Diffusion Tensor Imaging and Neuromodulation: DTI as Key Technology for Deep Brain Stimulation

CHAPTER TEN

Diffusion Tensor Imaging andNeuromodulation: DTI as KeyTechnology for Deep BrainStimulationVolker Arnd Coenen*,†,1, Thomas E. Schlaepfer‡,}, Niels Allert},Burkhard Mädler**Division of Stereotaxy and Functional Neurosurgery, Department of Neurosurgery, BonnUniversity MedicalCenter, Bonn, Germany†German Center for Neurodegenerative Diseases (DZNE), Bonn University Medical Center, Bonn, Germany‡Department of Psychiatry and Neurological Rehab Center “Godeshohe” Psychotherapy, Bonn UniversityMedical Center, Bonn, Germany}Departments of Psychiatry and Mental Health, The Johns Hopkins University, Baltimore, Maryland, USA}Neurological Rehab Godeshohe, Bonn, Germany1Corresponding author: e-mail address: [email protected]

Contents

1.

InternISSNhttp:/

Overview

ational Review of Neurobiology, Volume 107 # 2012 Elsevier Inc.0074-7742 All rights reserved./dx.doi.org/10.1016/B978-0-12-404706-8.00011-5

208
2. Diffusion Weighted and Diffusion Tensor Magnetic Resonance Imaging 211 3. Fiber Tractography 214 4. Deterministic and Probabilistic Fiber Tracking 215 5. Accuracy of Diffusion Tensor Imaging Fibertracking and Its Use in Neurosurgery 217 6. Electric Field Simulation 218 7. Applications 219
7.1
Identification of “true” target structures 219 7.2 Tremor and the dentatorubrothalamic tract 220
8.
Development of a New Target Region Utilizing the DTI Technique: A New DBSTarget to Treat Major Depression 222
9.
DTI FT and the Concept of Network Balances 225 10. Summary 227 Acknowledgments 229 References 229
Abstract

Diffusion tensor imaging (DTI) is more than just a useful adjunct to invasive techniqueslike optogenetics which recently have tremendously influenced our understanding ofthe mechanisms of deep brain stimulation (DBS). In combination with other technolo-gies, DTI helps us to understand which parts of the brain tissue are connected to others

207

http://dx.doi.org/10.1016/B978-0-12-404706-8.00011-5

208 Volker Arnd Coenen et al.

and which ones are truly influenced with neuromodulation. The complex interaction ofDBS with the surrounding tissues—scrutinized with DTI—allows to create testablehypotheses that can explain network interactions. Those interactions are vital for ourunderstanding of the net effects of neuromodulation. This work naturally was first donein the field of movement disorder surgery, where a lot of experience regarding therapeu-tic effects and only a short latency between initiation of neuromodulation and alleviationof symptoms exist. This chapter shows the journey over the past 10 years with firstapplications in DBS toward current research in affect regulating network balances andtheir therapeutic alterations with the neuromodulation technology.

Until the middle of the present century anatomists relied principally on fourmethods for demonstrating connections of the brain. Identified, respectively, withthe names of Gudden, Golgi, Marchi, and Nissl [. . .].

Paul MacLean 1989, The triune brain in evolution - Role in paleocerebral functions.

