The Effect of Chiari Malformation Type I on CerebrospinalFluid Dynamics

Author:Lloyd, Robert

Publication Date:2019

DOI:https://doi.org/10.26190/unsworks/3909

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The Effect of Chiari Malformation Type I

on Cerebrospinal Fluid Dynamics

Authored by Robert A. Lloyd

A thesis in fulfilment of the requirements for the degree of

Doctor of Philosophy

Prince of Wales Clinical School

Faculty of Medicine

Supervisor: Lynne E. Bilston

Co-supervisor: David F. Fletcher

Submitted: September 2019

i

Thesis/Dissertation sheet

Surname/Family Name : Lloyd

Given Name/s : Robert Arthur

Abbreviation for degree as give in the University calendar : Doctor of Philosophy (Research)

Faculty : Medicine

School : Prince of Wales Clinical School

Thesis Title : The Effect of Chiari Malformation Type I on Cerebrospinal Fluid Dynamics

Abstract 350 words maximum:

Chiari malformation type I is a disorder in which the cerebellar tonsils herniate through the

foramen magnum. A majority of patients with this condition also develop a fluid filled

cavity (syrinx) within the spinal cord. Syrinxes are associated with both sensory and motor

disturbances, with extreme cases leading to autonomic dysfunction. The mechanisms that

lead to the accumulation of fluid remain unknown. There is no universally accepted

treatment and current surgical treatments have variable, but often unsatisfactory, outcomes

and side effects are common. There is a need to understand cerebrospinal fluid (CSF)

circulation in Chiari malformation to identify the mechanism(s) that cause syrinx

formation. This would enable a mechanistically based treatment to be developed.

This thesis presents six interrelated studies investigating CSF circulation and the influence

of tonsillar herniation on CSF dynamics, to better understand potential mechanisms for

syrinx formation. Methods used include novel real-time magnetic resonance imaging

(MRI) of CSF and blood flow, morphological assessments of anatomical MRI, subject-

specific computational models of the spinal subarachnoid space, and idealised models of

the perivascular space.

The subject-specific models showed that in Chiari patients the peak systolic CSF pressure

was increased and occurred earlier in the cardiac cycle, compared with controls. The

perivascular space model suggested that these subarachnoid pressure characteristics may

cause increased flow into the cord that is favourable for syrinx formation in Chiari patients.

Increased overcrowding below the foramen magnum in syrinx free subjects led to an

earlier systolic pulse, whereas in syrinx subjects it caused a delay. This difference in

behaviour may be related to syrinx patients having a smaller midsagittal cross-sectional

area and could explain why not all patients develop a syrinx. Real-time imaging of CSF

flow in controls were inconsistent with the currently accepted mechanism for respiratory

CSF circulation, suggesting instead that respiratory CSF flow is primarily dependent on the

balance of the thoracic and lumbar spinal pressures. Real-time imaging in Chiari patients

found coughing and straining produced high velocity cranial flow at the foramen magnum,

which may be a marker for Chiari associated headache, but whether this influences syrinx

formation requires further investigation.

ii

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thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.

I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts

International (this is applicable to doctoral theses only). …………………………………………… Signature

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FOR OFFICE USE ONLY

Date of completion of requirements for Award:

iii

Originality statement

‘I hereby declare that this submission is my own work and to the best of my knowledge it

contains no materials previously published or written by another person, or substantial

proportions of material which have been accepted for the award of any other degree or diploma

at UNSW or any other educational institution, except where due acknowledgement is made in

the thesis. Any contribution made to the research by others, with whom I have worked at

UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual

content of this thesis is the product of my own work, except to the extent that assistance from

others in the project's design and conception or in style, presentation and linguistic expression is

acknowledged.’

Signed ……………………………………………..............

Date …………………………………………….................

Copyright statement

‘I hereby grant the University of New South Wales or its agents the right to archive and

to make available my thesis or dissertation in whole or part in the University libraries in

all forms of media, now or here after known, subject to the provisions of the Copyright

Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use

in future works (such as articles or books) all or part of this thesis or dissertation.

I also authorise University Microfilms to use the 350 word abstract of my thesis in

Dissertation Abstract International (this is applicable to doctoral theses only).

I have either used no substantial portions of copyright material in my thesis or I have

obtained permission to use copyright material; where permission has not been granted I

have applied/will apply for a partial restriction of the digital copy of my thesis or

dissertation.'

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Date …………………………………………….................

Authenticity statement

‘I certify that the library deposit digital copy is a direct equivalent of the final officially

approved version of my thesis. No emendation of content has occurred and if there are any

minor variations in formatting, they are the result of the conversion to digital format’

Signed ……………………………………………..............

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iv

Inclusion of publications statement

UNSW is supportive of candidates publishing their research results during their candidature

as detailed in the UNSW Thesis Examination Procedure.

Publications can be used in their thesis in lieu of a Chapter if:

The student contributed greater than 50% of the content in the publication and is the

“primary author”, i.e. the student was responsible primarily for the planning, execution

and preparation of the work for publication

The student has approval to include the publication in their thesis in lieu of a Chapter

from their supervisor and Postgraduate Coordinator.

The publication is not subject to any obligations or contractual agreements with a third

party that would constrain its inclusion in the thesis

Please indicate whether this thesis contains published material or not.

☐ This thesis contains no publications, either published or submitted for

publication

☐ Some of the work described in this thesis has been published and it has been

documented in the relevant Chapters with acknowledgement.

☒ This thesis has publications (either published or submitted for publication)

incorporated into it in lieu of a chapter and the details are presented below

CANDIDATE’S DECLARATION

I declare that:

I have complied with the Thesis Examination Procedure

Where I have used a publication in lieu of a Chapter, the listed publication(s) below

meet(s) the requirements to be included in the thesis.

Name

Signature

Date (dd/mm/yy)

Postgraduate Coordinator’s Declaration (to be filled in where publications are used in

lieu of Chapters)

I declare that:

the information below is accurate

where listed publication(s) have been used in lieu of Chapter(s), their use complies

with the Thesis Examination Procedure

the minimum requirements for the format of the thesis have been met.

PGC’s Name

PGC’s Signature Date (dd/mm/yy)

v

Details of publication #1:

Full title: Chiari Malformation may Increase Perivascular Cerebrospinal Fluid Flow into the

Spinal Cord: A Subject-Specific Computational Modelling Study

Authors: Lloyd, R.A., Fletcher, D.F., Clarke, E.C. & Bilston, L.E.

Journal or book name: Journal of Biomechanics

Volume/page numbers: 65, 185-193

Date accepted/ published: 8th December 2017

Status Published ✔ Accepted and

In press

In progress

(submitted)

The Candidate’s Contribution to the Work

Lloyd, R. A. provided conception and design of study, and collected, processed, analysed,

and interpreted the data. He also drafted manuscript and revisions.

Location of the work in the thesis and/or how the work is incorporated in the thesis:

The publication has been included in lieu of Chapter 2

Primary Supervisor’s Declaration

I declare that:

• the information above is accurate

• this has been discussed with the PGC and it is agreed that this publication can be

included in this thesis in lieu of a Chapter

• All of the co-authors of the publication have reviewed the above information and have

agreed to its veracity by signing a ‘Co-Author Authorisation’ form.

Supervisor’s name

Supervisor’s signature

Date (dd/mm/yy)

vi

Details of publication #2:

Full title: The Effects of Variation in the Arterial Pulse Waveform on Perivascular Flow

Authors: Lloyd, R.A., Stoodley, M. A., Fletcher, D.F. & Bilston, L.E.

Journal or book name: Journal of Biomechanics

Volume/page numbers: 90, 65-70

Date accepted/ published: 11th June 2019

Status Published ✔ Accepted and

In press

In progress

(submitted)

The Candidate’s Contribution to the Work

Lloyd, R. A. provided conception and design of study, and collected, processed, analysed,

and interpreted the data. He also drafted manuscript and revisions.

Location of the work in the thesis and/or how the work is incorporated in the thesis:

The publication has been included in lieu of Chapter 3

Primary Supervisor’s Declaration

I declare that:

• the information above is accurate

• this has been discussed with the PGC and it is agreed that this publication can be

included in this thesis in lieu of a Chapter

• All of the co-authors of the publication have reviewed the above information and have

agreed to its veracity by signing a ‘Co-Author Authorisation’ form.

Supervisor’s name

Supervisor’s signature

Date (dd/mm/yy)

vii

Table of contents

Thesis/Dissertation sheet ............................................................................................................. i

Originality statement ................................................................................................................. iii

Copyright statement .................................................................................................................. iii

Authenticity statement ............................................................................................................... iii

Inclusion of publications statement .......................................................................................... iv

Table of contents ....................................................................................................................... vii

Acknowledgements ..................................................................................................................... x

Publications from thesis ............................................................................................................ xi

Presentations from thesis ......................................................................................................... xii

Other publications ................................................................................................................... xiii

List of figures ............................................................................................................................ xiv

List of tables............................................................................................................................. xvii

1. Introduction and literature review .................................................................................... 1

Background of healthy anatomy and physiology of the CSF spaces ............................ 2

The cerebrospinal fluid spaces ............................................................................. 2

Subarachnoid space circulation ............................................................................ 4

Perivascular flow .................................................................................................. 8

Chiari malformation and syringomyelia ....................................................................... 8

Chiari malformation ............................................................................................. 8

Syringomyelia ....................................................................................................... 9

Mechanisms for syrinx formation: Clinical studies ............................................ 10

Treatment ............................................................................................................ 12

Magnetic resonance imaging studies .......................................................................... 13

Structural anatomy of Chiari malformation ....................................................... 13

The effect of Chiari on CSF flow ........................................................................ 18

The purpose and reliability of computational modelling ............................................ 21

Idealised modelling ............................................................................................. 28

Subject-specific modelling .................................................................................. 29

The importance of subarachnoid structures on CSF flow .................................. 30

Mechanism for syrinx development: Engineering studies .......................................... 32

The tonsillar piston ............................................................................................. 32

Venturi effect and elastic jump ............................................................................ 33

Mechanism for perivascular inflow .................................................................... 33

Summary ..................................................................................................................... 35

The effects of Chiari malformation on CSF dynamics: Thesis Aims ......................... 36

2. Chiari malformation may increase perivascular cerebrospinal fluid flow into the

spinal cord: a subject-specific computational modelling study ..................................... 37

Introduction ................................................................................................................. 37

Methods....................................................................................................................... 39

MR Imaging and flow measurements .................................................................. 39

Computational modelling of the spinal subarachnoid space .............................. 40

Statistical analysis............................................................................................... 43

Results ......................................................................................................................... 43

viii

Subarachnoid model validation ........................................................................... 43

CSF dynamics in Chiari subjects ......................................................................... 44

Arterial pulse delay and perivascular flow ......................................................... 44

The effect of subarachnoid space pressure-time features on perivascular flow .. 48

Discussion .................................................................................................................... 48

Implications for syrinx development .................................................................... 48

Study limitations .................................................................................................. 50

Conclusion ................................................................................................................... 51

3. The effects of variation in the arterial pulse waveform on perivascular flow .............. 53

Introduction ................................................................................................................. 53

Methods ....................................................................................................................... 55

Modelling perivascular flow ................................................................................ 55

Systematic variation of the arterial pulse wave ................................................... 56

Arterial pulse amplitude ...................................................................................... 56

Statistical analysis ............................................................................................... 57

Results ......................................................................................................................... 59

Discussion .................................................................................................................... 60

Influence of arterial pulse waveform on the ‘leaky valve’ mechanism ................ 60

Study limitations .................................................................................................. 61

Conclusion ................................................................................................................... 62

4. The shape of the posterior fossa and its effects on cerebrospinal fluid dynamics ........ 63

Introduction ................................................................................................................. 63

Methods ....................................................................................................................... 68

MR imaging and anatomical segmentation ......................................................... 68

Spinal canal taper ................................................................................................ 69

Subarachnoid pressure-time data ........................................................................ 69

Statistical analysis ............................................................................................... 70

Results ......................................................................................................................... 70

Posterior fossa morphology ................................................................................. 70

Spinal canal taper ................................................................................................ 72

The effect of craniocervical obstruction on the subarachnoid pressure waveform

72

Discussion .................................................................................................................... 74

Study limitations .................................................................................................. 75

Conclusion ................................................................................................................... 76

5. Respiratory cerebrospinal fluid flow is driven by the thoracic and lumbar spinal

pressures ............................................................................................................................. 77

Introduction ................................................................................................................. 77

Methods ....................................................................................................................... 79

Subjects ................................................................................................................ 79

Respiratory manoeuvres ...................................................................................... 79

MRI scans ............................................................................................................ 79

Pressure measurements ....................................................................................... 80

Data analysis ....................................................................................................... 81

Statistical analysis ............................................................................................... 82

ix

Results ......................................................................................................................... 84

CSF and venous blood flow during expiratory efforts ........................................ 84

CSF and venous blood flow during inspiratory efforts ....................................... 85

Similarity of MRI waveforms during the sampled manoeuvres .......................... 86

Differences in the magnitude of CSF dynamics and trunk pressures during

different strength manoeuvres ............................................................................................. 87

Regression modelling .......................................................................................... 89

Discussion ................................................................................................................... 91

Mechanics of inspiratory CSF flow .................................................................... 91

Mechanics of expiratory CSF flow ...................................................................... 93

Study limitations .................................................................................................. 94

Clinical implications ........................................................................................... 94

Conclusion .................................................................................................................. 95

6. Simulating respiratory effects on the cervical CSF pressures ....................................... 96

Introduction ................................................................................................................. 96

Methods....................................................................................................................... 96

Results ......................................................................................................................... 98

Discussion ................................................................................................................... 99

Conclusion ................................................................................................................ 100

7. Pilot study: The effects of coughing and Valsalva on cerebrospinal fluid flow in

Chiari I malformation ..................................................................................................... 101

Introduction ............................................................................................................... 101

Methods..................................................................................................................... 101

MR imaging and flow measurements ................................................................ 102

Computational modelling of the spinal subarachnoid space ............................ 103

Results ....................................................................................................................... 105

CSF flow studies................................................................................................ 105

Subarachnoid space models .............................................................................. 110

Discussion ................................................................................................................. 115

Evaluation of imaging protocol ........................................................................ 115

Sources of error in the coughing models .......................................................... 116

Conclusion ................................................................................................................ 117

8. Summary and conclusions .............................................................................................. 118

Implications for CSF physiology .............................................................................. 118

Implications for Chiari malformation and syringomyelia ......................................... 119

Implications for research methods ............................................................................ 120

Imaging ............................................................................................................. 120

Modelling .......................................................................................................... 121

Future directions ....................................................................................................... 122

Understanding syrinx formation ....................................................................... 122

Understanding coughing-associated headache ................................................ 123

Conclusion ................................................................................................................ 124

References ................................................................................................................................ 125

x

Acknowledgements

Firstly, I would like to thank Professor Lynne Bilston for her mentorship and guidance

throughout my PhD. I am grateful to have had the opportunity to study with Lynne, and to have

been part of such a welcoming research group. I am also thankful for her continued interest in

my professional development, for which her suggestions to improve my choice of language

while presenting will surely help.

I would also like to thank my co-supervisor Professor David Fletcher, for his mentorship,

guidance and willingness to advise me and troubleshoot both modelling and grammatical errors.

I would also like to thank Professor Marcus Stoodley and the Macquarie syrinx research group.

I would like to thank Barbara Toson for her statistical expertise. I would like to thank the staff at

the NeuRA imaging centre. I am also thankful to the participants who were willing to contribute

to my research, in spite of the discomfort long imaging sessions or nasogastric catheters may

have caused them.

Thank you to the Column of Hope and NeuRA for funding my time as a PhD student.

Surprisingly, I would wish to thank David Cameron and his government, for convincing me to

pursue research opportunities outside my country.

To my parents, I would like to thank you for your long-standing support allowing me to pursue

further education, as well as your lack of basic knowledge as to the proper use of contraceptive

measures allowing me to exist. On the note of existing, I would also like to thank the kind New

Yorker, who allowed me to return to Sydney and complete my thesis.

xi

Publications from thesis

I. Lloyd, R. A., Fletcher, D. F., Clarke, E. C., and Bilston, L. E. ‘Chiari malformation may

increase perivascular cerebrospinal fluid flow into the spinal cord: A subject-specific

computational modelling study’. Journal of Biomechanics. 2017 Oct; 65, 185-193.

II. Lloyd, R. A., Stoodley, M. A., Fletcher, D. F., and Bilston, L. E. ‘The effects of variation in

the arterial pulse waveform on perivascular flow’. Journal of Biomechanics. 2019 Jun, 90,

56-70

xii

Presentations from thesis

I. Bilston, L., Lloyd, R., and Fletcher, D. 2017 ‘Computational modelling of spinal

perivascular flow: Relationships between perivascular transport and subarachnoid space

pressure, pulse timing and pulse shape’ Presented at 4th International CSF Dynamics

Symposium, Atlanta, USA, June 19th-20th

II. Lloyd, R., Fletcher, D., and Bilston, L. 2017 ‘The effect of Chiari Malformation and

syringomyelia on CSF flow: CFD subject-specific models for a cohort of Chiari patients

and controls’ Presented at 4th International CSF Dynamics Symposium, Atlanta, USA, June

19th-20th

III. Lloyd, R., Fletcher, D., and Bilston, L. 2017 ‘The effect of Chiari malformation and

syringomyelia on perivascular flow: A computational modelling study’ Presented at 45th

Annual Tow Research Awards, Sydney, November 24th

IV. Lloyd, R., Ball, I., and Bilston, L. 2017 ‘Using TimeSLIP and Real-time PC-MRI to

quantify respiratory effects on the cerebrospinal fluid dynamics’ Presented at The

Australian and New Zealand Society for Magnetic Resonance, Kingscliff, NSW, Dec 2nd-6th

V. Lloyd, R., Fletcher, D., Clarke, E and Bilston, L. 2018 ‘The effects of Chiari malformation

and syringomyelia on Perivascular flow’ Presented at the 8th World Congress of

Biomechanics, Dublin, Ireland, July 8th-12th

VI. Lloyd, R., Ball, I., Stoodley, M., Fletcher, D., and Bilston, L. 2018 ‘The effects of coughing

and Valsalva on cerebrospinal fluid flow and cranial venous drainage’ Presented at the 8th

World Congress of Biomechanics, Dublin, Ireland, July 8th-12th

VII. Lloyd, R., Stoodley, M., Fletcher, D., and Bilston, L. 2019 ‘Mechanisms of syrinx

formation C: Engineering studies’, Presented as an invited talk at CSF flow at Niagara Falls,

Niagara Falls, USA, June 26th-28th

VIII. Lloyd, R., Butler, J., Gandevia, S., Stoodley, M., and Bilston, L. 2019 ‘CSF flow during

respiration is influenced by both thoracic and abdominal pressures’ Presented at 5th

International CSF Dynamics Symposium, Oslo, Norway, July 1st-2nd

IX. Lloyd, R., Fletcher, D., Butler, J., Gandevia, S., and Bilston, L. 2019 ‘The significance of

abdominal pressure on respiratory cerebrospinal fluid dynamics’ Presented at the 16th

International Symposium on Computer Methods in Biomechanical and Biomedical

Engineering and the 4th Conference on Imaging and Visualization, New York City, USA,

August 14th-16th

xiii

Other publications

I. Mowlavi. S., Engmann. J., Burbige. A., Lloyd. R., Hayoun. P., Le Reverend. B., and

Ramaioli. M. ‘In vivo observations and in vitro experiments on the oral phase of

swallowing newtonian and shear thinning liquids’, Journal of Biomechanics, 2016 Dec:

49(16), 3788-3795

II. Cirovic, S., Lloyd, R., Jovanovik, J., Volk, H. A., and Rusbridge, C. ‘Computer simulation

of syringomyelia in dogs’. BMC Veterinary Research, 2018 Mar; 14(1), 82

III. Dawes, B. H., Lloyd, R. A., Rogers, J. M., Magnussen, J. S., Bilston, L. E., and Stoodley,

M. A. “Cerebellar tissue strain in Chiari malforamtion with headache”. World

Neurosurgery, 2019 May; [In Press]

xiv

List of figures

Figure 1.1 A) Gross anatomy of the central nervous system and the CSF spaces (Elliott et al.,

2013). B) Detailed diagram of the meninges within the spinal canal. ......................... 3

Figure 1.2 Diagram of perivascular spaces within the brain (Standring, 2015) highlighting

the structures of the meninges, subarachnoid space, and perivascular spaces............. 4

Figure 1.3 Schematic of the Monro-Kellie doctrine (QA = arterial flow, Qv = venous flow and

QCSF = CSF flow). ........................................................................................................ 5

Figure 1.4 Schematic of the epidural veins and radiographs of there function. ............................ 6

Figure 1.5 Schematic of the effects of respiration on the venous blood flow in the epidural

veins. ............................................................................................................................ 7

Figure 1.6 Serial radiographs of the lumbar spine. ........................................................................ 7

Figure 1.7 Illustrated cases of canalicular syrinxes in a patient with Chiari I malformation,

(A) communicating and (B) non-communicating. (C) provides an example of

extracanalicular non-communicating syringomyelia. .................................................. 9

Figure 1.8 Gardner’s mechanism for syrinx development. ......................................................... 10

Figure 1.9 Williams pressure dissociation mechanism for syrinx development. ........................ 11

Figure 1.10 A) Ball & Dayan’s mechanism for syrinx development, suggesting increased

subarachnoid pressure forces CSF into the cord via the perivascular spaces. B)

Tonsillar piston mechanism for syrinx formation. .................................................... 12

Figure 1.11 Common morphometric measurements performed in the literature. ....................... 15

Figure 1.12 Example cardiac CSF pulse from a control. ............................................................. 20

Figure 1.13 Examples of flow jets and recirculation patterns in Chiari patients from Bunck et

al. (2012). ................................................................................................................... 20

Figure 1.14 A) Example of poor agreement between CFD and MRI .......................................... 30

Figure 1.15 Example of the different methods used to model the effects of nerver roots and

denticulate ligaments on CSF flow (Pahlavian et al., 2014; Sass et al., 2017;

Stockman, 2005). ....................................................................................................... 31

Figure 1.16 The effects of arachnoid trabeculae of CSF flow and fluid mixing. ........................ 32

Figure 2.1 (A) An illustration of the perivascular anatomy, highlighting a single penetrating

vessel and the potential channel for fluid transport ................................................... 38

Figure 2.2 The Venc prescribes the maximum and minimum velocities that correspond to a

phase .......................................................................................................................... 40

Figure 2.3 (A) Midline sagittal MR scan of a Chiari subject with a syrinx. (B) Magnified

view of midline scan with segmented model area overlaid. ...................................... 42

Figure 2.4 Example of the measured MRI and model velocity-time profiles used for

validation, taken from a control subject. ................................................................... 45

xv

Figure 2.5 Bland-Altman plots assessing the model error (modelled variable – MRI

measurement), each data point is the average error measured across the 10

locations sampled around the subarachnoid space in a subject (Figure 2.3C). ......... 46

Figure 2.6 Group means with their 95% confidence intervals from one-way ANOVA for;

parameters measured from subarachnoid pressures .................................................. 47

Figure 2.7 Relationship between pressure wave features and net pumping rates calculated

with an arterial delay of 4%. ..................................................................................... 48

Figure 2.8 Effect of peak pressure timing on net perivascular flow, for selected cases with a

4% delay in arterial expansion. ................................................................................. 50

Figure 3.1 Illustration demonstrating how the interactions of the forward travelling and

reflected waves may alter the shape of the arterial pressure pulse, adapted from

London and Pannier (2010)....................................................................................... 54

Figure 3.2 Annotated schematic of the axisymmetric perivascular space model. ...................... 55

Figure 3.3 Example of the arterial displacement waveforms used in the parametric analysis. ... 56

Figure 3.4 Calculated pumping rates. Group means with 95% confidence intervals are

shown. ....................................................................................................................... 60

Figure 4.1 Common morphometric measurements performed in the literature. ......................... 65

Figure 4.2 Example posterior fossa volume of a Chiari patient with a syrinx. ........................... 69

Figure 4.3 Relationship between the timing (A-D) and magnitude (E-H) of the subarachnoid

pressures and the measures of obstruction. ............................................................... 73

Figure 5.1 Schematic of the Monro-Kellie hypothesis (QA=arterial flow, Qv=venous flow and

QCSF=CSF flow). ....................................................................................................... 77

Figure 5.2 Schematic of the effects of respiration on the venous blood flow in the epidural

veins. ......................................................................................................................... 78

Figure 5.3 Diagrammatic presentation of the experimental set-up. ............................................ 80

Figure 5.4 Sample measurements of pressures and velocity in an individual subject for each

of the respiratory manoeuvres. .................................................................................. 83

Figure 5.5 Example of one subject’s repeated coughs, followed by a normal breath (marked

by red dashed line). ................................................................................................... 84

Figure 5.6 Example of one subject’s repeated expiratory sniffs, followed by a normal breath

(marked by red dashed line). ..................................................................................... 85

Figure 5.7 Example of one subjects repeated inspiratory sniffs, followed by a normal breath

(marked by red dashed line). ..................................................................................... 86

Figure 5.8 The cross-correlation coefficients for the coughs, expiratory and inspiratory sniffs

in all participants comparing the manoeuvre waveforms of CSF velocity to both

(A) epidural and (B) IJV acceleration, and (C) epidural to IJV acceleration. ........... 86

xvi

Figure 5.9 Average peak displacements plotted against the average peak intrathoracic and

abdominal pressures generated during inspiratory and expiratory efforts. ................ 90

Figure 5.10 Schematic of the effects of inspiration on the change in the venous blood volume

within the thoracic spinal canal. ................................................................................ 92

Figure 6.1 Annotated schematic of the simple spinal model. ...................................................... 97

Figure 6.2 Input displacements for the baseline simulation. ....................................................... 98

Figure 6.3 Example of simulated CSF velocities and displacements at C3. ............................... 98

Figure 6.4 The peak CSF displacements at C3 plotted against the relative difference in

volume change between thoracic and lumbar spine. ................................................. 99

Figure 7.1 Shape of the coughing pulse applied to the caudal end of the model. ..................... 105

Figure 7.2 Group median and interquartile range for the peak cranial (upper) and caudal

(lower) velocities during inspiration and expiration. ............................................... 106

Figure 7.3 Sample measurements of the CSF velocities during a Valsalva manoeuvre at C3,

in two control subjects (A and B), a Chiari patient with (C) and without a syrinx

(D)............................................................................................................................ 106

Figure 7.4 Group median and interquartile range for the peak cranial (Left: A,C,D, and F)

and caudal (Right: B, E, and G) velocities before (pre: A,B), during and after

(post: F, G) Valsalva. ............................................................................................... 108

Figure 7.5 Sample measurements of the CSF velocities during a cough in the cervical spinal

canal, in a Control (A), Chiari patient without (B) and two with a syrinx (C & D).109

Figure 7.6 Group median and interquartile range for the peak cranial (upper: A, B, and C)

and caudal (lower: D, E, and F) velocities before (pre: A, D), during (B, E), and

after (post: C, F) a cough. ........................................................................................ 110

Figure 7.7 A) Example of measured MRI and model velocity for a control subject at C3 for

a cardiac cycle. ........................................................................................................ 111

Figure 7.8 Bland-Altman plots assessing the model error (modelled variable – MRI

measurement) for the cardiac models plotted against the MRI measurement. ........ 111

Figure 7.9 Example CSF velocities in a Chiari patient with a syrinx, calculated with a

coughing pulse applied to the caudal end of the model compared with the subjects

measured MRI velocities. ........................................................................................ 112

Figure 7.10 Bland-Altman plots assessing the model error (modelled variable – MRI

measurement) for the coughing models plotted against the MRI measurement. ..... 113

Figure 7.11 Outline of the model cross-sectional area overlaid on the corresponding axial

slice at the foramen magnum, for subjects A (Left) and B (Right) of Figure 7.10.. 114

Figure 7.12 Group median and interquartile range of the pressure drop between the cervical

spine and foramen magnum, for a cardiac and coughing pulse. .............................. 114

xvii

List of tables

Table 1.1 Summary of morphometric studies. ............................................................................ 16

Table 1.2 Summary of spinal taper measurements. .................................................................... 18

Table 1.3 Summary of the magnitude and timing features of CSF flow in Chiari patients both

with and without a syrinx present. ............................................................................ 19

Table 1.4 Summary of computational models investigating characteristics of normal CSF

dynamics. .................................................................................................................. 22

Table 1.5 Summary of computational models investigating the effect of pathology on CSF

dynamics. .................................................................................................................. 24

Table 2.1 Summary of participant information ........................................................................... 39

Table 2.2 Summary of the agreement between model and in vivo velocity-time profiles.......... 45

Table 3.1 Alterations made to displacement wave, displayed as: variable value (percentage

change from baseline model). ................................................................................... 58

Table 4.1 Summary of Chiari morphometric studies. ................................................................. 66

Table 4.2 Summary of participant information ........................................................................... 68

Table 4.3 Descriptive statistics of the morphological measures that were normally

distributed. ................................................................................................................ 71

Table 4.4 Descriptive statistics of the morphological measures that were non-normally

distributed. ................................................................................................................ 71

Table 4.5 Median spinal tapers measured within the cohort. ...................................................... 72

Table 5.1 Mean and 95% confidence intervals of the peak variables during different

respiratory manoeuvres, in both the cervical and lumbar regions. ............................ 89

Table 5.2 Pairwise comparisons from the linear mixed model for both expiratory and

inspiratory Sniffs, looking at the effects of both the location, strength and their

interaction. ................................................................................................................ 89

Table 7.1 Summary of the agreement between model and MRI velocity-time profiles for the

cardiac models. ....................................................................................................... 111

Table 7.2 Summary of the agreement between the peak model and MRI velocity, at both the

foramen magnum (FM), and at C3 for the coughing models. ................................. 113

1

1. Introduction and literature review

Chiari malformation type I is a condition where the cerebellar tonsils herniate (by ≥ 5 mm) into

the upper cervical spinal canal, causing symptoms of headaches, neck pain and sensory and

motor disturbances (Fernández et al., 2009; Milhorat et al., 1999). A large number of these

patients also develop syringomyelia, fluid filled cavities (syrinxes) within the spinal cord. The

continued growth of the syrinx is believed to compress or damage the nervous tissue (Milhorat

et al., 1995), giving rise to additional symptoms such as persistent pain, loss of motor function,

sensory disturbances and occasionally autonomic dysfunction (Sakushima et al., 2012).

The true prevalence of Chiari Type I is unknown. Using the anatomical definition of Chiari as

having 5 mm or greater tonsillar herniation through the foramen magnum, imaging studies have

estimated Chiari to affect between 0.04-0.90% of the population (Morris et al., 2009; Vernooij

et al., 2007). However, this likely overestimates the clinically relevant cases, as symptomatic

Chiari occurs less frequently (Aitken et al., 2009; Strahle et al., 2011). Within the Chiari

population between 57-78% are diagnosed with syrinxes (Ellenbogen et al., 2000; Milhorat et

al., 1999; Tubbs et al., 2004).

While the symptoms are assumed to originate from abnormal anatomy, neither the severity of

tonsillar herniation nor skull morphology appear to be related to whether patients experience

symptoms (Khalsa et al., 2018; Meadows et al., 2000). Additionally, the mechanisms which

cause coughing and straining associated headaches are poorly understood (Alperin et al., 2015;

Leung et al., 2016). Similarly, the mechanisms which cause a syrinx to develop or enlarge

remain unknown, and the severity of tonsillar herniation appears unrelated to whether a syrinx is

likely to develop (Masur et al., 1995; Stovner and Rinck, 1992).

As the pathophysiology of Chiari malformation and syringomyelia are poorly understood, there

is not a universally accepted treatment, leading to variable, and often unsatisfactory (2.60-

38.4%) outcomes (Aghakhani et al., 2009; Dubey et al., 2009; Jia et al., 2019; Parker et al.,

2013). Positive outcomes from these treatments are commonly attributed to the normalisation of

the posterior cranial volume and cerebrospinal fluid flow (CSF) through the foramen magnum.

There have been many hypotheses proposed for the mechanisms by which Chiari malformation

gives rise to a syrinx, most of which lack supporting evidence, and some have since been

discredited (Ball and Dayan, 1972; Gardner, 1965). Thus, there is a need to understand normal

CSF circulation and the effect of Chiari malformation, to identify mechanisms that could cause

patient symptoms and syrinx development. As this would enable a mechanistically-based

treatment to be developed.

2

Background of healthy anatomy and physiology of the CSF spaces

The cerebrospinal fluid spaces

The brain and spinal cord are suspended in a bath of CSF. This is thought to protect the central

nervous system (CNS) from shock and facilitate solute transport and the homeostasis of the

CNS. CSF is a Newtonian fluid with properties similar to water (density = 1000 kg.m-3,

viscosity = 0.8 mPa.s) (Bloomfield et al., 1998), which is secreted from a highly vascularised

structure within the ventricles called the choroid plexus. It is reabsorbed into the venous blood

through the arachnoid villi (Figure 1.1), and along the spinal nerve roots. Although, more

recently clinical and experimental studies have suggested that the CSF may be produced and

absorbed across the walls of capillaries in the CNS (Brinker et al., 2014). Additionally, dural

lymphatic vessels have been identified (Absinta et al., 2017), which have been demonstrated to

provide an alternate source of rapid CSF drainage (Ma et al., 2017; Ma et al., 2019).

Classically the difference between CSF production and drainage is believed to drive a slow

creeping flow. CSF from the lateral ventricles can flow through the foramen of Monro into the

third ventricle, then through the aqueduct of Sylvius into the fourth ventricle, and out of the

foramen of Magendie (midline outlet) and foramina of Lushka (lateral outlets) into the cisterna

magna, and the subarachnoid space which encases the CSF volume external to the brain and

spinal cord (Figure 1.1) (Brodbelt and Stoodley, 2007). CSF can potentially flow from the

fourth ventricle into the central canal of the spinal cord via the obex, however the central canal

is commonly occluded in adults (Milhorat et al., 1994).

3

A B

Figure 1.1 A) Gross anatomy of the central nervous system and the CSF spaces (Elliott et al., 2013). B) Detailed

diagram of the meninges within the spinal canal. The arachnoid mater (A) lines the thicker dura mater (D). The

arachnoid trabeculae (IL) connects to the pia mater lining the cord. This layer is fenestrated consisting of sheets and

pillar structures connecting, blood vessels (V) and nerve roots. The pia mater also coats the blood vessels within the

subarachnoid space (Nicholas and Weller, 1988). A) Reproduced from Elliot et al., Journal of Fluids and Structures,

2013, 40:1-24 with permissions from Elsevier. B) Reproduced from Nicholas and Weller, Journal of Neurosurgery,

1988, 69:276-282 with permissions from http://thejns.org.

The subarachnoid space is bounded by two layers of fibrous connective tissue (meninges). The

inner layer is the pia mater, which lines the surface of the brain, spinal cord, and blood vessels

which cross the subarachnoid space and penetrate the nervous tissue (Figures 1.1B and 1.2)

(Cloyd and Low, 1974; Nicholas and Weller, 1988). The outer boundary is the arachnoid mater,

which is connected to the pia mater by the arachnoid trabeculae (arachnoid web). The arachnoid

trabeculae consist of both fenestrated sheets and pillar like collagenous structures, which

surround and support blood vessels (Figure 1.1B) (Cloyd and Low, 1974; Nicholas and Weller,

1988). The arachnoid web partitions the subarachnoid space into different cisterns, however the

fenestrations and varied density allows free CSF circulation (Brodbelt and Stoodley, 2007). The

arachnoid mater is overlaid by the dura mater, a thick elastic membrane which provides strength

to the fluid space. Within the cranium the dura coats the surface of the skull, as it progresses

through the foramen magnum into the spine, the dura is separated from the bone by a layer of

fat that allows movement (Weller, 2005). The difference in the support provided to the cranial

and spinal dura mater results in the spinal subarachnoid space being more compliant, allowing

4

the volume of the spinal subarachnoid space to change in response to the variation in CSF

pressure (Henry–Feugeas et al., 2000).

See Standring (2015) Section 3, Chapter 18, Figure 18.14on page 227.

Alternative versions in Zhang et al. (1990) Figure 10, PMID: 2254158,

or in Elliott et al. (2013) Figure 4, Available at DOI:

https://doi.org/10.1016/j.jfluidstructs.2013.01.010

Figure 1.2 Diagram of perivascular spaces within the brain (Standring, 2015) highlighting the structures of the

meninges, subarachnoid space, and perivascular spaces.

