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ANATOMICAL BRAIN CONNECTIVITY TO ASSESS COGNITIVE

DYSFUNCTION IN MULTIPLE SCLEROSIS

Journal: Journal of Neurology, Neurosurgery, and Psychiatry

Manuscript ID: jnnp-2012-302246

Article Type: Research paper

Date Submitted by the Author:

10-Jan-2012

Complete List of Authors: Bozzali, Marco; Fondazione Santa Lucia, IRCCS, Neuroimaging Laboratory Spano', Barbara; Fondazione Santa Lucia, IRCCS, Neuroimaging Laboratory Parker, Geoff; University of Manchester, Imaging Science &

Biomedical Engineering Castelli, Maura; University of Rome "Tor Vergata", Department of Neuroscience Basile, Barbara; Fondazione Santa Lucia, IRCCS, Neuroimaging Laboratory Rossi, Silvia; University of Rome "Tor Vergata", Department of Neuroscience Serra, Laura; Fondazione Santa Lucia, IRCCS, Neuroimaging Laboratory Magnani, Giuseppe; San Raffaele Scientific Institute, Department of Neurology Nocentini, Ugo; Santa Lucia Foundation, Rome, Italy, Department of

Clinical and Behavioural Neurology Caltagirone, Carlo; Santa Lucia Foundation, Rome, Italy, Department of Clinical and Behavioural Neurology Centonze, Diego; University of Rome "Tor Vergata", Department of Neuroscience Cercignani, Mara; Brighton & Sussex Medical School, University of Sussex, Clinical Imaging Sciences Centre

<b>Specialty</b>: Demyelinating diseases

Keywords: MULTIPLE SCLEROSIS, MRI, NEUROPSYCHOLOGY

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DYSFUNCTION IN MULTIPLE SCLEROSIS

M. Bozzali1, B. Spanò

1, G.J.M. Parker

2,3, M. Castelli

4, B. Basile

1, S. Rossi

4, L. Serra

1, G.

Magnani5, U. Nocentini

6, C. Caltagirone

4,6, D. Centonze

4, M. Cercignani

1,7.

1Neuorimaging Laboratory, Santa Lucia Foundation, Rome, Italy.

2Imaging Science & Biomedical Engineering, University of Manchester, United Kingdom.

3Biomedical Imaging Institute, University of Manchester, United Kingdom.

4Department of Neuroscience, University of Rome "Tor Vergata", Rome, Italy.

5Department of Neurology, San Raffaele Scientific Institute, Milan, Italy.

6Department of Clinical and Behavioural Neurology, Santa Lucia Foundation, Rome, Italy.

7Brighton & Sussex Medical School, Clinical Imaging Sciences Centre, University of Sussex,

Brighton BN1 9RR

Running title: ACM in MS brains.

Key words: Anatomical connectivity; Diffusion; Imaging; MS; cognition; PASAT

Text word count: 2752

Correspondence to:

Dr. Marco Bozzali, Via Ardeatina 306, 00179 Rome, Italy. Telephone number: #39-06-

5150 1324; Fax number: #39-06-5150 1213; E-mail address: m.bozzali@hsantalucia.it

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ABSTRACT

Objective: To investigate structural brain disconnection in relapsing-remitting (RR)-MS by

using anatomical connectivity mapping (ACM), a recently developed method based on

diffusion weighted (DW)-MRI tractography This approach overcomes the limitations of

measuring lesion loads or microscopic indexes of local white matter (WM) damage, which

do not quantify the actual loss of “brain connectivity”.

Methods: For this cross-sectional study, thirty-five RRMS patients and 25 healthy controls

underwent MRI at 3T, including conventional images, T1-weigthed volumes, and DW-

MRI. Volumetric scans were coregistered to fractional anisotropy (FA) images, and used to

obtain parenchymal FA maps from white (WM) and grey matter segments. Probabilistic

tractography, based on Q-ball and on the probabilistic index of connectivity, was initiated

from all parenchymal voxels, and ACM maps were obtained by counting the total number

of tractography streamlines passing through each voxel, and normalising it by the total

number of streamlines initiated. ACM maps transformed into standard space were used for

between-group comparison and for correlation analysis between patients' ACMs and

performance at the Paced Auditory Serial Addition Test (PASAT).