1. OVERVIEW

With the development of a new and sophisticated neuroscientific

method, namely, optogenetics (Deisseroth, 2012; Gradinaru, Mogri,

Thompson, Henderson, & Deisseroth, 2009; Lobo, Nestler, &

Covington, 2012), our understanding of the mechanisms underlying the

function of the brain has tremendously increased. The same increase of

insight holds true for the application of the same technique to understand

the mechanisms of altering brain function with the deep brain stimulation

(DBS) technology (Gradinaru et al., 2009) that is predominantly applied

therapeutically for chronic illnesses in the field of movement disorders

(Parkinson’s disease, dystonias, tremor) and has been used regularly for

almost 20 years (Benabid, Benazzous, & Pollak, 2002; Benabid, Deuschl,

Lang, Lyons, & Rezai, 2006). Only, recently, DBS has been approved in

Europe for the use in partial onset epilepsy (Fisher et al., 2010). As

landmark research in optogenetics has been performed also in the field of

DBS, we have now further confirmation about the mechanisms of its

action: Other than believed before DBS does not only create a functional

lesion through the proved mechanism of a depolarization block. The

mechanisms of DBS are complex, and the functional inactivation of the

target by this mechanism has been clearly demonstrated. It may,

however, not be the main mechanism of DBS under some circumstances

(Benabid et al., 2002). DBS increases the activity in axonal structures that

are reached with chronic high-frequency stimulation, and probably via

this mechanism has a larger impact on the network sum effects than the

209Diffusion Tensor Imaging and Deep Brain Stimulation

depolarization block as has been elegantly proved with optogenetics

(Gradinaru et al., 2009). The truth, however, likely is much more

complicated and the epiphenomena resulting from this activation are

complex and hard to understand. Diffusion tensor imaging (DTI)—which

in conjunction with the DBS technique is the topic of this chapter—is

more than just a useful adjunct to the developing invasive techniques. Its

totally noninvasive approach allows—in combination with other

technologies like electric field simulation (Butson, Cooper, Henderson, &

McIntyre, 2007; Butson & McIntyre, 2005; Madler & Coenen, 2012;

McIntyre & Grill, 2002; McIntyre, Mori, Sherman, Thakor, & Vitek,

2004)—to understand which parts of the individual’s brain tissue are truly

influenced with DBS. In turn, the complex interaction of DBS with

the surrounding tissues that are scrutinized with DTI in the living human

brain allows to create testable hypotheses that explain network

interactions which are vital in our understanding of the net effects of

neuromodulation. Naturally, this work had first to be done in the field of

movement disorders, where there is a lot of therapeutic experience and

only a short latency between the start of neuromodulation and alleviation

of a symptom (e.g., tremor) or the elicitation of a side effect (e.g.,

capsular effect) (Coenen, Allert, & Madler, 2011; Coenen et al., 2009;

Coenen, Madler, Schiffbauer, Urbach, & Allert, 2011). With this

experience, the identification of a neuroanatomical structure and the

allocation of a (side-) effect are much clearer than in the field of

psychiatric research where most stimulation results have to be dug out

under the dust of a patient’s individual and often distracting personality

with help of standardized neuropsychological test batteries. The rise of

new and often experimental applications in the psychiatric field

(depression, OCD, Gilles de la Tourette syndrom) mandate an

understanding of emotional networks and the identification of neuralgic

intervention points that will in the future help DBS to unfold a better

efficacy (better effects, less energy used) than at today’s target regions

(Coenen, Schlaepfer, Maedler, & Panksepp, 2010). With the results from

the movement disorder research in mind, one can now extrapolate to

even more complex interactions in emotional networks where effects of

DBS have traditionally been seen from weeks to months after initiation of

high-frequency stimulation (Lozano et al., 2008; Mayberg et al., 2005;

Neimat et al., 2008; Schlaepfer & Lieb, 2005). In a way, we and other

groups (Barkhoudarian et al., 2010; Henderson, 2012) have gaged these

instruments by testing them in the movement disorders before applying


them to psychiatric indications. To do so, one has to assume that the brain

functions on the basis of the same neurophysiological mechanisms

regardless of the system (motor, sensory, pain, emotion, etc.) we look at

(Panksepp, 1985). Furthermore, the sophisticated use of this technology

might allow to in the future place DBS electrodes under general anesthesia

(Madler & Coenen, 2012). This chapter sums up a journey of the past

10 years with first applications of the DWI/DTI technique in DBS

(Coenen et al., 2006; Sillery et al., 2005) toward current research in the

field of emotional networks (Coenen et al., 2006, 2001; Coenen,

Schlaepfer, Maedler, & Panksepp, 2010; Gutman, Holtzheimer, Behrens,

By-passingaxons of other

circuit

DB

S

DB

S

By-passingaxons of other

circuit

Afferent neurons Afferent neurons

A B

Figure 10.1 Twoways of looking at themechanism of deep brain stimulation (DBS). TheDBS electrode is placed inside a group of nerve cell bodies (nucleus, blue). In theclassical view (A) high-frequency stimulation (HFS) creates a “functional lesion” byinfluencing the cell membranes and inducing a “refractory” state. In the extended view(B)—according to the new results from optogenetic neuromodulation togetherwith DBS in rodents (Gradinaru et al., 2009) that just confirm long suspectedmechanisms—the high-frequency stimulation creates a functional lesion inside the field(cf. (A) but—while the electric field reaches beyond the nucleus itself—activates affer-ent and efferent as well as by-passing axonal pathways. The sum of both mechanismscreates the network effect. In this simplistic depiction, the degree of myelination andits effect on activation properties of the HFS are not considered.