There is a fluid space which surrounds blood vessels that penetrate the parenchyma of the brain

and spinal cord, which extends to the arterioles and venules (Figure 1.2) (Lam et al., 2017;

Zhang et al., 1990). These ‘perivascular’ spaces provide a fluid pathway which is continuous

with the subarachnoid space. CSF tracer studies have demonstrated that solutes injected in the

subarachnoid space rapidly migrate into and stain the perivascular spaces. These pathways are

suspected to play a role in the solute transport and exchange between the interstitial fluid and

CSF, in the homeostasis of the CNS (Diem et al., 2016; Hawkes et al., 2011; Iliff et al., 2013a).

Subarachnoid space circulation

CSF oscillates within the subarachnoid space, driven by pressure changes created by the

cardiovascular and respiratory systems. It is generally accepted that the fast (~1 Hz) pulsations

are cardiac driven and respiration drives slower CSF oscillations (<<1 Hz) (Hamer et al., 1977;

Takizawa et al., 2017), although sudden respiratory events such as coughing and straining can

also lead to a rapid displacement of CSF (Hamilton et al., 1936; Williams, 1976; Williams,

1981a; Yildiz et al., 2017).

Cardiac CSF pulsations are primarily driven by the volume balance within the cranial

compartment (the Monro-Kellie doctrine). Over the cardiac cycle the net volume of blood

(arterial inflow minus venous return) within the cranium changes, and CSF is displaced through

the foramen magnum into or out of the spinal subarachnoid space to compensate (Figure 1.3)

5

(Alperin et al., 2005a; Alperin et al., 2005b). Although the spinal cord also pulsates (Dunbar et

al., 1966; Nakamura et al., 1997; Nakamura et al., 1998), it has a less significant effect on CSF

flow compared with cranial pulsations as the spinal canal is more compliant (Henry–Feugeas et

al., 2000).

Figure 1.3 Schematic of the Monro-Kellie doctrine (QA = arterial flow, Qv = venous flow and QCSF = CSF flow). (A)

Labelled schematic demonstrating a hypothetical initial or resting state. The cranial volume is constant, as the net

blood volume increases CSF is displaced into the spinal canal (B). Conversely, a decrease in blood volume draws

CSF cranially (C).

The current literature suggests that there are two mechanisms which drive respiratory CSF flow,

one for normal breathing and one for sudden expiratory efforts (e.g. coughing or the Valsalva

manoeuvre [a forced exhalation against a closed epiglottis]). In the case of forced expiration the

CSF pressure is increased (Hamilton et al., 1944; Hamilton et al., 1936) driving cranial flow

(Du Boulay et al., 1972; Martins et al., 1972; Williams, 1976; Williams, 1981a, b). The

pressures of the trunk are assumed to be transmitted to the spinal canal via the epidural veins

(Henriques, 1962). The epidural veins are a valveless network of veins which span the length of

the spine, occupying a large volume of the extradural fat (Figure 1.4) (Groen et al., 2004; Groen

et al., 2005; Groen et al., 1997). When thoracic and abdominal pressures are increased, blood

flow from the inferior to superior vena cava is prevented, being diverted through the spinal

epidural veins (Figure 1.4) (Batson, 1940; Henriques, 1962; Shah, 1994). This increase in blood

volume will act to compress the dura mater, increasing spinal pressure, and displacing CSF

(Figure 1.5) (Du Boulay et al., 1972; Martins et al., 1972; Reitan, 1941).

6

See Groen et al. (1997) Figure 1.

Available at DOI:

https://doi.org/10.1002/(SICI)1097-

0185(199710)249:2<285::AID-

AR16>3.0.CO;2-K

See Henriques (1962) Figures 11 A & B.

PMID: 13906534

Figure 1.4 Schematic of the epidural veins and radiographs of there function. A) Simplified diagram of anterior

epidural veins and their connection to the larger veins out of the spine (1 jugular vein, 2 vertebral vein, 3 azygos

veins, 4 superior vena cava, 5 inferior vena cava, 6 left renal vein, 7 ascending lumbar vein, 8 femoral veins) (Groen

et al., 1997). B) Radiograph showing venous blood flow in a dogs vena cava under rest. C) Radiograph showing that

when pulmonary pressure was increased, blood flow from the inferior to superior vena cava was prevented, being

diverted into the valveless spinal epidural veins (Henriques, 1962).

CSF flow during normal respiration is hypothesised to be driven by the blood volume balance

within the cranium (Figure 1.3). Thoracic pressures become negative during inspiration,

decreasing the central venous pressure (Hamer et al., 1977; Hamit et al., 1965), increasing

venous return from the cranium (Dreha-Kulaczewski et al., 2017), and CSF flows cranially to

compensate (Dreha-Kulaczewski et al., 2017; Dreha-Kulaczewski et al., 2018; Yamada et al.,

2013). Conversely, during exhalation CSF flows caudally as venous return decreases. However,

this proposed mechanism fails to account for the cases where CSF above the diaphragm flows

caudally during inspiration (Aktas et al., 2019; Dreha-Kulaczewski et al., 2017; Dreha-

Kulaczewski et al., 2018). Aktas et al. (2019) demonstrated that diaphragmatic breathing

resulted in a larger magnitude of cranial CSF flow compared with thoracic, attributing this to

increased abdominal pressures driving a greater volume of blood into the lumbar epidural veins,

and therefore a greater displacement of CSF. Previously it has been shown that inspiration

decreases the cervical and thoracic epidural pressures (Usubiaga et al., 1967), therefore by the

same reasoning it is possible that a sufficient decrease in thoracic pressure could remove a

greater volume of blood from the epidural veins, creating a decrease in spinal pressure that

would draw CSF caudally (Figure 1.5C).

A C B

7

Figure 1.5 Schematic of the effects of respiration on the venous blood flow in the epidural veins. A) Resting

condition, highlighting the possible directions of venous blood flow (solid black arrows) in the respective vessels. B)

With a cough or Valsalva manoeuvre, both intrathoracic and abdominal pressures become positive, driving blood into

the epidural veins, compressing the dural sac driving CSF cranially. C) During inspiration the intrathoracic pressures

becomes negative, increasing venous return in the internal jugular veins (IJV) and epidural veins, removal of blood

from the thoracic epidural veins decreases the pressure in the spinal canal, if this is greater than the increased lumbar

spinal pressures CSF may flow caudally.

The overall effect of respiration on CSF circulation is likely a balance between the dynamic

effects of respiration on the spinal pressures (Figure 1.5), and the coupled cardiovascular effects

on the intracranial pressures (Figure 1.3). For example, when venous return is hindered (e.g.

persistent straining or jugular compression) intracranial pressure is increased by a decrease in

venous drainage, driving CSF flow into the spinal canal (Figures 1.6A-i & B-v) (Bedford, 1935;

Reitan, 1941; Shah, 1994). This can also be achieved by increasing the mean arterial pressure

(e.g. hypercapnia or in response to releasing Valsalva) (Figure 1.6B-iii) (Martins et al., 1972;

Williams, 1981a, b). Alternately, CSF can be drawn cranially by a reduction in mean arterial

pressures, which may occur during hyperventilation (Figure 1.6B-ii).

A - i ii B - i ii iii iv v

Figure 1.6 Serial radiographs of the lumbar spine. A) Reproduced from Reitan (1941) with permsions from

www.tandfonline.com: i – caudal displacement and bulging with jugular compression, ii – cranial displacement of

CSF when abdominal pressure is increased with jugular compression. B) Reproduced from Martins et al. (1972) with

markers to indicate net direction flow: i – base line location of bolus, ii – post 2 minute hyperventillation, iii – post 2

minutes breathing 10% CO2, iv – During a Valsalva, v – post 15 seconds of jugular compression. B) Reproduced

from Martins et al., Journal of Neurology, Neurosurgery & Psychiatry, 1972, 35:468-473 with permissions from the

BMJ publishing Group Ltd.

8

Perivascular flow

Fluid from the spinal subarachnoid space can flow into the spinal cord via the perivascular

spaces. This flow will be in part dependent on the difference between the subarachnoid and

interstitial pressures, although the mechanisms which facilitate this flow are unclear. Currently,

it is believed that the arterial pulsations within the perivascular space actively drive flow into

the cord. In a sheep model, Stoodley et al. (1997) demonstrated this dependence by ligating the

brachiocephalic trunk (large artery that branches off the aorta and supplying blood to the

cranium in sheep), reducing the arterial pulse pressure while maintaining the mean arterial and

subarachnoid pressure, finding tracer deposition along the perivascular spaces was diminished

compared with the control animal. Another ligation study in mice demonstrated the same

behaviour in the brain, perpetuating this hypothesis (Iliff et al., 2013b). Additionally, recent

intravital imaging studies have shown perivascular tracers pulsate in phase with the cardiac

cycle (Bedussi et al., 2018; Mestre et al., 2018). However, the significance of arterial pulsation

as a driver of perivascular flow is not clear. In the sheep model presented by Stoodley et al.

(1997), ligation also reduced the subarachnoid pulse pressure, therefore the significance of

arterial or subarachnoid pulsations on perivascular flow cannot be discerned. On a similar note,

in surface pial arteries the pulse amplitude of the arteries appears too small (~1% of arterial

diameter) (Mestre et al., 2018) to mechanically drive the flow.

Within the parenchyma most fluid motion is driven by diffusion, as the tight junctions and

narrow, tortuous nature of the extracellular space restrict advective flow (Brinker et al., 2014;

Tao and Nicholson, 1996). However, tracer studies have shown that fluid within the cord that

reaches the perivascular spaces will preferentially flow along them (Liu et al., 2018; Wong et

al., 2012), potentially being dispersed along the pathway to the outlets by the pulsatile flow. It

has been hypothesised that a pressure difference between the arteries and veins within the

parenchyma could drive flow through the extracellular space, drawing fluid in along the

periarterial and out through the perivenous spaces (Iliff et al., 2012). However, the evidence

supporting this remains controversial, and the mechanisms which facilitate fluid outflow from

the cord remain poorly understood (Brinker et al., 2014; Liu et al., 2014).

Chiari malformation and syringomyelia

Chiari malformation

As noted above in the Introduction, Chiari I Malformation is a congenital condition

characterised by the herniation of the cerebellar tonsils by 3-5 mm or more below the foramen

magnum (Fernández et al., 2009). The cause of the herniation is believed to be the abnormal

development of the posterior fossa, combined with an otherwise normal cerebellum giving rise

to overcrowding (Milhorat et al., 1999). The disease presents most commonly with symptoms

9

such as suboccipital headaches, neck pain and vertigo. These symptoms can be exacerbated by

Valsalva manoeuvre and head movements (Fernández et al., 2009; Milhorat et al., 1999).

Syringomyelia

Figure 1.7 Illustrated cases of canalicular syrinxes in a patient with Chiari I malformation, (A) communicating and

(B) non-communicating. (C) provides an example of extracanalicular non-communicating syringomyelia.

Communicating syrinxes (Figure 1.7A) appear as a dilation of the central canal, lined entirely or

partially by ependymal cells. The caudal end of the cavity is obstructed by a stenosis and

cranially unobstructed allowing passive flow to and from the fourth ventricle. This type of

syrinx commonly is asymptomatic, as the cavity rarely ruptures into the spinal tissue (Milhorat

et al., 1995). Communication with the fourth ventricle is an infrequent occurrence in cases of

syringomyelia (West and Williams, 1980). This communication is commonly found in

association with obstructions to CSF flow through the outlets of the fourth ventricle

(hydrocephalus and Chiari II malformation), suggesting that excess CSF leads to the dilation of

the central canal (Milhorat, 2000; Milhorat et al., 1995).

Non-communicating canalicular syrinxes (Figure 1.7B) are dilations of the central canal

enclosed both caudally and cranially by a stenosis (obstructing flow to the fourth ventricle).

Unlike communicating syrinxes, with growth the syrinx is likely to rupture and dissect the

parenchyma, leading to additional neurological defects (Milhorat et al., 1995). They are found

in association with obstructions of flow at the level of or below the foramen magnum, such as

with Chiari I malformation and arachnoiditis (Milhorat, 2000), although the causal link is poorly

understood.

Non-communicating extracanalicular syrinxes (Figure 1.7C) initiate within the parenchyma of

the spinal cord and do not communicate with the central canal. The initial cavitation is often

associated with spinal cord trauma, although the mechanism in which the cavity fills with fluid

is unknown. Rodent models of injury suggest that spinal trauma causes a prolonged disruption

of the blood-spinal cord barrier, and increased the expression of water channels within the

spinal cord (such as Aquaporin-4 [AQP4]; a protein channel that facilitates transport of water

A B C

10

across the blood-spinal cord barrier), which would allow for greater fluid accumulation and may

aid syrinx formation (Hemley et al., 2013; Hemley et al., 2012; Hemley et al., 2009).

Additionally, arachnoiditis and scarring at the site of injury may influence fluid influx

(Milhorat, 2000; Naftel et al., 2013; Najafi et al., 2016; Wong et al., 2016).

Mechanisms for syrinx formation: Clinical studies

Communicating syrinx formation

Initial suggestions of a mechanistic link between the structural abnormalities and syrinx

formation came from clinical observations. Gardner (1965) hypothesised that if the outlets of

the fourth ventricle were sealed, the pulsation of the choroid plexus would force fluid into the

central canal and produce a syrinx (Figure 1.8). Using this understanding of how a syrinx

develops, Gardner (1965) suggested that Chiari associated syrinxes could be treated by

removing a portion of the occipital bone (posterior fossa decompression; done to increase CSF

volume of cisterna magna), removing obstructions to the fourth ventricle, sealing the obex, and

expanding the dura mater with a duraplasty (a patch of material used as artificial dura, with the

aim of increasing CSF volume).

Figure 1.8 Gardner’s mechanism for syrinx development. A) During the systolic phase, pulsation of the choroid

plexus forces fluid from the fourth ventricle to the central canal of the spinal cord, expanding the syrinx. B) During

diastolic phase syrinx fluid is prevented from returning to fourth ventricle.

The positive surgical outcomes from posterior fossa decompression earned the hypothesis

popularity (Klekamp, 2002), although further studies showed that the same level of success was

found when the obex was left unobstructed (questioning the role of central canal communication

in syrinx formation) (Ball and Dayan, 1972). Additionally, this mechanism relied on the

permanent blockage of the fourth ventricle, which would result in hydrocephalus (Williams,

1969), which does not occur in the majority of syrinx patients (Williams, 1980). In cases

associated with Chiari II malformation and hydrocephalus, this mechanism is still accepted

(Milhorat, 2000; Milhorat et al., 1995).

A B

11

Williams (1969) developed the concept of CSF communication via the fourth ventricle,

suggesting that the cerebellar tonsils could act as a one-way valve, allowing cranial flow but

obstructing caudal flow (Figure 1.9). During this caudal flow obstruction a pressure difference

between the cranial and spinal subarachnoid spaces would develop (commonly referred to as a

“pressure dissociation” in clinical literature), and in order to equalise the pressure difference,

CSF would flow into the cord via the central canal (Williams, 1981a, b). This ‘suck’ effect was

demonstrated with invasive pressure measurements, finding cranial pressures to be higher than

in the lumbar spine post-Valsalva. This effect was attributed to the tonsillar obstruction, but it

should be noted that this study used no control subjects, thus which characteristics are

pathological is not clear. Heiss et al. (1999) later confirmed the occurrence of this pressure

dissociation, performing jugular compression on controls and Chiari subjects to increase

intracranial pressure, showing a delay in the rise of the lumbar spinal pressures in Chiari

patients compared with controls. However, MRI scans from this study showed syrinxes are

compressed in systole and do not expand during Valsalva manoeuvres, which contradicts what

would be expected under the mechanisms suggested by either Gardner or Williams.

Figure 1.9 Williams pressure dissociation mechanism for syrinx development. A) While coughing or straining CSF is

displaced cranially. B) At rest the tonsils descend preventing CSF from flowing caudally and maintaining increased

intracranial pressure. To normalise the pressure CSF flows from the fourth ventricle into spinal cord via the central

canal, forming the syrinx.

Mechanism for perivascular inflow

As communicating syringomyelia is uncommon among Chiari type I patients, Ball and Dayan

(1972) proposed that the perivascular spaces could provide an alternate pathway to the central

canal. Ball and Dayan (1972) noted that in cords with a syrinx, the lumen of the perivascular

spaces were abnormally dilated, this was taken to indicate that the perivascular spaces had been

under pressure for a prolonged period. From this, it was assumed that increased subarachnoid

pressure could force CSF into the cord, leading to the development of a syrinx (Figure 1.10A).

With the use of MRI and ultrasonography Oldfield et al. (1994) & Heiss et al. (1999) identified

that the cerebellar tonsils pulsate in phase with the caudal CSF flow. It was hypothesised that

A B

12

the cerebellar tonsils could act like a piston, sending large pressure waves along the spinal

canal, forcing fluid into the cord via the perivascular spaces (Figure 1.10B). In support of their

hypothesis, patients that underwent posterior fossa decompression had reduced hindbrain

motion and the syrinx diameter decreased (Oldfield et al., 1994). Additionally, it was shown

that amount of contrast fluid that migrated from the spinal subarachnoid space into the syrinx

cavity decreased post decompression (Heiss et al., 2018). However, it has been shown that the

amplitude of tonsillar pulsation does not differ between patients with and without a syrinx

(Leung et al., 2016), failing to explain why this mechanism would not cause a syrinx in all

Chiari patients. Similarly, elevated pressure alone fails to explain why fluid would be retained

in the cord when the subarachnoid pressures decrease during the diastolic phase (Heiss et al.,

1999).

Figure 1.10 A) Ball & Dayan’s mechanism for syrinx development, suggesting increased subarachnoid pressure

forces CSF into the cord via the perivascular spaces. B) Tonsillar piston mechanism for syrinx formation. During the

systolic phase or coughing the cerebellar tonsils pulsate caudally increasing the cervical spinal pressure and forcing

fluid into the cord.

Treatment

Typically, Chiari malformation is treated by performing a posterior fossa decompression, to

increase the volume of the cisterna manga and normalise CSF flow. At its simplest this may

involve only removing a portion of the occipital bone and upper cervical spinal processes (bony

decompression). More commonly the dura will also be dissected and expanded with a

duraplasty (a synthetic patch or a piece of muscle) to further expand the posterior volume.

Additional steps may involve dissecting that arachnoid and resecting the cerebellar tonsils (Villa

et al., 2019). Retrospective clinical studies are not conclusive as to the optimal approach,

showing similar levels of positive outcome and complications between the different methods

(Zhao et al., 2016). Common complications include CSF leaks, or bleeding within the

subarachnoid space, which causes post-operative arachnoid scarring and further complications

(Aghakhani et al., 2009; Dubey et al., 2009; Jia et al., 2019). The reduction of the syrinx and the

A B

13

relief of symptoms are typically attributed to the normalisation of CSF flow (Dolar et al., 2004;

Iskandar et al., 2004) and tissue motion (Dawes et al., 2019; Lawrence et al., 2018), although a

mechanism for this link has yet to be established.

Shunting can be used to ensure flow from the fourth ventricle, or across the foramen magnum

(Guyotat et al., 1998). More typically it is used to drain the syrinx when the associated

pathology is deemed inoperable or previous interventions have failed to reduce the syrinx

(Cacciola et al., 2009; Davidson et al., 2018). The shunt can be placed in the syrinx to drain

fluid into the pleural, peritoneal, or subarachnoid spaces (Cacciola et al., 2009; Chumas et al.,

1993; Davidson et al., 2018). The collapse of syrinxes with shunting into the subarachnoid

space may provide insight as to the mechanics of syrinx growth (Davidson et al., 2018). Syrinx

growth can be thought to be an imbalance between the normal levels of fluid flow into and out

of the spinal cord. Shunting into the subarachnoid provides direct communication with the

syrinx, and if increased subarachnoid pressure alone could increase fluid uptake, the syrinx

would be expected to grow. Since syrinxes are successfully treated with this method, it would

suggest that there is either some additional mechanism required to increase fluid uptake, or there

may be a mechanism restricting normal fluid outflow from the cord.

Magnetic resonance imaging studies

Structural anatomy of Chiari malformation

Tonsillar herniation is a clinical definition of Chiari, however the 5 mm threshold falls within

the range seen in the wider healthy population (Smith et al., 2013). Additionally, the degree of

herniation shows no relation to whether the patient experiences symptoms (Khalsa et al., 2018;

Meadows et al., 2000), or develops a syrinx (Masur et al., 1995; Stovner and Rinck, 1992).

Other morphometric measures of the occipital bone and the soft tissues within the posterior

fossa have also been used to characterise Chiari (Figure 1.11; Table 1.1). Common findings are

that Chiari patients compared with controls appear to have; a shorter clivus (Figure 1.11; line 3)

with a shallower angulation (Figure 1.11; angles 9 & 11), a shorter occipital bone (Figure 1.11;

line 6) with a steeper angulation (Figure 1.11; angles 10 & 13), in addition to a larger fraction of

the fossa volume occupied by the hindbrain (over-crowding). The corpus callosum, pons, and

fastigium of the fourth ventricle have also been found to be closer to the foramen magnum

(Figure 1.11; lines 16, 17 & 18). The proximity of these structures to the foramen magnum has

generally not been addressed, and it has likely been assumed that their relative position is the

result of an abnormal posterior fossa causing the caudal descent of these structures, similar to

the cerebellar tonsils (Houston et al., 2018; Urbizu et al., 2014). However, it could also indicate

that supratentorial abnormalities are influencing hindbrain descent and overcrowding (Heiss,

2014). As in the analogous condition Chiari-like malformation found in brachycephalic toy

14

breeds of dog, it has been shown that rostral changes in bone structure contribute to

overcrowding, and tonsillar herniation (Knowler et al., 2017; Rusbridge et al., 2018).

Chiari patients with a syrinx have been found to have a narrower foramen magnum

(Eppelheimer et al., 2018) and greater clivus incline (Yan et al., 2016) compared with patients

without a syrinx (Figure 1.11; line 7 and angle 9). These subtle differences could affect the CSF

dynamics and contribute to syrinx formation; however, these findings are not consistent across

the two studies (Table 1.1). Additionally, not controlling for the presence of a syrinx could

contribute to conflicting findings within the literature. Most studies have either not mentioned

or failed to control for the differences between Chiari patients with and without a syrinx (see

Table 1.1).

Similarly to tonsillar herniation, no relationship has been demonstrated between the morphology

of the posterior fossa and the symptoms of the Chiari patient (Alperin et al., 2014; Alperin et al.,

2015; Khalsa et al., 2018). However, Chiari headaches are associated with a significant decrease

in intracranial compliance (Alperin et al., 2014; Alperin et al., 2015), and increased hindbrain

(Leung et al., 2016) and spinal cord (Lawrence et al., 2018) pulsation, although the mechanisms

which facilitate this relationship, and whether they contribute to syrinx growth is not fully

understood.

15

Linear

1) Height of PCF = perpendicular distance from the foramen magnum [Line 7] to the superior aspect of the tentorium.

2) Width of PCF = distance between the dorsum sellae of the clivus to the internal occipital protuberance.

3) Clivus length = distance between the dorsum sellae (superior point) to the basion (inferior point), also equals the sum of 4

and 5.

4) Basisphenoid length = dorsum sellae to the sphenoocciput.

5) Basiocciput length = basion to the sphenoocciput.

6) Supraocciput length (Occipital bone length) = the opisthion (posterior point of foramen magnum) to the internal occipital

protuberance.

7) Foramen magnum (McRae line) = anterior-posterior diameter between the basion and opisthion.

Angular

9) Clivus incline (Boogard angle) = angle between lines 3 and 7.

10) Tentorial angle = angle between lines 15 and 6.

11) Basal angle = angle between line 3 and a line extending along the anterior cranial fossa from the dorsum sellae.

12) Wackenheim angle = angle between line 3 and a tangent line connecting the posterior aspect of the dens to the caudal point

of the C2 vertebra.

13) Twining’s-tentoral angle = angle between lines 2 and 15.

Soft tissue 14) Tonsillar herniation below foramen magnum [Line 7]. (Distance below line is taken as positive.)

15) Tentorium length = From the internal occipital protuberance to the superior end of the tentorium.

16) Fastigium height = perpendicuar distance between the foramen magnum [Line 7] and the fastigium of the fourth ventricle.

17) Pons height = perpendicular distance between the foramen magnum [Line 7] the superior aspect of the pons at the

invagination before the midbrain.

18) Corpus callosum height = perpendicuar distance between the foramen magnum [Line 7] to the inferior aspect of the

splenium of the corpus callosum.

Areas

19) PCF area = area enclosed by lines 3,7,6,15,8

20) PCF osseous area = area enclosed by lines 3,7,6,2

Volumes

21) PCF volume = volume segmented form the same bounds as 19.

22) Hindbrain volume within the PCF volume [21].

23) CSF volume in PCF [21] = volume 21 minus volume 22.

24) Ratio of hindbrain volume:PCF volume – relative measure of overcrowding [Volumes 22/21].

Figure 1.11 Common morphometric measurements performed in the literature. Numbering scheme used to simplify

summary of study findings in Table 1.1. Measurements are divided into five categories, linear, angular, area, and

volumetric measures of PCF, in addition to measures of soft tissue location.

16 Table 1.1 Summary of morphometric studies. Table contains symbolic representations of the commonly investigated characteristics (for clarity, some insignificant measures or those used in few

studies were neglected). Significant findings are shown as; decrease (↓), increase (↑), no significant difference (NS), and measure not studied (-).Measurement codes corresponded to Figure 1.11.

For studies where Chiari patients both with and without a syrinx were grouped, the proportion of syrinx patients are noted (? Indicates where syrinx patients were included but the number of

patients was not quoted). *Indicates a measurement that was calculated in the original study but can be calculated from the paper. 1Highlights that the study includes 2 subjects with fourth

ventricle herniation which may affected the results. 2Study uses a total 364 Chiari patients in the study but only 50 were used for quantitative analysis.

Study Number of subjects Comparison Linear measures Angular measures Soft tissue measurements Area Volume

1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Dagtekin et al. (2011)

Control = 25

Chiari = 15

Syrinx = 9/15

Chiari v.

Control - - NS ↓ ↑ ↓ ↑ - NS - - - - - - NS - - - - - - -

Alperin et al. (2014) Control = 37

Chiari = 36

Chiari v.

Control - ↓ ↓ - - ↓ NS - - - - - - - - - - - - ↓ NS - ↑

Alperin et al. (2015) Control = 37

Chiari = 63

Chiari v.

Control - - ↓ - - ↓ - - - - - - - - - - - - - ↓ NS - ↑

Nishikawa et al.

(1997)

Control = 50

Chiari = 301

Syrinx = 26/30

Chiari v.

Control - - NS - NS ↓ - - - - - ↑ - - - NS - - - NS NS - ↑

Nishikawa et al.

(1997)

Control = 50

Chiari = 301

Syrinx = 26/30

Chiari w/

basilar invagination v.

Control - - ↓ - ↓ ↓ - - - - - ↑ - - - NS - - - NS NS - ↑

Milhorat et al. (1999)

Control = 50

Chiari = 502

Syrinx = ?/50

Chiari v.

Control - - ↓ - - ↓ - - ↑ - - - ↑ - - - - - - ↓ NS ↓ ↑*

Karagoz et al. (2002)

Control = 21

Chiari = 22

Syrinx = 13/22

Chiari v.

Control - ↓ ↓ - - ↓ NS ↑ NS ↑ - ↑ - - - - - NS ↓ - - - -

Sekula et al. (2005) Control = 25

Chiari = 22

Chiari v.

Control - NS ↓ ↓ ↓ NS NS - - - - ↑ - - - NS - - - - - - -

Hwang et al. (2013)

Controls = 24

Chiari = 12

Syrinx = ?/12

Chiari v.

Control - ↓ ↓ - - NS ↓ ↑ ↑ - - - - - - NS - - - - - - -

Urbizu et al. (2014)

Controls = 50

Chiari = 100

Syrinx = 45/100

Chiari v.

Control ↓ NS ↓ NS NS ↓ NS - ↓ ↑ NS - ↑ ↓ ↓ ↓ ↓ ↓ ↓ - - - -

Milhorat et al. (2010) Controls = 80

Chiari = 388

Chiari v.

Control - - ↓ - - ↓ NS - - - - - - - - - - - - ↓ NS - ↑*

Aydin et al. (2005)

Control = 30

Chiari = 60

Syrinx = 46/60

Chiari v.

Control ↓ ↓ ↓ - - NS ↑ - - - - - - - - - - - - - - - -

Dufton et al. (2011) Control = 107

Chiari = 81

Chiari v.

Control - - ↓ - - - ↑ ↑ - - - - ↑ - - - - - - - - - -

Yan et al. (2016) Control = 40

Chiari = 36

Chiari v.

Control - ↓ ↓ - - ↓ NS NS - - - - - - - - - - - - - - -

17

Table 1.1 Continued summary of morphometric studies. Showing the studies that investigated the difference in morphology with sex, and the differences seen in syrinx patients. Significant

findings are shown as; decrease (↓), increase (↑), no significant finding (NS), and measure not studied (-).For studies where Chiari patients both with and without a syrinx were grouped, the

proportion of syrinx patients are noted (? Indicates where syrinx patients were included but the number of patients was not quoted). 3Study reassessed after reducing the data set to control for

age, race and body mass index.

Study Number of

subjects Comparison

Linear measures Angular measures Soft tissue measurements Area Volume

1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Schady et al. (1987) Control = 26

Chiari = 13

Male

Chiari v.

Control - NS ↓ - - - NS ↑ - ↑ - - - - - - - - ↓ - - - -

Stovner et al. (1993) Control = 14

Chiari = 11

Male

Chiari v.

Control - NS - - - - - - - - - - - - - - - - NS - - - -

Vega et al. (1990)

Control = 23

Chiari = 25

Syrinx = ?/25

Male

Chiari v.

Control - - ↓ - - - NS ↑ - ↑ - - - - - - - - ↓ ↓ - - -

Houston et al. (2019)

Control = 26

Chiari = 26

Syrinx = 8/26

Male

Chiari v.

Control ↓ ↑ ↓ - - NS NS ↑ ↓ NS ↓ - ↑ NS ↓ ↓ ↓ NS NS - - - -

Schady et al. (1987) Control = 23

Chiari = 19

Female

Chiari v.

Control - NS NS - - - NS ↑ - ↑ - - - - - - - - ↓ - - - -

Stovner et al. (1993) Control = 26

Chiari = 22

Female

Chiari v.

Control - NS - - - - - - - - - - - - - - - - ↓ - - - -

Vega et al. (1990)

Control = 23

Chiari = 17

Syrinx = ?/25

Female

Chiari v.

Control - - ↓ - - - NS ↑ - NS - - - - - - - - ↓ NS - - -

Houston et al. (2019)

Control = 26

Chiari = 26

Syrinx = 6/26

Female

Chiari v.

Control

NS NS ↓ - - NS NS ↑ NS NS ↓ - ↑ NS ↓ ↓ ↓ NS NS - - - -

Houston et al. (2018)

Controls = 131-140

Chiari = 155-162

Syrinx = 28/162

Female

Chiari v.

Control ↓ NS ↓ - - NS ↑ ↑ NS ↑ ↓ - ↑ NS ↓ ↓ ↓ ↓ ↓ - - - -

Houston et al. (2018)3

Controls = 100-108

Chiari = 115-121

Syrinx = 21/121

Female

Chiari v.

Control ↓ NS ↓ - - NS ↑ ↑ NS ↑ ↓ - ↑ NS ↓ ↓ ↓ NS NS - - - -

Eppelheimer et al. (2018) Controls = 140

Syrinx = 38

Syrinx v.

Control NS NS NS - - NS NS NS NS NS NS - NS NS NS ↓ NS ↓ NS - - - -

Yan et al. (2016) Control = 40

Syrinx = 48

Syrinx v.

Control - ↓ ↓ - - ↓ NS ↑ - - - - - - - - - - - - - - -

Eppelheimer et al. (2018) Chiari =198

Syrinx = 38

Syrinx free v.

Syrinx NS NS NS - - NS ↑ NS NS NS NS - NS NS NS NS NS NS NS - - - -

Yan et al. (2016) Chiari = 19

Syrinx = 48

Syrinx free v.

Syrinx - NS NS - - NS NS ↑ - - - - - - - - - - - - - - -

18

Several studies have measured the taper of the spinal canal (change in the anterior-posterior

diameter of the spinal subarachnoid space per vertebrae along the spine) to determine how it

may relate the syrinx formation (Gadde et al., 2017; Hammersley et al., 2012; Hirano et al.,

2012; Struck et al., 2016; Zhu et al., 2014). A common finding is that the diameter of the spinal

canal increases faster between the C4 and C7 vertebrae in syrinx patients compared with

controls and syrinx free Chiari patients (Table 1.2). These studies suggest there may be subtle

differences in the pressure gradients in the cervical spine in Chiari subjects, which could

facilitate syrinx development. Although there is some conflict within the literature, in part this

may be attributed to the small sample of several studies (Gadde et al., 2017; Hammersley et al.,

2012; Hirano et al., 2012). Additionally, as the tapers are defined by the spinal level, differences

in the caudocranial height of the vertebra has been neglected, which may have a significant

effect on the rate of change (and pressure gradients) between subjects. These studies did not

take into account whether there is any variation in the size of the epidural space associated with

different subarachnoid tapers. This could also affect CSF dynamics by causing local changes in

the spinal compliance or by altering the sensitivity to respiratory forces.

Table 1.2 Summary of spinal taper measurements. The table contains a symbolic representation of the studies

findings. Significant findings are shown as; decrease in the anterior-posterior diameter caudally (↓), increase in the

anterior-posterior diameter caudally (↑), no significant difference (NS), and measure not studied (-).

Study Number of subjects Comparison C1-

C4 C4-C7 C1-C7 C1-T1

Gadde et al. (2017) Control = 21

Pre-syrinx = 21

Pre-syrinx v.

Control NS ↑ NS -

Gadde et al. (2017) Chiari = 21

Pre-syrinx = 21

Pre-syrinx v.

Chiari NS NS NS -

Hammersley et al. (2012) Chiari with scoliosis = 2

Syrinx with scoliosis = 20

Chiari with scoliosis v.

Syrinx with scoliosis NS - NS -

Thompson et al. (2016) Chiari = 101

Syrinx = 49

Syrinx v.

Chiari NS ↑ ↓ -

Struck et al. (2016) Control = 50

Idiopathic syrinx = 50

Idiopathic syrinx v.

Control NS NS NS -

Zhu et al. (2014) Syrinx distended = 44

Syrinx not distended = 33

Syrinx distended v.

Syrinx not distended ↓ ↑ ↓ -

Hirano et al. (2012) Control = 9

Chiari = 9

Chiari v.

Control - - - NS

Hirano et al. (2012) Control = 12

Syrinx = 12

Syrinx v.

Control - - - ↑

Hirano et al. (2012) Syrinx = 7

Idiopathic syrinx = 7

Idiopathic syrinx v.

Control - - NS

The effect of Chiari on CSF flow

Early cardiac-gated phase-contrast MRI (PC-MRI) studies were conducted in the midsagittal

plane, finding reduced CSF flow at the level of the foramen magnum (Armonda et al., 1994;

Ellenbogen et al., 2000), supporting the hypothesis that Chiari malformation restricts CSF flow

(Gardner, 1965; Williams, 1969). Subsequent axial and 4D (flow through a volume over time)

PC-MRI scans have since demonstrated that although flow though the anterior CSF space may

be restricted, high velocity jets are commonly found in the anterolateral spaces (Bunck et al.,

2012; Bunck et al., 2011; Haughton et al., 2003; Iskandar et al., 2004; Quigley et al., 2004).

19

Other abnormal features of CSF flow have been identified in Chiari patients, however the

majority of studies have grouped patients both with and without a syrinx, removing the

possibility of identifying differences related to syrinx formation (Haughton et al., 2003;

Hofmann et al., 2000; Iskandar et al., 2004; Quigley et al., 2004). The findings of the few

studies which have investigated the difference in CSF flow between the two patient groups are

summarized in Table 1.3. Figure 1.12 shows a typical cardiac CSF pulse annotated with the

characteristics investigated. In general, Chiari patients without a syrinx have been shown to

have increased peak caudal velocities, which occur earlier in the cardiac cycle compared with

controls, whereas patients with a syrinx have normal peak velocities, with delays in timing

features when compared with both controls and syrinx free patients (Table 1.3). However, there

is disagreement as to whether syrinx subjects have normal or increased peak velocities (Table

1.3).