Results: RRMS patients had reduced ACM in the corpus callosum, in the left prefrontal

lobe, and in the head of the caudate nucleus bilaterally. A significant direct association

between ACM and performance at PASAT was found in the anterior cingulate and in the

anterior thalamic radiation bilaterally.

Conclusion ACM opens a new perspective for clarifying in a more direct way the

contribution of anatomical brain disconnection to clinical and cognitive disabilities in MS.

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Introduction

There is an intense debate about the neurobiological substrate of cognitive disability

in multiple sclerosis (MS). Cognitive dysfunctions are traditionally regarded as cortical or

sub-cortical1, with both mechanisms (i.e., local grey matter [GM] damage and white matter

[WM] disconnection) occurring in MS brains. So far, GM involvement in MS has been

investigated by volumetric techniques2,3,4,5,6,7

or other neuronal markers (e.g., N-acetyl-

aspartate, NAA8), or by assessing (at high magnetic field) the presence of cortical lesions

9.

On the other hand, structural brain disconnection has been so far estimated by measuring

lesion loads (LL) or microscopic indexes of WM integrity, such as mean diffusivity and

fractional anisotropy (FA) as derived by diffusion-weighted (DW)-MRI6,10,11,12

. However,

all these quantities reflect only a local measure of GM and WM integrity, but do not

attempt to quantify the loss of structural “brain connectivity”. The concept of anatomical

connectivity concerns GM and WM tissues as parts of an integrated system, with cognitive

functions (and dysfunctions) relying on neuronal networks rather than specialized brain

areas communicating to each other1. We recently developed a novel approach based on

DW-MRI and tractography, namely anatomical connectivity mapping (ACM), to obtain a

scalar measure of structural brain connectivity at the level accessible to tractography

methods13

. An exploratory investigation in patients with Alzheimer’s disease has revealed

intriguing results, showing expected patterns of reduced ACM, but also changes suggestive

for putative phenomena of brain plasticity.

Aim of this study was to assess, in a group of patients with relapsing-remitting (RR)

MS, the patterns of anatomical brain disconnection and its impact on the observed cognitive

disabilities. The advantage of this approach, compared with previous studies, is that ACM

is potentially able to highlight the remote effects of macro- and microscopic lesions in brain

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regions lying far from the location of damage.

Methods

Subjects

Twenty-five patients (F/M=19/6) with a diagnosis of clinically definite MS

according to McDonald’s criteria14

attending the specialist outpatient clinic of the

University of Rome "Tor Vergata" (Rome, Italy) were recruited for this study (Table 1). All

patients had a RRMS course. Their mean (SD) age was 35 (8.6) years, and their median

Expanded Disability Status Scale score (EDSS15

) was 2.0 (range: 0.0-4.5). Their median

(range) disease duration (based on the onset of the first symptom clearly associated with

MS) was 7 years (range=2-16). All patients were treated with immunomodulatory

medication (13 with b-interferons, 9 with Glatiramer acetate, and 3 with Natalizumab).

None of the recruited patients had any relapse or cortico-steroid treatment over the three

months preceding MR acquisition. Additionally, all patients had a T1-weighted brain scan

performed in the two weeks preceding their recruitment, showing the absence of active

lesions.

After recruitment, all MS patients were assessed by the Multiple Sclerosis

Functional Composite (MSFC)16

score which includes also the paced auditory serial

addition test (PASAT)17

. For the proposes of the current study, PASAT raw scores only

(corrected for age, education and gender according to the Italian normative data18

) were

used to test for correlations with ACM data. Within 48 hours after

clinical/neuropsychological assessment, patients underwent MR scanning.

Twenty five healthy subjects [HS; F/M=13/12; mean (SD) age=31.8 (8.0) years]

with no history of neurological disorders and normal neurological examination were also

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recruited and served as controls. They underwent the PASAT and the same MRI protocol

as the MS patients.

The study was approved by the Local Ethics Committee, and written informed

consent was obtained from all the subjects before study entry.