Johansen-Berg, & Mayberg, 2009; Johansen-Berg et al., 2007; Schoene-

Bake et al., 2010) and toward a better understanding of what the DBS

technique truly does: (Fig. 10.10). Influencing balanced network

interactions (Coenen, Panksepp, Hurwitz, Urbach, & Madler, 2012;

Coenen et al., 2010).

2. DIFFUSION WEIGHTED AND DIFFUSION TENSORMAGNETIC RESONANCE IMAGING

Measuring diffusion properties of molecules with nuclear magnetic

resonance (NMR) and later on spatially resolved with magnetic resonance

imaging (MRI) methods has a long tradition. They root date back to the

pioneering work of Stejskal and Tanner (1965). Compared to other methods

like chemical or radioactive tracer studies, MRI has the advantages of being

noninvasive, produces good spatial resolution, and does not adhere to a fixed

coordinate system or following specific predetermined axis or pathways, for

example, dissected slices, arbitrary volume subsamples.

Since the work from the mid-1980s of Wesbey, LeBihan, Breton,

Merboldt, Taylor, and others (Jones, 2011; Le Bihan et al., 1986;

Merboldt & Hanicke, 1985; Taylor & Bushell, 1985; Wesbey, Moseley, &

Ehmann, 1984) (Diffusion MRI, p. 58), it is possible to generate spatially

resolved in vivo diffusion maps with MRI from animals as well as from

human subjects. The first human studies produced so-called diffusion-

weighted images where the brightness of each voxel corresponds to the

inverse of the apparent diffusion coefficient (ADC) of water in the tissue,

hence dark voxels illustrating a relatively fast diffusion of water whereas

very bright regions correspond with no or very slow water diffusivity.

Followed by the reported acute reduction in ADC in ischemic tissue in

cat brain within a few hours after introduced ischemia from Moseley et al.

(1990a, 1990b), the first human clinical relevant DWI-studies focused on

acute local or global cerebral ischemia as a result of cardiac arrest, stroke,

hemorrhage, or thrombosis. Shortly after this introduction as a new MR-

image contrast mechanism, it was found that the ADC showed a

directional dependence, first on the applied gradient encoding direction

and second also on the tissue orientation (Chenevert, Brunberg, & Pipe,

1990; Doran et al., 1990; Moseley et al., 1990a, 1990b). It, therefore,

suggests treating also the cerebral tissue of animal and human as a highly

heterogeneous and hence anisotropic tissue. Since the ADC is inherently

coupled with the mean squared displacement via Einstein’s famous


equation (hr2i prop.D*t) (Einstein, 1905), it was concluded that a structural

and/or anatomical hindrance of the water diffusion pathway in cerebral

tissue is the reason for this observation. As this effect is primarily

observed in white matter of cerebral tissue, whereas CSF and gray matter

exhibit a more or less isotropic behavior (no orientational dependence) of

water diffusion, it was initially concluded that barriers constituted by the

myelin sheaths surrounding WM-tissue, local susceptibility gradients,

axonal cytoskeleton, and fast-axonal transport are the primary causes for

this behavior (Thomsen, Henriksen, & Ring, 1987). Myelin itself,

however, seems to play only a marginal rule to cause anisotropic diffusion

behavior as studies from immature rat brains that have yet no myelin

sheath developed (Prayer et al., 1997; Wimberger et al., 1995). Later,

Beaulieu and coworkers ruled out the influence of internal susceptibility

gradients, axonal cytoskeleton, and fast-axonal transport from a series of

experiments on excised frog and giant squid nerves (Beaulieu & Allen,

1994a, b, 1996). They concluded that the main contribution of water

diffusion anisotropy in nervous tissue is the mere presence of intact cell

membranes and that myelination does only serve as a mild modulator of

anisotropy. Quantitatively this was confirmed by Madler et al. in

correlating quantitative in vivo measures for the degree of myelination in

the human brain with various anisotropy measures of water diffusion

(Madler, Drabycz, Kolind, Whittall, & MacKay, 2008).