Table 1.3 Summary of the magnitude and timing features of CSF flow in Chiari patients both with and without a

syrinx present. Features of interest are annotated on Figure 1.12. Significant findings are shown as; decrease (↓),

increase (↑), no significant difference (NS), and measure not studied (-). Table divided into comparisons between the

three different subject groups.

Study

Peak

cranial

velocity

Peak

caudal

velocity

Time of

cranial

peak

Time of

caudal

peak

Time of

cranial

onset

Time of

caudal

onset

Caudal

flow

duration

Ch

iari

v.

Co

ntr

ol

Pinna et al.

(2000) - - - - - - NS

Bunck et

al. (2012) ↑ ↑ - NS - - NS

Clarke et

al. (2013b) NS ↑ NS ↓ NS ↓ NS

Sy

rin

x v

.

Co

ntr

ol

Pinna et al.

(2000) - - - - - - NS

Bunck et

al. (2012) ↑ ↑ - ↑ - - NS

Clarke et

al. (2013b) NS NS ↑ NS NS ↑ NS

Ch

iari

v.

Sy

rin

x

Pinna et al.

(2000) - - - - - - ↓

Bunck et

al. (2012) NS NS - ↓ - - NS

Clarke et

al. (2013b) NS ↑ NS ↓ ↓ ↓ NS

20

Figure 1.12 Example cardiac CSF pulse from a control. Showing annotations for characteristics of the velocity-time

profile used for analysis. Image edited and reproduced from Clarke et al. (2013b).

Overall, exceptionally high CSF velocities (4.9-37.9 cm.s-1) were found by Bunck et al. (2012)

compared with conventional 2D flow studies (~4-10 cm.s-1) (Clarke et al., 2013b; Haughton et

al., 2003; Iskandar et al., 2004), suggesting that the limited field of view of 2D imaging is prone

to missing key flow features. However, from the circular flow jets shown in the example images

provided (Figure 1.13), it is unclear whether the region of interest used to quantify flow

excluded the blood vessels such as the anterior epidural veins (Groen et al., 1997). Failing to do

so could account for the large discrepancy in the measured velocities and explain why the

greatest velocities were measured at C1, instead of near the constriction at the foramen magnum

as expected.

Figure 1.13 Examples of flow jets and recirculation patterns in Chiari patients from Bunck et al. (2012). A) shows

velocity vectors on the coronal plane, b) shows axial slice in same subject at obstruction (approx. C1), highlighting

dark circular jets. D) shows another patient example in coronal plane, with corresponding axial view C). High

velocity jets had aliasing errors and were interpreted as CSF jets, however this is unclear and they may be blood

vessels. Reproduced from Bunk et al.,European Radiology,2012, 22:1860-70 with permissions from Springer Nature.

21

Bhadelia et al. (2016) used real-time pencil beam imaging to investigate the effect of coughing

on CSF flow in Chiari patients. This imaging technique measures the through plane velocity at

axial cross-sections along the length of a cylindrical volume. While scanning, participants

would breathe normally for approximately 20 seconds, followed by six forceful coughs, then

return to normal breathing. With this protocol, it was demonstrated that the CSF pulse

amplitude was decreased post-cough in Chiari patients compared with controls (Bhadelia et al.,

2016). Similarly, it was shown that in Chiari patients with headache associated symptoms, there

was a greater decrease in the stroke volume (net volume of CSF displaced per cardiac beat)

post-cough compared with patients without specific symptoms (Bezuidenhout et al., 2018).

These findings were interpreted to support Williams (1969) mechanism of pressure dissociation

(Figure 1.9), suggesting that post-cough the cerebellar tonsils would move and obstruct CSF

flow, although this valving motion was not explicitly demonstrated in the study. There may be

other factors which could contribute to this behaviour, such as the increased duration of

inspiration post-cough shown in the example case provided by Bhadelia et al. (2016).

Additionally, after the release of sustained elevated trunk pressures (i.e. persistent coughing or

straining) there are changes in heart rate and mean blood pressure which may be confounding

factors (Dawson et al., 1999; Korner et al., 1976), particularly as the CSF pulse amplitudes are

not comparable between groups during coughing, which may suggest different strength

expiratory efforts, and could have contributed to the differences found in the post-cough CSF

flow (Bezuidenhout et al., 2018). Whether these cough-dependent changes in CSF flow

contribute to syrinx development has not been assessed.

The purpose and reliability of computational modelling

Computational models are used to examine physical phenomena which could play a role in

syrinx development. This can be achieved by either using idealised models to illustrate new

mechanisms, or subject-specific models which can provide additional information that current

imaging techniques cannot. Models investigating aspects of normal CSF dynamics are

summarised in Table 1.4 and models studying the influence of pathology are summarised in

Table 1.5.

22 Table 1.4 Summary of computational models investigating characteristics of normal CSF dynamics.

Study Numerical

method Anatomy Boundary inputs Validation Focus of Investigation Outcome Limitations Sweetman and

Linninger

(2011) Finite

element Semi-

idealised Cardiac displacements applied to lateral ventricles and circle

of Willis Model agrees with

MR flow at two

locations How cranial pulsations drive

CSF flow Estimate of pressures and flows

Indication of increased mixing with

flow transition from cranial to caudal flow

-

Tangen et al.

(2015) Finite

Volume Subject-

specific Cardiac displacements applied to brain surface, choroid plexus

and lower spine Model agrees with MR flow at three

locations How nerve roots and

arachnoid web influence flow Nerve roots and web increase fluid

recirculation Web density can increase flow

resistance

Arachnoid web assumed to be rigid

cylinders. Potentially over-estimating

there resistance to flow and on mixing.

Gupta et al.

(2009) Finite

Volume Subject-

specific MR acquired CSF flows

applied to inlets Comparisons to

literature values Determine flow path in

cranial and cervical junction Determine influence of

arachnoid web

Estimates of flow paths and CSF

mixing Arachnoid density increases pressures

required for a given flow

Arachnoid web assumed to be rigid cylinders and applied as a porous

resistance. Likely over estimating there resistance and neglecting any

dynamic effect. Gupta et al.

(2010)

Sass et al. (2017)

Finite Volume

Subject-specific

Highest CSF flow rate in

cervical region applied to

foramen magnum Model over-

estimates flow

rates Determine the influence of

anatomical nerve roots List hydrodynamic factors of the

model. N=1. No comparisons made to model

without nerve roots so effects cannot

be assessed Pahlavian et

al. (2014) Finite

Volume Subject-

specific Highest CSF flow rate in

cervical region applied to foramen magnum

Poor matching

with 4D MRI data Determine whether addition

of nerve roots would fix

discrepancy between models

and MR

Insignificant effect on pressures. Introduce flow jets.

Introduced recirculation during flow

reversal.

Large differences between MR and

model geometries, causing validity problems

Yiallourou et

al. (2012) Finite

Volume Subject-

specific Highest CSF flow rate in

cervical region applied to foramen magnum

Poor matching

with 4D MRI data Reliability study into CFD

model Poor agreement between models and

MR. Attributed to lack spinal structures

Large differences between MR and

model geometries, causing validity problems

Khani et al. (2017)

Finite Volume

Subject-specific

0 Pa outlet and dura mater was

displaced to fit a approximated volume change measured by

MRI flow data

Good matching

between model

and MR data New method to accurately

model CSF velocity’s Model could match average flow

profiles well, although there are still local decencies likely due to nerve

roots and web

Model applies an unphysiological motion to the dura. Forcing the

correct velocities independent of the

geometry. No pressure data. reported

Loth et al.

(2001) Finite

Volume Semi-

idealised MR acquired CSF flows

applied to inlet

Comparison to

literature values

and qualitative assessment of

PCMRI

Characterise dynamic within

the spinal canal Flow inertia dominated.

Eccentricity of the cord has little

effect on pressure by influence local

flow field -

Khani et al.

(2018) Finite

Volume Subject-

specific 0 Pa outlet and dura mater was displaced to fit a approximated

volume change measured by

MRI flow data

Good forced agreement

between model

and MR Determine the effects nerve

roots on CSF flow

Nerve roots create flow jets and

increase local mixing during flow

reversal. Fine obstructions increase the effect

of steady streaming See Khani et al. (2017)

23

Table 1.4 Summary of computational models investigating characteristics of normal CSF dynamics continued.

Study Numerical

method Anatomy Boundary inputs Validation Focus of Investigation Outcome Limitations

Cheng et al. (2014)

Finite Volume

Subject-specific

MR acquired CSF flows applied to inlet

Good agreement at T2 level

Determine the effect of cord motion

Cord motion could change local

velocity field based on eccentricity. Negligible effect on the CSF

pressures

Excluded fine structures from

subarachnoid which could influence

cord motion. Stockman

(2005) Lattice

Boltzmann Idealised CSF flow from literature

applied to inlet Comparison to

literature values Influence of fine structures

on flow Introduction of fine structures has a negligible effect on average flow. Increase in plane velocities and

mixing

Fine structures assumed to be rigid cylinders, likely overestimating there

resistance and neglecting any dynamic

effect. Stockman (2007)

Sánchez et al.

(2018) Analytical Idealised CSF flow from literature

applied to inlet Comparison to

literature and

mechanical model Influence of eccentricity on

bulk flow. Cumulative effects of convective

acceleration can result in a creeping

flow when the cord is eccentric.

Creeping velocity is likely negligible compared with psychological changes

in net flow. Fine structures and changes in spine

curvature will significantly affect this

as a mechanism for bulk flow.

Kuttler et al.

(2010) Finite

Volume Semi-

idealised

Literature based cardiac flow

rate applied to cranial inlet. Lower spine has a moving

boundary to simulate

respiratory compression

Comparison to

literature Influence of oscillatory flow

on drug circulation

Input condition result in a non-zero

mean velocity in spinal canal. CSF pulsation play a larger role in

solute transport then injection

method

Fluid transport under CSF pulsation

not actually calculated. Solute flow calculated based on time averaged CSF flow. Abstract results,

cannot be directly compared with.

Martin et al.

(2016) Finite

Volume Subject-

specific Highest CSF flow rate in

cervical region applied to

foramen magnum None

To assess the significance of

inter-operator variability

when generating subject-specific models

CSF velocities and pressures

relatively insensitive to small errors

in the volume of the subarachnoid space

No validation was conducted. There is

good agreement between operators, but

with no in vivo comparison, the

operator’s performance cannot be

assessed.

Cheng et al.

(2012) Finite

Volume Subject-

specific MR acquired CSF flows

applied to inlet Model agrees with

MR flow at two locations

Determine how arachnoiditis

and it permeability effect CSF dynamics.

Model demonstrated that

arachnoiditis increases resistance to

flow, increasing the magnitude and introducing timing delays in CSF

pressure. Also introduces complex flow patterns, i.e. anterior-posterior

bidirectional flow.

Yeo et al. (2017) found peak CSF flow to occur earlier compared with control

in a larger cohort of PTS syrinx

patients. Suggesting that modelling arachnoiditis a porous obstruction,

does not accurately capture it effects

on CSF dynamics.

24 Table 1.5 Summary of computational models investigating the effect of pathology on CSF dynamics.

Study Numerical

method Anatomy Boundary inputs Validation Focus of Investigation Outcome Limitations

Cirovic (2009) Analytical Idealised Harmonic waves assumed Compared with waves speeds

calculated in

Bertram et al. (2005)

Influence of geometry and

material properties on wave

propagation Syrinx growth slows wave

propagation in the spinal cord and

increases radial pulsation

Governing equations are simplified assuming that the wave amplitudes

are small. i.e. CSF velocity << pulse

speed With respiratory driven CSF flow

this may not be true, and the non-

linear convective term cannot be

neglected.

Cirovic and Kim (2012) Analytical Idealised

1) Cranial 100 Pa and 2)

caudal 10 kPa Half sine pulses at 100Hz.

Verified against literature values of

wave speed

(models and in vivo)

To Assess whether 1D

models can be used in place

of finite element. Characterise wave

propagation and influence of

a syrinx

Syrinx growth slows wave

propagation in the spinal cord and

increases radial pulsation

Model assumes small deformations.

In the lumbar and sacral spine with respiration this assumption does not

hold true (Martins et al., 1972)

Linge et al. (2010) Finite Volume

Semi-idealised

Sinusoid applied to the

craniocervical junction. Caudal

peak velocity ≈ 2 cm.s-1

Flow field qualitatively

compared with in

vivo data by a

neuroradiologist

To provide a proof of concept generalised spinal

model for future assessment

of pathological changes in

geometry - -

Linge et al. (2011) Finite

Volume Semi-

idealised Same as Linge et al. (2010) Same as Linge et

al. (2010) Determine the effect of

extending the tonsils

caudally on CSF dynamics

Creates more complex flow fields

(i.e. flow jets and bidirectional

flow) and increases the pressure gradient

Only tested one herniated state.

Unknown how gradual progression

from normal levels of herniation to pathological effects CSF dynamics.

Linge et al. (2013) Finite Volume

Semi-idealised

Asymmetric waveform. F= 80

& 120 bpm. For slower heart rate caudal velocity 2x greater

than cranial, and diastolic phase 2x longer than systole.

Fast heart rate becomes

symmetric.

Same as Linge et al. (2010)

Determine the effect of

elevated heart rate on CSF dynamics

Creates more complex flow fields

(i.e. flow jets and bidirectional flow) and increases the pressure

gradient

Increased cranial velocity with higher

heart rate unsupported and unlikely to occur in vivo. This will affect the

pressure gradients reported

Linge et al. (2014) Finite

Volume Semi-

idealised Same as 80 bpm case from

Linge et al. (2013) None To assess how different level

of posterior fossa

decompression affect CSF

dynamics

All levels of decompression reduced CSF velocities and flow

complexity, and the pressures

returned to normal levels

Post decompression treated as rigid,

ignoring the effects of a duraplasty. Tonsils do not ascend in post-

decompression as expected. Both

factors may have an effect on the CSF dynamics.

25

Table 1.5 Summary of computational models investigating the effect of pathology on CSF dynamics continued.

Study Numerical

method Anatomy Boundary inputs Validation Focus of Investigation Outcome Limitations

Clarke et al. (2013a)

Finite Volume

Subject-specific

MR acquired CSF flows applied to inlet

Model agrees with

MR flow at two

locations Determine the effects of

Chiari and syringomyelia on

the CSF pressure profile

Peak pressures occurred earlier and would be elevated in the Chiari

subject. The syrinx delayed the pressure pulse and reduced it amplitude

A single subject was used for each

group and may not represent the wider

population.

Martin et al.

(2013) Finite

Volume Subject-

specific MR acquired CSF flows

applied to inlet Compared with

unvalidated models To determine an appropriate parameter for diagnose and

surgical assessment

Pressure gradient and CSF impedance greater in Chiari

subjects. Surgery reduced these parameters to

a varying degree, still elevated

compared with controls

2 Chiari and 1 control subject used. As

such results may not be representative.

Shaffer et al. (2014)

Finite Volume

Subject-specific

MR acquired CSF flows applied to inlet None

To assess the CSF impedance

as an indicator for Chiari

symptomology

Tonsillar herniation is a poor

indicator for CSF impedance. Impedance is greater in Chiari

subjects although there is overlap

and no difference between asymptomatic and symptomatic

patients.

Hydraulic impedance was the only

measure assessed, which can be biased by the assumptions behind its

calculation. Other factors relating to

the CSF velocity and pressure waveforms may be related to tonsillar

herniation.

Pahlavian et al. (2015)

Finite Volume

Subject-specific

MR acquired CSF flows applied to inlet None

To assess the importance of

tonsillar pulsation in subject-

specific modelling Tonsillar pulsation results in small

changes in CSF pressure close to the

foramen magnum

N=1, with mild herniation, so may not

characterise the effect of more severe

Chiari. However, tonsillar pulsation is unlikely to have a dominating effect

over the pulsation of the whole brain

in the cranium.

Cheng et al.

(2012) Finite

Volume Subject-

specific MR acquired CSF flows

applied to inlet Model agrees with

MR flow at two

locations Determine how arachnoiditis

and it permeability effect

CSF dynamics.

Model demonstrated that

arachnoiditis increases resistance to flow, increasing the magnitude and

introducing timing delays in CSF

pressure. Also introduces complex flow

patterns i.e. anterior-posterior

bidirectional flow.

Yeo et al. (2017) found peak CSF

flow to occur earlier compared with

control in a larger cohort of PTS syrinx patients. Suggesting that

modelling arachnoiditis a porous

obstruction, may not accurately capture it effects on CSF dynamics.

Roldan et al.

(2009) Boundary

Element Subject-

specific

MR acquired CSF flows applied to inlet and 0 Pa at

outlet during systole. Boundary

conditions flipped during

diastole Same volume flow rate for

Chiari and control.

Flow field qualitatively

compared with in

vivo data

To use subject models to

characterise the effect of

Chiari on CSF dynamics

Given the same volume flow rate, the reduction in area caused by

Chiari increases CSF pressures and

velocity.

Poor age and sex matched subject leading to the models being scaled for

comparison, which may introduce

error. Boundary conditions assigned for

diastole are unphysiological. BEM neglects fluid inertia.

26 Table 1.5 Summary of computational models investigating the effect of pathology on CSF dynamics continued.

Study Numerical

method Anatomy Boundary inputs Validation Focus of Investigation Outcome Limitations

Rutkowska et

al. (2012) Finite

Element Subject-

specific Same time varying pressure

gradient applied to all model.

(Not explicitly shown in paper)

Model and MR

peak velocities

show good agreement

To use a cohort of subject

specific models to

characterise the effects of Chiari on CSF dynamics

CSF velocities are greater in Chiari subjects. At the level of the foramen

magnum pressures are elevated in

Chari subjects. Posterior fossa decompression normalised CSF

pressure.

Use of predefined pressure difference

instead of measured CSF velocities

for the boundary conditions, may result in spurious results.

Stoverud et al. (2013)

Finite Element Idealised

Inlet and outlets assigned a

velocity, either constant or

sinusoidal. None

A parametric study investigating the relationship

between pressure drop,

velocity and the severity of a subarachnoid obstruction.

Increasing the level of obstruction

increases the pressure gradient across the obstruction and

introduced a timing delay. -

Stoverud et al. (2016)

Finite Element

Subject-specific

MR acquired CSF flows applied to inlet None

To see the effect of Chiari on CSF dynamics in both the

cervical subarachnoid space

and within the CSF spaces of the posterior fossa.

Chiari subjects were found to have increased pressures and velocities in

both the fossa and spinal canal. Chiari was found to delay the CSF

pulse.

Inlet boundary conditions have been

shifted from timings present in raw

data (To potentially fix timing difference of triggering signal i.e.

PPU vs ECG). Therefore, findings of

phase difference are unreliable. Some inlet flow were estimated,

causing potential errors in the velocity

and pressure magnitudes.

Bertram et al.

(2005) Finite

Element Idealised 100 Pa half sine pulse at 2.5

and 5 Hz and a triangle wave at

20, and 200 Hz, applied to the cranial inlet.

None To determine the effects of

changing the properties of the

dura, spinal cord and central canal on the wave speed.

Increasing rigidity of dura and cord, increase wave speed. Inclusion of

central canal had little effect on the

wave speed. Travelling waves dissipate along

spinal canal, elastic jump unlikely.

-

Bertram et al. (2008)

Finite Element Idealised 100 Pa triangle wave at 200 Hz

applied to the cranial inlet. None To assess the effect of

arachnoid scaring (radial cord tethering) on CSF pressure

and cord stress.

Dura-cord tethers increase radial

tension within the cord. Making the

spinal cord pressure more negative.

Increased dural stiffness increased this effect.

Model focuses on the effects in the spinal cord. The stress created by the

tethers are low and unlikely to

damage cord tissue. Effects on CSF dynamics not properly assessed.

Artificially stiff region of cord to

make tethers stable, will influence wave propagation and cord stress.

27

Table 1.5 Summary of computational models investigating the effect of pathology on CSF dynamics continued.

Study Numerical

method Anatomy Boundary inputs Validation Focus of Investigation Outcome Limitations

Bertram (2009)

Finite Element Idealised 100 Pa triangle wave at 200 Hz

applied to the cranial inlet. Verified against

wave speeds calculated from

MRI and models.

To determine the effect of a

syrinx on wave propagation

and assess the feasibility increased syrinx fluid

momentum dissecting the

cord (‘slosh’).

Syrinx introduces wave reflections and slows forward travelling

waves. Momentum generated in the

syrinx unlikely to expand the cavity.

Crude geometry likely exaggerated the magnitude of wave refection

created by a syrinx. These reflections

intern effect the relative velocity of the syrinx.

Bertram

(2010) Finite

Element Idealised

100 Pa triangle wave at 200 Hz

or a 1000 Pa sine2 wave at

2.5Hz. Either applied to the cranial inlet or at lumbar

region to external dural

surface.

None To assess whether the

addition of a stenosis is

likely to increase the syrinx

‘slosh’.

Increased restriction of the stenosis

created pressure gradients in the subarachnoid along the length of

the syrinx. This did not

significantly contribute to syrinx ‘slosh’.

-

Bertram and Heil (2016)

Finite Element Idealised 500 Pa sine wave at 2.5 Hz at

cranial inlet None To determine the effect a

stenosis would have on flow

into and through a syrinx,

accounting for the porosity of the cord.

The stenosis creates pressure

gradients which recirculating flow from subarachnoid into the syrinx

cavity. Slowly changing the

cavities volume. Suggesting this mechanism does not require

perivascular spaces.

Mechanism requires a cavity, and is sensitive to its location to the

stenosis. Also requires a significant

occlusion. Assumes flow through a permeable tissue, the perivascular

spaces not the extracellular spaces

would be the least resistive component.

Heil and

Bertram (2016)

Elliott et al. (2017) Analytical Idealised Harmonic waves assumed

Verified against

literature values of wave speed (in

vivo data)

Effect of material properties of the spinal canal, degree of

subarachnoid obstruction,

and syrinx size on the wave propagation.

With a syrinx and obstruction,

syrinx fluid and the cord move out of phase. Suggesting slosh could

play a role in syrinx progression.

Interpretation of difference in in vivo

wave speed in relation to validation and overall model behaviour may be

incorrect. Pathology may contribute

to the higher wave speed in Williams (1976), however coughing also

changes spinal compliance and

therefore wave speed. So this may still be within the range of normal

controls.

28

Idealised modelling

Idealised models make assumptions that simplify the biomechanics being modelled. These

simplifications reduce the complexity of the problem, making it easier to identify how

parametric changes (assumed effects of the pathology) alter the model outputs (characteristics of

CSF dynamics, for example velocity, pressure, wave speed etc.). Typical approaches are to

either treat the subarachnoid space as a series of resistances and capacitances (lumped

parameter/zero-dimensional modelling) (Chang and Nakagawa, 2003, 2004; Elliott et al., 2011),

or reduce the spinal canal to a series of flexible/rigid axisymmetric coaxial tubes (one-

dimensional modelling) (Berkouk et al., 2003; Carpenter et al., 2003; Cirovic, 2009; Cirovic

and Kim, 2012; Elliott et al., 2009; Elliott et al., 2017; Toro et al., 2018). The main aim of these

models has been to identify how changes to the material properties and compliance of the spinal

canal affects wave propagation.

Coaxial models of the spinal canal have been used to characterise how pressure pulses would

propagate along the spinal subarachnoid space, identifying four distinct wave modes. One of the

slower waves causes the dura to distend and compresses the spinal cord, contributing the most

to the longitudinal motion of the cord. The next fastest wave, results in the compression of both

the cord and dura with little influence on the axial motion of the cord. The fastest wave mode is

primarily associated with the longitudinal displacement of the dura mater, with negligible effect

on the cord or CSF flow. The slowest wave mode acts similar to the second mode in the radial

direction, although this mode is dependent on a patent central canal which is uncommon in

healthy adults (Milhorat et al., 1994) with a syrinx it becomes more dominant (Bertram, 2009;

Cirovic, 2009; Cirovic and Kim, 2012). These travelling waves will reflect at boundaries (i.e.

foramen magnum and lumbar cistern), and at discontinuities in the stiffness of tissues. The

interaction of these travelling and reflected waves may stress tissues and locally increase the

CSF pressures, which could influence syrinx formation. Additionally, these models have

demonstrated reductions in the relative size of the subarachnoid space increases the wave speed,

whereas the presence of a syrinx causes a reduction (Bertram, 2009; Cirovic, 2009; Cirovic and

Kim, 2012), which may inform our understanding of how pathology influences the

characteristic differences in the timing and magnitude of CSF pulses (Clarke et al., 2013b; Yeo

et al., 2017).

Typically, the pulse wave velocity of an idealised model is compared with MRI measurements

(Greitz et al., 1999; Kalata et al., 2009) to demonstrate the behaviour of the model is

representative of in vivo conditions (Tables 1.4 and 1.5), however this may not be a valid

approach. The compliance of the spinal canal is in part dependent on the pressure acting on the

external surface of the dura mater. Since the extradural pressure varies with respiration, the

compliance of the spinal canal, and the pulse wave velocity will also vary (Usubiaga et al.,

29

1967). This potentially explains the noticeable difference between the pulse wave velocities

measured at rest (~4 m.s-1 in cardiac gated MRI (Greitz et al., 1999; Kalata et al., 2009)) and

while coughing (~13 m.s-1 (Williams, 1976)). Additionally, this highlights a problem with

models that use high pulse wave velocity to model pathological conditions (Elliott et al., 2017),

as it is unknown whether the value measured by Williams (1976) is within the range expected in

healthy controls. How changes to the normal variation in spinal compliance affect CSF

dynamics could have implications for syrinx development.

Subject-specific modelling

Subject-specific computational models are an attractive method for investigating CSF flow

disorders, as they can provide a means to determine the impact of pathological changes that

cannot be readily assessed with the current technologies. Therefore, it is important to ensure

they accurately reproduce in vivo behaviours. A popular method for subject-specific modelling

is to create a 3D geometry from the subjects anatomical MRI scans, and to apply CSF velocities

measured at a cranial location to the inlet of the model (Cheng et al., 2014; Cheng et al., 2012;

Clarke et al., 2013a; Pahlavian et al., 2014; Sass et al., 2017; Yiallourou et al., 2012). Via this

method, the simulated pressures and velocities are sensitive to the cross-sectional areas along

the length of the model (Loth et al., 2001), yet several studies have managed to demonstrate

good agreement between MRI and simulated velocity measurements (Cheng et al., 2014; Cheng

et al., 2012; Clarke et al., 2013a; Pahlavian et al., 2016; Rutkowska et al., 2012). Whereas

others have noticed large discrepancies between the in vivo and simulated velocities (Pahlavian

et al., 2014; Sass et al., 2017; Yiallourou et al., 2012), attributing this to the absence of the finer

structures within the spinal canal, however discrepancies between the MRI and model

geometries likely play a large role (Figure 1.14).

30

Figure 1.14 A) Example of poor agreement between CFD and MRI , highlighing poor agreement between velocities

and differences in cross-sectional area (Yiallourou et al., 2012), Left – control model, Right – Chiari model. B)

Example of good agreement between CFD and MRI, highlighting good matching between the cross-sectional areas

(Pahlavian et al., 2016).

As an alternative to using MRI flow data as an input condition, other researchers have opted to

actively drive CSF flow. Sweetman and Linninger (2011) and Tangen et al. (2015) aimed to

drive CSF flow by simulating the volume change of the brain during perfusion, by assigning a

displacement to the pial surfaces of the brain, based on a cerebral blood flow waveform scaled

to match the volume change expected in a caudal location. This approach demonstrated good

agreement between MRI and simulated velocities. Recently, Khani et al. (2017) demonstrated

that by applying non-uniform displacements to the surface of the dura mater, a low level of error

can be seen in the calculated velocities. However, there are significant issues with this approach,

limiting its application and wider use. Firstly, the method changes the volume of the model to

match the measured change in volume (the integrated difference between flow rates at two

locations; ∆𝑉 = (𝑄2 − 𝑄1) × ∆𝑡), which forces the model to match the measured MRI

velocities. Thus, with this method good agreement between the velocities could be achieved

even with an incorrect geometry of the subarachnoid space, however the CSF pressures and

streamlines are likely to be inaccurate. Additionally, this applied motion does not agree with the

known drivers of cardiac CSF flow (Alperin et al., 2005a; Alperin et al., 2005b; Henry–Feugeas

et al., 2000).

The importance of subarachnoid structures on CSF flow

Subject-specific models generally neglect various subarachnoid space structures, to make

analysis simpler and streamline the workflow to reduce the time needed to simulate large

A B

31

subject cohorts. Therefore, it is important to understand how these structures affect CSF flow,

and that their exclusion will not introduce significant error into a simulation.

In a rigid boundary model Loth et al. (2001) demonstrated that the relative position of the spinal

cord could cause significant changes to the local flow field, but the CSF pressures were

unaffected. In reality the cord is compliant (Bilston and Thibault, 1995) and some interaction

between the CSF flow and cord deflection may be expected. Using a fluid-structure-interaction

(FSI) model of the cervical spine in a healthy control, Cheng et al. (2014) showed that a non-

uniform flow field about the cord would induce small displacements (~2 mm), which further

disturbed the local flow field, however similar to the rigid model this did not significantly

change the subarachnoid pressures (< 1 Pa absolute change; ~1.3% change).

The influence of nerve roots and denticulate ligaments (a caudocranially-orientated thick

collagen membrane which extends from the pia to the dura, providing support to the spinal cord

(Weller, 2005)) on flow has been assessed with varying levels of complexity (e.g. tubes, sheets,

aerofoils or anatomical structures based on cadaver studies; Figure 1.15) (Khani et al., 2018;

Pahlavian et al., 2014; Sass et al., 2017; Stockman, 2005; Tangen et al., 2015). The inclusion of

these structures has been shown to alter the local flow field, potentially creating jets between the

nerve bundles increasing local velocity peaks (~ 2 cm.s-1 increase) (Khani et al., 2018;

Pahlavian et al., 2014), and during flow reversal (caudal to cranial or vice versa) the obstruction

can create recirculation zones that increase local mixing of CSF (Khani et al., 2018; Pahlavian

et al., 2014; Tangen et al., 2015). However, the inclusion of the nerve roots and denticulate

ligaments has been shown to have little effect on the average flow profile (Stockman, 2005) and

subarachnoid pressure (< 10 Pa increase; < 15% increase in peak pressure drop along the spine)

(Pahlavian et al., 2014).

Figure 1.15 Example of the different methods used to model the effects of nerver roots and denticulate ligaments on

CSF flow (Pahlavian et al., 2014; Sass et al., 2017; Stockman, 2005). Left: Reproduced from Stockman, Journal of

Biomechanical Engineering, 2005, 128:106-114 with permission from ASME.

The arachnoid web is distributed throughout the subarachnoid space (Cloyd and Low, 1974;

Nicholas and Weller, 1988; Weller, 2005), and its influence on CSF flow has typically been

modelled as a simplified uniform or random array of fine tubes, with diameters ranging from

30-137 µm (Gupta et al., 2009; Stockman, 2005). CSF flows radially and circumferentially

about these obstructions, altering the local flow field and increasing fluid mixing (Figure 1.16),

32

however as the arachnoid web occupies a small volume fraction of the subarachnoid space, the

average caudocranial flow is unchanged (Stockman, 2005, 2007; Tangen et al., 2015).

Modelling the arachnoid web as rigid inclusions has suggested that the web may increase

resistance to flow, meaning a larger pressure is required to achieve the same flow rate, and if the

arachnoid is neglected the simulated pressures would likely underestimate in vivo conditions

(Gupta et al., 2009; Tangen et al., 2015). However, the arachnoid web is a compliant structure

(Mortazavi et al., 2018), so the rigid approximation likely overestimates the resistance to flow,

and underestimates CSF mixing, as a flexible structure will create more complex flows due to

the two-way interaction between tissue displacement and CSF flow (Balint and Lucey, 2005;

Cheng et al., 2014; Hübner et al., 2004).

Figure 1.16 The effects of arachnoid trabeculae of CSF flow and fluid mixing. A) Flow profile at different times in

the oscillatory cycle [s] and different mean velocities [cm.s-1] (grey scale dark-low to light-high) (Stockman, 2005).

B) Dispersion of a line of tracer introducing into the spinal canal, Left – model with trabeculae causing rapid

dispersion of the tracer, Right – model with no structures within the spinal canal, oscillations cause some dispersion

however bolus is still well defined (Stockman, 2007). A) Reproduced from Stockman, Journal of Biomechanical

Engineering, 2005, 128:106-114 with permission from ASME. B) Reproduced from Stockman, Journal of

Biomechanical Engineering, 2007, 129:666-675 with permission from ASME.

These studies suggest that the structures within the subarachnoid space primarily affect the local

flow field, increasing fluid mixing, and do not have a significant effect on the simulated

pressures. As such, these structures can generally be neglected, unless they contribute to the

pathology being studied (i.e. arachnoid scarring, tethering, or local compressions) (Bertram et

al., 2008; Cheng et al., 2012), or the model aims to investigate the effects of CSF mixing on

drug dispersion (Khani et al., 2018; Kuttler et al., 2010; Sánchez et al., 2018). Provided a

reliable method is used to quantify the volume of the subarachnoid space, a subject-specific

model consisting of just the CSF space, treating the dura mater and spinal cord as rigid

boundaries may accurately reproduce in vivo cardiac driven CSF flow (Cheng et al., 2014;

Cheng et al., 2012; Clarke et al., 2013a; Pahlavian et al., 2016; Rutkowska et al., 2012).

Mechanism for syrinx development: Engineering studies

The tonsillar piston

Although supporting evidence is lacking, the hypothesis that the pulsation of the cerebellar

tonsils actively drives CSF from the subarachnoid space into the spinal cord (Figure 1.10B) is

still discussed widely by clinicians (Heiss et al., 2018). Pahlavian et al. (2015) used a subject-

specific model to simulate the effect of tonsillar pulsation. The model showed that tonsillar

33

pulsation locally altered flow at the level of the obstruction, however there was only a small

change in the pressure drop across the craniocervical junction (<10 Pa), approximately four

times smaller than the pressure drop caused by the systolic CSF pulse. This would be expected

as the volume of the upper cervical spinal canal is both smaller and more compliant than the

cranium, therefore pulsations of the cerebellar tonsils would cause a smaller pressure change

compared with the pulsation of the cranial vasculature. The increased pulse pressure in Chiari

subjects (Clarke et al., 2013a) can be, in part, attributed to similar volume of CSF flow being

driven through a foramen magnum with a reduced cross-sectional area (Linge et al., 2011).

Venturi effect and elastic jump

Two mechanisms for syrinx development, the elastic jump and Venturi effect, were derived

from analytical mathematical models but were quickly dismissed. The Venturi effect describes

the process where fluid velocity increases and its pressure decreases through a local

constriction. Greitz (2006) postulated that constrictions in the subarachnoid space (e.g. Chiari

malformation, vertebral fracture or scarring) would cause a localised pressure drop which would

‘suck’ the tissues of the cord and lead to syrinx formation. However, computational (Bertram,

2010) and mechanical models (Martin et al., 2010; Martin and Loth, 2009) of severe

subarachnoid obstructions have failed to demonstrate a significant Venturi effect which could

contribute to syrinx growth.

The elastic jump hypothesis was developed from a simplified model of coaxial tubes (Berkouk

et al., 2003; Carpenter et al., 2003). The model suggested that the leading edge of pressure

waves travelling along the spinal cord would steepen, and when obstructed a reflection would

create transiently elevated pressures within the cord which could damage tissues and cause

syrinx formation (Carpenter et al., 2003). However, this behaviour was highly dependent on the

size of the subarachnoid space, which was subsequently shown to be unrealistically narrow in

the original model. When tested with a more physiologically likely range of subarachnoid

dimensions, it was shown that the spinal canal would have to be several meters long for an

elastic jump to form, and unlikely to contribute to syrinx development (Elliott et al., 2009).

Mechanism for perivascular inflow

Peristalsis

Ligation studies demonstrated that perivascular tracer deposition depends on arterial pulsation,

and it was concluded that the pulsations actively drove flow along the perivascular spaces (Iliff

et al., 2013b; Stoodley et al., 1997). This active mechanism was used to explain how CSF tracer

could flow into a syrinx cavity against its high pressure (Stoodley et al., 1999; Stoodley et al.,

2000). Furthermore, Bilston et al. (2003) suggested that peristaltic waves travelling along the

surface of the arteries could propel fluid along the perivascular space, even against an adverse

34

pressure gradient. However, the wavelength used (≤ 300 µm) to achieve peristalsis is unlikely

to occur in vivo, since the arterial wave speed is greater than 2 m.s-1 (Borlotti et al., 2010;

Gladdish et al., 2005; Hoeks et al., 1999; Wang and Parker, 2004), so for a typical heart rate of

60 bpm the wavelength would be on the order of meters. Wang and Olbricht (2011) attempted

to expand upon this mechanism including a hydraulic resistance to the perivascular space to

account for obstructive material situated within the perivascular space, and such features were

later shown to exist in electron microscopy studies (Lam et al., 2017). However, as the model

used a similarly unrealistic wavelength, it too likely does not realistically represent the

mechanics of perivascular flow.