MRI acquisition and pre-processing

All imaging was obtained using a head-only 3.0 T scanner (Siemens Magnetom

Allegra, Siemens Medical Solutions, Erlangen, Germany), equipped with a circularly

polarised transmit-receive coil. The maximum gradient strength is 40 mT m-1

, with a

maximum slew rate of 400 T m-1

s-1

. The MRI session included for every subject: (1) a

dual-echo turbo spin echo (TSE) (TR=6190 ms, TE1=12 ms, TE2=109ms, echo train length

(ETL)=5; matrix=256x192; FOV=230x172.5 mm2; 48 contiguous 3 mm thick slices); for

lesion identification and segmentation (scan time: approximately 4 min); (2) a fast fluid

attenuated inversion recovery (FLAIR) (TR=8170ms, TE=96ms, TI=2100 ms; ETL=13;

same FOV, matrix and number of slices as TSE); to use as a reference for lesion

identification (scan time: 5 min); (3) a magnetisation prepared rapid gradient echo

(MPRAGE) sequence (TR=2500ms; TE=2.74 ms; inversion time=900 ms; Flip angle=8°;

matrix=256 x208x176; FoV=256x 208x 176mm3, scan time: 7 min); 4) a Diffusion

weighted twice-refocused19

SE EPI (TR=170ms, TE=85ms, maximum b factor=1000 smm-

2, isotropic resolution 2.3mm

3; matrix=96x96; 60 slices), collecting 7 images with no

diffusion weighting (b0) and 61 images with diffusion gradients applied in 61 non collinear

directions20

(scan time: 11 min).

T2-hyperintense lesions were identified by consensus by two observers on the short

echo images of the TSE, for every patient. Lesions were outlined on the same scan using a

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semi-automated local thresholding contouring software (Jim 4.0, Xinapse System,

Leicester, UK, http://www.xinapse.com/). FLAIR and T2-weighted scans were always used

as a reference to increase confidence in lesion identification. Lesion masks, where a value

of 1 was assigned to every voxel corresponding to a lesion, and a value of zero was

assigned elsewhere, were created for every patient. TSE images were warped into standard

space and the same transformation was applied to the corresponding lesion mask. A

probabilistic lesion map, indicating the percentage of patients with a lesion in a given area,

was obtained by combining every patient’s lesion masks (see Figures 1 and 2).

Diffusion data were corrected for misalignment between volumes according to the

following steps: i) b=0 images were realigned to the first volume with a rigid body

transformation computed using the FMRIB's Linear Image Registration Tool (FLIRT)21

,

and averaged; ii) the 61 DW volumes were averaged and coregistered to the scalp stripped

mean b=0 image, to yield an average transformation (Tx1) matching the mean DW image to

the mean b=0; iii) each DW volume was realigned to the mean DW image (with a rigid

body transformation, described by Tx2), and the transformation matching each DW volume

with the b=0 image was obtained by combining Tx2 with Tx1. The b matrices were rotated

accordingly22

. All the remaining processing was done using the Camino toolkit

(www.camino.org.uk), if not otherwise specified. The diffusion tensor was estimated in

every voxel23

, and maps of fractional anisotropy (FA) were obtained. Each FA map was

used as a reference for registering the corresponding T1-weighted anatomical image. Once

registered, the anatomical images were segmented into WM, GM and cerebrospinal fluid

using SPM8 (www.fil.ion.ucl.ac.uk/spm). As a by-product of segmentation, SPM8

estimates the transformation that warps the images to standard space. These parameters

were recorded to be used later on the ACMs. For every subject a binary parenchymal mask

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was obtained by adding GM and WM segments. The resulting image, whose intensity

reflects each voxel’s probability of containing parenchyma, was thresholded to retain only

those voxels with intensity greater than 0.8, and binarized. As in our preliminary study13

,

we used the Q-ball algorithm24

to process the DTI data, as it provides a model of diffusion

able to resolve more than one direction per voxel. Tuch's original radial-basis function

formulation24

was employed, as implemented in Camino with the default parameter

settings25

.