From all the above listed phenomena, we must conclude that it is inad-

equate to describe water diffusivity specifically in nervous tissue as a single

scalar value that does not reflect any of the orientational dependencies

described above. Furthermore, different patient positions and orientations

inside aMRI-scanner would lead to different observations of the ADC. This

leads to the introduction of the diffusion tensor, a mathematical vehicle that

helps to understand and describe the three-dimensionality of the diffusion

problem. Pioneered by Basser et al. in the early 1990s, the concept of

DTI was introduced where the 3D properties of diffusion directionality

are reflected by the 3�3 matrix of the diffusion tensor D¼ (Dxx. . .Dzz).

The so-called diagonal elements Dxx, Dyy, Dzz correspond to the diffusivity

along the three orthogonal axes in a Cartesian coordinate systemwhereas the

off-diagonal elements reflect the correlation of diffusivity between those

orthogonal axes, for example, x and y. The DT is symmetric, meaning its

corresponding off-diagonal elements are the same (e.g., Dxy¼Dyx), as we

cannot distinguish directionality of diffusion along one axis (�x or þx)

by observation of the hr2i displacement only.


When all three (six) off-diagonal values are zero, the tensor is aligned

with the principal axis of diffusion for the specific tissue. The diffusion tensor

transformation into the coordinate system of the principal diffusion axis of

the tissue is referred to as diagonalization. The remaining diagonal elements

Dxx, Dyy, and Dzz in the diagonalized form, the eigenvalues of the diffusion

tensor, are labeled l1, l2, and l3. The normalized directions of the orthogonal

axis in this diagonalized form are called eigenvectors. Any second rank ten-

sor (3�3 matrix elements) can be transformed into such representation of

eigenvalues and eigenvectors. If we order the eigenvalues according to their

magnitude, the three corresponding orthogonal eigenvectors v1, v2, v3

multiplied with the square root of their eigenvalues span a 3D ellipsoid with

its largest axis (sqrt(l1)) along v1. This geometric representation of the diag-

onalized diffusion tensor is also referred to as the diffusion ellipsoid. The

isosurface boundary of this ellipsoid represents the displacement of an en-

semble of molecules per measurement time—the diffusion coefficient—in

two dimensions, it could be visualized by the spread of an ink-drop on paper

with increasing observation time.

Although the idea to describe diffusion in an anisotropic medium as a

tensor property dates back to the early work of Stejskal and Tanner

(1965), it was not until 1992 when Basser and colleagues introduced a

method to measure the diffusion tensor directly from a series of DW-

measurements (Basser & Bihan, 1992) and combined this information into

what is now known as DTI (Basser, Mattiello, & Le Bihan, 1994; Basser,

Mattiello, & LeBihan, 1994).

In practice, however, the patient or tissue reference of diffusion does not

coincide with the principal or main direction of diffusion. Therefore, one

must consider the coupling of the nondiagonal elements from different gra-

dient directions (reference frame) and therefore b-matrix bij with the

nondiagonal elements of Dij. To determine the six independent elements

of D, measurements are made along at least six noncollinear gradient direc-

tions and one measurement without diffusion weighting to accommodate

the impact of various imaging gradients additionally to the diffusion weighting

gradients apparent in the sequence onto the signal equation. In general, the

measurement involves multiple b-values or gradient strength (M) along N

noncollinear gradient directions. From such an experiment, a system of linear

equations in matrix form can be created and solved for the six tensor elements

Dij as well as for the signal amplitude S(0) without diffusion weighting.

This relatively simple form becomes much more complex if one also

considers the effect of imaging and/or background gradients that are not


considered in the Stejskal–Tanner equation. A more general formalism is

used instead to solve the Bloch–Torrey equation (Torrey, 1956) which

might become analytically very complex. Therefore, Le Bihan and co-

workers suggested early on to summarize all gradient effects over time in

the general b-matrix (Le Bihan et al., 1986).

Generally speaking, the gradients are chosen to uniformly sample the

Cartesian 3D space or at least one hemisphere (ambiguity between directions

of diffusion). For a predefined number of gradient directions, one can

employ the electrostatic repulsion model of equally charged particles on a

unit sphere (Jones, 2011). Minimizing the energy for the entire particle

assembly leads to an arrangement where all charged particles have more

or less the same distance to each other.

Nevertheless, several disagreeing results onto the optimal number and

directions of gradient encoding schemes do not leave a unique and universal

recommendation. Jones summarizes these partially diverse recommenda-

tions, as there is a benefit for improved precision in the estimated principal

eigenvectors and trace values with higher number of direction schemes,

however, with diminishing returns for more than 30 noncollinear direc-

tions. The asymptotic number of orientations for anisotropy measures is

already reached at about 20 (Jones, 2004; Papadakis, Murrills, Hall, Huang,

& Adrian Carpenter, 2000).