Asgari et al. (2016) assessed whether arterial pulsation alone could transport fluid through the

perivascular space. Using an axisymmetric model, arterial pulsation was simulated with a

moving boundary at the inner radius of the perivascular space, applying zero volume flux across

the domain. The model showed that arterial expansion would displace a volume of fluid, but

upon relaxation the fluid would return to its initial position, potentially aiding fluid mixing but

unlikely to drive fluid flow in isolation.

Arterial vs subarachnoid space pressure phase differences

Bilston et al. (2010) suggested an alternate mechanism using a realistic wavelength for arterial

pulsation. Based on the magnitude of pulsation seen elsewhere in the body (Millasseau et al.,

2002), the model demonstrated that penetrating arteries could act as a partial valve, expanding

to reduce the perivascular space during systole and relaxing in diastole. This model was used to

demonstrate that timing differences between the onset of the subarachnoid space pressures and

arterial expansion could act to either increase or restrict CSF flow into the spinal cord.

Subsequent imaging (Clarke et al., 2013b; Yeo et al., 2017) and modelling studies (Cheng et al.,

2012; Clarke et al., 2013a; Martin et al., 2010; Stoverud et al., 2013) have shown pathological

conditions to introduce such changes in timing, which would be favourable for syrinx formation

via this mechanism. A parametric analysis with this model showed that if the systolic CSF

pressure was increased and sustained for a longer period while the artery is relaxed fluid uptake

would likely increase (Clarke et al., 2017). Analytical lumped parameter models also

demonstrated that decreases in spinal compliance could cause timing shifts favourable for fluid

accumulation via this mechanism (Elliott et al., 2011; Martin et al., 2012). However, this

mechanism has yet to be demonstrated to occur in vivo. The pulse amplitude of penetrating

vessels remains unknown, although recent measurements of the surface pial arteries in the brain

have shown the pulse amplitude to be less than 1% of the initial diameter (Mestre et al., 2018),

making it unclear whether there would sufficient motion to form a valving mechanism.

35

Summary

Chiari malformation remains poorly understood, with many of the clinically accepted

mechanisms for syrinx development such as the ‘tonsillar piston’ (Heiss et al., 2018), having

little evidence to support the underlying biomechanics of the mechanism. As a result the current

treatments are still based around restoring free CSF flow at the foramen magnum, a treatment

that was originally based on the work of Gardner (1965) and Williams (1969). This treatment is

associated with wide variation in outcomes between institutions and frequent unfavourable

outcomes (Aghakhani et al., 2009; Dubey et al., 2009; Jia et al., 2019; Parker et al., 2013).

To further understand the pathogenesis of syringomyelia in Chiari patients, it is important to

establish the mechanistic link between the abnormal morphology and altered CSF dynamics.

The morphology of the posterior fossa has been extensively investigated (Table 1.1), however

commonly only the midsagittal structures are investigated, although PC-MRI flow studies have

demonstrated that the lateral anatomy has a significant effect on CSF dynamics (Bunck et al.,

2012; Bunck et al., 2011; Haughton et al., 2003; Iskandar et al., 2004; Quigley et al., 2004).

Additionally, few studies have identified differences between the anatomy of Chiari patients

with and without a syrinx (Eppelheimer et al., 2018; Yan et al., 2016), making it unclear as to

why syrinxes do not occur in all patients (Speer et al., 2003) if overcrowding is the cause.

Positive outcomes after decompression surgery have been shown to coincide with the

‘normalisation’ of CSF flow at the foramen magnum (Dolar et al., 2004; Iskandar et al., 2004).

This has furthered the assumption that abnormal CSF flow contributes to syrinx formation,

although a causal link has yet to be demonstrated. Understanding how CSF flow differs between

patients with and without a syrinx may help understand the mechanism which causes their

development. However, to date only three studies have examined the differences between these

patient groups (Bunck et al., 2012; Clarke et al., 2013b; Ellenbogen et al., 2000). Syrinx patients

are shown to have a delayed caudal peak flow compared with syrinx free patients (Bunck et al.,

2012; Clarke et al., 2013b), however whether these differences are related to the structures of

the craniocervical junction, or cause syrinx formation has not been investigated.

Since coughing and straining associated headaches are common in Chiari patients, abnormal

changes in CSF flow under different respiratory manoeuvres have commonly been hypothesised

to relate to both patient symptoms and syrinx pathophysiology (Bhadelia et al., 2016; Williams,

1981b). However, how respiration drives CSF flow in the healthy population is still poorly

characterised, making it difficult to correctly identify abnormal behaviour which could

contribute to the disorder.

36

The effects of Chiari malformation on CSF dynamics: Thesis Aims

The overall aims of this thesis are to use MRI imaging and computational models to improve

the understanding of the effects of Chiari malformation on CSF dynamics, to provide a basis for

future mechanistically-based treatment options for Chiari and syringomyelia. Specifically, the

aim of Chapter 2 is to use subject-specific computational models of healthy controls, and

Chiari patients with and without a syrinx, to estimate the effect of Chiari and syringomyelia on

the subarachnoid pressures. In addition, to using an idealised model of the perivascular space to

determine whether features of the pressure-time profile would be favourable for increased CSF

flow into the cord, and syrinx formation. In Chapter 3 a parametric study is conducted to assess

how sensitive the ‘leaky valve’ model of the perivascular space is to variation in the arterial

pulse wave. In Chapter 4 the morphology of the posterior cranial fossa is measured in healthy

controls, and Chiari patients with and without a syrinx, to determine whether bone structure or

tonsillar overcrowding are related to the characteristic difference in the subarachnoid pressure

profiles. The aim of Chapter 5 is to use real-time PC-MRI to measure CSF and epidural blood

flow, to identify the driving mechanism of respiratory CSF in healthy controls. Chapter 6 aims

to demonstrate that the relative difference in the magnitude of compression and expansion

applied to the lumbar and thoracic dural sac respectively, could drive CSF cranially and

caudally as hypothesised in Chapter 5. With the secondary aim of determining the caudal

boundary condition required to simulate coughing in subject specific models. Finally, Chapter

7 aims to demonstrate that real-time PC-MRI could be used to identify characteristic differences

in CSF flow which may be indicators for syrinx development or coughing associated headaches.

It has the secondary aim of demonstrating feasibility of reproducing the effects of coughing in

subject specific models of healthy controls and Chiari patients.

37

2. Chiari malformation may increase perivascular cerebrospinal fluid

flow into the spinal cord: a subject-specific computational

modelling study

Publication I

Lloyd, R.A., Fletcher, D.F., Clarke, E.C. & Bilston, L.E. Chiari Malformation May Increase

Perivascular Cerebrospinal Fluid Flow into the Spinal Cord: A Subject-Specific Computational

Modelling Study. Journal of Biomechanics. 2017 Oct; 65, 185-193.

Declaration

I certify that this publication was a direct result of my research towards this PhD and that

reproductoin in this thesis does not breach copyright regulations.

……………………………………………….

Rob Lloyd [candidate]

Introduction

Chiari malformation Type I is a disorder in which the cerebellar tonsils herniate through the

foramen magnum. A majority (65−80%) of Chiari patients also develop a fluid filled cavity

(syrinx) within the spinal cord (Speer et al., 2003). Syrinxes are associated with both sensory

and motor disturbances, and occasionally with autonomic dysfunction (Sakushima et al., 2012).

The mechanisms that lead to syrinx formation in only some of the population remains unknown,

with current treatments for syringomyelia being unsatisfactory (Aghakhani et al., 2009).

Previous experimental studies showed that cerebrospinal fluid (CSF) flows from the spinal

subarachnoid space into the spinal cord via the perivascular spaces (PVS) (Ball and Dayan,

1972), and that this flow is dependent on arterial pulsation (Stoodley et al., 1997; Stoodley et

al., 1999). A simple computational model was used to demonstrate that the arteries could act as

a ‘leaky valve’, increasing resistance to flow in the PVS by dilating during systole and vice

versa (Figure. 2.1) (Bilston et al., 2010). Via this mechanism, modest changes in the timing of

the arterial and subarachnoid pressures could lead to the enhanced influx of CSF into the spinal

cord required for syrinx formation (Clarke et al., 2017).

38

Figure 2.1 (A) An illustration of the perivascular anatomy, highlighting a single penetrating vessel and the potential

channel for fluid transport , re-printed from Bilston et al., Journal of Neurosurgery, 2010, 112:808-813 with

permission from http://thejns.org. (B) A simplified diagram of the perivascular anatomy (Figure 2.1A) demonstrating

how the potential ‘leaky valve’ mechanism in the perivascular space (PVS) could alter CSF inflow, and the basis for

the design of the perivascular model shown in Figure 2.3G.

Phase-contrast magnetic resonance imaging (PC-MRI) studies have shown that both the timing

and velocity of CSF flow in the subarachnoid space are altered in Chiari patients. However, the

subarachnoid pressures cannot be measured noninvasively, so computational models are used to

provide an estimate. Idealised models have been used to demonstrate; how obstructions to flow

in the subarachnoid space delay the onset and increase the amplitude of peak pressures

(Stoverud et al., 2013), that greater tonsillar herniation increases the pressure gradient in the

subarachnoid space (Linge et al., 2011), and that the presence of a syrinx in the cord may delay

the pressure pulses (Cirovic and Kim, 2012).

Subject-specific models provide greater accuracy, as they rely on fewer assumptions and can be

validated directly. Of the subject-specific Chiari modelling studies in the literature (Clarke et al.,

2013a; Martin et al., 2013; Pahlavian et al., 2015; Shaffer et al., 2011; Shaffer et al., 2014;

Støverud et al., 2016), only Clarke et al. (2013a) studied how a syrinx influenced the

subarachnoid pressure-time profile, showing both timing and magnitude changes compared with

healthy controls. These subject-specific pressure-time profiles were used in conjunction with a

model of the PVS, to show that the increased duration of the positive pressure in syrinx patients

could promote CSF flow into the spinal cord (Clarke et al., 2017). However, only one model

was used per subject group, so the findings may not be representative of the patient population.

39

The aim of this study was to use a larger series of subject-specific models to investigate how

Chiari I malformation, with and without a syrinx, affects the magnitude and timing of the

subarachnoid pressure-time profiles compared with healthy controls, and to assess how

characteristics of the subarachnoid pressure influence perivascular flow. We hypothesise that

magnitude and timing of subarachnoid pressure in Chiari patients without a syrinx will be

significantly altered, leading to a greater influx of CSF into the spinal cord.

Methods

MR Imaging and flow measurements

This study used the MRI scan data from Clarke et al. (2013b) and reanalysed the subjects

modelled in in Clarke et al. (2013a) and Clarke et al. (2017). The University of New South

Wales Human Research and Ethics Committee approved all experimental protocols. All

participants gave written informed consent. Twenty-four participants underwent MRI scanning

of the head and neck; 9 healthy controls and 15 symptomatic Chiari I patients, 7 with and 8

without syringomyelia (Table 2.1).

Table 2.1 Summary of participant information, showing age, weight and heart rate during MRI. Data displayed as

means ± S.D. (range).

Subject Group Age Weight Heart Rate N

[Years] [Kg] [Bpm]

Control 37.8 ± 11.5 (24 -60) 68.5 ± 12.4 (55-85) 68.4 ± 12.1 (49.8-88.1) 9

Chiari w/o syrinx 36.0 ± 11.2 (22-60) 64.5 ± 7.6 (55-78) 67.4 ± 9.2 (51.9-81.3) 8

Chiari w/ syrinx 40.3 ± 10.9 (27-58) 78.0 ± 15.1 (58-100) 70.3 ± 8.6 (60.5-87.8) 7

3D isotropic T1 weighted sagittal anatomical MRI scans of the cervical spine were acquired,

parameters for the scan include; 0.94 mm voxels, matrix = 288×288, FOV = 270×270, TR/TE =

5.5/2.5 ms and 180 slices of 0.94 mm thickness. Axial cardiac gated cine PC-MRI scans (30

phases/cycle) were acquired at the following locations and encoded velocities (Venc); the base of

the skull (Venc = 12 cm.s-1), 5 mm cranial to the tip of the cerebellar tonsils (Venc=10 cm.s-1), mid

C2 (Venc = 9 cm.s-1) and mid C5 (Venc = 13 cm.s-1). Additional scanning parameters were; matrix

= 240×176, FOV = 250×250, TR/TE = 21/6.8 ms and slice thickness = 5 mm. For further details

on the scanning protocol see Clarke et al. (2013a).

Blood flow measurements taken from mid-C5 were used to provide an estimate of the relative

timing of the arterial and CSF pressures, by assuming that blood flow and pressure were in

phase. As for a typical artery of 4 mm diameter (Reymond et al., 2009) with blood oscillating at

a frequency of 60 bpm and a peak velocity of 30 cm.s-1 (density 1054 kg.m-3 and viscosity

4 mPa.s (McDonald, 1955)), the Reynolds and the Womersley numbers would be 316 and 2.57

respectively. For a Womersley number less than 3, the boundary layer would occupy

approximately half the artery’s radius, reducing the inertial effects on the flow, therefore

40

laminar flow with the pressure and flow in phase can be assumed (Lighthill, 1975). The blood

flow was analysed from the C5 plane as the vertebral artery at this location runs approximately

parallel to the spinal canal and therefore the flow would be primarily through-plane as required.

However, the Venc set to measure CSF velocities was too low to capture the blood flow, leading

to phase wrapping in blood vessels (Figures 2.3C, 2.3D). To remove the phase wraps (Figures

2.2A, 2.2B), multiples of 2π were manually added to the affected voxels (Figures 2.2C, 2.2D)

(Bioucas-Dias and Valadao, 2007).

The velocity in the vertebral artery at C5 was measured from the corrected PC-MRI data using

the freely available software Segment (Heiberg et al., 2010). The velocity data was fitted with

cubic smoothing splines (MATLAB v8.6, The MathWorks Inc., MA) to interpolate the offset

between the R wave and systolic uptake (tOffset; Figure 2.2D).

Figure 2.2 The Venc prescribes the maximum and minimum velocities that correspond to a phase in the range from –

π to + π. In the case where the measured velocity exceeds the Venc, the phase ‘wraps around’ to the bottom of the

range. This is called phase wrap, which is visualised here as discontinuities in the plots of the phase (A, B).

Correction of the phase wrap removes these discontinuities (B) providing a smooth representation of the flow

velocities across the artery. The figure shows examples of wrapped (A-B) and unwrapped (C-D) blood velocity data

in the vertebral artery at C5. A and C show the variation in velocity across the lumen of the artery at the systolic peak

(where X and Y are the pixel coordinates of the sampled region of interest). B and D show the encoded velocity-time

profiles for the vessel centre line. (D) Highlighting the foot of systolic uptake (t Offset), the crosses mark the acquired

data points and the solid line is the interpolated profile.

Computational modelling of the spinal subarachnoid space

The pressure-time profiles were calculated using the modelling protocols in Clarke et al.

(2013a). In summary, for each subject a 3D geometry of the subarachnoid space (Figure 2.3)

was constructed from point cloud data, manually segmented from the anatomical MRI. The

models spanned from 5 mm cranial to the tip of the cerebellar tonsils or the base of the skull to

10 mm past mid-C5, with a 5 mm extension to the cranial inlet to provide a smooth entry zone.

The models were imported into ANSYS CFX (v17.1, ANSYS Inc., PA) to calculate the CSF

velocities and pressures. CSF in this study was modelled as a Newtonian fluid with 0.8 mPa.s

41

viscosity and 1000 kg m-3 density (Bloomfield et al., 1998). PC-MRI flow data from 5 mm

cranial to the tip of the tonsils or the base of the skull was applied to the cranial end of the

model as the inlet boundary condition. The caudal outlet was set to have a reference pressure of

0 Pa. The spinal cord and dura mater were treated as solid wall boundaries. The CSF flow was

assumed to be laminar. All models were calculated with the measured time scales, but as there

was minimal variation in heart rate between subject groups (ANOVA, p = 0.22), the output

timescales were subsequently normalised for comparison. The models were validated by

comparing features of the calculated velocity-time profiles against the MRI data, taken from 10

corresponding locations at mid-C2 and mid-C5 of the subarachnoid space (Figure 2.3C). The

monitor points (Figure 2.3C) were positioned in the centre of the annulus of the spinal canal,

avoiding areas close to solid structures in order to minimise the mixing effects introduced by

structures such as nerve roots and possible partial volume effects in the MRI data. The features

of interest were; the time when the caudal flow begins, the timing of the peak caudal flow, the

time when flow changes from caudal to cranial and the peak caudal and cranial velocities.

Characteristics of the pressure-time profiles (Figure 2.3F) from mid-C2 were compared between

the subject groups. The features of interest were the timing of; the onset of positive pressure (t

On), the peak positive pressure (t Peak) and the transition from positive to negative pressure (t Off),

as well as the magnitude of the peak positive (P Max) and negative (P Min) pressures. Additionally,

the cumulative pressure acting over time (area under the pressure curve; AUC) during the

positive (A +) and negative (A –) segments, as well as the overall net value (A Net) were

calculated.

A steady state run provided initial conditions for the transient simulation. A solution was

accepted once a time periodic state was reached in the second cycle, with normalised residuals

converging to below 10–5 at each time step (5 ms). The CFD code is a 3D Navier-Stokes solver

that uses a finite volume approach. The equations were solved using a pressure-velocity coupled

solver. Spatial and time derivatives were calculated with second order differencing schemes.

Double precision was applied to minimise rounding errors.

42

Figure 2.3 (A) Midline sagittal MR scan of a Chiari subject with a syrinx. (B) Magnified view of midline scan with

segmented model area overlaid. White lines show the scan planes used for PC-MRI at the base of the skull, C2 and

C5. (C) Magnified view of the PC-MRI at C5, showing the 10 locations used to compare measured and simulated

velocities for model validation. (D) Model cross section overlaid on the C5 PC-MRI. The white arrows on C and D

highlight the phase wrapped vertebral arteries. (E) 3D model of the subarachnoid space, with the planes

corresponding to the PC-MRI scans highlighted in red. (F) An example of the simulated subarachnoid pressure

profile (PSAS(t)) for a Chiari subject without a syrinx taken from the C2 plane (Figure 2.3E), with key features

annotated, where tpeak = time of peak positive pressure, PMax= Peak positive pressure, PMin = Peak negative pressure,

ton = time of positive pressure onset, toff = time of negative pressure onset, A+ = Area under positive pressure curve,

A− = Area under cranial negative curve. (G) Schematic of the axisymmetric perivascular space model. The inlet was

assigned the simulated subarachnoid pressure-time profile, similar to the example in Figure 2.3F. Arterial expansion

was modelled by applying a time dependent displacement (H) to the inner wall of the perivascular space.

Modelling the perivascular space

Following the strategy in Clarke et al. (2017) perivascular transport was modelled with a

previously published idealised model of the perivascular space (Bilston et al., 2010). In

summary, the model is an axisymmetric representation of a penetrating artery, the surrounding

perivascular space, spinal cord and surrounding subarachnoid space (SAS) (Figure 2.3G). The

spinal cord parenchyma at the outer wall of the annulus was treated as rigid. The arterial

expansion was simulated as a prescribed moving boundary, by applying a time dependent

displacement with an amplitude of 10 µm (Figure 2.3H) to the wall at the inner radius. As the

wavelength of the arterial pulse is orders of magnitude larger than the length of the model, the

displacement could be assumed to act uniformly along the wall. The calculated pressures from

mid-C2 (Figure 2.3F) were applied to the inlet of the perivascular model. The outlet was given a

reference pressure of 0 Pa.

Prior studies have illustrated that the relative timing of arterial and subarachnoid pressures

influences perivascular flow (Bilston et al., 2010; Clarke et al., 2017). Since the in vivo timing

43

is unknown, a range of offsets were simulated. Arterial expansion was delayed relative to the R

wave in steps of 2%, from 0–20% of the cardiac cycle.

The models were solved in ANSYS CFX (v17.1, ANSYS Inc., PA), the high resolution

bounded second order scheme was used for the advection term and the second order backward

Euler scheme for the transient term. For convergence, normalised residuals were reduced below

2 × 10-5 at each time step. For further detail of the model configuration and assumptions see

Bilston et al. (2010) and Clarke et al. (2017). The flow rate was integrated over the cardiac cycle

to provide the net mass of fluid transported along the PVS per cardiac cycle (pumping rate).

Statistical analysis

Agreement between the velocity-time profiles of the MRI measurements and subarachnoid

models, were assessed using the Bland-Altman method for repeated measures (Bland and

Altman, 2007). Bland-Altman plots provide the average model error and the 95% limits of

agreement (±1.96×S.D.). Model error was calculated as the model result minus the MRI

measurement. The velocity-time profiles (Figure 2.4) and the within subject variation of the

measured MRI data were used to determine whether the model error was acceptable.

Features of the pressure-time profiles (Figure 2.3F) and the perivascular model were compared

between subject groups with a one-way ANOVA using MATLAB (v8.6, The MathWorks Inc.,

MA). When the ANOVA model returned a significant difference (p < 0.05), a Bonferroni post-

hoc test was used to identify significant differences between the three groups. Stepwise multiple

regression was used to determine whether features of the subarachnoid pressures were

associated with the net pumping rate.

Results

Subarachnoid model validation

The simulated and measured MRI velocities show an acceptable level of agreement (Figure 2.4)

for most comparison points. The greatest deviation between the models and the MRI data was in

the posterior and inferior segments of the models (Figures 2.4, 2.5 and Table 2.2), where the

models would predict a uniform flow field around the spinal cord, while the MRI data showed

significant variation around the cord. The models showed no proportional or a preferential bias

for any subject group (Figure 2.5). The model error was less than the measured variation around

the cord (Table 2.2). On average the models overestimated the timing of the peak caudal flow

and when the flow reversed (Figure 2.5 and Table 2.2). This can be attributed to deviation from

the in vivo velocities around the spinal cord (Figure 2.4).

44

CSF dynamics in Chiari subjects

Figure 2.6 shows the average measured pressure-time characteristics (Figure 2.3F) for each

group. The timing of the positive peak pressure in Chiari patients without a syrinx were shown

to occur earlier than controls (ANOVA, p = 0.045) and patients with a syrinx (ANOVA,

p = 0.037). The peak positive pressures in Chiari patients without syringomyelia were greater

than in controls (ANOVA, p = 0.029). In patients with a syrinx the transition from positive to

negative pressure was delayed compared with those without (ANOVA, p = 0.038). Chiari

malformation and syringomyelia had no influence on the AUC.

Arterial pulse delay and perivascular flow

Delaying the onset of arterial expansion between 0 – 14% of the cardiac cycle, increased flow

into the spinal cord (Figure 2.6D). Chiari patients without a syrinx had significantly greater CSF

inflow (ANOVA, p < 0.05) than (i) controls for a delay between 4 – 14%, (ii) patients with a

syrinx for a delay of 4 – 10%.The phase corrected blood flow maintained an approximately

parabolic flow profile throughout the cardiac cycle (Figure 2.2C), which confirms that the

pressure and velocity are in phase, as expected for low Womersley numbers. Systolic uptake in

the vertebral artery occurred 4.7 ± 0.1% after the R wave in controls. This was similar

(ANOVA, p = 0.45) in both patients with (4.4 ± 0.5%) and without (5.1 ± 0.4%) a syrinx. The

average delay for all subjects (N = 24) was 4.7 ± 0.2%.

45

Figure 2.4 Example of the measured MRI and model velocity-time profiles used for validation, taken from a control

subject. Each figure shows data at both the C2 (Left) and C5 (Right) levels, at five regions around the spinal cord and

the average annular velocity (Bottom). The dashed lines mark the standard deviation, crosses indicate transitions

between caudal and cranial flow and diamonds indicate peak caudal velocities.

Table 2.2 Summary of the agreement between model and in vivo velocity-time profiles. Model error displayed as the

bias ± 95% limits of agreement. Measured spread displayed as the 95% limits of agreement of the within subject

variation in the MRI data.

Velocity-time feature Model error Measured spread

Plane C2 Plane C5 Plane C2 Plane C5

Time of initial caudal flow [%] 0.19 ± 3.00 -0.17 ± 5.71 3.53 9.36

Time of peak caudal flow [%] 3.11 ± 6.07 0.99 ± 8.81 7.94 12.4

Time of return to cranial flow [%] 2.06 ± 12.5 2.58 ± 16.3 18.2 25.1

Peak caudal velocity [cms-1] -0.10 ± 0.88 -1.39 ± 2.56 1.09 3.61

Peak cranial velocity [cms-1] 0.17 ± 0.64 1.14 ± 1.78 0.79 2.34

46

Figure 2.5 Bland-Altman plots assessing the model error (modelled variable – MRI measurement), each data point is

the average error measured across the 10 locations sampled around the subarachnoid space in a subject (Figure 2.3C).

The solid line marks the average model bias and the dashed lines show the 95% limits of agreement (Table 2.2). The

first and second columns display the results taken from the C2 and C5 planes respectively. Features of the velocity-

time profile used for validation were; (A - B) the time caudal flow starts, (C - D) the time of peak caudal flow, (E - F)

the time cranial flow restarts, (G -H) the peak caudal velocity and (I - J) the peak cranial velocity

47

47

Figure 2.6 Group means with their 95% confidence intervals from one-way ANOVA for; parameters measured from subarachnoid pressures at C2 (A - C) and predicted perivascular pumping

rates (D). Measured parameters include; (A) the timing features, (B) the peak positive and negative pressures, (C) the pressure duration (AUC). Annotated dashed lines across the time axis

indicate the 95% confidence intervals of the measured time delay until systolic uptake (tOffset) for all subjects (D). Significant comparisons are indicated by an asterisk (* = p < 0.05).

48

The effect of subarachnoid space pressure-time features on perivascular flow

The pumping rates were weakly correlated with the; timing of the peak pressure, amplitude of

peak pressure, positive and net AUC (Figure 2.7). The timing of the peak pressure, amplitude of

peak pressure and net AUC were significantly associated with net pumping rates (Regression

analysis, p < 0.0001; RAdj2 = 0.85).

Figure 2.7 Relationship between pressure wave features and net pumping rates calculated with an arterial delay of

4%. (A) Relation with time of the positive pressure peak (tPeak), (B) Relation with peak positive pressure (PMax), (C)

Relation with positive AUC (A +), (D) Relation with net AUC (A Net), grey dashed line highlights separation between

the normal levels and a noticeable increase in pumping rate. Labelled crosses indicate cases (a) to (c) in Figure 2.8

Discussion

Implications for syrinx development

This study presents a series (N = 24) of patient-specific CFD models of Chiari patients and

controls, which demonstrate how subarachnoid pressures are altered by Chiari malformation

and syringomyelia. We found that the peak positive pressures were elevated and occurred

earlier, in patients without a syrinx than those with a syrinx and healthy controls. The coupled

49

CFD and perivascular space model showed that the increased peak pressures and changes in

timing introduced by tonsillar herniation are likely to promote CSF flow into the spinal cord and

contribute to the formation of a syrinx. These findings were consistent across the range of

arterial pulse delays measured with PC-MRI.

Studies have consistently demonstrated that obstructions to flow in the subarachnoid space and

craniocervical junction result in elevated peak pressures (Cheng et al., 2012; Clarke et al.,

2013a; Stoverud et al., 2013). Results for timing are less consistent. Two studies have shown

that obstructions could delay the timing of pressure pulses (Cheng et al., 2012; Stoverud et al.,

2013), in contrast with the findings of the current study (Figure 2.6A and 2.7A), other subject-

specific modelling (Clarke et al., 2013a) and in vivo imaging studies (Clarke et al., 2013b).

These differences may be due to how the specific type of obstruction investigated influences the

compliance of the spinal canal. The timing of the flow profile is influenced by the compliance

of the cranial and spinal compartments. Increases in compliance would act to delay the

propagation of travelling waves, whereas decreases would accelerate wave propagation. The

herniated tonsils are likely to affect the compliance at the foramen magnum, and increase the

wave propagation across this region, whereas wave propagation in more caudal locations is

affected by spinal canal compliance.

Posterior fossa decompression is the most common treatment for Chiari malformation, and can

collapse a syrinx; however unfavourable surgical outcomes are frequent (Aghakhani et al.,

2009). The surgery aims to restore normal flow by increasing the volume of the craniocervical

junction. This may reduce the tonsillar obstruction and increase craniocervical junction

compliance due to duraplasty. Both of these would normalise the compliance, delaying wave

transmission and may reduce flow into the spinal cord (Figure 2.7A).

Introduction of a fluid cavity in the spinal cord increases the compliance of the spinal canal,

reducing the peak amplitude and mean subarachnoid pressure (Elliott et al., 2011; Martin et al.,

2010; Martin and Loth, 2009), and causing delays in pressure transmission (Cirovic and Kim,

2012; Martin and Loth, 2009). This is consistent with the observation that the magnitude and

timing of the subarachnoid pressures (Figure 2.6A and 2.6B) and velocities (Clarke et al.,

2013b) in patients with a syrinx are more similar to control subjects than patients without a

syrinx, suggesting that the formation of a syrinx could act to normalise subarachnoid dynamics

and hinder further growth.

Taking the average measured delay until systolic uptake together with the linked results of the

perivascular model, we conclude that patients with Chiari malformation without a syrinx may

have a greater influx of fluid into the spinal cord via perivascular spaces compared with controls

and patients with a syrinx (Figure 2.6D), as previously predicted by Bilston et al. (2010). It

50

would be interesting to follow Chiari patients to determine whether this parameter predicts

syrinx formation.

Our analysis extends findings from (Clarke et al., 2017), which showed that the net AUC

influences perivascular flow, however it has also shown that the shape of the pressure profile is

important (Figure 2.7). This is likely due to the relatively small timing delay between the R

wave and systolic uptake, which was not previously considered by Clarke et al. (2017). Figure

2.8 illustrates that arterial expansion provides increased resistance to flow from an early time

point in the cardiac cycle; so perivascular flow would be sensitive to changes that give higher

pressures earlier in the cardiac cycle.

Figure 2.8 Effect of peak pressure timing on net perivascular flow, for selected cases with a 4% delay in arterial

expansion. All cases have comparable net AUC, with the peak times progressing from early (A) to later (C) in the

cardiac cycle. The net pumping rates for the cases are highlighted with crosses in Figure 2.7.

Study limitations

The limitations of the subarachnoid space models are discussed in Clarke et al. (2013a). In this

study, the subarachnoid space was modelled as having rigid boundaries, and internal structures

such as the nerve roots and any arachnoid webs were neglected. The inclusion of these features

can cause variation in the local flow field, which could account for some of the differences in

the velocities between our models and the PC-MRI data; however, their influence on the

subarachnoid pressures has been shown to be minimal (Cheng et al., 2014; Pahlavian et al.,

2014). To create a high-quality mesh, the segmented subarachnoid space was smoothed. This

may account for some deviation between the simulated and measured velocities.

51

The timing of the blood pressures was measured in the vertebral artery at mid-C5, but may

differ in the penetrating arteries further down the arterial tree (London and Pannier, 2010). The

wave speed in the vertebral artery is approximately 9 m.s-1 (Wang and Parker, 2004), assuming

the length of the vessel to be no more than 10 cm, the timing delay between the locations can be

estimated to be +10 ms (+1% of the cardiac cycle at 60 bpm), and thus likely have a negligible

effect on the interpretation of the results.

The arterial displacement used for the perivascular model was derived from finger

photoplethysmograph measurements (Millasseau et al., 2002). Although this simplification may

overestimate the displacement during late diastole (Hirata et al., 2006), due to the distribution of

the subarachnoid pressure profile (Figure 2.8), perivascular flow can be expected to be

insensitive to variation during diastole. As the offset between the onset of subarachnoid and

arterial pressure is relatively small, the effective ‘sealing’ of the perivascular space may be

affected by heart rate and vessel properties (Bombardini et al., 2008; Kelly et al., 1989;

Wilkinson et al., 2000) and should be considered in future studies. Additionally, the model

neglected anatomical variation in the perivascular space and assumed a reference pressure of

0 Pa within the spinal cord parenchyma, effectively equating central cord pressure to the caudal

subarachnoid pressure. These assumptions may overestimate flow within the perivascular space

if the spinal cord pressure is higher than caudal subarachnoid pressure, which may occur as

syrinx pressure increases (Heiss et al., 1999), giving a proportional decrease in perivascular

flow. This issue is discussed further in Bilston et al. (2010) and Clarke et al. (2017).

In this study, variation in the shape of the subarachnoid pressure pulse has been attributed to the

general differences between the subject groups. Investigating how the pressure pulse is

influenced by specific changes in anatomy (occipital bone morphology, tonsillar herniation,

etc.) and physiology (heart rate, respiratory stress, etc.) may provide further insight into the

drivers of perivascular flow.

The sample size used in this study (N = 24), although larger than most computational studies, is

nevertheless a modest group of participants and may not represent all Chiari patients.

Additionally, this study used data from a single time point MRI study, and additional

information about the prior history (such as duration of symptoms) and subsequent progression

of the disease may provide further insight in the pathogenesis of syringomyelia.

Conclusion

This study has demonstrated that Chiari malformation could lead to peak pressures occurring

earlier and being significantly increased compared with those from controls, whereas patients

who also have a syrinx tend to have pressure-time profiles more similar to healthy controls. The

idealised model of the perivascular space indicated that for a range of arterial pressure delays

52

spanning those measured in the vertebral artery, Chiari patients without a syrinx may have a

significantly greater CSF inflow than controls and syrinx patients. The model demonstrated that

extended periods of high pressure occurring earlier in the cardiac cycle could increase

perivascular inflow; as such these features may underlie the formation of a syrinx. Further

analysis of how patient pressure profiles change over time prior to and during the development

of a syrinx, as well as in response to treatment, are likely to provide further insight into the

pathogenesis of syringomyelia.

53

3. The effects of variation in the arterial pulse waveform on

perivascular flow

Publication II

Lloyd, R.A., Stoodley, M. A., Fletcher, D.F. & Bilston, L.E. The Effects of Variation in the

Arterial Pulse Waveform on Perivascular Flow. Journal of Biomechanics. 2019 April; 90, 65-

70.

Declaration

I certify that this publication was a direct result of my research towards this PhD and that

reproduction in this thesis does not breach copyright regulations.

……………………………………………….

Rob Lloyd [candidate]

Introduction

The perivascular spaces are fluid channels within the brain and spinal cord which surround

arteries and veins (Lam et al., 2017). These structures provide a conduit between the

cerebrospinal fluid (CSF) in the subarachnoid space and parenchymal interstitial fluid,

facilitating solute transport and waste clearance (Diem et al., 2016; Hawkes et al., 2011; Iliff et

al., 2013a). Tracer studies have indicated that arterial pulsations are involved in perivascular

transport (Iliff et al., 2013b; Rennels et al., 1985; Stoodley et al., 1997; Stoodley et al., 1999).

Identifying the normal function of the perivascular spaces and how flow can be disrupted is vital

to understanding healthy neural function, and abnormal fluid accumulation in nervous tissues.

Syrinxes are fluid filled cavities which form in the spinal cord, they are commonly associated

with subarachnoid space obstructions. Syrinxes can cause sensory and motor disturbances, and

current surgical treatments are frequently unsuccessful (Aghakhani et al., 2009). Fluid

accumulation in a syrinx is the net result of an imbalance between fluid inflow and outflow, and

since the mechanisms which cause fluid accumulation are unknown, this hinders treatment

improvements.

As CSF flow through the perivascular spaces depends on arterial pulsation (Iliff et al., 2013b;

Rennels et al., 1985; Stoodley et al., 1997; Stoodley et al., 1999), computational models have

been used to explore the biomechanics, showing that arterial pulsation alone cannot actively

drive flow in the absence of a pressure gradient (Asgari et al., 2016; Bilston et al., 2003).

However, the artery could act as a ‘leaky’ valve. While an artery expands the perivascular space

54

narrows, increasing resistance to flow driven by dynamic subarachnoid space pressure (Bilston

et al., 2010). As such, flow through the perivascular spaces will be related to the timing

differences between the arterial and subarachnoid pressures (Clarke et al., 2017; Lloyd et al.,

2017).