Computation of the anatomical connectivity maps (ACMs)

To generate the ACMs, probabilistic tractography based on the probabilistic index

of connectivity (PICo) method26

, was used. The algorithm accounts for the uncertainty

associated with the determination of the principal directions of diffusion in every voxel by

generating a probability density function (PDF) of estimated fiber alignment derived from

Q-ball modelling of each voxel25

. This provides voxel-wise estimates of confidence in fiber

tract alignment, which are then used in the probabilistic tract-tracing procedure. Streamline

tracking was then performed, with 10 Monte Carlo iterations from all voxels in the

parenchymal mask.

ACMs were obtained as previously described in detail13

. Briefly, the total number of

streamlines passing through each voxel was counted. Then, connectivity maps were divided

by the maximum possible count (sum of the number of voxels tracked, weighted by the

number of diffusion directions per voxel, times the number of Monte Carlo iterations) to

normalize for brain volume.

The resultant ACMs were warped into standard space using the normalization

parameters obtained during segmentation, and then smoothed using a 8 mm3 Gaussian

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kernel before statistical analysis.

Statistical Analysis

Voxel-wise statistics was carried out using SPM8 to assess: 1) the presence of

between-group ACM differences; 2) the correlation between ACM and performance

obtained by patients at the PASAT. In both analyses age, gender and number of voxels in

the parenchymal mask were entered as covariates of no interest. T2-LL was entered as

additional covariate in the correlation analysis performed within the patient group.

In both analyses, statistical threshold was set to p-FWE-corrected<0.05 at cluster

level (cluster size defined using an initial voxel-level threshold p=0.005 uncorrected).

Results

MS patients and controls were well matched for age (p=0.8). Gender distribution was

slightly different between the two groups without reaching the statistical significance (Chi-

square = 3.125; p=0.08). To correct for this potential bias, gender and number of voxels in

the parenchymal mask (a proxy for brain volume) were always entered as covariates of no

interest in the statistical models.

The majority of MS patients (96%) performed poorly at the PASAT, reporting scores

outside the range of normality18

.

All HS performed normally at the PASAT, and none of them had any macroscopic

abnormality on T2-w and FLAIR scans.

The average T2-lesion volume in MS patients was 7.85 mL (SD=8.3).

ACM

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Voxel-wise group comparison of ACM maps revealed reductions of connectivity in

the corpus callsum, in the left prefrontal lobe, and in the head of the caudate nucleus

(bilaterally) of MS patients compared to controls. No regions of increase in ACM were

observed in MS patients. These results are illustrated in Figure 1 (red areas), together with

an image of the probabilistic lesion map of the corresponding sections.

Correlation analysis between patients’ ACM and performance at the PASAT

(calculated as number of correct responses), showed areas of direct association in the

anterior portion of the cingulate gyrus and in the anterior thalamic radiation bilaterally

(Figure 2, yellow areas). The corresponding lesion probability map is also shown in Figure

2. The inverse correlation did not produce any significant result.

Discussion

In the current study, we used for the first time ‘ACM’ to investigate brain tissue

changes in patients with MS. Our cohort of patients showed only a moderate average T2-

lesion load, although most of the patients reported abnormal scores at the PASAT. On one

hand, this confirms the PASAT as a highly sensitive instrument to detect cognitive

dysfunctions in MS27

. On the other, this confirms that the clinical disability observed in MS

is only partially explained by the presence and extension of macroscopic brain lesions. This

is consistent with a large body of literature, showing that microscopic abnormalities to the

so-called NAWM and NAGM play a relevant role in determining clinical and

neuropsychological deficits in MS6,7,11

. So far, structural investigations of the NAWM and

NAGM by quantitative MR techniques have been used to assess local tissue changes, and

to correlate them with clinical or behavioural measures. From a different perspective,

functional MRI (fMRI) has been used in MS to assess the impact of structural brain damage

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on broad neuronal networks as defined by specific experimental tasks or by the so-called

resting state fMRI (i.e., functional brain connectivity). Both structural MRI and fMRI

studies have increased our knowledge on how the brain tissue damage observed in MS

translates into patients’ clinical dysfunction and disability. However, a big gap exists

between structural and functional information, and a unified interpretation of these findings

often requires complex speculations. We propose here a new method, namely ACM, which

might contribute in reconciling the structural and functional information obtained from MS

brains. ACM provides a measure of structural brain connectivity, based on diffusion MRI

tractography, and accounts for changes occurring to the WM and/or GM tissue as an

integrated system.