Lumped parameter models have shown that decreased spinal canal compliance induces a timing

shift in the subarachnoid pressures favourable for increased fluid accumulation (Elliott et al.,

2011; Martin et al., 2012). Our perivascular space model together with simulated subject-

specific pressure data (Lloyd et al., 2017), predicted that the timing changes introduced by

Chiari malformation could significantly increase inflow compared with controls.

Research has focused on how changes in subarachnoid pressure influence perivascular flow but

not examined how variations in the arterial pulse wave affect flow. The arterial waveform is

comprised of the forward and reflected travelling pulse waves (Figure 3.1). Reflections are

produced by impedance differences along the vascular tree. For further details of what

influences the pulse waveform shape, see (Kim et al., 2017; London and Pannier, 2010).

Changes in the arterial waveform may alter perivascular space dimensions across the cardiac

cycle, and thus affect the efficiency of the ‘leaky valve’ mechanism. This study used an

idealised model of the perivascular space with subject-specific subarachnoid pressure profiles to

assess how features of the arterial pulse waveform influence perivascular flow, in order to

determine whether the ‘leaky valve’ mechanism is sensitive to the arterial pulse waveform. We

hypothesised that pulse wave variations within typical physiological ranges would minimally

affect net CSF inflow.

Figure 3.1 Illustration demonstrating how the interactions of the forward travelling and reflected waves may alter the

shape of the arterial pressure pulse, adapted from London and Pannier (2010). (A) A baseline example with the travel

time of the reflected wave (TR) being 0.29 s. (B) The effects of increasing the stiffness of the aorta, reducing the

travelling time of the reflected wave to 0.18 s, shifting the peak into late systole and reducing the diastolic pressure.

55

Methods

Modelling perivascular flow

A previously published idealised model of the perivascular space was used (Bilston et al.,

2010). The model comprises an axisymmetric annulus of fluid surrounding a representative

penetrating artery of the spinal cord (Figure 3.2). Previously calculated subarachnoid space

pressures derived from subject-specific CSF flows for 9 controls, 7 Chiari patients with a syrinx

and 8 without (Figure 3.2B), were applied to the inlet of the model (Lloyd et al., 2017). The

outlet in the cord parenchyma was set at the reference gauge pressure (0 Pa). Arterial expansion

was simulated by applying a time-dependent displacement to a moving inner annular wall

(Figure 3.3A). Since the wavelength of the arterial pulse is orders of magnitude greater than the

length of the perivascular space, the displacement was applied uniformly along its length. The

soft tissue of the spinal cord (the outer radius) was modelled as a rigid wall boundary, since the

cord is nearly incompressible with a Young’s modulus (~1 MPa (Bilston and Thibault, 1995))

orders of magnitude greater than the perivascular pressures (<100 Pa).

Systolic uptake was set at 4% of the cardiac cycle, within the range of timing measured in vivo

(5 ± 1 of the cardiac cycle) (Lloyd et al., 2017). The model was solved in ANSYS CFX (v17.1,

ANSYS Inc, Pittsburgh, PA, USA); the high resolution bounded second order scheme was used

for the advection term and the second order backward Euler scheme for the transient term. At

each time step the normalised residuals were required to be below 2 × 10-5 for convergence.

CSF was modelled as a Newtonian fluid (ν = 0.8 mPa.s, ρ = 1000 kgm-3 (Bloomfield et al.,

1998)). The flow rate was integrated over the cycle to calculate the net mass flux per cardiac

cycle. Positive values indicate net inflow towards the central canal. See Bilston et al. (2010),

Clarke et al. (2017) and Lloyd et al. (2017) for further details.

Figure 3.2 Annotated schematic of the axisymmetric perivascular space model. Flow within the perivascular space is

oscillatory, as such fluid can either flow through the outlet towards the central canal (left on schematic), or return

through the outlet towards the subarachnoid space (SAS; right on schematic). The applied pressure PSAS(t) is one of

the 24 different subject-specific pressure-time profiles calculated in Lloyd et al. (2017). B) Shows the ensemble-

averaged waveforms for the three subject groups.

56

Systematic variation of the arterial pulse wave

Parametric analysis was conducted to assess how the arterial waveform affected perivascular

flow. A simplified version of the displacement waveform used in Bilston et al. (2010) was used

as a baseline (Figure 3.3A). Features of the displacement wave were systematically altered to

mimic in vivo waveform characteristics, while maintaining the baseline value for remaining

features (Figure 3.3 and Table 1). Each displacement wave was applied to the model for all 24

subject-specific pressure profiles.

Figure 3.3 Example of the arterial displacement waveforms used in the parametric analysis. (A) Annotated baseline

waveform, highlight the features of interest in this study. (B) Example of a profile with an earlier peak displacement

(tpeak) being sustained for a longer time (Δt) (increased AUC in uptake; AUCUptake). (C) Example of a profile with a

rapid decline in pressure during late systole and diastole (decreased AUC in decline; AUCDecline), by imposing an

artificial dicrotic notch to the waveform at 40% of the cardiac cycle. (D) Example waveforms for changing the time

of the peak displacement while keeping the total area the same.

Arterial pulse amplitude

The arterial wall deformation within the spinal cord penetrating arteries is unknown, and an

amplitude of 10µm has been assumed previously (Bilston et al., 2010; Clarke et al., 2017).

There, the pulse waveform was scaled to 1.25, 2.5, 5, and 20µm peak amplitude.

Systolic peak time

Systolic peak timing was shifted by between −18 to +10% from baseline (Figure 3.3D; Table

1), to span values in the vertebral artery (first peak = 9±2% and second peak = 25±7% of the

cardiac cycle after systolic uptake), and carotid artery (−8 to 5% change from baseline, from the

youngest to oldest, respectively) (Kelly et al., 1989). Resulting variation in the total area under

the curve (AUC) from baseline was kept below 4%.

57

Systolic peak time and duration (Area under systolic uptake)

The AUC during systolic uptake was increased from 9-35% from baseline, spanning the

increase between 30-70 years (Kelly et al., 1989), by shifting the systolic peak 8-18% earlier.

The peak displacement was maintained until the beginning of systolic decline, which was held

at 28% of the cardiac cycle (Table 1). AUC during uptake was not decreased as this would

require the wave to be altered in late systole and diastole.

Area under systolic decline and diastolic run-off

The AUC during decline was decreased by 15 to 49% from the baseline, to cover a range of

values (–39 to –11% change from baseline, from the oldest to youngest respectively) estimated

from Kelly et al. (1989), by altering the magnitude of the displacement at the dicrotic notch

(Figure 3.3C). The dicrotic notch was assumed to occur at 40% of the cardiac cycle (38±8% of

the cardiac cycle in vertebral artery profiles).

Statistical analysis

The effect of arterial waveform on net perivascular flow was assessed using a generalised linear

mixed model (GLMM) with Bonferroni correction (IBM Statistics v24, IBM Corp., Armonk,

NY). p<0.05 was considered significant.

58 Table 3.1 Alterations made to displacement wave, displayed as: variable value (percentage change from baseline model). Highlighted cells indicate variables altered, where AUCUptake is the area

under the curve during systolic uptake, AUCDecline is the area under the curve during systolic decline and diastole, and tpeak is the time when peak displacement occurs.

Amplitude Peak time Peak duration (Δt) AUC in uptake AUC in Decline Total AUC

Wave Name [µm (% change)] [% R-R interval (% change)] [% R-R interval (% change)] [µm (% change)] [µm (% change)] [µm (% change)]

Baseline 10 28 0 1.62 4.3 5.93

Amp 1.25µm 1.25 (−87.5) 28 0 0.20 (−87.5) 0.50 (−87.5) 0.74 (−87.5)

Amp 2.5 µm 2.5 (−75) 28 0 0.40 (−75.0) 1.10 (−75.0) 1.48 (−75.0)

Amp 5 µm 5 (−50) 28 0 0.81 (−50.0) 2.20 (−50.0) 2.96 (−50.0)

Amp 20 µm 20 (100) 28 0 3.23 (100) 8.60 (100) 11.9 (100)

AUCUptake +9% 10 20 (−8.0) 8 (8.0) 1.75 (8.52) 4.30 6.07 (2.32)

AUCUptake +21% 10 14 (−14) 14 (14) 1.96 (21.0) 4.30 6.27 (5.73)

AUCUptake +35% 10 10 (−18) 18 (18) 2.18 (34.9) 4.30 64.9 (9.51)

AUCDecline −49% 10 28 0 1.62 2.20 (−49.0) 3.81 (-35.7)

AUCDecline −33% 10 28 0 1.62 2.90 (−32.8) 4.52 (-23.8)

AUCDecline −15% 10 28 0 1.62 3.70 (−14.9) 5.29 (-10.8)

tPeak −18% 10 10 (−18) 0 0.40 (−75.0) 5.60 (30.9) 6.05 (2.03)

tPeak −14% 10 14 (−14) 0 0.59 (−63.3) 5.40 (24.9) 5.98 (0.85)

tPeak −8% 10 20 (−8) 0 0.97 (−39.7) 5.00 (16.1) 5.98 (0.87)

tPeak +10% 10 38 (10) 0 2.26 (39.5) 3.90 (-9.98) 6.14 (3.52)

59

Results

Figure 3.4 shows the effect of the arterial wave variations on net perivascular inflow. Figure

3.4A shows that reducing the pulse amplitude decreases the net inflow. Conversely, increasing

the amplitude slightly increased CSF inflow. Earlier arrival of the systolic peak reduces CSF

flow into the spinal cord (Figure 3.4B). Perivascular flow decreased with increasing AUC

during systolic uptake (Figure 3.4C), and decreased AUC during systolic decline and diastole

(Figure 3.4D).

In controls, there was net outflow of fluid when either; the systolic peak times were earlier than

14% of the cardiac cycle, the AUC during systolic uptake was increased more than 26%, or the

AUC during systolic decline and diastole was decreased more than 23%. Chiari patients without

a syrinx maintained a significantly greater influx of CSF compared with controls and syrinx

patients for all conditions. However, for amplitudes less than 5 µm and systolic peaks occurring

earlier than 14% into the cardiac cycle, the 95% confidence intervals overlap.

There were no interaction effects between the subject groups and the characteristics of the

arterial pulse wave (GLMM: Amplitude; p = 0.146, AUC uptake; p = 0.455, AUC decline; p =

0.959, systolic peak time; p = 0.65). Therefore, differences between groups can be attributed to

differences in the input subarachnoid pressures.

60

Figure 3.4 Calculated pumping rates. Group means with 95% confidence intervals are shown. The pumping rates

were calculated by varying the following parameters from the baseline value; (A) arterial expansion amplitude, (B)

area during systolic uptake, (C) area during systolic decline and diastole, (D) the timing of the peak displacement.

Dashed vertical line indicates the baseline results. Syrinx patient results removed for clarity as they superimpose the

control results. Significant comparisons are indicated by an asterisk (* = p < 0.05, ** = p < 0.001).

Discussion

Influence of arterial pulse waveform on the ‘leaky valve’ mechanism

These models suggest that the ‘leaky valve’ mechanism is relatively unaffected by physiological

variations in the arterial pulse waveform. While the ‘leaky valve’ mechanism is most sensitive

to changes in the timing of the systolic peak, a major reduction in perivascular flow requires an

61

unphysiologically early systolic peak (Kelly et al., 1989). Chiari subjects had a significantly

higher inflow over a physiologically likely range of parameters (Figure 3.4).

Our wave amplitude results are in agreement with previous animal studies showing that

diminished arterial pulse amplitude reduces CSF tracer penetration into the spinal cord

(Stoodley et al., 1997; Stoodley et al., 1999). Given the relative timing of the CSF and arterial

pulses, the positive pressure that drives CSF into the spinal cord occurs during a period of low

resistance (late diastole – early systole), whereas the backflow driven by the negative pressures

faces the largest resistance due to arterial expansion. Therefore, the reduced perivascular flow

seen at low amplitude can be attributed to increased backflow. Conversely, increasing the

amplitude above 10 µm also increased resistance to inflow, causing a minimal increase in CSF

inflow (Figure 3.4A). The sensitivity to the timing of the peak displacement (Figure 3.4B), is

similarly affected by the change to inflow resistance.

The low impedance of the cerebral vascular bed (Kim et al., 2017) allows for small penetrating

arteries to maintain pulsatile pressures (Blanco et al., 2017; Geurts et al., 2018). As such, the

pressure waveform would likely have a pronounced peak in systole, with a gradual decline

through late systole and diastole. Measurements from the vertebral and carotid artery (Kelly et

al., 1989) suggest that the baseline waveform (Figure 3.3A) used in previous studies (Bilston et

al., 2010; Clarke et al., 2017; Lloyd et al., 2017) may overestimate the efficiency of the ‘leaky

valve’ mechanism (Figure 3.4D). However, group differences between Chiari subjects and

controls are not altered (Figure 3.4), suggesting that the ‘leaky valve’ is robust to

physiologically reasonable variations in the arterial pulse waveform.

Study limitations

The subarachnoid pressures were based on cardiac-gated MRI scans, neglecting any effects of

respiration (Hamer et al., 1977; Yildiz et al., 2017). If respiration alters the net subarachnoid

pressures, fluid inflow could vary with the respiratory cycle and this should be included in

future studies.

In part, spinal subarachnoid flow is driven by arterial pulsation within the cranium (Alperin et

al., 2005a; Alperin et al., 2005c). Therefore, changes in the arterial pulse may also affect the

subarachnoid pulse shape, which has not been considered here as these effects are not known.

The presence of a syrinx has been repeatedly shown to normalise CSF dynamics (Cirovic and

Kim, 2012; Clarke et al., 2013b; Elliott et al., 2011; Lloyd et al., 2017; Martin et al., 2010;

Martin and Loth, 2009), but there is no in vivo data for perivascular flow. Therefore,

perivascular flow arising from CSF pressures in syrinx patients would be expected to be similar

to control subjects, as found here and by Lloyd et al. (2017). However, the CSF dynamics in the

pre-syrinx state are unknown, so this study uses the syrinx free subjects as an approximation of

62

that initial state, although there are likely differences as these subjects have not yet developed a

syrinx. It was not possible to obtain longitudinal clinical or imaging data to confirm this. It

follows from this that these results do not provide a complete picture of the mechanisms of

syrinx formation or enlargement, as they are based on cross-sectional data. Future studies

should address this issue.

This model neglected variation in cross-sectional area and the bundles of collagen fibres found

in the perivascular space (Lam et al., 2017). The models may thus underestimate perivascular

resistance to flow.

We did not model temporal variation in the internal pressure of the spinal cord, effectively

assuming the cord pressure curve follows the caudal subarachnoid pressure. A syrinx would

elevate the pressure within the spinal cord, either in or out of phase with the subarachnoid

pressure (Heiss et al., 1999), altering the pressure gradient and reducing perivascular flow. This

would have a comparable effect to altering the shape of the subarachnoid pressure waveform in

isolation, the effects of which has been explored by Clarke et al. (2017).

Solute transport has not explicitly been simulated in this study. However, tracer particles such

as Ovalbumin (Tao and Nicholson, 1996) would be dominated by convective flow (Péclet

number ≈ 1250, for a typical 5 mm.s-1 velocity) and closely follow bulk flow, as seen in recent

intravital imaging studies (Bedussi et al., 2018; Mestre et al., 2018). Modelling particle

deposition or flow into the parenchyma should be a focus of future work.

Conclusion

In this parametric analysis, it has been demonstrated that Chiari patients without a syrinx

maintain a significantly greater perivascular flow into the cord over a physiologically realistic

range of arterial pulse wave shapes, indicating that the error introduced using idealised arterial

pulse shapes in previous studies is likely to be small. The results also suggest that changes in the

arterial pulse associated with aging (i.e. increased late systolic pulse amplitude and faster

diastolic decay) may slightly decrease perivascular flow.

63

4. The shape of the posterior fossa and its effects on cerebrospinal

fluid dynamics

Introduction

Chiari malformation type I is defined clinically by the herniation of the cerebellar tonsils

through the foramen magnum (Milhorat et al., 1999). Many patients with the condition are also

diagnosed with syringomyelia, a condition where a fluid filled cavity (syrinx) forms within the

spinal cord (Speer et al., 2003). Syrinxes have been associated with both sensory and motor

deficits (Sakushima et al., 2012). Although the mechanistic link between the two conditions is

unknown, it is assumed that changes in the cerebrospinal fluid (CSF) dynamics, caused by the

obstruction formed in the foramen magnum, play a significant role in the pathogenesis of a

syrinx.

Although 3-5mm of tonsillar herniation is used clinically to diagnose Chiari, it has been shown

to have no relation to a patient’s symptoms (Khalsa et al., 2018; Meadows et al., 2000) or the

risk of syrinx development (Masur et al., 1995; Stovner and Rinck, 1992). Other morphometric

measures of bony landmarks and position of soft tissues within the posterior cranial fossa (PCF;

Figure 4.1; Table 4.1), as well as the taper of the cervical subarachnoid space have been

suggested as characteristics that could affect CSF dynamics and result in syrinx formation.

Syrinx patients have been found to have a narrower foramen magnum (Eppelheimer et al.,

2018), and a greater clivus incline (Yan et al., 2016) compared with syrinx free Chiari patients

(Figure 4.1; line 7 and angle 9). Additionally, the anterior-posterior diameter of the

subarachnoid space decreases faster from the C7 to C4 vertebrae in syrinx patients (Gadde et al.,

2017; Thompson et al., 2016; Zhu et al., 2014).

Invasive in vivo pressure monitoring studies have documented that the spinal subarachnoid

pressures are elevated in Chiari patients (Heiss et al., 1999; Heiss et al., 2012), attributing the

pressure increase to the tonsils acting as a piston, driving a high pressure wave into the spinal

subarachnoid space. In contrast, computational models have demonstrated that tonsillar

pulsation produces a relatively small change in pressure relative to a static herniation (Linge et

al., 2011; Pahlavian et al., 2015). Additionally, Leung et al. (2016) demonstrated that there is no

difference between the tonsillar motion in syrinx and syrinx free Chiari patients. It was shown

with a series of subject-specific models that Chiari patients without a syrinx had an elevated

pulse pressure occurring earlier in the cardiac cycle, attributing this effect to the over-crowding

of the posterior fossa (Lloyd et al., 2017). However, despite numerous morphological (Table

4.1) and computational studies (Linge et al., 2011; Pahlavian et al., 2015), there remains no

clear understanding of how structures of the posterior fossa and cerebellar anatomy influence

CSF dynamics in the region of the foramen magnum.

64

The aim of this study was to use a series of subject specific anatomical models to investigate

how the anatomy of the craniocervical junction influences the subarachnoid pressure-time

profiles in Chiari patients, with and without a syrinx, compared with healthy controls. We

hypothesised that overcrowding in the posterior cranial fossa (PCF) reduces compliance and

causing the pressure peak to occur earlier in the cardiac cycle.

65

Linear

1) Height of PCF = perpendicular distance from the foramen magnum [Line 7] to the superior aspect of the tentorium.

2) Width of PCF = distance between the dorsum sellae of the clivus to the internal occipital protuberance.

3) Clivus length = distance between the dorsum sellae (superior point) to the basion (inferior point), also equals the sum of 4 and

5.

4) Basisphenoid length = dorsum sellae to the sphenoocciput.

5) Basiocciput length = basion to the sphenoocciput.

6) Supraocciput length (Occipital bone length) = the opisthion (posterior point of foramen magnum) to the internal occipital

protuberance.

7) Foramen magnum (McRae line) = anterior-posterior diameter between the basion and opisthion.

Angular

9) Clivus incline (Boogard angle) = angle between lines 3 and 7.

10) Tentorial angle = angle between lines 15 and 6.

11) Basal angle = angle between line 3 and a line extending along the anterior cranial fossa from the dorsum sellae.

12) Wackenheim angle = angle between line 3 and a tangent line connecting the posterior aspect of the dens to the caudal point of

the C2 vertebra.

13) Twining’s-tentoral angle = angle between lines 2 and 15.

Soft tissue 14) Tonsillar herniation below foramen magnum [Line 7]. (Distance below line is taken as positive.)

15) Tentorium length = From the internal occipital protuberance to the superior end of the tentorium.

16) Fastigium height = perpendicuar distance between the foramen magnum [Line 7] and the fastigium of the fourth ventricle.

17) Pons height = perpendicular distance between the foramen magnum [Line 7] the superior aspect of the pons at the invagination

before the midbrain.

18) Corpus callosum height = perpendicuar distance between the foramen magnum [Line 7] to the inferior aspect of the splenium

of the corpus callosum.

Areas

19) PCF area = area enclosed by lines 3,7,6,15,8

20) PCF osseous area = area enclosed by lines 3,7,6,2

Volumes

21) PCF volume = volume segmented form the same bounds as 19.

22) Hindbrain volume within the PCF volume [21].

23) CSF volume in PCF [21] = volume 21 minus volume 22.

24) Ratio of hindbrain volume:PCF volume – relative measure of overcrowding [Volumes 22/21].

Figure 4.1 Common morphometric measurements performed in the literature. Numbering scheme used to simplify

summary of study findings in Tables 4.1,3 &4. Measurements are divided into five categories, linear, angular, area,

and volumetric measures of PCF, in addition to measures of soft tissue location.

66 Table 4.1 Summary of Chiari morphometric studies. Table contains symbolic representations of the commonly investigated characteristics (for clarity, some insignificant measures or those used

in few studies were neglected). Significant findings are shown as; decrease (↓), increase (↑), no significant difference (NS), and measure not studied (-).Measurement codes corresponded to

Figure 4.1. For studies where Chiari patients both with and without a syrinx were grouped, the proportion of syrinx patients are noted (? Indicates where syrinx patients were included but the

number of patients was not quoted). *Indicates a measurement that was not calculated in the original study but can be calculated from the paper. 1The study includes 2 subjects with fourth

ventricle herniation which may affect the results. 2Study includes 364 Chiari patients but only 50 were used for quantitative analysis.

Study Number of subjects Comparison Linear measures Angular measures Soft tissue measurements Area Volume

1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

This Study Control = 12

Chiari = 12

Chiari v

Control ↓ NS NS - - NS NS NS NS ↑ NS NS ↑ NS ↓ ↓ ↓ NS NS NS NS NS NS

Dagtekin et al. (2011)

Control = 25

Chiari = 15

Syrinx = 9/15

Chiari v.

Control - - NS ↓ ↑ ↓ ↑ - NS - - - - - - NS - - - - - - -

Alperin et al. (2014) Control = 37

Chiari = 36

Chiari v.

Control - ↓ ↓ - - ↓ NS - - - - - - - - - - - - ↓ NS - ↑

Alperin et al. (2015) Control = 37

Chiari = 63

Chiari v.

Control - - ↓ - - ↓ - - - - - - - - - - - - - ↓ NS - ↑

Nishikawa et al.

(1997)

Control = 50

Chiari = 301

Syrinx = 26/30

Chiari v.

Control - - NS - NS ↓ - - - - - ↑ - - - NS - - - NS NS - ↑

Nishikawa et al.

(1997)

Control = 50

Chiari = 301

Syrinx = 26/30

Chiari w/

basilar invagination v.

Control - - ↓ - ↓ ↓ - - - - - ↑ - - - NS - - - NS NS - ↑

Milhorat et al. (1999)

Control = 50

Chiari = 502

Syrinx = ?/50

Chiari v.

Control - - ↓ - - ↓ - - ↑ - - - ↑ - - - - - - ↓ NS ↓ ↑*

Karagoz et al. (2002)

Control = 21

Chiari = 22

Syrinx = 13/22

Chiari v.

Control - ↓ ↓ - - ↓ NS ↑ NS ↑ - ↑ - - - - - NS ↓ - - - -

Sekula et al. (2005) Control = 25

Chiari = 22

Chiari v.

Control - NS ↓ ↓ ↓ NS NS - - - - ↑ - - - NS - - - - - - -

Hwang et al. (2013)

Controls = 24

Chiari = 12

Syrinx = ?/12

Chiari v.

Control - ↓ ↓ - - NS ↓ ↑ ↑ - - - - - - NS - - - - - - -

Urbizu et al. (2014)

Controls = 50

Chiari = 100

Syrinx = 45/100

Chiari v.

Control ↓ NS ↓ NS NS ↓ NS - ↓ ↑ NS - ↑ ↓ ↓ ↓ ↓ ↓ ↓ - - - -

Milhorat et al. (2010) Controls = 80

Chiari = 388

Chiari v.

Control - - ↓ - - ↓ NS - - - - - - - - - - - - ↓ NS - ↑*

Aydin et al. (2005)

Control = 30

Chiari = 60

Syrinx = 46/60

Chiari v.

Control ↓ ↓ ↓ - - NS ↑ - - - - - - - - - - - - - - - -

Dufton et al. (2011) Control = 107

Chiari = 81

Chiari v.

Control - - ↓ - - - ↑ ↑ - - - - ↑ - - - - - - - - - -

Yan et al. (2016) Control = 40

Chiari = 36

Chiari v.

Control - ↓ ↓ - - ↓ NS NS - - - - - - - - - - - - - - -

67

Table 4.1 Continued summary of morphometric studies. Showing the studies that investigated the difference in morphology with sex, and the differences seen in syrinx patients. Significant

findings are shown as; decrease (↓), increase (↑), no significant finding (NS), and measure not studied (-). For studies where Chiari patients both with and without a syrinx were grouped, the

proportion of syrinx patients are noted (? Indicates where syrinx patients were included but the number of patients was not quoted). 3Study reassessed after reducing the data set to control for

age, race and body mass index.

Study Number of

subjects Comparison

Linear measures Angular measures Soft tissue measurements Area Volume

1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Schady et al. (1987) Control = 26

Chiari = 13

Male

Chiari v.

Control - NS ↓ - - - NS ↑ - ↑ - - - - - - - - ↓ - - - -

Stovner et al. (1993) Control = 14

Chiari = 11

Male

Chiari v.

Control - NS - - - - - - - - - - - - - - - - NS - - - -

Vega et al. (1990)

Control = 23

Chiari = 25

Syrinx = ?/25

Male

Chiari v.

Control - - ↓ - - - NS ↑ - ↑ - - - - - - - - ↓ ↓ - - -

Houston et al. (2019)

Control = 26

Chiari = 26

Syrinx = 8/26

Male

Chiari v.

Control ↓ ↑ ↓ - - NS NS ↑ ↓ NS ↓ - ↑ NS ↓ ↓ ↓ NS NS - - - -

Schady et al. (1987) Control = 23

Chiari = 19

Female

Chiari v.

Control - NS NS - - - NS ↑ - ↑ - - - - - - - - ↓ - - - -

Stovner et al. (1993) Control = 26

Chiari = 22

Female

Chiari v.

Control - NS - - - - - - - - - - - - - - - - ↓ - - - -

Vega et al. (1990)

Control = 23

Chiari = 17

Syrinx = ?/25

Female

Chiari v.

Control - - ↓ - - - NS ↑ - NS - - - - - - - - ↓ NS - - -

Houston et al. (2018)

Controls = 131-140

Chiari = 155-162

Syrinx = 28/162

Female

Chiari v.

Control ↓ NS ↓ - - NS ↑ ↑ NS ↑ ↓ - ↑ NS ↓ ↓ ↓ ↓ ↓ - - - -

Houston et al. (2018)3

Controls = 100-108

Chiari = 115-121

Syrinx = 21/121

Female

Chiari v.

Control ↓ NS ↓ - - NS ↑ ↑ NS ↑ ↓ - ↑ NS ↓ ↓ ↓ NS NS - - - -

Houston et al. (2019)

Control = 26

Chiari = 26

Syrinx = 6/26

Female

Chiari v.

Control

NS NS ↓ - - NS NS ↑ NS NS ↓ - ↑ NS ↓ ↓ ↓ NS NS - - - -

This Study Controls = 12

Syrinx = 9

Syrinx v.

Control ↓ NS NS - - NS NS NS NS NS NS NS ↑ NS ↓ ↓ ↓ NS NS NS NS NS NS

Eppelheimer et al. (2018) Controls = 140

Syrinx = 38

Syrinx v.

Control NS NS NS - - NS NS NS NS NS NS - NS NS NS ↓ NS ↓ NS - - - -

Yan et al. (2016) Control = 40

Syrinx = 48

Syrinx v.

Control - ↓ ↓ - - ↓ NS ↑ - - - - - - - - - - - - - - -

This Study Chiari = 12

Syrinx = 9

Syrinx free v.

Syrinx NS NS NS - - NS NS NS NS NS NS NS NS NS NS NS NS ↓ NS NS NS NS NS

Eppelheimer et al. (2018) Chiari =198

Syrinx = 38

Syrinx free v.

Syrinx NS NS NS - - NS ↑ NS NS NS NS - NS NS NS NS NS NS NS - - - -

Yan et al. (2016) Chiari = 19

Syrinx = 48

Syrinx free v.

Syrinx - NS NS - - NS NS ↑ - - - - - - - - - - - - - - -

68

Methods

MR imaging and anatomical segmentation

This study included analysis of previously collected anatomical MRI data from Clarke et al.

(2013a) and prospectively collected data that are part of a separate ongoing study. For

anatomical MRI data acquired in Clarke et al. (2013a), the experimental protocols were

approved by the University of New South Wales Human Research and Ethics Committee. The

South Eastern Sydney Local Health District Human Research Ethics Committee approved all

experimental protocols for the new data sets. All participants gave written informed consent to

enter the study. Thirty-two participants underwent MRI scanning of the head and neck; 12

healthy controls and 21 symptomatic Chiari I patients, 9 with and 12 without syringomyelia

(Table 4.2). 3D isotropic T1 weighted sagittal anatomical MRI scans of the cervical spine were

acquired. Parameters for the scan include; 0.94mm voxels, matrix = 288×288, FOV = 270×270,

TR/TE =5.5/2.5 ms and 180 slices of 0.94 mm thickness.

Table 4.2 Summary of participant information, showing age, weight and heart rate during MRI. Data displayed as

‘means ± S.D’ (range).

Subject Group Age Weight Heart Rate N

[Years] [Kg] [Bpm]

Control 37.3 ± 11.2 (24 -60) 66.9 ± 12.0 (53-85) 67.6 ± 10.8 (49.8-88.1) 12

Chiari w/o syrinx 33.6 ± 10.1 (22-60) 65.1 ± 10.0 (55-87) 66.7 ± 9.5 (51.9-81.3) 12

Chiari w/ syrinx 40.0 ± 9.76 (27-58) 73.5 ± 15.4 (55-100) 70.5 ± 9.5 (58.9-87.8) 9

Morphometric measurements

The structure of the posterior fossa and hindbrain were quantified taking linear, angular and area

measures on the midsagittal plane (when required images were reformatted to give the

midsagittal slice). The 21 measures taken are defined in Figure 4.1. The basisphenoid [line 4]

and basiocciput [line 5] lengths were excluded as their net effect is well described by the length

of the clivus [line 3]. Measurements were performed by a single observer with the open source

software 3D Slicer (https://www.slicer.org).

A single user segmented the fossa and hindbrain volumes as follows. The structures were

outlined in the axial plane at 5 mm intervals, the space between these was interpolated then

smoothed with a 2 mm Gaussian kernel. Boundaries were inspected and manually corrected.

Volume regions were defined (Figure 4.2), 1) parallel to the inferior aspect of the C1 vertebrae

to the foramen magnum (C1-FM), 2) from the foramen magnum to the Line 2 (osseous volume;

volume corresponding to previously measured osseous area [Area 20]), 3) from the foramen

magnum to the superior aspect of the tentorium (Fossa volume; volume corresponding to

previously measured posterior cranial fossa area [Area 19]). As the craniocervical junction is

69

continuous when calculating overcrowding (tissue volume / total volume) the C1-FM volumes

were added to the osseous (C1-Osseous) and fossa (C1-Fossa) volumes.

Figure 4.2 Example posterior fossa volume of a Chiari patient with a syrinx. Left: midsagittal view with the outline

of the segmented volume overlayed, blue = total volume of fossa, yellow = tissue volume. The volumes were divided

into the segments: A) between the C1 vertebra (1) and foramen magnum (2), B) from the foramen magnum (2) to the

anterior-posterior diameter of the fossa (3), and C) from line 3 to the superior aspect of the tentorium. Right: shows

the corresponding segmented volumes. A = C1-FM, A+B = C1-Osseous, A+B+C = C1-Fossa, B+C = volumes 21-23.

Spinal canal taper

The anteroposterior diameters of the cervical spinal canal from C1 to C7 were measured on the

midsagittal plain, this was measures perpendicular the spinal subarachnoid space at the mid-

point of each vertebrae. The caudocranial heights of the vertebra were measured between the

midpoints of the anteroposterior diameters at each level. Similar, to studies in the literature a

least square fit was used to estimate the spinal tapers (mm/level) between the C1-C7, C1-C4,

and C4-C7 regions. Spinal tapers were also calculated by the change in both anteroposterior

diameter and height ((𝑪𝒂𝒖𝒅𝒂𝒍 𝑫𝒊𝒂 − 𝑪𝒓𝒂𝒏𝒊𝒂𝒍 𝑫𝒊𝒂)

𝑪𝒂𝒖𝒅𝒐𝒄𝒓𝒂𝒏𝒊𝒂𝒍 𝑯𝒆𝒊𝒈𝒉𝒕⁄ ) for the same regions, to account for the

variation in height between subjects.

Subarachnoid pressure-time data

The effect of tonsillar obstruction on CSF dynamics was assessed against each subject’s

matched pressure-time data (derived from 24 previously published (Lloyd et al., 2017) and 8

original subject-specific computational models). In summary, 32 models of the cervical

subarachnoid space were manually segmented from the anatomical MRI. Cardiac-gated cine

phase-contrast MRI (PC-MRI) of CSF flow at the level of the cerebellar tonsils or foramen

magnum were used as an inlet boundary condition, the outlet was set with a 0 Pa reference

pressure. The CSF velocities and pressures were solved in ANSYS CFX (v17.1, ANSYS Inc.,

PA), and validated against MRI velocities measured in the cervical spine. The magnitude and

timing of the peak pressures calculated at the C2 level were analysed in this study. Cardiac PC-

MRI was not available for one Chiari subject which was excluded from modelling.

70

Statistical analysis

The morphometric measures were compared between subject groups with a one-way ANOVA,

using a Bonferroni post hoc test to identify group differences when a significant difference (p <

0.05) was found. For variables which were non-normally distributed a Kruskal-Wallis test was

performed, and with a post-hoc Mann-Whitney U test to identify group differences. Linear

regression was used to determine whether features of the subarachnoid pressure profiles were

significantly associated with the morphometric measures of overcrowding. Analyses were

conducted with IBM Statistics (v24, IBM Corp., Armonk, NY).

Results

Posterior fossa morphology

All morphological measurements are shown in Tables 4.3 and 4.4, for the normally and non-

normally distributed variables respectively. Both Chiari patients with and without a syrinx had

a; greater tonsillar herniation [line 14], lower fastigium [line 16], pons [line 17], and a greater

ratio of the volume occupied by tissue between the C1-FM, C1-Osseous, and C1-Fossa

segments. Table 4.1 contains a summary of significant trends for comparison with the other

studies in the literature. The midsagittal cross-sectional area of the posterior fossa was found to

be significantly smaller (area 19 = ‒13%) in syrinx patients compared with syrinx free subjects.

71

Table 4.3 Descriptive statistics of the morphological measures that were normally distributed. Linear measures are in

mm, angular measures are in degrees, the areas are in mm2, volumes in ml, and ratios are non-dimensional. None of

the variables in this table were significantly different between groups.

Measure Control Chiari w/o syrinx Chiari w/ syrinx

Mean 95% CI Mean 95% CI Mean 95% CI

Linear 7 37.3 35.3-39.3 36.9 35.1-38.7 36.2 33.3-39.2

Volume 21 192.3 181.6-202.9 181.9 166.8-197.0 182.8 167.9-197.8

C1-Fossa

Volume Total 201.2 189.9-212.5 190.1 174.8-205.6 191.5 174.9-208.1

Table 4.4 Descriptive statistics of the morphological measures that were non-normally distributed. Linear measures

are in mm, the areas are in mm2, volumes in ml, and ratios are non-dimensional. Significant comparisons are in bold,

italicised, and indicated with asterisks, * p < 0.05, ** p < 0.01, and *** p < 0.001. Comparison N-C is between

controls and Chiari patients without a syrinx, N-S is between controls and patients with a syrinx, and C-S is between

patients with and without a syrinx.