The direct comparison between MS patients and healthy controls returned a pattern

of ACM reduction involving the isthmus and midbody of the corpus callosum, the head of

the caudate nuclei and the left prefrontal cortex. As shown in Figure 1, this pattern of

abnormalities was mainly located in brain regions that were, on average, spared by MS

lesions, thus suggesting that ACM changes cannot be merely explained by the presence of

macroscopic tissue damage. The corpus callosum is the largest commissural bundle that

connects cortical and subcortical regions of the brain. The corpus callosum is implicated in

the interhemispheric transfer of auditory, sensory and motor information, and represents

one of the major anatomical substrates for cognitive networks. The caudate nucleus is

known to be involved not only in fine motor control, but also in cognitive functions. The

pattern of reduced ACM we observed in MS patients might therefore account for both

deficits in the higher level control of motor abilities (midbody of the corpus callosum;

caudate nuclei) and cognitive dysfunctions (isthmus of the corpus callosum; caudate nuclei;

left prefrontal lobe). With respect to motor dysfunction, our analysis did not detect any

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ACM reduction in the corticospinal tracts of MS patients, despite these WM connections

are known to be extensively damaged by the disease6,28

. This lack of sensitivity fits well

with our expectations, as the corticospinal tract is monosynaptic, and its pathological

damage is not expected to result in any remarkable change of ‘structural connectivity’. This

suggests that ACM should be used to investigate pathways characterized by multiple

connections, such as those sub-serving cognitive functions. In this perspective, the selective

reduction of ACM we found in the left prefrontal lobe of MS patients (together with ACM

reductions in the isthmus of the corpus callosum and in the caudate nuclei) might represent

the neurobiological substrate of the typical cognitive impairment observed in MS, which

includes attention information processing efficiency, executive functioning, processing

speed, and long-term memory29

. This is moreover consistent with previous studies in MS,

indicating an association between frontal and parietal lesion burdens and performance on

tests of sustained attention and verbal working memory30

, and an association between

macroscopic abnormalities in the caudate nuclei and the presence of cognitive deficits31

.

Interestingly, our correlation analysis between ACM and patients’ performance at

the PASAT revealed a well defined pattern of associations, including the anterior cingulate

and the anterior thalamic radiations. Several previous studies revealed that PASAT

performance is based on attentional processing controlled by the anterior cingulate12,32,33

.

The anterior cingulate is indeed known to be part of a distributed attentional network,

maintaining strong reciprocal interconnections with the lateral prefrontal cortex (BA 46/9),

the parietal cortex (BA 7), and the premotor and supplementary motor areas34

. Moreover,

the anterior cingulate has been previously implicated in spatial attention, spatial working

memory, selection-for-action, and conflict monitoring3,36

. A selective structural

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disconnection of the anterior cingulate is therefore likely to contribute to the cognitive

profile typically observed in MS.

The anterior thalamic radiations, whose ACM values were also associated to

patients’ performance at PASAT, have been previously reported to be damaged in MS37

.

These WM tracts are part of the thalamic projections between the mediodorsal thalamic

nuclei and the frontal cortex, and between the anterior thalamic nuclei and the anterior

cingulated.38

The basal ganglia and the thalamus are thought to play a critical role in

information-processing39,40

and their reciprocal disconnection might be relevant for the

deficits of information-processing speed observed in patients with MS. To reinforce this

hypothesis, It should be noted that information processing speed is a cognitive domain that

critically contributes to the level of performance obtained by PASAT, whose scores were

used for the correlation analysis tested here17,39

.

The areas where ACM was associated with PASAT in patients do not correspond to

regions of reduced ACM in the group comparison with healthy controls. This is not

surprising, as the voxel-wise comparison only highlights areas of the brain where ACM is

equally reduced across the whole group. By contrast, the association between the PASAT

score and ACM is explained by their co-variance in specific regions of the brain that are

known to be implicated in information-processing and attention.

In conclusion, the current study supports, on a “structural” basis, the role of brain

disconnection as responsible for the cognitive impairment in MS1. In all ACM analyses, T2

LL and number of segmented voxels were always used as covariates of no interest. This

suggests that ACM does not merely provide a surrogate measure of gross tissue damage,

but some additional information on the actual brain connectivity. This approach might be

useful for studies on the clinical evolution of MS and for clinical trial monitoring.

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Acknowledgments

The Neuroimaging Laboratory of the Santa Lucia Foundation is supported in part by the

Italian Ministry of Health

Competing interests

The authors declare that they have no actual or potential conflicts of interest.

Copyright licence statement

The Corresponding Author has the right to grant on behalf of all authors and does grant on

behalf of all authors, an exclusive licence (or non exclusive for government employees) on

a worldwide basis to the BMJ Publishing Group Ltd to permit this article (if accepted) to be

published in JNNP and any other BMJPGL products and sublicences such use and exploit

all subsidiary rights, as set out in our licence.

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FIGURE LEGENDS

Figure 1. Voxel-wise comparison of ACM values between MS patients and HS.

Reductions of structural connectivity in patients are shown in red. The reductions are

located in the corpus callsum (A, B, C), in the left prefrontal lobe (A, B, C), and in the head

of the caudate nuclei (bilaterally) (A). No areas of increased ACM were observed in MS

patients compared to controls. In the inset of panel A, the probabilistic lesion map is shown,

indicating the percentage of patients with a lesion in a given area. ACM changes were

mainly found in brain regions spared by macroscopic damage.

Statistical threshold set to p-FWE-corr.<0.05 at cluster level. Spatial coordinates are in the

figure are in MNI space.

See text for further details.

Figure 2. Direct associations (yellow areas) between ACM values and scores obtained by

MS patients at the PASAT. The areas of significant association are located in the anterior

portion of the cingulate gyrus (A, B, C) and in the anterior thalamic radiation bilaterally (A,

B). In the inset of panel A the probabilistic lesion map is shown, indicating the percentage

of patients with a lesion in a given area.

Statistical threshold set to p-FEW-corr.<0.05 at cluster level.

Spatial coordinates are in the figure are in MNI space.

See text for further details.

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Table 1. Principal demographic and clinical characteristics of studied subjects.

Patients with RRMS

(N=25)

Healthy controls

(N=25)

Mean (SD) age: years

34.3 (8.2)

32.9 (8.1)

Gender (F/M) 19/6 13/12

Mean (SD) EDSS score 2.1 (1.3) -

Median (range) disease duration [years] 7.0 (2-18) -

PASAT: Mean (SD) score 41.16 (10.1) 48.1 (8.2)

Abbreviations: EDSS = expanded disability status scale; PASAT= Paced Auditory Serial Addition

Test; RRMS=relapsing remitting multiple sclerosis; SD=standard deviation.

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Voxel-wise comparison of ACM values between MS patients and HS. Reductions of structural connectivity in patients are shown in red. The reductions are located in the corpus callsum (A, B, C), in the left prefrontal lobe (A, B, C), and in the head of the caudate nuclei (bilaterally) (A). No areas of increased ACM were observed in MS patients compared to controls. In the inset of panel A, the

probabilistic lesion map is shown, indicating the percentage of patients with a lesion in a given area. ACM changes were mainly found in brain regions spared by macroscopic damage.

Statistical threshold set to p-FWE-corr.<0.05 at cluster level. Spatial coordinates are in the figure are in MNI space.

See text for further details.

254x190mm (96 x 96 DPI)

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Direct associations (yellow areas) between ACM values and scores obtained by MS patients at the PASAT. The areas of significant association are located in the anterior portion of the cingulate gyrus

(A, B, C) and in the anterior thalamic radiation bilaterally (A, B). In the inset of panel A the probabilistic lesion map is shown, indicating the percentage of patients with a lesion in a given area.

Statistical threshold set to p-FEW-corr.<0.05 at cluster level. Spatial coordinates are in the figure are in MNI space.

See text for further details. 254x190mm (96 x 96 DPI)

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