Measure Control Chiari w/o syrinx Chiari w/ syrinx Comparison

Median IQR [Q1-Q3] Median IQR [Q1-Q3] Median IQR [Q1-Q3] N-C N-S C-S

Linear

1 66.6 64.7-70.3 61.8 57.1-68.5 59.8 59.0-65.1 * *

2 83.5 82.2-88.5 81.6 80.9-86.2 83.9 81.8-87.9

3 44.1 42.2-48.7 43.1 40.8-44.5 41.1 39.8-45.0

6 39.8 37.4-46.4 39.2 36.3-42.1 41.1 39.9-44.9

Angular

9 117.1 111.7-118.4 119.7 114.9-123.7 117.6 111.5-133.3

10 98.0 90.9-107.3 98.1 89.8-103.8 93.9 89.1-95.0

11 113.0 108.2-119.3 120.0 117.4-124.7 115.8 109.9-120.4 *

12 151.1 147.8-157.8 150.0 147.0-154.3 150.5 136.6-160.7

13 40.5 37.4-44.8 41.3 39.7-46.3 38.8 37.3-40.1

Soft tissue

14 –2.57 –5.22- –1.28 6.91 1.78-9.62 7.89 5.51-10.7 *** ***

15 49.4 47.2-51.4 49.2 46.9-50.3 45.6 43.7-46.9

16 32.7 31.0-34.2 28.3 25.8-29.5 28.3 25.6-29.1 *** ***

17 43.1 41.6-44.9 39.4 36.5-40.8 40.6 32.8-42.4 *** *

18 62.9 61.8-64.1 60.0 55.7-62.6 57.7 54.4-60.1 * **

Area 19 3595 3219-3873 3650 3262-3937 3178 2996-3499 *

20 2217 1804-2416 2035 1864-2161 1869 1674-2249

Volume

22 169.0 150.0-175.2 155.0 143.0-179.7 159.2 149.5-168.5

23 27.4 23.6-32.3 21.5 18.8-27.3 24.2 16.7-30.4

24 0.85 0.82-0.88 0.87 0.85-0.89 0.89 0.84-0.90

C1-FM

Volume

Tissue 2.51 1.85-3.02 3.87 3.56-4.72 4.89 4.01-6.25 *** ***

Total 9.13 7.62-9.67 8.03 7.59-9.36 7.86 6.96-10.5

Ratio 0.27 0.24-0.28 0.45 0.40-0.66 0.56 0.40-0.65 *** ***

C1-Osseous

Volume

Tissue 129.7 117.4-141.8 122.7 107.1-138.7 129.9 114.6-140.5

Total 152.7 146.5-167.2 141.5 122.6-158.3 147.6 126.4-164.9

Ratio 0.83 0.80-0.86 0.87 0.84-0.89 0.89 0.85-0.90 ** **

C1-Fossa

Volume

Tissue 171.4 151.8-177.7 159.8 146.5-184.3 164.1 152.8-174.5

Ratio 0.83 0.79-0.85 0.85 0.84-0.88 0.88 0.83-0.88 * *

72

Spinal canal taper

Table 4.5 shows the measured spinal canal taper, for each of the defined regions. No significant

differences were found between subject groups, for either the tapers which accounted for

variance in the vertebral height or normalised by spinal level.

Table 4.5 Median spinal tapers measured within the cohort.

C1-C7 C1-C4 C4-C7

Group Median IQR [Q1-Q3] Median IQR [Q1-Q3] Median IQR [Q1-Q3]

Ta

per

[mm

/mm

] Control –0.03 –0.04- –0.02 –0.07 –0.09- –0.05 0.002 –0.02-0.03

Chiari w/o

syrinx –0.04 –0.06- –0.02 –0.08 –0.12- –0.06 –0.003 –0.02-0.04

Chiari w/

Syrinx –0.03 –0.06- –0.02 –0.06 –0.11- –0.02 –0.011 –0.03- –0.02

Ta

per

[mm

/lev

el]

Control –0.58 –0.81- –0.43 –1.4 –1.8- –0.92 0.05 –0.34-0.37

Chiari w/o

syrinx –0.64 –1.1- –0.50 –1.6 –2.2- –1.1 –0.10 –0.20-0.40

Chiari w/

Syrinx –0.75 –1.1- –0.26 –1.4 –2.2- –0.51 –0.05 –0.50-0.37

The effect of craniocervical obstruction on the subarachnoid pressure waveform

The relationships between tonsillar obstruction and the subarachnoid pressures are shown in

Figure 4.3. In controls or syrinx free patients the measures of obstruction (herniation [line 14],

and the volume ratios) were neither correlated with the magnitude or timing of the peak

pressures. Pooling syrinx free patients and controls together showed the magnitude and timing

to be weakly associated with herniation (Figure 4.3A and E) and the ratio of the volume

occupied between C1-FM (Figure 4.3B and F), whereas in syrinx patients the peak timing was

moderately correlated with the tonsillar herniation (Figure 4.3A) and strongly related to the

volume occupied between C1-FM (Figure 4.3B).

73

Figure 4.3 Relationship between the timing (A-D) and magnitude (E-H) of the subarachnoid pressures and the measures of obstruction. Linear relationships shown A-F are all significant

(p<0.05). No significant relationships were found between the C1-Osseous/C1-Fossa overcrowding and the peak pressure for either subjects with or without a syrinx. R2 is the coefficient for

determination for the linear regression lines shown. Relationships in syrinx free subjects are shown as a solid line, the dashed line indicated a relationship in the syrinx patients.

74

Discussion

This study found in general that the morphology of the posterior cranial fossa and the spinal

canal in Chiari patients were in the range of healthy controls. However, the structures of the

brain were located closer to the foramen magnum, which may be a contributing factor to why

the posterior fossa in Chiari patients was occupied by a greater fraction of nervous tissue.

Similar to other studies, the position of the pons, fastigium of the fourth ventricle and splenium

of the corpus callosum were found to be closer to the foramen magnum in Chiari patients

compared with controls (Eppelheimer et al., 2018; Houston et al., 2018; Urbizu et al., 2014).

This has generally been overlooked, likely attributing the descent of the hindbrain to structural

differences in the posterior fossa. However, Chiari patients have been found to have shorter

intracranial heights (Eppelheimer et al., 2018; Houston et al., 2019; Houston et al., 2018;

Taştemur et al., 2017) and widths (Taştemur et al., 2017) compared with controls, suggesting

that supratentorial abnormalities and other structures in the cranium could affect posterior fossa

overcrowding (Heiss, 2014).

The absolute value of the measurements are within the ranges of previous investigations, for

both the linear, angular (Houston et al., 2018; Urbizu et al., 2014; Yan et al., 2016) and

volumetric (Alperin et al., 2014; Khalsa et al., 2018; Khalsa et al., 2017; Nishikawa et al., 1997)

measures. In contrast to previous studies only the posterior cranial fossa height [linear 1] and

basal angle [angle 11] were significantly different between Chiari patients and controls (Table

4.1). Additionally, the total volume of the posterior fossa and the volumes of the hind brain (C1-

Osseous and C1-Fossa) were not different compared with controls, however Chiari patients

were still significantly more overcrowded (Table 4.4). These results indicate that the group wise

differences are related to the overall shape of the cranial volume rather than the absolute size of

the structures. This would allow for various structures to be within normal ranges and still cause

overcrowding.

Interestingly, in syrinx free patients the midsagittal area [area 19] of the posterior fossa was not

different from controls, but it was significantly smaller in patients with a syrinx compared with

patients without (Table 4.4). These differences in shape will alter the overall distribution of the

cranial volume, which may cause local variations in overcrowding, restricting and forming

alternate fluid pathways. This in turn may contribute to the difference in CSF dynamics between

those with and without a syrinx (Figure 4.3A, B, E, and F) and play a role in syrinx

development.

In syrinx patients a strong association was seen between the timing delay of the peak pressure

pulse and the obstruction of the subarachnoid space below the foramen magnum (Figure 4.3B).

Previously this delay has been attributed to the increase in spinal compliance introduced by the

75

fluid cavity (Cirovic and Kim, 2012; Martin and Loth, 2009). Although the dimensions of

syrinx cavity were not quantified in this study, the syrinx patient with the longest delay had a

visually smaller syrinx compared with the patient with the earliest timing. This could suggest

that the delay seen is primarily the result of the obstruction, similar to that seen in various

models of stenosis (Stoverud et al., 2013) and arachnoiditis (Cheng et al., 2012).

Study limitations

The sample size of this study (N = 33) compared with other studies is underpowered to detect a

small effect size between morphological measures. This may explain why our results differ from

previous studies in different patient populations, and limited inference should be taken from the

results when assessing the performance of a morphological measurement as a clinical marker

for Chiari malformation. However, even between the larger studies there are conflicting findings

and large variance in the effect size of the differences between controls and Chiari patients

(Houston et al., 2018; Karagoz et al., 2002; Urbizu et al., 2014), suggesting these measures are

unreliable for clinical use. A contributing factor to this may be related to the different

demographics sampled in those studies (Bogdanov et al., 2019; Brickell et al., 2006; Roller et

al., 2015), and this needs to be assessed in future studies. An additional limitation of the cross-

sectional study design is that the data is taken from a single time point and provides no

information as to how the morphology may cause change in the CSF dynamics over time. All

data in this study was collected manually by a single user which, while consistent, may

introduce a source of human error and bias.

CSF dynamics are likely influenced by the size of the syrinx cavity (Martin et al., 2010; Martin

and Loth, 2009) and supratentorial structures (Heiss, 2014). However, due to the limited field of

view used for the acquisition of the anatomical images, these features could not be quantified.

Imaging protocols have since been altered so their influence can be fully investigated in future

studies.

The current analysis of Chiari morphology is limited by its reliance on the analysis of the

midsagittal plane, and it has been shown that these are poor predictors of the overall volume

(Alperin et al., 2014). More advanced shape analysis techniques such as spherical harmonic

decomposition (SPHARM) could be used in future investigations. These methods parameterise

the volume, allowing for the comparison of local differences about the whole volume (Paniagua

et al., 2013; Styner et al., 2006). Although the available software is still under development and

requires validation, SPHARM may help identify better clinical markers of Chiari malformation.

The relationship between the posterior fossa morphology and subarachnoid pressures was

assessed using cardiac-gated data. With the development of real-time MR imaging techniques,

the significance of respiration on CSF dynamics has been emphasised (Aktas et al., 2019;

76

Dreha-Kulaczewski et al., 2017; Dreha-Kulaczewski et al., 2018; Yamada et al., 2013).

Coughing and straining is known create large changes in the subarachnoid pressures, driving

CSF cranially (Williams, 1976; Williams, 1981a). As such, understanding how overcrowding

alters the response to a sudden increase in pressure may inform our understanding of Chiari

symptomology and syrinx development.

Conclusion

The morphometric analysis showed that the craniocervical junction was overcrowded, and that

the mid-sagittal area of the posterior fossa was significantly smaller in syrinx patients compared

with syrinx free Chiari patients. This suggests that there are differences in the overall shape of

the posterior cranial fossa between the two patient groups, which could create local changes to

the CSF pathways and dynamics. The results of this study suggest that the timing of the

subarachnoid pressures may be influenced by the degree of tonsillar obstruction, particularly in

the base of the posterior fossa. Structural differences in the posterior fossa significantly

influence the pressure timing and may influence whether a syrinx is formed. Future

morphological assessments of the occipital bone and syrinx cavity are planned to provide

further insight into the pathophysiology of Chiari malformation and syringomyelia.

77

5. Respiratory cerebrospinal fluid flow is driven by the thoracic and

lumbar spinal pressures

Introduction

Both the cardiovascular and respiratory systems contribute to the pulsatile flow of cerebrospinal

fluid (CSF). The cardiac component is well understood to be dependent on the blood volume

balance within the cranium (the Monro-Kellie hypothesis; Figure 5.1) (Mokri, 2001), as the

cranium is rigid and of a fixed volume, so as the volume of arterial and venous blood varies

over the cardiac cycle, CSF is either displaced into or out of the spinal canal to compensate

(Alperin et al., 2005a; Alperin et al., 2005b).

Figure 5.1 Schematic of the Monro-Kellie hypothesis (QA=arterial flow, Qv=venous flow and QCSF=CSF flow). (A)

Labelled schematic demonstrating a hypothetical initial or resting state. The cranial volume is constant, as the net

blood volume increases CSF is displaced into the spinal canal (B). Conversely, a decrease in blood volume draws

CSF cranially (C).

Respiratory-driven CSF flow depends on two different mechanisms. In the case of forced

expiratory efforts such as coughing or a Valsalva manoeuvre, spinal pressures are increased

(Hamilton et al., 1944; Hamilton et al., 1936), driving CSF cranially (Du Boulay et al., 1972;

Martins et al., 1972; Williams, 1976; Williams, 1981a, b). This is facilitated by the epidural

veins (Figures 5.2A and 5.2B), a valveless network spanning the length of the spine that allows

backflow from the vena cava (Batson, 1940; Henriques, 1962). When the intrathoracic or

abdominal pressures are increased, blood flows into the spinal canal and compresses the dural

sac, displacing CSF (Du Boulay et al., 1972; Martins et al., 1972; Reitan, 1941). On the other

hand, CSF flow during normal respiration is commonly accepted to be driven by the Monro-

Kellie hypothesis (Figure 5.1). During inspiration CSF has been shown to flow cranially (Aktas

et al., 2019; Dreha-Kulaczewski et al., 2017; Dreha-Kulaczewski et al., 2018; Yamada et al.,

2013), compensating for the increase in venous return from the cranium (Dreha-Kulaczewski et

al., 2017) as a result of decreased intrathoracic pressures. Conversely, during expiration CSF

flows caudally as venous return decreases.

78

Figure 5.2 Schematic of the effects of respiration on the venous blood flow in the epidural veins. A) Resting

condition, highlighting the possible directions of venous blood flow (solid black arrows) in the respective vessels. B)

With a cough or Valsalva manoeuvre, both intrathoracic and abdominal pressures become positive, driving blood into

the epidural veins, compressing the dural sac driving CSF cranially. C) During inspiration the intrathoracic pressures

becomes negative, increasing venous return in the internal jugular veins (IJV) and epidural veins, removal of blood

from the thoracic epidural veins decreases the pressure in the spinal canal, if this is greater than the increased lumbar

spinal pressures CSF may flow caudally.

However, the Monro-Kellie hypothesis fails to account for why CSF would flow caudally

during inspiration as has been reported in several imaging studies (Aktas et al., 2019; Dreha-

Kulaczewski et al., 2017; Dreha-Kulaczewski et al., 2018). Aktas et al. (2019) demonstrated

that diaphragmatic breathing resulted in greater cranial CSF flow compared with thoracic

breathing, suggesting that increased epidural pressure (Usubiaga et al., 1967) as a result of

greater abdominal pressures caused the increase in CSF flow. Therefore, by the same logic it is

possible that the decrease in the thoracic epidural pressure during inspiration could act to draw

CSF caudally (Usubiaga et al., 1967; Figure 5.2C).

CSF plays an important role in the transport of solutes and metabolic waste. CSF flows

continuously between the ventricles, to the subarachnoid space of both the cranium and spinal

canal. Understanding the mechanisms which drive CSF circulation is critical for clinical

problems such as identification of a mechanistic link between obstructive CSF disorders and the

formation of fluid filled cavities within the spinal cord (syringomyelia), or establishing the

kinematics of intrathecal drug injections and their efficacy.

The aim of this study was to determine the mechanisms which drive respiratory CSF flow in

healthy human subjects. Real-time phase-contrast magnetic resonance imaging (PC-MRI) is a

non-invasive method for the quantification of fluid velocities, and is uniquely capable of

capturing the changes in CSF and blood flow velocities in response to respiration. The

intrathoracic and abdominal pressures were also recorded, so the pressures required to drive

flow could be assessed. We hypothesised that respiratory CSF flow is dependent on the change

in the epidural blood volume within the spinal canal, and not only the Monro-Kellie hypothesis.

79

Methods

Subjects

Ten healthy subjects were enrolled in the study, consisting of five females (age 32±10 years;

weight 65±14 kg; height 174±9 cm; mean ± S.D.) and five males (age 31±6 years; weight

86±8 kg; height 181±3 cm; mean ± S.D.). Subjects underwent MRI scans to quantify arterial,

CSF and venous flow in response to respiratory changes. In a separate session, the same

manoeuvres were performed with a pressure transducer used to measure the intrathoracic and

abdominal pressures. The South Eastern Sydney Local Health District Human Research Ethics

Committee approved all experimental protocols, and these were conducted according to the

Declaration of Helsinki (2013) with the exception of clause 35. All participants gave written

informed consent and had no contraindication for MR imaging.

Respiratory manoeuvres

While supine, participants were instructed to perform either; a sniff (a nasal inspiratory effort,

with close mouth), an expiratory sniff (a nasal expiratory effort, with close mouth), or a weak

cough (equivalent to a throat clear) between normal breaths. The manoeuvres were performed at

the end of expiration, to ensure all manoeuvres started from approximately the same expiratory

lung volume. The inspiratory and expiratory sniffs were performed several times at low,

medium, and high intensities (which were self-reported to scale by approximately a factor of

two between each intensity level), to assess effect respiratory effort has on the magnitude of

CSF flow. Before either the MRI scan or pressure measurements participants were informed of

and practiced the respiratory manoeuvres.

MRI scans

MRI data were collected using a 3T Philips Achieva 3TX (Philips Healthcare, Best, The

Netherlands). Subjects lay supine and were asked to keep still and breathe quietly, with the

Frankfort plane vertical. Foam pads were placed around the head to reduce head motion. The

16-channel neurovascular head coil was used to image flow in the cervical spinal canal. The 16-

channel torso coil was used to capture flow in the lumbar spinal canal. Respiratory motion was

recorded concurrently with a respiratory monitoring band which measures the displacement of

the thorax, positioned below the xiphoid process of the sternum. During the real-time scans the

subjects were instructed to perform the various respiratory manoeuvres as the scans were

triggered. The timing was confirmed with the respiratory band signal. Scanning parameters for

the real-time PC-MRI include; flip angle = 20°, matrix=96×81, C3 FOV=192×192mm, L2

FOV=210×300mm, TR/TE = 13/7ms and a slice thickness of 10mm. The scanning planes were

positioned mid-C3 and mid-L2 perpendicular to the spinal canal (Figure 5.3). The encoding

velocities (Venc) were set to 10-45 cm s-1 for CSF measurements and 55-105 cm s-1 for blood

flow, acquiring 200 phases at 70 and 140ms intervals at C3 and L2 respectively. Scans were

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repeated with a higher Venc when aliasing artefacts were present, but small errors were still

present in some scans, which were corrected manually by adding or subtracting 2π to affected

pixels (Bioucas-Dias and Valadao, 2007). Velocity was calculated from the PC-MRI scans

using Segment software (Heiberg et al., 2010). Regions of interest were manually drawn and

tracked over the spinal subarachnoid space and epidural veins (Figure 5.3). In the cervical

region, additional regions of interest included the vertebral arteries, internal carotid arteries, and

the internal jugular veins (IJV).

Figure 5.3 Diagrammatic presentation of the experimental set-up. A) Participants lay supine of the scanning bed. For

cervical scans the neurovascular head coil (dashed lines) is used, and the imaging plane is positioned in the middle of

the C3 vertebrae perpendicular to the spinal canal. For lumbar scans the torso coil (dotted lines) is used, and the

imaging plane is positioned mid-L2 perpendicular to the spinal canal. The respiratory band was positioned at the

sternum and used during both the cervical and lumbar scans. Images B and D show cropped views of the imaging

planes for mid-C3 and mid-L2 respectively, with the regions of interest annotated.

Pressure measurements

While seated, lignocaine (Co-phenylcaine Forte 5%) was administered nasally to numb the

nasopharynx. A catheter with two the pressure sensors (CTO/4E-2, Gaeltec, Dunvegan,

Scotland) was inserted via one nostril. The subjects were placed in the supine position and

instructed to perform some coughs and sniffs, to ensure that the proximal and distal pressure

sensors were in the oesophagus and stomach respectively. The catheter was then taped at the

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nose and forehead. Subjects were left to acclimatise to the catheter for 2-9 minutes until a

regular respiratory rhythm was resumed. Subjects were given similar instructions as during the

MRI scans, to perform the respiratory manoeuvres at the end of expiration, being given real-

time feedback to perform manoeuvres at the end of expiration, and the amount of effort to give

in order to achieve three consistent repeats. Subjects were similarly instructed to increase or

decrease the effort of a manoeuvre to ensure that the high, medium, and low intensity

inspiratory/expiratory sniff peak pressures scaled by a factor of 2. The pressure sensor was

calibrated against the hydrostatic pressure of a column of water. The signal was amplified

(S7b/2, Gaeltec, Dunvegan, Scotland) and converted from an analogue to a digital input (Power

1401, Cambridge Electronic Design, Cambridge, UK) being sampled at 100 Hz with Spike2

(v7, Cambridge Electronic Design, Cambridge, UK).

Data analysis

The flow data were segmented into epochs containing the respiratory manoeuvres (i.e. cough,

expiratory, or inspiratory sniff). The signal from the respiratory band was used to identify the

timing of the event. The time-series mean was calculated from the raw 200 phases collected,

and an event was confirmed if the peak CSF velocities were ±2 S.D. greater than the time-series

mean. Internal jugular vein flows where contralateral flow was in opposing directions were

excluded from analysis. The CSF velocities were integrated with the trapezium method to

estimate the CSF displacement. The following features of interest were compared: peak cranial

and caudal CSF velocities, and peak CSF displacement. Pressure recordings were also divided

into epochs of the manoeuvres (i.e. cough, expiratory, or inspiratory sniff excluding the normal

breaths), taking the peak intrathoracic and abdominal pressures for comparison.

Pre-processing

A cross-correlation can be used to compare the similarity of time-series data, but a requirement

is that the signal is stationary. Our hypothesis is that CSF is displaced by the compression of the

dural sac and would have a similar waveform to the epidural blood flow. However, the CSF

displacements were non-stationary signals. To make the signals stationary they can be

differentiated, comparing the CSF velocity with the venous acceleration. The time scales were

normalised to the duration of the respiratory manoeuvre (i.e. duration of a cough, expiratory or

inspiratory sniff). On a normalised time scale all signals were fitted by a 4th order Fourier series,

to smooth and reduce the data to the same number of time points. The venous blood velocities

were then numerically differentiated. To further remove noise from the data, for each subject an

ensemble average was taken from the repeated measures of each respiratory manoeuvre (cough

or high intensity expiratory/inspiratory sniff).

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Statistical analysis

Cross-correlations were performed between the ensemble averaged CSF and venous time-series

data for coughs and high intensity expiratory/inspiratory sniffs using MATLAB (v8.6, The

MathWorks Inc, MA), taking the maximum correlation coefficient within a lag 20% of the

normalised event duration. A linear mixed model (LMM) was used to compare whether the CSF

velocities/displacement or intrathoracic/abdominal pressures were different between the high

and medium or medium and low intensity respiratory manoeuvres. The LMM accounts for

missing values, and bootstrapping with 1000 samples was used to correct for non-normally

distributed variables. The model incorporated the fixed effects of the location (spinal location

C3 or L2, or catheter probe location abdominal or thoracic), manoeuvre intensity and their

interaction. The LMM also accounts for the within-subject similarity between the repeated

measurements taken at C3/L2 and at different manoeuvre intensities. Pairwise comparisons

were performed with a Bonferroni correction, considering p < 0.05 to be statistically significant

(IBM Statistics v24, IBM corp., Armonk, NY). Linear regression analysis was performed

between the median peak CSF displacements and median peak trunk pressures of each subject

(assuming data collected from manoeuvres at the same intensity were paired), for inspiratory

and expiratory manoeuvres. Similarly, stepwise multiple regression analysis was performed to

determine the relationship between the CSF displacements and trunk pressures pooling

inspiratory and expiratory efforts. A variable was included in the model if p < 0.05 and

excluded when p > 0.10. Heteroscedasticity was checked using the Goldfeld-Quandt test. Where

data were heteroscedastic, regression analysis was performed using bootstrapping methods

(with 1000 samples), which does not assume homoscedasticity.

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Figure 5.4 Sample measurements of pressures and velocity in an individual subject for each of the respiratory

manoeuvres. A) MRI velocities at mid-C3 in the carotid (red) and vertebral (pink) arteries, subarachnoid space

(green), and the epidural (dark blue) and internal jugular (light blue) veins. B) Intra-abdominal (black) and

intrathoracic (grey) pressures. C) MRI velocities at mid-L2 in the subarachnoid and epidural veins. A cranial

displacement/velocity is positively signed, and a negative sign indicates a caudal displacement/velocity. Note cervical

MRI (A), lumbar MRI (C) and pressure measurements (B) were not collected concurrently, so absolute time scales

and respiratory manoeuvres are not the same. Arrows are used to indicate the timing of the peaks which correspond to

the respiratory manoeuvres (cough/expiratory and inspiratory sniffs).

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Results

Figure 5.4 shows typical data obtained from the MRI scans and pressure transducer in a subject

during sequences of coughs, expiratory sniffs, and sniffs. The epidural and IJV velocity

waveforms follows the abdominal and intrathoracic pressure traces. Similarly, the relatively

large responses in CSF flow are seen to occur at the same time as in the venous flow.

CSF and venous blood flow during expiratory efforts

Figures 5.5 and 5.6 shows an example of a representative subject performing several coughs and

expiratory sniffs. During the expiratory efforts both the intrathoracic and abdominal pressures

are elevated (Figure 5.5/6 C and F). This corresponds to an influx of venous blood into both the

cervical and lumbar epidural veins (Figure 5.5/6 B and E), giving a cranial displacement of CSF

(Figure 5.5/6 A and D). In the lumbar spine both cranial and caudal epidural flow indicate an

influx of blood, however whether it fills the thoracolumbar (cranial) or lumbosacral (caudal)

sections depends on the pressure gradient in the trunk relative to the imaging plane. The

expiratory sniffs showed the same response as the coughs, with a lower magnitude change.

Figure 5.5 Example of one subject’s repeated coughs, followed by a normal breath (marked by red dashed line). A

and D show the CSF displacement, B and E show the corresponding epidural velocities, C and F show the

intrathoracic and abdominal pressures respectively. A cranial displacement/velocity is positively signed, and a

negative sign indicates caudal a displacement/velocity. The expiratory-sniffs show the same response (Figure 5.6).

The cervical MRI, lumbar MRI and pressure measurements are not measured concurrently, so duration of a cough

will differ between panels.

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Figure 5.6 Example of one subject’s repeated expiratory sniffs, followed by a normal breath (marked by red dashed

line). A and D show the CSF displacement, B and E show the corresponding epidural velocities, C and F show the

intrathoracic and abdominal pressures respectively. A cranial displacement/velocity is positively signed, and a

negative sign indicates caudal a displacement/velocity. The cervical MRI, lumbar MRI and pressure measurements

are not measured concurrently, so duration of an expiratory sniff will differ between panels.

CSF and venous blood flow during inspiratory efforts

Figure 5.7 shows the same representative subject performing several sniffs. During the sniff the

abdominal pressure increased and the thoracic pressures became negative (Figure 5.7C and F).

Similar to the expiratory efforts in the lumbar spine, this corresponded to an influx of venous

flow into the spinal canal and a cranial displacement in CSF flow (Figure 5.7D and E). In the

cervical spine, there was an increase of venous flow from the cranium to the thoracic spine

(Figure 5.7B). For the majority of sniffs CSF was initially drawn in the same direction as the

blood flow (caudally), and transitions to a cranial displacement in the later phase of the sniff

(Figure 5.7A). However, for two sniffs there was a large cranial displacement of CSF. Note

there appears to be no difference in the epidural blood flow corresponding to these sniffs.

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Figure 5.7 Example of one subjects repeated inspiratory sniffs, followed by a normal breath (marked by red dashed

line). A and D show the CSF displacement, B and E show the corresponding epidural velocities, C and F show the

intrathoracic and abdominal pressures respectively. A cranial displacement/velocity is positively signed, and a

negative sign indicates caudal displacement/velocity. During some sniffs cervical CSF was displaced cranially (A),

these are displayed as a dashed line. The epidural blood flow corresponding to those events are also shown as dashed

lines (B), note that the dashed lines appear similar to the solid lines (although one example is slightly delayed),

suggesting no functional difference in cervical epidural blood flow between the two cases. The cervical MRI, lumbar

MRI and pressure measurements are not measured concurrently, so duration of a sniff will differ between panels.

Similarity of MRI waveforms during the sampled manoeuvres

Cross-correlations between the CSF displacements, and both the internal jugular and epidural

veins during the period of the manoeuvre (cough / expiratory / inspiratory sniff) are summarised

in Figure 5.8. Due to the small size of the epidural veins, poor signal quality, and irregular

lumbar anatomy, epidural blood flow was not collected in all subjects (C3 = 9/10 during sniffs,

7/10 during cough/expiratory sniff; L2 = 7/10 for all manoeuvres). Similarly, flow data in the

internal jugular veins were not collected for one subject during an expiratory sniff, and excluded

from analysis in 1 subject’s coughs, 2 subjects’ expiratory sniffs and 1 subject’s sniffs.

Figure 5.8 The cross-correlation coefficients for the coughs, expiratory and inspiratory sniffs in all participants

comparing the manoeuvre waveforms of CSF velocity to both (A) epidural and (B) IJV acceleration, and (C) epidural

to IJV acceleration. Note epidural blood flow was not available in all subjects (C3: sniff N = 9, cough/expiratory sniff

N = 7, L2: N=7 for all manoeuvres). Similarly, cases of missing IJV data (1 expiratory sniff) and excluded

contralateral IJV flow (2 expiratory sniffs, 1 cough, and 1 sniff) results in decreased sample in panels B (cough/sniff

N = 9, expiratory sniff N = 7) and C (sniff N = 9, cough = 6, and expiratory sniff N = 4).

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Correlations during expiratory efforts

In the lumbar spinal canal, during an expiratory effort there was an increase in epidural blood

flow and CSF was displaced cranially. In two subjects the epidural blood flow was also in phase

flowing cranially, in the remaining five subjects epidural flow was directed caudally (Figure

5.8A). The correlation between the waveforms ranged from moderate to strong for both the

coughs (Cross-correlation; 0.62 – 0.95) and expiratory sniffs (Cross-correlation; 0.65 - .93).

Whereas, in the cervical spinal canal there was a more consistent response. For all subjects

during an expiratory effort there would be an increase in cranial epidural blood flow and CSF

displacement. Being in phase, the coughing (Cross-correlation; 0.77 – 0.96) and expiratory sniff

(Cross-correlation; 0.50 – 0.85) traces showed strong and moderate correlations respectively. In

all but one subject the cranial displacement of CSF was accompanied by an increase venous

drainage via the internal jugular veins (Figure 5.8B). Similarly, in all subjects epidural and

internal jugular flow were out of phase (Figure 5.8C; in the subject who exhibited cranial flow

in the internal jugular in response to a cough/expiratory sniff, epidural flow data was not

available).

Correlations during inspiratory efforts

In the lumbar spinal canal, increased epidural blood flow corresponded with an increase in CSF

displacement, with both being directed cranially and in phase in all subjects (Cross-correlation;

0.84 – 0.96; Figure 5.8A). Whereas, in the cervical spinal canal the behaviour was less

consistent. In five of the nine subjects an increase of venous return through the epidural veins

was accompanied by the caudal displacement of CSF, in the remaining subjects CSF was

displaced cranially. This mixed response gave a weaker correlation between the waveforms

(Cross-correlation; 0.35 – 0.90), particularly in subjects where CSF would flow either cranially

or caudally between repeated measurements (Figure 5.7). Similarly, CSF compared with

internal jugular veins showed a mixed response with 3 subjects being in phase and 6 out of

phase (Figure 5.8B). In 8/9 subjects during inspiration there was an increase in venous return

via both the internal jugular and epidural veins (Figure 5.8C), however in one subject with

increased drainage via the epidural vein there was retrograde flow in the internal jugular vein

(Figure 5.4A).

Differences in the magnitude of CSF dynamics and trunk pressures during

different strength manoeuvres

During an expiratory sniff, the peak CSF velocities and displacement were greater at C3 than L2

(Table 5.2). Increasing the intensity of the expiratory sniff, from low to medium, and medium to

high, saw a consistent increase in the magnitude of the peak cranial velocity and CSF

displacement between the different levels of effort (Table 5.2). The peak caudal velocity was

greater in the high intensity expiratory sniffs compared with the medium effort (-1.71 cm s-1;

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LMM; p = 0.011), but between the medium and low strength efforts there was no significant

difference (LMM; p = 0.265). The expiratory sniff resulted in a comparable increase in the

abdominal and intrathoracic pressures (LMM; p = 0.211). Increasing the intensity of the

expiratory sniff saw the abdominal/intrathoracic pressure to increase by an average of

28 cmH2O between both low to medium and medium to high intensity efforts. The effect of

expiratory sniff intensity was not different depending the spinal/catheter location (LMM;

p > 0.05).

During a sniff, the peak caudal velocity was greater at C3 than L2 (–1.50 cm s-1; LMM;

p = 0.004). There was a greater cranial displacement of CSF in the lumbar spinal canal

compared with the cervical spinal canal (0.978 cm; LMM; p = 0.004). Increasing the intensity

of the sniff form medium to high resulted in a significant increase in the peak velocities and

displacement (Table 5.2), however there was no difference between the low and medium

intensity efforts. Increasing the sniff intensity had a different effect on the lumbar CSF

displacement compared with the in the cervical displacement (LMM; p = 0.014). In the lumbar

spinal canal increasing the sniff intensity from medium to high resulted in a larger cranial

displacement of CSF (1.58 cm; LMM; p < 0.001), but in the cervical spinal canal there was no

difference in the magnitude of displacement between the intensities. During the low intensity

sniffs the CSF velocity in the cervical spinal canal did not exceed the ±2 S.D. threshold, so any

effect could not be discerned from normal cardiac flow and was excluded from analysis. During

a sniff the intrathoracic pressure was significantly negative compared with the abdominal

pressure (–65 cmH2O; LMM; p < 0.001). On average, between each level (low to high) of

intensity the pressure dropped by 9 cmH2O. The change in sniff intensity had a different effect

on the intrathoracic and abdominal pressures (LMM; p < 0.001). On average between each sniff

intensity (low to high) the intrathoracic pressures decreased by 30 cmH2O (Table 5.2). Whereas

the abdominal pressure increased by 12.9 cmH2O (LMM; p = 0.007) between the medium and

high intensity sniffs, but showed no difference between the low and medium intensity efforts

(LMM; p = 0.079).

Table 5.1 shows the mean and 95% confidence intervals all respiratory manoeuvres, at all

measurement locations and manoeuvre intensities. Table 5.2 shows estimated mean differences

and their corresponding p values for all pairwise comparisons made with the linear mixed

model.

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Table 5.1 Mean and 95% confidence intervals of the peak variables during different respiratory manoeuvres, in both

the cervical and lumbar regions. C3 and L2 indicate the scanning planes for the MRI variables, whereas for the

pressure measurements C3 and L2 indicate the intrathoracic and abdominal pressures respectively. H, M, and L

indicated high, medium and low intensity manoeuvres respectively. For the coughs only one intensity was used. The

(-) indicates that that no change in CSF dynamics were measured for low intensity sniffs in the cervical spinal canal.

Cranial velocity

[cm.s-1]

Caudal velocity

[cm.s-1]

Displacement

[cm]

Pressure

[cmH2O]

Type Loc Strength mean 95% CI mean 95% CI mean 95% CI mean 95% CI

Co

ug

h C3 - 14.1 12.1-16.3 –7.40 –9.18- –5.97 2.64 2.20-3.09 83.2 76.3-90.5

L2 - 8.77 6.88-10.8 –4.77 –5.32- –4.14 1.74 1.35-2.12 85.8 78.1-93.8

Ex

pir

ato

ry

Sn

iff

C3

H 11.5 10.3-12.7 –6.00 –6.75- –5.24 2.44 2.17-2.70 68.2 62.3-74.0

M 8.37 6.06-10.7 –4.23 –5.77- –2.69 1.77 1.24-2.29 43.3 37.6-49.0

L 3.13 –0.361-6.62 –2.56 –4.81- –0.301 .639 –0.155-1.43 16.3 10.2-22.4

L2

H 8.94 7.46-10.4 –4.28 –5.23- –3.34 1.90 1.56-2.23 75.7 69.8-81.5

M 4.36 2.55-6.17 –2.63 –3.80- –1.46 1.00 0.590-1.41 44.7 39.0-50.4

L 2.59 –0.495-5.67 –1.21 –3.19-0.783 0.422 –0.278-1.12 16.5 10.4-22.6

Sn

iff

C3

H 9.41 8.31-10.5 –5.50 –6.29- –4.71 0.795 0.457-1.13 –79.6 –85.4- –73.8

M 4.34 2.33-6.35 –3.28 –4.72- –1.83 0.361 –0.257-0.979 –48.5 –54.3- –42.6

L - - - - - - –21.4 –27.4- –15.3

L2

H 11.2 10.0-12.4 –5.18 –6.03- –4.33 2.82 2.46-3.18 27.1 21.3-33.0

M 4.27 2.82-5.73 –2.12 –3.17- –1.07 1.24 0.796-1.69 14.2 8.37-20.0

L 2.63 0.958-4.30 –1.37 –2.57- –0.165 0.605 0.092-1.12 4.65 –1.14-10.72

Table 5.2 Pairwise comparisons from the linear mixed model for both expiratory and inspiratory Sniffs, looking at

the effects of both the location, strength and their interaction. Shown as the mean difference (∆�̅�) and p value for each

variable. C3 and L2 indicate the scanning planes for the MRI variables, whereas for the pressure measurements C3

and L2 indicate the intrathoracic and abdominal pressures respectively. H, M, and L indicated high, medium and low

intensity manoeuvres respectively. Significant comparisons (p < 0.05) are shown in bold. The (-) indicates when the

LMM found no significant interaction effect (p > 0.05), and a pairwise comparison was not conducted.

Cranial velocity

[cm.s-1]

Caudal velocity

[cm.s-1]

Displacement

[cm]

Pressure

[cmH2O]

Type Effects Comparison ∆�̅� P value ∆�̅� P value ∆�̅� P value ∆�̅� P value

Ex

pir

ato

ry

Sn

iff

Location C3-L2 2.37 0.017 –1.56 0.016 0.507 0.024 –3.05 0.211

Strength H-M 3.85 <0.001 –1.71 0.011 0.783 <0.001 27.9 <0.001

M-L 3.51 0.039 –1.55 0.265 0.853 0.024 27.7 <0.001

Location &

Strength

C3 H-M - - - - - - - -

M-L - - - - - - - -

L2 H-M - - - - - - - -

M-L - - - - - - - -

Sn

iff

Location C3-L2 0.829 0.25 –1.50 0.004 –0.978 <0.001 –65.1 <0.001

Strength H-M 6.01 <0.001 –2.64 <0.001 1.01 <0.001 –9.11 0.007

M-L 1.68 0.34 –1.33 0.24 0.198 1.00 –8.77 0.012

Location & Strength

C3 H-M - - - - 0.434 0.23 –31.2 <0.001

M-L - - - - - - –27.1 <0.001

L2 H-M - - - - 1.58 <0.001 12.9 0.007

M-L - - - - 0.639 0.198 9.55 0.079

Regression modelling

Figure 5.9 shows the relationships between the trunk pressures and CSF displacement in both

the cervical and lumbar spine during inspiratory and expiratory efforts (the slope (β) of the

relationships are in units of cm/cmH2O). The Goldfeld-Quant test confirmed that for all

respiratory manoeuvres, as the magnitude of the trunk pressures increased there was an increase

in the variability of the lumbar CSF displacements (heteroscedasticity) (p < 0.05). Therefore,

linear regression was performed using bootstrapping methods (95% CI of regression shown on

Figures 5.9C and D).

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Figure 5.9 Average peak displacements plotted against the average peak intrathoracic and abdominal pressures

generated during inspiratory and expiratory efforts. Where cranial and caudal CSF displacement are shown as

positive and negative, respectively. Solid grey lines are the linear regression, dotted grey lines indicate the 95% CI of

the regression when the data are heteroscedastic.

Trunk pressures and CSF displacement during expiratory efforts

During an expiratory effort, cervical CSF displacement was significantly related to the pressures

of the trunk, with larger displacements associated with both increased intrathoracic (β = 0.033)

and abdominal (β = 0.031) pressures. Similarly, lumbar CSF displacement was also significantly

related to the pressures of the trunk, with greater displacement associated with both increased

thoracic (β = 0.022; CI = 0.017 to 0.029) and abdominal (β = 0.022; CI = 0.016 to 0.027)

pressures. However, for the same change in the trunk pressure there is a larger displacement of

CSF at C3 compared with L2.

Trunk pressures and CSF displacement during inspiratory effort

During inspiratory efforts, cervical CSF displacement was related to the trunk pressures, giving

larger displacements for a more negative intrathoracic (β = –0.010) and greater abdominal

(β = 0.026) pressures. Lumbar CSF displacement was also shown to be significantly associated

with the intrathoracic (β = –0.031; CI = –0.046 to –0.021) and abdominal (β = 0.085;

CI = 0.057 to 0.124) pressures. However, for the same change in the trunk pressures, there is a

greater displacement in the lumbar spine compared with the cervical spine.

Multivariable linear regression

Since the trunk pressures vary continuously through inspiratory and expiratory efforts, a

stepwise linear regression was performed pooling both inspiratory and expiratory manoeuvres.

In this model, cranial displacement of CSF in the cervical spine was only significantly

91

associated with the abdominal pressures (β = 0.031; R2 = 0.82; p < 0.001), whereas

displacement in the lumbar spine was significantly related to both the intrathoracic and

abdominal pressures (𝑅𝐴𝑑𝑗2 = 0.78; p < 0.001). While increased abdominal and reduced

intrathoracic pressures increase cranial CSF displacement, an increase in intrathoracic pressure

reduces the magnitude of cranial CSF displacement (βAbd = 0.041; βThor = –0.020; p < 0.001).

Discussion

This study demonstrated that during inspiration, CSF could flow either cranially or caudally,

independent of the venous drainage from the cranium (Figure 5.7). We also found that increased

abdominal pressure was associated with the cranial displacement of CSF, but if the intrathoracic

pressure was sufficiently negative CSF would be drawn caudally (Figure 5.9A and B). These

data suggest that respiratory CSF flow is primarily driven by the spinal pressures rather than

cranial fluid volume balance, as dictated by the Monro-Kellie hypothesis.

Mechanics of inspiratory CSF flow

During inspiration, the negative intrathoracic pressure results in an increase of venous drainage

from the cranium, and decreased intracranial pressure (Hamer et al., 1977; Lawley et al., 2017).

Similarly, the thoracic extradural pressures also decrease during inspiration (Usubiaga et al.,

1967). As there are a greater number of outlets for venous blood to return to the vena cava from

the thoracic spinal canal compared with the cranium (Groen et al., 1997), it is possible for the

thoracic spinal pressures to become more negative than the cranium, causing CSF to flow

caudally (Figure 5.2C) as previously reported (Aktas et al., 2019; Dreha-Kulaczewski et al.,

2017; Dreha-Kulaczewski et al., 2018).

Previous imaging studies have shown that CSF flows cranially during inspiration, however this

was not consistent between subjects (Aktas et al., 2019; Dreha-Kulaczewski et al., 2017; Dreha-

Kulaczewski et al., 2018; Yamada et al., 2013). Some of the variability may come from the

specific breathing protocol used, a prolonged period of ‘deep’ inspiration followed by slow

exhalation. With slower low magnitude changes in the intrathoracic and abdominal pressures,

the intracranial cardiac pulsations have greater influence on CSF flow and introduce large

variation between repeated manoeuvres (Figures 5.6A and 5.7A). Alternatively, some of these

cases will be the result of inspiration driving caudal CSF flow, similar to the subjects in this

study whose intrathoracic pressure was sufficiently negative compared with the abdominal

pressure (Figure 5.9). This behaviour is dependent on the balance of inflow and outflow of

epidural blood. As the segments of the spine are continuous, the volume of blood being drained

from the thoracic spine during inspiration is also replaced by both cervical and lumbar venous

blood (Figure 5.7). When the intrathoracic pressure is sufficiently negative, there is greater

92

outflow from the thoracic epidural veins than is replaced, decreasing the thoracic spinal pressure

and drawing CSF caudally (Figure 5.10).

Figure 5.10 Schematic of the effects of inspiration on the change in the venous blood volume within the thoracic

spinal canal. (A) Resting condition at the end of expiration. Epidural veins are continuous throughout the length of

the spine, so as thoracic blood is drained during inspiration, cervical and lumbar epidural blood replaces the lost

volume. If the flow rate from the thoracic spinal canal is greater than what can be replaced, the volume of the epidural

space would be reduced decreasing the subarachnoid pressure, and increasing the capacitance of the spinal canal to

store CSF displaced from the lumbar spinal canal and potentially drawing cervical CSF caudally (B). If there is

greater or equal flow from the lumbar and cervical veins than being lost from the thoracic spinal canal, there would

be a limited change in both the volume and pressure of the extradural space, reducing the spinal canals capacity to

store CSF, and promoting en bloc cranial displacement of CSF (C).

Aktas et al. (2019) showed that on average, ‘diaphragmatic’ compared with ‘thoracic’ breathing

resulted in larger cranial displacements of CSF. In this study we have found a similar

relationship, whereby increased abdominal pressure promotes greater cranial displacement of

CSF (Figure 5.9). This can be attributed to the lumbar spinal canal having a larger epidural

space (Hirabayashi et al., 1997; Lee et al., 2007) and internal vertebral venous plexus (Groen et

al., 2005; Groen et al., 1997) than the thoracic spinal canal. This would give the lumbar spine a

larger capacitance for venous blood, allowing for larger compressions of the dural sac (Du

Boulay et al., 1972; Martins et al., 1972), than in the thoracic spinal canal. Additionally, with

greater abdominal pressures it is likely that during inspiration there is more blood flow from the

lumbar epidural veins than is being drained from the thoracic epidural veins. This will prevent

the thoracic epidural pressure decreasing, maintaining the compliance of the spinal canal (how

much the dural sac will expand for a given change in subarachnoid pressure), and promoting en

bloc cranial displacement of CSF dependent on the lumbar spinal pressures (Figure 5.10C). This

mechanism may explain why in the upper spinal canal cranial inspiratory flow occurred more

frequently in this study (Figure 5.9A and B) and in others in the literature (Aktas et al., 2019;

Dreha-Kulaczewski et al., 2017; Dreha-Kulaczewski et al., 2018).

In the all but one of subjects, during inspiration there was an increase in venous drainage via

both the epidural and internal jugular veins (Figure 5.8), independent of the direction of CSF

flow. This suggests that, although it may not be the main mechanism which drives respiratory

CSF flow, the blood volume balance within the cranium may still have a small influence on the

net direction of flow. Additionally, sustained respiratory effects which alter the mean arterial or

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venous pressures within the cranium (therefore the overall intracranial pressure) (i.e.

hyperventilation, increased CO2 inspiration, jugular compression, or a sustained Valsalva) have

been shown to alter the balance between the spinal and cranial pressures and change the net

direction of CSF flow (Bedford, 1935; Martins et al., 1972; Reitan, 1941; Shah, 1994).

However, in one subject internal jugular vein flow was shown to compensate for changes in

epidural blood flow during normal breaths and both the expiratory and inspiratory efforts

(Figure 5.4A). Although not universal, this further supports the hypothesis that the spinal

pressures primarily drive respiratory CSF flow.

Mechanics of expiratory CSF flow

As originally hypothesised by Reitan (1941), the increase in trunk pressure caused by expiratory

efforts, such as a cough, resulted in an influx of epidural blood into the spinal canal, and a

cranial displacement of CSF (Figures 5.2B, 5.5 and 5.6). Usubiaga et al. (1967) showed that

during a cough the pressure change in the thoracic epidural space was greater than in the

cervical or lumbar spine, suggesting this was the result of being closer to the pleural cavity.

However, the results of this study would suggest rather that the thoracic epidural space had a

higher pressure due to the additive effect of the blood vessels filling and the influx of CSF from

the lumbar spine. Similarly, central venous pressure is also elevated when the expiratory

pressure is positive, due to increased load on the vena cava preventing venous return (Hamit et

al., 1965; Shah, 1994). However, Figures 5.4A and 5.8C demonstrate that a dynamic rise in

trunk pressure results in an increase in venous return via the internal jugular veins. This

increased flow would contribute to the instantaneous rise in central venous pressure, particularly

as central venous pressure has been recorded from an internal jugular catheter (Hamilton et al.,

1944; Hamit et al., 1965; Shah, 1994).

Unlike forced manoeuvres like coughing, expiration during normal breathing is passive,

relaxing the diaphragm and intercostal muscles allowing the abdominal and intrathoracic

pressures to return to their resting state (Figure 5.7C and F). Previous imaging studies reported

that expiratory CSF flow is caudal, although at locations above the diaphragm this was not

consistent between subjects (Aktas et al., 2019; Dreha-Kulaczewski et al., 2017; Dreha-

Kulaczewski et al., 2018). Being a passive action, the net direction of CSF flow during

expiration is dependent on the direction CSF was displaced during inspiration. For example, in

the lumbar spinal canal during inspiration, the increased abdominal pressure consistently

displaces CSF cranially, therefore when this pressure is removed, CSF moves caudally returning

to its initial position (Figures 5.5, 5.6 and 5.7). The same occurs in the cervical spinal canal,

although the direction is not consistent. As previously explained, the direction of cervical CSF

flow is dependent on the balance between the intrathoracic and abdominal pressures. Therefore,

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if the inspiratory pressures result in a cranial displacement of CSF, when the pressures are

reduced during expiration CSF will return caudally, or vice versa (Figures 5.5, 5.6 and 5.7).

Study limitations

The relationships between the intrathoracic/abdominal pressures and CSF displacements

presented in this study are estimates, as the data were not recorded concurrently. Differences

between the environment of the lab and scanner (scanner noise and encased by the torso coil)

may influence how the respiratory manoeuvres were performed between sessions. However,

waveforms of the pressures and CSF displacements are similar (Figures 5.5, 5.6 and 5.7), but

future studies would benefit from using MR compatible pressure catheters.

In our regression analysis we assumed that for respiratory manoeuvres performed at the same

intensity, the CSF displacements and peak pressures were paired. However, our analysis showed

no significant change in CSF displacement and abdominal pressure between the different

intensities of sniffing (Table 5.2). Specifically, the abdominal pressures were not different

between medium and low strength sniffs, but the intrathoracic pressures measured concurrently

were significantly different. This could be the result of a difference in breathing strategy, being

more reliant on thoracic versus diaphragmatic breathing (Konno and Mead, 1967). For example,

the intrathoracic pressure in one subject performing a medium sniff was 26 cmH2O more

negative than when performing a low intensity sniff, but the abdominal pressure was only 1

cmH2O greater. Similarly, this could affect the lumbar CSF displacements. In the cervical spine

there were no differences in the magnitude of CSF displacement between the high and medium

sniff intensities. One explanation could be that under the proposed mechanism (Figure 5.2C) as

the intrathoracic pressure decreases the spinal canal becomes more compliant, this allows for a

large change in the volume of dural sac for a relatively small change in the subarachnoid

pressure, normalising the effect of different inspiratory pressures. It is also possible that in the

scanner participants did not achieve different strength sniffs, and this may introduce a source of

bias in the regression analysis.

To capture the large dynamic range of the CSF and blood velocities during respiration without

aliasing errors, high encoding velocities were used. This resulted in a low signal to noise ratio

for the low velocities during the resting breaths. To mitigate this, analysis focused on data

recorded during a high velocity event (i.e. cough, expiratory and inspiratory sniff). This also

prevented automatic tracking of the regions of interest, requiring manual tracking.

Clinical implications

These data suggest how much the dural sac will expand in response to changes in the spinal

subarachnoid pressure (compliance of the spinal canal) will be largely dependent on the epidural

pressures, which vary with respiration (Shah, 1994; Usubiaga et al., 1967). During inspiration

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the outflow of venous blood from the thoracic spinal canal decreases the extradural pressure

(Usubiaga et al., 1967), making the thoracic spinal canal more compliant and introducing a

volume change that decreases the cervical and thoracic spinal pressures (Figure 5.10). In the

case of spinal trauma resulting in a stenosis, this natural change in spinal compliance may be

disturbed by either increased resistance to venous blood flow, or preventing changes in the

volume of the subarachnoid space. This would result in a persistently higher spinal pressure,

similar to a mild expiratory effort with every breath. This could cyclically stress the spinal cord,

potentially contributing to the formation of fluid filled cavities (syrinxes).

The dispersion of intrathecal drug injections can be unpredictable, so understanding the normal

circulation may help prevent unexpected outcomes (Hocking and Wildsmith, 2004; Kamran and

Wright, 2001; Kuttler et al., 2010). Recent modelling studies have identified steady streaming (a

creeping flow resulting from oscillatory flows) to play a significant role in the cranial dispersal

of subarachnoid solutes (Khani et al., 2018; Lawrence et al., 2019; Sánchez et al., 2018),

achieving flows of ~0.1 mm.s-1 in line with in vivo human tracer studies. These velocities are

based on qualitative studies that started imaging between 10-15 minutes after the initial lumbar

injection, noting the tracer had reached the foramen magnum at this point (Greitz and Hannerz,

1996; Ringstad et al., 2017), so the actual rate of transport remains unknown. The results of our

study indicate that a bolus can be displaced between 1-2 cm in a second during a normal breath

(Figures 5.4C, 5.5D, 5.6D, and 5.7D), and further with either large negative inspiratory or

positive expiratory pressures (Figures 5.5, 5.6, and 5.7). Dispersion of the drug will also be

increased by the arachnoid web and nerve roots (Pahlavian et al., 2014; Stockman, 2005, 2007;

Tangen et al., 2015). Our data would suggest that respiratory effects need to be accounted for

when administering drugs by intrathecal injection, especially if the patient is ventilated, as the

increased intrathoracic pressures may promote a rapid dispersion of the drug towards the brain

(Figure 5.9).

Conclusion

Respiratory CSF flow is primarily dependent on the blood volume balance within the thoracic

and lumbar spinal canal, and the cranial fluid balance plays a lesser role. The net direction of

CSF flow in the upper spinal canal is dependent on the balance between the outflow of thoracic

epidural blood and the influx of lumbar epidural blood. Large increases in abdominal pressure

cause an influx of epidural blood, compressing the dural sac and displacing CSF cranially.

However, if the intrathoracic pressure is sufficiently negative, there is greater venous return

from the thoracic epidural veins, reducing the spinal pressure and drawing CSF caudally from

the upper spinal canal. The findings of this study provide a more complete description of normal

of CSF circulation. This is needed to understand the effects of obstructions to CSF flow, the

dynamics of drug dispersion, and how to manipulate CSF pressures.

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6. Simulating respiratory effects on the cervical CSF pressures

Introduction

Chapter 5 demonstrated that respiratory CSF flow is dependent on the volume of blood within

the spinal canal. With increasing trunk pressures, an influx of blood via the epidural veins

compresses the dural sac and displaces CSF. Additionally, it was hypothesised that cranial CSF

displacement during inspiration was the result of the lumbar spinal pressure becoming more

dominant, whereas the caudal flow was the result of the thoracic spinal canal pressure becoming

sufficiently negative to counteract the increased lumbar spinal pressure and draw CSF caudally

from the cranium. Although the data presented support this hypothesis, there was no imaging of

the thoracic spine, measurements of the extradural pressures, or the dural displacements, which

would support the feasibility of these mechanisms.

Subject-specific modelling studies have previously used cardiac-gated PC-MRI data as the

cranial input and assumed 0 Pa reference pressure at the caudal end. For cardiac CSF pulsations

in isolation this is a reasonable assumption, as the pressure variation originates from the volume

balances within the cranium (Alperin et al., 2005a; Alperin et al., 2005b). However, respiration

introduces periodic variation in the spinal pressures (Hamer et al., 1977), which has been shown

to have a more significant effect on CSF flow (Aktas et al., 2019). Therefore, a static reference

pressure is not appropriate, and to simulate respiratory effects a dynamic boundary condition is

required.

The aim of this study is to use a simplified model of the spinal subarachnoid space to simulate

the effect of respiratory changes in epidural pressure on CSF flow to demonstrate the feasibility

of the proposed mechanism for CSF circulation, and to determine a suitable caudal boundary

condition to simulate respiratory events in future subject-specific models. Expecting the

subarachnoid pressures to be proportional to the rate of change in the subarachnoid volume.

Methods

The model simplifies the spinal subarachnoid space to an axisymmetric annulus (Figure 6.1),

the dimensions of the spine are estimated from data taken from the visible Human project

(Bertram et al., 2005; Loth et al., 2001). The surface of the spinal cord and inner surface of the

dura mater were modelled as rigid boundaries. The end of the sacral spine was assigned as a

zero-flow boundary. The outlet at the cranial end was set a 0 Pa reference pressure. Respiration

was simulated by applying a time-dependent displacement (Figure 6.2) in the radial direction to

the outer radius, at both the thoracic and lumbar spinal regions. The models were solved in

ANSYS CFX (v19.2, ANSYS Inc, PA) to provide the CSF velocities and pressures. CSF was

modelled as a Newtonian fluid with 0.8 mPa.s viscosity and 1000 kg.m-3 density. The CSF flow

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was assumed to be laminar, which is appropriate as even for peak coughing velocities (30 cm.s-

1) the Reynolds number was 1875 (assuming a typical hydraulic diameter of 5 mm) sufficiently

below transition. Additionally, evidence of turbulence was not seen in the MRI data. Velocity

and pressure data were taken from a plane corresponding to the C3 vertebra, and verified against

the MRI velocities measured in vivo.

Figure 6.1 Annotated schematic of the simple spinal model. Flow is simulated by applying the time displacements

shown in Figure 6.2 to the thoracic and lumbar regions of the model.

The applied displacement was derived from a subject’s epidural blood flow during a sniff

followed by a normal breath. As there was no significant timing delay between the epidural

blood flow and CSF displacements, it was assumed that the pressure (and therefore

displacement) was in phase with the blood flow. A cough has been shown to reduce the volume

of the thecal sac by ~50% (Du Boulay et al., 1972), inspiration also compresses the lumbar

dural sac only to a lesser extent (Reitan, 1941). To approximate the effect of a high to medium

strength sniff (Table 5.2), the peak displacement in the lumbar region was set to reduce the outer

radius by 10% (0.75 mm). This relatively small displacement prevented the need for mesh

remodelling, simplifying the model and reducing computational time. The same waveform was

applied to the thoracic spine, so only the differences in the amplitude between the two regions

affected the flow. For the baseline test, the amplitude of the thoracic displacement (0.29 mm)

was scaled so that the applied volume change was equal between the two regions. To determine

whether CSF would flow either caudally or cranially depending on the relative volume change

in between the two spinal regions, the thoracic displacement was scaled so that the absolute

volume change ranged between 10% smaller to 10% greater than in the lumbar spine, in steps of

5%.

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Figure 6.2 Input displacements for the baseline simulation. A negative displacement causes compression of the dural

sac, and a positive expands it.

The CFD code (v19.2, CFX ANSYS) uses a 3D Navier-Stokes solver which couples the

pressure and velocity equations, using the finite volume approach. For convergence normalised

residuals were reduced below 10-5 for each time step (5 ms), accepting a solution once a quasi-

steady state was reached in the second cycle. Spatial and time derivatives were calculated with

second order differencing schemes, and double precision was used to reduce rounding errors.

Results

Figure 6.3 shows an example of the simulated CSF velocities and displacements for two test

cases. When the volume change in the thoracic spine is 10% less than in the lumbar spine, CSF

is displaced cranially. In contrast, when the thoracic volume change was 10% greater, CSF

moves caudally.

Figure 6.3 Example of simulated CSF velocities and displacements at C3. For a relative volume change in the

thoracic spine 10% smaller and larger than in the lumbar spine. A positive value indicates cranial flow, and negative

caudal flow.

Figure 6.4 shows how the relative difference in the volume change in the two regions alters the

magnitude and direction of CSF displacement, demonstrating a linear relationship with the

volume balance between the two regions. Additionally, the results indicate that, given the

correct balance, respiration can result in a negligible effect on CSF dynamics.

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Figure 6.4 The peak CSF displacements at C3 plotted against the relative difference in volume change between

thoracic and lumbar spine. A positive value indicates cranial flow, and negative is caudal flow.

Discussion

To simulate the effect of inspiration on CSF flow, this study used moving boundaries to mimic

the effect of changing blood volumes within the thoracic and lumbar extradural spaces has on

the spinal subarachnoid space, thereby demonstrating that these changes in volume can be used

to reproduce the patterns of CSF flow seen in vivo (Figures 5.4, 5.7, and 6.3). The results also

indicate that if the volume change in the thoracic spine is comparatively small, then the lumbar

displacement becomes dominant, driving a cranial flow (Figure 6.4), whereas, if the expansion

of the thoracic subarachnoid space is sufficiently large CSF is drawn caudally, supporting the

hypothesis that the direction of cervical CSF flow depends on the balance of epidural blood

flow within the thoracic and lumbar spine (Chapter 5). The balance between the volumes of the

thoracic and lumbar spinal subarachnoid spaces can change the direction of CSF flow

supporting the findings of Chapter 5. Further to this, Figure 6.4 demonstrates a range where

given differences in timing or magnitude, respiration can have a negligible effect on CSF

motion, as the change in volume of the thoracic spine can act as a sink for lumbar CSF. This

could be one reason why normal breathing has been seen to have a limited effect of CSF flow

(Bhadelia et al., 2016; Dreha-Kulaczewski et al., 2015, 2017; Yildiz et al., 2017), requiring

stronger manoeuvres to demonstrate a consistent effect. Additionally, differences in local spinal

anatomy (i.e. epidural vein size and impedance, or size of spinal compartments) could

contribute to the inter-subject variability (Groen et al., 1997; Hirabayashi et al., 1997; Lee et al.,

2007).

In other subject-specific modelling studies, non-uniform boundary motion has been used to

match the simulated and measured CSF velocities (Khani et al., 2018; Khani et al., 2017).

However, the displacements used were not validated and unphysiological, as cardiac-driven

CSF pulsations are predominantly driven from the cranium into the spinal canal (Henry–

Feugeas et al., 2000), and the dural pulsations are created from the internal pressure changes not

the extradural pressures. Although these methods would be appropriate for simulating

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respiratory flow (flow driven by the extradural forces Figure 5.2), the actual dural displacements

are unknown, and the method itself is computationally costly. An alternative method would be

to apply a pressure pulse to the caudal end of the model which incorporated the effects dural

motion would have on the subarachnoid pressures. As the spinal canal is a closed system, filled

with an incompressible fluid, the volume flow rate (Figure 6.3) and therefore pressures would

be proportional to the rate of change in the volume of the fluid domain. Therefore, an

asymmetric sine wave can be applied to the caudal boundary of the model and scaled in

magnitude to match the simulated velocities to the MRI data. Since, the flow is laminar the

exact shape of the applied wave can be estimated from in vivo MRI data.

Conclusion

This model demonstrates that the pressure balances required to allow CSF to flow both cranially

and caudally during inspiration are physiologically feasible. Additionally, it has provided a

sensible estimate of the waveform required at the caudal end of subject-specific models to

simulate respiratory effects without significant additional computational cost.

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7. Pilot study: The effects of coughing and Valsalva on cerebrospinal

fluid flow in Chiari I malformation

Introduction

Chiari malformation type I is clinically defined by the cerebellar tonsil anatomy, accompanied

by the appearance of overcrowding, however a link between the anatomy and the symptoms of

Chiari or the pathogenesis of Chiari-associated syringomyelia has yet to be demonstrated.

Invasive measurement techniques have been used to examine the common association between

Chiari and coughing triggered headache. These studies have suggested that during coughing the

cerebellar tonsils obstruct normal flow, which creates pressure gradients between the cranium

and spinal canal that could contribute to headaches and syrinx formation (Sansur et al., 2003;

Tachibana et al., 1992; Williams, 1981b). Few studies have since investigated the effects of

respiration on CSF dynamics in Chiari patients, and whether such a flow obstruction exists.

Bhadelia et al. (2016) recently demonstrated that in Chiari patients the amplitude of cardiac

pulsation driven CSF flow is decreased post-cough. Bhadelia et al. (2016) suggested that this

was evidence that coughing caused the tonsils to descend and further restrict flow through the

foramen magnum, as originally hypothesised by Williams (1969). This mechanism has yet to be

proven, but this change in behaviour is a potentially useful marker which may help to further the

understanding of Chiari associated headache (Bezuidenhout et al., 2018). In Chapter 5, it was

shown that in controls the effects of coughing were transient, with no lasting effects post-cough

(Figures 5.4 and 5.5; Chapter 5), however only single coughs were performed and it is unclear if

the behaviour described by Bhadelia et al. (2016) is related to a sustained elevated trunk

pressure during repetitive coughs causing coupled cardiovascular effects (post-Valsalva

overshoot/rebound: a transient increase in the mean arterial pressure), nor is it clear the extent to

which this differs between healthy subjects and those with Chiari malformation (Dawson et al.,

1999; Korner et al., 1976; Williams, 1981a).

The aim of this pilot study was to demonstrate that real-time phase contrast MRI could be used

to identify the characteristics of CSF flow in Chiari patients both with and without

syringomyelia. It was hypothesised that the reduced area of the foramen magnum would result

in higher velocities in Chiari patients during respiratory manoeuvres such as coughing and

Valsalva. A secondary aim was to demonstrate that subject-specific computational models could

reliably simulate the effects of coughing in both patients and controls.

Methods

The South Eastern Sydney Local Health District Human Research Ethics Committee approved

all experimental protocols, and all participants gave written informed consent. Ten participants

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enrolled in the study, consisting of four Chiari patients without a syrinx (age 29 ± 9 years;

weight 67 ± 14 kg), two with a syrinx (age 44 ± 2 years; weight 60 ± 5 kg), and four age and sex

matched controls (age 34 ± 9 years; weight 61 ± 6 kg) (a fifth control subject was excluded from

analysis). Subjects underwent MRI scanning of the head and neck to quantify CSF flow in

response to respiratory manoeuvres.

MR imaging and flow measurements

Respiratory manoeuvres

While in the supine position, three respiratory manoeuvres were assessed. Initial scans collected

data during phases of normal quiet breathing. Scans were also collected while participants

performed a Valsalva manoeuvre, and during coughing. During the Valsalva manoeuvres

participants were instructed to breathe normally and count to three, then perform a Valsalva and

continue to strain for five seconds, and then to release and breathe normally for the remainder of

the scan. When performing coughs participants were originally instructed to breathe normally

and count to three, then perform three consecutive coughs, inhaling in between each cough, and

then breathe normally for the remainder of the scan. This was adjusted in the most recent

participants to the protocol used in Chapter 5, i.e. subjects were instructed to cough between

normal breaths at the end of expiration. This approach was adopted as it was found that

participants were less likely to forget to breathe between coughs, preventing the summation of

coughs.

Imaging parameters

MRI data was collected using a 3T Philips Achieva 3TX (Philips Healthcare, Best, The

Netherlands). 3D isotropic T1 sagittal anatomical MRI scans were acquired from the cervical

spine, parameters for the scan include; 0.94 mm voxels, matrix = 288×288, FOV = 270×270,

TR/TE = 5.5/2.5 ms and 180 slices of 0.94 mm thickness. Both cardiac gated and real-time PC-

MRI scans were collected from at the base of the skull (or 5 mm superior to the tip of the

cerebellar tonsils in Chiari subjects) and at the C3 vertebra perpendicular to the spinal canal.

The encoding velocities (Venc) for the cardiac gated scans were set to 12 cm.s-1, 10 cm.s-1 and

13 cm.s-1 at the base of the skull, 5 mm superior to the tip of the cerebellar tonsils and C3

vertebra respectively. Additional scanning parameters include; matrix = 240×176, FOV =

250×250, TR/TE=21.6/6.8 ms, 30 phases/cycle and slice thickness = 5 mm. For further details

on the scanning protocol see Clarke et al. (2013b). For real-time PC-MRI scans the encoding

velocity was scaled between to 10-40 cm.s-1 at all levels acquiring 200 phases at 70 ms intervals.

Scans were repeated with higher Venc when aliasing artefacts were present. Other scanning

parameters include; flip angle = 20°, matrix = 96×81, FOV = 192×192mm, TR/TE = 13/7 ms

and a slice thickness of 10 mm.

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Flow data processing

CSF velocity data was calculated from the PC-MRI scans using the freely-available software

Segment (Heiberg et al., 2010). Regions of interest were manually drawn and tracked over the

spinal subarachnoid space. The resulting flow data were segmented into epochs containing

either normal breaths or the respiratory manoeuvre. Valsalva data were segmented into the pre,

onset, hold, and post phases of the Valsalva. The coughing scans were similarly separated into

the pre, cough, and post cough phases. With the original coughing protocol, two subjects did not

take a breath between coughs, resulting in the summation of multiple coughs, therefore only the

first cough in the sequence was assessed, and the caudal peak was excluded (which could be

influenced by the subsequent coughs). The peak cranial and caudal velocities were compared

both within and between groups.

Statistical analysis

In a larger study with a sufficient number of participants, statistical testing would likely be

performed using a linear mixed model (LMM) approach, as the LMM can take into account the

repeated measures, multiple levels and potential missing data. However, given the sample size

of this pilot study statistical tests would not be valid (Kraemer, 2019), therefore statistical

analysis beyond basic descriptive statistics has not been performed.

Computational modelling of the spinal subarachnoid space

Subject-specific models and the initial calculation of the pressure-time profiles were obtained

following the protocols outlined in Clarke et al. (2013a) and Chapter 2. For each subject, a 3D

geometry of the spinal subarachnoid space was generated from point cloud data, manually

segmented from the anatomical MRI. The models spanned from the base of the skull (or 5 mm

superior to the tip of the cerebellar tonsils) to 10 mm caudal to the middle of C5, with a 5 mm

extension applied to the rostral inlet to provide a smooth entry zone.

The models were solved in ANSYS CFX (v18.0, ANYS Inc., PA). CSF in this study was

assumed to be a Newtonian fluid with a viscosity of 0.8 mPa.s and 1000 kg.m-3 density

(Bloomfield et al., 1998). The spinal cord and dura mater were treated as rigid walls. With an

average hydraulic diameter of 5 mm and peak CSF velocity of 30 cm.s-1 during a cough, the

Reynolds number was calculated as 1875, so laminar flow was assumed. Each subject-specific

model was solved under two different load conditions; a cardiac-driven CSF pulse (used for

validation of the model geometry and boundary conditions) and a caudal coughing pressure

pulse (see Chapter 6).

A steady state run provided the initial conditions for the transient simulations. In these, the

normalised residuals were required to converge to be below 10-5 at each time step (5 ms). The

CFD code is a 3D Navier-Stokes solver that uses a finite volume approach. The equations were

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solved using a pressure-velocity coupled solver. Spatial and time derivatives were calculated

with second order differencing schemes. Double precision was applied to minimise rounding

errors.

Cardiac flow models

For the cardiac pulsation driven models, fitted flow data taken from the cranial scan was applied

to the cranial inlet of the model. At the caudal outlet a reference pressure of 0 Pa was set. A

solution was accepted once a periodic state was reached in the second cycle. The models were

validated by comparing the average velocity-time profiles against the MRI data, taken at mid-

C3. Features of the velocity profile which were assessed include; the time when caudal flow

begins, the timing of peak caudal and cranial flow and when the flow changes from caudal to

cranial, in addition to the peak caudal and cranial velocities (Figures 1.12 and 2.4). In two

subjects, cardiac-gated scans were not collected, so an average cardiac pulse taken from the

real-time scans during normal breathing was used as an input, and the models were compared

against the peak velocities since timing data was not available.

Coughing models

To mimic the coughing protocol performed during the MRI scans, the caudal boundary

condition was switched between a resting and coughing state. The resting state was used to

approximate the normal breath between coughs, assuming normal breathing to have a minimal

effect on CSF flow (Figure 5.4A). For this phase, the total pressure calculated at the cranial inlet

with the initial cardiac model was applied as an inlet boundary condition, and at the caudal

outlet a reference pressure of 0 Pa was applied. To simulate a cough, a generic pressure pulse

was applied to the caudal end of the model (Figure 7.1). Chapter 6 assessed the effects of dural

compression on CSF velocities, suggesting that the pressure pulse applied at the caudal end of

the model to simulate respiratory events would be proportional to the rate of change in the dural

compression. Assuming that the compression of the dural sac is proportional to the inflow of

epidural blood, the generic cough pulse was approximated from the derivative of a typical

epidural velocity-time profile during a cough, measured in Chapter 5 (for example see Figure

5.5B).

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Figure 7.1 Shape of the coughing pulse applied to the caudal end of the model. Shape approximated from the

derivative of the epidural venous velocity during a cough. Duration of the cough was held at 1 second. The amplitude

of the peak pressures were scaled between 25, 50, and 75 Pa.

Initially two cardiac pulses were run to ensure the model was in a quasi-steady state. The cough

pulse was then applied at the beginning of the next cardiac pulse (synchronous with onset of the

systolic CSF pulse), and repeated every three cardiac pulses, giving a total of three coughs

followed by two cardiac pulses. The increase in caudal subarachnoid pressure created by a

cough is unknown, therefore the amplitude of the three coughing pulses were scaled to 25, 50

and 75 Pa to provide an estimate (these pressures were estimated from an initial trial in a model

of a control subject, assuming the coughing force to be comparable between controls and Chiari

patients). The coughing pulses were validated by comparing the peak cranial and caudal CSF

velocities at both the foramen magnum and mid-C3. The sequence with the best match was then

analysed. The peak pressure drop between the foramen magnum and C3 which would drive both

cranial and caudal flow was compared between groups.

Statistical analysis

Agreement between the MRI velocities and the subarachnoid space models was assessed using

the Bland-Altman method. Bland-Altman plots provide the average model error and 95% limits

of agreement (±1.96×S.D.). Due to the small sample size this pilot study was underpowered to

perform more complex statistical tests.

Results

CSF flow studies

Normal breathing

Figure 7.2 shows the median CSF velocities for each group during both the inspiratory and

expiratory phases of quiet breathing. The peak cranial and caudal velocities are within the same

range in both inspiration and expiration. Both the Chiari patients with and without a syrinx

appear to have higher velocities, although there is a large overlap between the interquartile

ranges.

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Figure 7.2 Group median and interquartile range for the peak cranial (upper) and caudal (lower) velocities during

inspiration and expiration. Black markers indicate measurements taken at the foramen magnum and the grey are taken

at C3. Each data point indicates an individual subject’s median velocity.

Valsalva manoeuvre

Figure 7.3 Sample measurements of the CSF velocities during a Valsalva manoeuvre at C3, in two control subjects

(A and B), a Chiari patient with (C) and without a syrinx (D). Cranial velocities are positive and caudal are negative.

The red dashed lines indicate the beginning and end of the Valsalva manoeuvre.

Figure 7.3 shows the effect of a Valsalva manoeuvre on CSF flow in examples of 2 controls,

and 2 patients, one with and one without a syrinx. Similar responses are seen for all subjects.

With the onset of the Valsalva there is a sudden high velocity (~10 cm.s-1) cranial flow of CSF.

The remainder of the response to the Valsalva was variable between subjects. During the

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maintained Valsalva two responses were commonly seen. 1) The cardiac pulse amplitude would

initially increase, and as the Valsalva persisted the amplitude decreased (Figures 7.3 A and C).

2) The pulse amplitude would be comparable with the normal cardiac pulse, and gradually

increase over time (Figures 7.3 B and D). After the release of the Valsalva CSF flow would

either normalise (Figures 7.3 A and B), or the pulse amplitude would transiently increase

(Figures 7.3 C and D). In the Chiari subjects post-Valsalva there were no cases that saw a

blockage to CSF flow. With these small subject numbers, it remains unclear whether the

differences in behaviour are related to the groups or difference in the strain effort during the

Valsalva.

Figure 7.4 shows the median cranial and caudal CSF velocities before, during and after a

Valsalva manoeuvre for each subject group. Within the subject groups the CSF velocities during

the maintained Valsalva and after its release appear comparable with those before the Valsalva.

The onset of the Valsalva results in a large cranial flow in all groups. At the level of the foramen

magnum the velocities appear to be higher in the patient groups but numbers are too small to be

definitive in this pilot study. The velocities at C3 are generally greater than at the foramen

magnum (except syrinx patients, in this small cohort), and there is a large overlap of the

interquartile ranges between the subject groups.

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Figure 7.4 Group median and interquartile range for the peak cranial (Left: A,C,D, and F) and caudal (Right: B, E,

and G) velocities before (pre: A,B), during and after (post: F, G) Valsalva. The onset of the Valsalva is labelled as

Valsalva (C), and the persistent strain as Hold (D, E). Black markers indicate measurements taken at the foramen

magnum and the grey are taken at C3. Each data point indicates an individual subject’s median velocity over the

duration of the Valsalva phase and repeated measurements.

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Coughing

Figure 7.5 shows typical data obtained from the MRI scans while coughing for one control

subject, and 3 Chiari subjects, two with and one without a syrinx. During the cough there is

initially an increase in cranial flow (~10 cm.s-1; Figures 7.5 A-C) then a caudal return (~5 cm.s-

1; Figures 7.5 A-C). After the cough the cardiac pulse quickly returns in all subjects, although in

one syrinx subject the amplitude of the cardiac pulses post-cough are small (Figure 7.5D) and it

is unclear whether cranial flow was achieved, as peak velocities trended toward 0 cm.s-1 during

the diastolic phase. Note in this case, to prevent aliasing, the encoding velocity was two times

greater than in the other examples (40 cm.s-1), increasing noise and reducing the sensitivity to

the lower velocities of the cardiac pulses.

Figure 7.5 Sample measurements of the CSF velocities during a cough in the cervical spinal canal, in a Control (A),

Chiari patient without (B) and two with a syrinx (C & D). Where cranial velocities are positive and caudal are

negative. The red dashed lines indicate the beginning and end of the coughs.

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Figure 7.6 Group median and interquartile range for the peak cranial (upper: A, B, and C) and caudal (lower: D, E,

and F) velocities before (pre: A, D), during (B, E), and after (post: C, F) a cough. Black markers indicate

measurements taken at the foramen magnum and the grey are taken at C3. Each data point indicates an individual

subject’s median velocity for the duration of the pre and post cough phases and a minimum of 3 coughs.

Figure 7.6 shows the group data for the peak cranial and caudal velocities, before, during and

after a cough. Within the subject groups, both the cranial and caudal CSF velocities were within

comparable ranges both before and after coughing. The cough resulted in a high velocity cranial

flow peak, followed by a large caudal peak in all subjects. At the foramen magnum, there was a

trend that the Chiari patients had both higher cranial and caudal velocities, but additional subject

numbers are required. In the cervical spine, the CSF velocities seem comparable across groups,

with a large overlap of the interquartile ranges.

Subarachnoid space models

Model validation

For the initial cardiac model, the simulated and measured MRI velocities at C3 showed an

acceptable level of agreement (Figure 7.7A), wherein the example presented the largest error

was at the caudal peak which was underestimated by 0.50 cm.s-1 (17% less than MRI peak

velocity). On average the model predicts the start of caudal flow, the time of peak caudal flow

and when flow transitions from caudal to cranial to occur later in the cardiac cycle than in vivo

(Figures 7.8A-C and Table 7.1). The timing features of these rigid boundary models are

dependent on the applied inlet flow, so these differences can be attributed to the larger error in

timing between flow at the foramen magnum and at C3 (Table 7.1). In general, the absolute

error between the model and MRI cranial and caudal peaks was low, with a tendency to

underestimate the caudal and overestimate the cranial peaks (Figures 7.8D and E; Table 7.1).

The poorest agreement was seen in a control model where the caudal peak was overestimated by

1.33 cm.s-1 (40% greater than MRI measurement).

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Using a pressure boundary condition at both the inlet and outlet can be unstable, converging on

a solution that is highly dependent on the initial estimates (ANSYS CFX, v18.0, Help System,

Solver Modelling Guide 2.3.2). With the pressure boundary at the inlet, the model converged on

the same solution for both the velocity (Figure 7.7B) and pressure (Figure 7.7C) terms.

Figure 7.7 A) Example of measured MRI and model velocity for a control subject at C3 for a cardiac cycle. Example

of simulated velocities (B) and pressures (C) in the case where flow data are applied to the cranial inlet compared

with when a pressure profile is applied to the inlet

Figure 7.8 Bland-Altman plots assessing the model error (modelled variable – MRI measurement) for the cardiac

models plotted against the MRI measurement. Solid line marks the average model bias, and the dash lines show the

95% limits of agreement (Table 7.1). Characteristics of the timing are on the upper panels, and velocity on the lower.

Table 7.1 Summary of the agreement between model and MRI velocity-time profiles for the cardiac models. Model

error displayed as the bias ±95% limits of agreement. Measured spread of MRI measurements displayed as maximum

range.

Model error Measured spread

Velocity-time feature Plane C3 FM – C3

Time of initial caudal flow [%] 1.14 ± 1.12 2.00

Time of peak caudal flow [%] 9.05 ± 7.20 14.0

Time of return to cranial flow [%] 3.87 ± 4.60 16.0

Peak caudal velocity [cm.s-1] −0.29 ± 0.56 –

Peak cranial velocity [cm.s-1] 0.10 ± 0.19 –

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Figure 7.9 shows a representative subject-specific model during a coughing pulse followed by a

period of rest. The model shows an acceptable level of error between the cranial peak, slightly

overestimating the velocity at the foramen magnum and underestimating at C3. The caudal peak

shows poorer matching, having a similar peak velocity, but showing a second peak which is not

present in the MRI data. This double peak has been seen in other MRI measurements

(Figure 7.5B), suggesting this error is the result of the summation of the caudal coughing peak

and the systolic CSF pulse, and is due to not matching the duration and timing of the subject’s

cough to the MRI data. The model captures no lasting transient effects after the cough pulse has

finished, with the regular cardiac pulse returning. The same is seen in the MRI data, although

the cardiac pulses match poorly, possibly due to timing difference and respiratory effects of

breathing after a cough.

Figure 7.9 Example CSF velocities in a Chiari patient with a syrinx, calculated with a coughing pulse applied to the

caudal end of the model compared with the subjects measured MRI velocities. The red dashed line indicates the peak

cranial velocity during a cough.

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Table 7.2 Summary of the agreement between the peak model and MRI velocity, at both the foramen magnum (FM),

and at C3 for the coughing models. Model error displayed as the bias ±95% limits of agreement.

Model error Model error

Velocity-time feature Plane FM Plane C3

Peak caudal velocity [cm.s-1] 2.75 ± 5.58 -0.67 ± 1.56

Peak cranial velocity [cm.s-1] 2.95 ± 2.62 -2.04 ± 1.50

Figure 7.10 Bland-Altman plots assessing the model error (modelled variable – MRI measurement) for the coughing

models plotted against the MRI measurement. Solid line marks the average model bias, and the dash lines show the

95% limits of agreement (Table 7.2). Error in cranial velocity in upper panels, and caudal in lower.

On average the simulated coughing pulse showed greater deviation from in vivo measurements

compared with the cardiac pulses (Tables 7.1 and 7.2). At mid-C3 there was better agreement

between measured and simulated coughing velocities, with a tendency to underestimate both the

peak cranial and caudal velocities. At the foramen magnum the error between measured and

simulated velocities increased (approximately by a factor of 2), typically overestimating the

cranial and caudal velocities.

Models of two subjects with large tonsillar herniations (subject A = 7 mm; subject B = 28 mm)

showed poor agreement at the foramen magnum. Subject A (Figure 7.10) underestimates the

cranial peak by 4 cm.s-1 at C3 and overestimated the peak at the foramen magnum by 10 cm.s-1.

Subject B (Figure 7.10) overestimates the caudal peak at C3 by 2 cm.s-1 and at the foramen

magnum by 17 cm.s-1. The error at C3 in these examples could be explained by some variation

in model geometry, or the pressure of the coughing pulse being incorrect. However, the large

error at the cranial end suggests that either there is poor agreement of the model geometry

(Figure 7.11), or the model is too simple to simulate the system’s behaviour at the cranial end.

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Figure 7.11 Outline of the model cross-sectional area overlaid on the corresponding axial slice at the foramen

magnum, for subjects A (Left) and B (Right) of Figure 7.10.

Subarachnoid pressures

Figure 7.12 shows the peak pressure differences between the foramen magnum and the cervical

spine for both a cardiac and coughing pulse. The coughing pulse resulted in a larger pressure

pulse driving cranial flow compared with the cardiac pulse, in all subject groups. Chiari patients

tended to have a greater pressure difference compared with controls, although there is

considerable overlap between the subject groups. The simulated pressures during the caudal

phase of the cough were comparable to the systolic cardiac pulse. The poor validation of the

models may lead to incorrect pressures being calculated, and characteristic differences not being

fully captured.

Figure 7.12 Group median and interquartile range of the pressure drop between the cervical spine and foramen

magnum, for a cardiac and coughing pulse. Here a positive pressure drop would drive cranial flow and negative

caudal.

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Discussion

Evaluation of imaging protocol

Valsalva manoeuvres

The Valsalva manoeuvre is thought to increase intracranial pressure by both decreasing venous

drainage from the cranium, and transient changes in the mean arterial pressure due to the trunk

pressure and baroreceptor reflexes (Dawson et al., 1999; Korner et al., 1976; Williams, 1981a).

The act of straining will also increase the spinal pressures (Martins et al., 1972; Williams,

1981a). As a result, CSF flows will depend on the balance between the intracranial and spinal

pressures, which change dynamically during respiratory manoeuvres. The different behaviours

(exemplified in Figure 7.3) are seen in all subject groups, and could be explained by this

pressure balance without the need of a tonsillar obstruction causing a ‘pressure dissociation’

(Williams, 1981b). However, the effects of intracranial or spinal pressure changes cannot be

easily identified from just CSF flow data. In future scans concurrent arterial and venous flow

data may help characterise the effect of cardiovascular changes on the intracranial pulsation,

since the cardiac component of CSF flow is dependent on the change in the net volume of blood

within the cranium (Alperin et al., 2005a; Alperin et al., 2005b), correlations between the CSF

and arterial blood flow can be attributed to cranial pressure changes.

The variable response to the Valsalva manoeuvre across subjects (Figure 7.3) could be related to

differences in the effort of the strain performed during the scan, or anatomical variation. To get

more consistent responses and to help determine whether differences are the result of

anatomical variation, participants may require more training prior to the scan. Additionally,

monitoring the expiratory pressure during the Valsalva may be required to ensure consistent

performance.

The results of this pilot study suggest that although the general response to a Valsalva may be

similar across both controls and Chiari patients, the velocities at the foramen magnum in the

patient groups may be greater (Figure 7.4). This requires further investigation to determine

whether this is consistent in a larger cohort, and if it corresponds to patient symptomatology

and/or changes with treatment.

Coughing

Single coughs cause a transient rise in spinal pressure which is not associated with a coupled

effect on the arterial pressure, and therefore intracranial pressure (Hamilton et al., 1944;

Hamilton et al., 1936; Williams, 1981a), unlike the Valsalva manoeuvres. As a result,

interpreting the effects of coughing on CSF flow is simplified. Additionally, the instructions for

the manoeuvre are simpler, reducing the chance of participant error, and giving a consistent

behaviour between subjects (Figure 7.5). These factors suggest the single cough protocol is the

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most suitable and reliable method to assess the effect of large changes in CSF pressure on

Chiari malformation and syringomyelia.

Recently, Bhadelia et al. (2016) has suggested that post-cough the cerebellar tonsils form an

obstruction resulting in a decrease in the amplitude of subsequent cardiac CSF pulsations. In the

current study, both the control subjects and Chiari patients’ cardiac CSF pulsations quickly

normalised post-cough. Additionally, in all participants during the cough there was a large

caudal flow peak following the initial cranial peak (Figures 7.5 and 7.6). This would suggest

that CSF flow is not restricted post-cough by some tonsillar valving mechanism. One subject

did appear to have restricted cranial CSF flow post-cough (Figure 7.5D), however the encoding

velocity was 40 cm.s-1 making the post-cough signal noisy, and unclear is whether this

measurement is accurate. This is an inherent limitation of current real time PC-MRI methods,

whereby the encoding velocities can only be set to reliably measure either the coughing or

cardiac component of CSF flow. As shown by Bhadelia et al. (2016) setting the encoding

velocity to 5 cm.s-1 to capture the lower velocities post cough will likely cause phase wrapping

errors during the coughs. Methods have been developed to take measurements at multiple

encoding velocities, however these require cardiac gating, and only correct for spatial difference

in velocity (Knobloch et al., 2014; Schnell et al., 2017).

The results of this limited cohort indicate that the studied Chiari patients may have higher CSF

velocities at the foramen magnum during a cough compared with controls. A larger sample is

required to determine whether this is representative of Chiari patients and related to the

pathology.

General limitation

Currently, the slice thickness of the axial scans is 10 mm. In Chiari patients this can result in

large partial volume errors at the level of the obstruction, due to the complex geometry of the

craniocervical junction, or the vertebral arteries which penetrate the dura at C1 obscuring the

majority of the anterior CSF space. Without further development, the current technique is

limited for use in the wider investigation of the effect of obstructive CSF flow disorders.

Sources of error in the coughing models

The simplistic assumed shape of the coughing pressure pulse (Figure 7.1), introduced some

error to the simulated velocities (Figure 7.9). Creating the coughing pulse from a subject’s own

epidural blood flow or CSF displacement (Chapter 5), matching the duration of the cough, and

the phase of the cardiac cycle that it occurs in, would likely result in closer agreement between

the models and in vivo data.

Two models showed reasonable agreement with the in vivo coughing velocities at C3, but

discrepancies at the cranial end. Incorrect segmentation of the model geometry could result in

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this deviation (Pahlavian et al., 2016; Pahlavian et al., 2014), however the small difference in

the cross-sectional area (Figure 7.11) would not be expected to cause such a large increase in

velocity. Therefore, it is likely that there is some important mechanism that is not currently

included in the models. These models assume the dura mater and spinal cord are rigid

boundaries and neglect the effects of intracranial compliance and tonsillar motion. For a cough,

a rigid dura mater is appropriate, as during a cough the extradural pressures are increased (Shah,

1994; Usubiaga et al., 1967) which decrease the compliance of the spinal canal. In Chapter 5 it

was shown that during a cough there is an influx of venous blood into the cranium via the

epidural veins, but also an increased drainage via the internal jugular veins (Figure 5.4). The net

difference between these flows will alter the effective cranial compliance, which may

significantly affect CSF dynamics and should be considered in future models. Additionally, the

increased spinal pressures during a cough could displace the spinal cord and cerebellar tonsils

(Cheng et al., 2014), which may vary the size of the fluid pathways and alter CSF flow and the

subarachnoid pressures. This may be particularly important for Chiari patients with severe

tonsillar herniation (i.e. subjects A and B; Figure 7.10). To incorporate this into future models,

the structures of the cranium may be needed, including the two-way effects of the fluid structure

interaction at the craniocervical junction in particular.

Conclusion

This pilot study demonstrated that real-time PC-MRI can be used to measure changes in CSF

flow during respiratory events such as coughing and Valsalva. Coughing provides a more

reliable measure of large changes in CSF pressure across the different subject groups, whereas

the Valsalva manoeuvre may help assess the effect of transient changes in the mean arterial

pressure. This preliminary data suggests Chiari patients have greater CSF velocities at the

foramen magnum during respiratory manoeuvres, but further investigation is required to

determine if this persists in a larger cohort and is related to patient symptoms.

With the current modelling approach, most of the models achieved an acceptable level of

agreement between the simulated and measured CSF velocities. However, larger discrepancies

in two Chiari patient models with substantial tonsillar obstructions indicates the models are not

completely capturing the biomechanics of the CSF space. Thus, the simulated pressures are

likely unreliable unless intracranial compliance and tonsillar motion are taken into account.

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8. Summary and conclusions

This thesis has presented a series of six studies which investigated the influence of

cardiovascular, respiratory and structural factors on healthy subjects and patients with Chiari

malformation Type I, which may further our understanding of the biomechanics of syrinx

formation. Within the respective chapters more detailed discussion of the results are presented.

In this section an overview and the wider implications of the findings is presented.

Implications for CSF physiology

Currently, two different mechanisms are thought to drive respiratory-related CSF flow. During

normal quiet breathing, CSF is thought to flow cranially to compensate for increased venous

return during inspiration, and that the opposite occurs during expiration (Dreha-Kulaczewski et

al., 2017; Yamada et al., 2013). During forced expiratory efforts (i.e. coughs, sneezing, and

straining), venous blood fills the epidural veins (Batson, 1940; Henriques, 1962), which

compresses the dural sac, increasing spinal pressure and displacing CSF cranially (Hamilton et

al., 1944; Martins et al., 1972; Reitan, 1941; Williams, 1981a). In Chapter 5 it was

demonstrated that both CSF flow behaviours could be explained by the effects of both the

intrathoracic and abdominal pressures on epidural blood flow into and out of the spinal canal.

With this mechanism it was possible to demonstrate that depending on the relative

distension/compression of the thoracic and lumbar dural sac, both the inspiratory cranial and

caudal CSF flow that is seen in vivo could be achieved (Chapter 6), which had previously not

been explained (Aktas et al., 2019; Dreha-Kulaczewski et al., 2017; Dreha-Kulaczewski et al.,

2018).

In Chapter 5 it was shown that variation in trunk pressure has a significant effect on the CSF

dynamics, and this suggests that accounting for its influence may aid in the understanding of

other research problems. For example, understanding the effect body position on CSF pressure

is important to prevent over drainage of shunts used for treatment of hydrocephalus.

Transitioning from a supine to an upright position causes a hydrostatic drop in intracranial

pressure, however models that only account for the hydrostatic forces tend to overestimate the

measured pressure drop in more upright positions (Bergsneider et al., 2004; Qvarlander et al.,

2013). Data from this thesis suggest that discrepancy may be able to be explained by postural

abdominal contractions (Montes et al., 2017) and increases in abdominal pressure (Cobb et al.,

2005; De Keulenaer et al., 2009), causing an influx of epidural blood into the spinal canal,

which increases lumbar pressure, and reduces the pressure difference with the cranium.

Intrathecal drug injections provide an effective way to deliver treatment directly to the central

nervous system, however the processes which facilitate the distribution of the drug within the

subarachnoid space remain poorly characterised, leading to large variation between patients

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(Hocking and Wildsmith, 2004; Shafer et al., 1998). The rate at which drugs spread cranially

from the initial lumbar injection site significantly contributes to this variation, and is expected to

be dependent on the mixing of CSF within the subarachnoid space (Eisenach et al., 2003). To

explain this phenomenon, computational models have suggested that cardiac pulsations enhance

dispersion (Hettiarachchi et al., 2011; Hsu et al., 2012), and the anatomical structure of the

subarachnoid space can result in an additional creeping flow in the cranial direction (Khani et

al., 2018; Kuttler et al., 2010; Lawrence et al., 2019; Sánchez et al., 2018). However, it should

be noted that the amplitude of the cardiac pulsation in the lower spinal canal is small (Khani et

al., 2018; Tanaka et al., 1998), so the effect of cardiac pulsation-driven dispersion in the lumbar

spine is likely minimal. In contrast, Chapter 5 demonstrated that variation in abdominal pressure

can result in large displacements of lumbar CSF, particularly during inspiration (Figure 5.7),

which would likely have a significant effect on drug dispersion from the thecal sac delivery

point. Understanding the effects of respiratory forces on drug dispersal may help predict drug

delivery patterns for individual patients, or that respiratory manoeuvres could be used to

facilitate the desired pattern of delivery.

Implications for Chiari malformation and syringomyelia

As noted throughout the thesis, not all Chiari patients develop a syrinx and the reasons for this

are not understood. For a syrinx to form or enlarge there is likely an imbalance between the

fluid flow into and out of the spinal cord. Timing differences between the arterial and CSF

pulsations was suggested as a mechanism which could facilitate an increase of fluid

accumulation (Bilston et al., 2010). In Chapters 2 and 3 this mechanism was assessed with

simulated subject-specific CSF pressures and an idealised model of the perivascular space. The

models showed that early peak pressures in Chiari patients could cause an increase in fluid

accumulation, and that the delay in arterial pulsation required occurs in vivo, providing support

for this mechanism. A limitation of this hypothesis is that it depends on arterial pulsation. This

dependency was derived from ligation studies which showed that with reduced arterial pulse

pressure deposition of tracer along the perivascular spaces diminished, however the ligation also

reduced CSF pulse pressure (Stoodley et al., 1997), so the significance of arterial pulsation

compared with CSF pulse pressure is unclear. Furthermore, recent intravital imaging studies

have shown in surface pial vessels the arterial pulsation to be ~1% of the original diameter

(Mestre et al., 2018), which would be insufficient for this hypothesised mechanism. As for pulse

amplitudes less than 10% of the vessel diameter, the artery during systole does not provide

sufficient resistance for the ‘leaky valve’, and there is no net flow (Figure 3.4A).

Using subject-specific models, it was demonstrated that for a cardiac pulse, the peak CSF

pressure was increased and occurred earlier in the cardiac cycle compared with controls and

syrinx patients, whereas syrinx patients had a pressure-time profile comparable with control

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subjects (Chapter 2). How the differences between the patient groups may relate to syrinx

development remains unclear. In cross-sectional studies it is typically assumed that the Chiari

patients without a syrinx are representative of a pre-syrinx state (Clarke et al., 2013a; Clarke et

al., 2013b), and that the decreased amplitude and delay in timing are attributed to the syrinx

increasing compliance (Cirovic and Kim, 2012; Elliott et al., 2011; Martin et al., 2010; Martin

and Loth, 2009). In Chapter 4 it was shown that the peak times were correlated with the fraction

of the craniocervical junction obstructed by the tonsils. For syrinx subjects, greater obstruction

correlated with an increase delay in peak time, whereas in syrinx free subjects the degree of

obstruction was associated with earlier timing of the CSF flow peak. Additionally, the

midsagittal cross-sectional area of the posterior fossa was shown to be significantly smaller in

the syrinx cohort. These findings suggest that the timing differences between patient groups

may be caused by the structural anatomy of the obstruction. While this may explain why only

some patients develop syrinxes (Ellenbogen et al., 2000; Milhorat et al., 1999; Tubbs et al.,

2004), this would also indicate that Chiari patients without a syrinx are a poor model of the pre-

syrinx state in patients that go on to develop one.

Williams (1969) suggested that the cerebellar tonsils could act as a one-way valve allowing CSF

to flow cranially, but restricting caudal flow during a cough. This dynamic action has yet to be

demonstrated, however it is still used to explain changes in CSF dynamics (Bhadelia et al.,

2016). In Chapter 7 it was demonstrated that during a cough in both patients with and without a

syrinx there is a large caudal flow at the level of the obstruction (Figures 7.5 and 7.6), which

may indicate that the tonsils do not restrict caudal flow while coughing. Although, this is a

small pilot study and requires further investigation, this initial evidence may prevent further

research into unsupported mechanisms.

Implications for research methods

Imaging

As part of this thesis, a standard protocol for investigating respiratory effects on spinal CSF

flow using real-time PC-MRI has been developed and applied to healthy subjects and a small

number of Chiari patients (Chapters 5 and 7). Short high intensity inspiratory and expiratory

efforts were shown to provide a repeatable response (Figures 5.5-5.7), allowing for the influence

of trunk pressures on CSF flow to be investigated. Single coughs appear to be a more robust

protocol than Valsalva manoeuvres, as they only cause a transient change in spinal pressure, and

are not associated with coupled effects on the arterial pressure (Hamilton et al., 1944; Hamilton

et al., 1936; Williams, 1981a), which may confound results (Chapter 7). Additionally, the

protocol for single coughs is simpler for participants to follow, which in a cohort with potential

cognitive difficulties (Allen et al., 2014; Rogers et al., 2018) may provide more reliable results.

In future work, it is recommended to include single sniffs in the imaging protocol, as

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demonstrated in Chapter 5 inspiratory efforts can cause an increase in venous drainage, and

either a cranial or caudal displacement of CSF. Contrasting these differences in blood and CSF

flow with coughing in Chiari patients may provide additional insight into the mechanisms of

coughing headache and syrinx development.

Morphological measurements of Chiari have failed to provide consistent markers of the

disorder, which could either further explain patient symptoms or pathology (Table 4.1).

Measurements that are currently used clinically are likely limited by their restriction to the

midsagittal plane (Alperin et al., 2014), as the lateral structures are known to have a significant

influence on CSF dynamics (Bunck et al., 2012; Bunck et al., 2011; Haughton et al., 2003;

Iskandar et al., 2004; Quigley et al., 2004). Three-dimensional shape analysis of the cranial

morphology is a promising avenue for better understanding the complexity of the posterior fossa

and cerebellum shape and the effects of CSF flow in the region. These methods have been

implemented for the analysis of the shape of long bones and the ventricles, however there is still

a need for methodological validation and improved implementation to enable use by non-

specialists (Heimann and Meinzer, 2009; Paniagua et al., 2013; Styner et al., 2006).

Modelling

Computational models can be used to examine the feasibility of proposed mechanisms, and as

such, could provide directions for future research. Additionally, models can be used to provide

data that cannot be collected without invasive methods. Thus, the main aim of modelling should

be to provide clinicians with new insights as to the pathophysiology of Chiari and

syringomyelia, which may lead to changes in the current clinical practices. In order to do this

clinicians need to be convinced that the models are reflective of real physiological processes and

are reliable.

Recently, several studies have been published which indicate that subject-specific models

cannot accurately reproduce the measured CSF velocities, attributing this poor performance to

the lack of anatomical detail (Pahlavian et al., 2014; Yiallourou et al., 2012) or failure to

incorporate key biomechanical processes (Khani et al., 2018; Khani et al., 2017). The modelling

protocol developed in Chapter 2 demonstrated that rigid boundary models, which exclude

additional anatomical features such as the nerve roots can accurately simulate cardiac CSF flow,

but may not be appropriate for modelling respiratory effects such as coughing (Chapter 7). As

the cranial compliance is likely to affect the model’s behaviour, it will need to be included,

either by a lumped-parameter model or explicitly modelling fluid structure interactions.

Models of the CSF space are often validated by comparing the pulse wave velocity of a model

with wave speeds calculated from cardiac-gated MRI measurements (which average over

several cardiac cycles, removing variation due to quiet breathing) (Tables 1.4 and 1.5), however

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this may not be appropriate, as the pulse speed of the spinal canal is unlikely to be constant. In

Chapter 5 it was demonstrated that respiratory changes in trunk pressure causes changes in the

volume of epidural blood, which will alter the extradural pressure (and the effective stiffness of

the dura) (Usubiaga et al., 1967) and likely compresses the dural sac to drive flow (Chapter 6).

These factors will cause variation in the CSF pulse wave speed with both time and location, as

the wave speed of the spinal canal is dependent on the relative size of the subarachnoid and the

stiffness of the dura (Bertram, 2009; Cirovic, 2009; Cirovic and Kim, 2012; Toro et al., 2018).

This variation needs to be accounted for in future models, particularly to identify if pathological

features alter wave propagation outside the normal limits of variation. To measure the pulse

speed non-invasively the imaging protocol outlined in Chapters 5 and 7 could be adapted to the

sagittal plane. This would enable flow at cranial and caudal positions to be measured

concurrently, which is required since the effect of inspiratory manoeuvres is variable in the

upper cervical spinal canal (Figure 5.7).

Future directions

Understanding syrinx formation

Tonsillar herniation is the most obvious sign of Chiari malformation, and as a result research

has focused on the structure and overcrowding of the posterior fossa (Table 4.1). The descent of

more superior structures of the brain such as the corpus callosum (Houston et al., 2018; Urbizu

et al., 2014), as well as reduced intracranial heights (Eppelheimer et al., 2018; Houston et al.,

2019; Houston et al., 2018; Taştemur et al., 2017) and widths (Taştemur et al., 2017) in Chiari

patients may indicate that supratentorial abnormalities contribute to caudal shift of the hind

brain, however this has yet to be investigated. In dogs with an analogous condition (Chiari-like

malformation), rostral abnormalities in bone structure have been confirmed to contribute to

overcrowding and tonsillar herniation (Knowler et al., 2017; Rusbridge et al., 2018). These

observations, together with the results of Chapter 4, suggest that the role of differences in

supratentorial structures should be investigated in Chiari patients, as abnormalities will likely

alter fluid pathways and CSF flow. This, in turn, may help understand the pathophysiology of

both syrinx formation and coughing triggered headaches that are a common symptom of Chiari

malformation.

In order to understand how the changes in CSF dynamics caused by pathology such as Chiari

malformation or arachnoiditis contribute to syrinx formation, the mechanisms which facilitate

fluid flow into the spinal cord needs to be understood, both in healthy subjects and in patients.

Recent intravital imaging studies have demonstrated that tracer from the subarachnoid space

flows into the perivascular spaces in a pulsatile motion that resembles the arterial pulsation

(Bedussi et al., 2018; Mestre et al., 2018). However, it is unclear whether CSF pressure

pulsations or spinal arterial pulsations drive this flow. To determine this with animal models,

123

comparisons between the effect of ligating the cranial (to reduce CSF pulsation) and spinal (to

reduce pulsation of spinal penetrating arteries only while maintaining cranially-derived CSF

pressure pulsation) arteries could help determine the relative importance of arterial and CSF

pulsation on perivascular flow. This may be technically challenging, and models could be used

to explore the influence of these factors in the first instance.

Currently, perivascular fluid inflow into the CNS is believed to be dependent on cardiovascular

factors, however respiration may have a significant effect and should be included into future

models and experimental studies. In particular, two effects of respiration need to be accounted

for. Firstly, during normal breathing the CSF pressures slowly oscillate (Hamer et al., 1977;

Hamit et al., 1965), and whether this slow variation in CSF pressure is sufficient to drive

periods of net inflow or outflow through the perivascular space, without arterial pulsation, needs

to be determined. Secondly, during inspiration there is an increase in venous drainage from the

cervical spine (Chapter 5), and this could affect perivascular flow by changing the compliance

of the spinal cord (Elliott et al., 2011), or could increase flow into the parenchyma by increasing

the pressure gradient between arteries and veins (Asgari et al., 2015; Holter et al., 2017; Jessen

et al., 2015; Jin et al., 2016). The influence of these factors is important for understanding the

normal fluid homeostasis of the spinal cord, but might also help determine how local changes in

the compliance of the spinal canal, such as those that occur in Chiari and syrinx patients,

contribute to increased fluid uptake in the cord (Berliner et al., 2019; Brodbelt et al., 2003).

Understanding coughing-associated headache

The cause of Chiari associated headache, or why some patient’s headaches are triggered by

coughing and straining is still poorly understood. Imaging studies have shown greater motion of

the spinal cord (Lawrence et al., 2018) and cerebellar tonsils (Leung et al., 2016) in Chiari

patients than matched controls. This increased motion is suspected to cause abnormal tissue

strains which have been suggested to cause headache, although mechanisms of this have not

been demonstrated, and the CNS tissue itself does not contain pain receptors. Additionally, no

difference in the magnitude of cardiac-driven tonsillar motion has been demonstrated between

patients with either persistent or coughing associated headaches (Dawes et al., 2019), however

this issue is likely better investigated using non-cardiac-gated methods to investigate respiratory

related tissue motion during manoeuvres similar to those that evoke headache in patients.

During a cough, spinal pressures are increased (Hamilton et al., 1944; Williams, 1981a) and

there is an influx of venous blood into the cranium via the epidural veins (Figure 5.5). Persistent

coughs or straining, such as in a Valsalva manoeuvre, will cause a transient rise in mean arterial

pressure (Dawson et al., 1999; Korner et al., 1976). Increased CSF pressure may introduce

abnormal tissue motion through the foramen magnum which could strain the hindbrain.

124

Whereas, increased blood pressure may lead to increased tissue pulsation which could strain the

cerebellar tonsils constrained by the craniocervical junction, or dilated blood vessels (i.e.

epidural venous plexus or vertebral arteries) could strain the meninges or compress tissues

within the crowded junction. Determining whether these different mechanisms occur, and their

relative importance, may help understand the triggers of coughing headache.

Conclusion

This program of research used magnetic resonance imaging and computational modelling

methods to better understand CSF circulation in healthy humans and Chiari patients, both with a

syrinx and without. The research has further elucidated the mechanism by which respiratory

manoeuvres drive CSF flow in the spinal canal and demonstrated the primary importance of

abdominal and thoracic pressures in driving this flow, likely via shifts in epidural blood volume.

The methods developed to study this in healthy subjects have been shown to be feasible to apply

in Chiari patients in the future. It has added some additional supporting evidence to the

hypothesis that inflow of fluid into the spinal cord could be altered by changes in the relative

timing of the arrival of CSF and arterial pressure pulses to a penetrating artery. The research has

also provided evidence that structural differences in morphology of the poster fossa between

patients with and without a syrinx could explain why only some patients develop a syrinx,

however this work also highlighted that standard linear measurements poorly characterise the

cranial morphology, and that more advanced techniques are required for future research.

125

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