In practice, we almost always face the challenge to adhere to a limited

scan time for a DTI-acquisition. The total examination time is proportional

to the number of gradient encodings and has to comply with certain limi-

tations in regards to clinical patients and examinations. Nowadays, with

high-performance MRI-scanners and powerful gradient coils, a full brain

single shot EPI DWI acquisition (128�128 matrix, 60 slices, isovolumetric

voxel size 2�2�2 mm3, b-value¼1000 s/mm2) takes about 10–15 s.

Multiplying this by the number of encoding directions leads to a total acqui-

sition time, for example, for 30 directions of approximately 6 min.

3. FIBER TRACTOGRAPHY

As pointed out earlier, diffusion of water in the CNS is characterized

not only by its magnitude but also its directional variance and orientation of

the main eigenvectors. As a reasonable assumption, it is generally assumed

that the direction of the greatest eigenvalue (largest diffusivity along of

the principal tensor axis—direction of corresponding eigenvector) is parallel

to the direction of the white matter fibers in large, homogeneous regions of

A B C

P

100

50

0

robabi

ity

l

Figure 10.2 Comparison of deterministic and probabilistic tracking results for the lefthuman pyramidal tracts generated with a single seed volume in the left central gyrus:(A) Deterministic tracking result from FACT and streamline visualization from a singlehuman subject. (B) Statistical confidence map (0–100%) of individually generateddeterministic tracking results from 45 human subjects transformed into standard brainspace (MNI152). The statistical confidence values (color scale) here are mainly based onindividual spatial variations of tracking results for each voxel of the structure. (C) Prob-abilistic tracking results using FSL (FMRIB, Oxford, GB). The color scale indicates theprobability that a streamline connects the start point (VOI) and each other voxel inthe brain.


the WM in CNS. For the complexity of crossing, kissing, or highly bended

regions in WM areas, we must refer to the literature.

A vast diversity of different approaches have been proposed to visualize

the directional organization of WM fibers based on DTI (vector maps,

glyphs, directional color encoded maps, etc.). The most advanced strategy

nevertheless is tractography which enables a more or less anatomical

3D-visualization of WM tracts. Tractography algorithms can be separated

into two groups, deterministic and probabilistic approaches (Fig. 10.2).

4. DETERMINISTIC AND PROBABILISTIC FIBER TRACKING

There are numerous ways of postprocessing DTI data to make them

clinically and anatomically more palatable than the first color-coded visual-

ization strategies. As early as 1999, Xue, van Zijl, Crain, Solaiyappan, and

Mori (1999) presented the first 3D reconstructions of known fiber pathways

in the rat brain using the deterministic fibertracking (FT) approach. The ear-

liest contributions in the field of deterministic FT in human normal brain

anatomy were probably reported by Mori and coworkers (Mori et al.,

2008; Wakana, Jiang, Nagae-Poetscher, van Zijl, & Mori, 2004; Xue

et al., 1999) and Ammunts and coworkers (Burgel et al., 2006).


A deterministic tractogram is generated by starting from one or more seed

location which can be combined with logical operators AND, NOT, OR, or

XOR. The simplest and still most practical approach proposed by Basser in

1998 (Basser, Pajevic, Pierpaoli, Duda, & Aldroubi, 2000) is the streamline

algorithm that uses the eigenvector of the largest eigenvalue to define the local

trajectory direction of fiber pathways from the diffusion tensor measurements.

It involves seamless integration along the vector field either by linear step-wise

algorithms such as FACT (fiber assignment by continuous tracking—Mori

et al., 2008) or Euler integration (Conturo et al., 1999). These algorithms

create continuous wire- or tube-wise structures that can be considered as

an anatomical portrayal of major WM tracts. Using only the largest

eigenvalue–eigenvector pair for estimating the tract’s direction works well

for highly anisotropic (cigar shaped) tensors but creates higher degrees of am-

biguity in regions of less anisotropy, for example, crossing, kissing fibers. For

such cases, the exploitation of the tensor’s full properties, for example, TEND

(tensor deflection—Lazar & Alexander, 2003; Lazar et al., 2003), may yield

more realistic tractograms. With the continuous growing number of largely

as well as mildly different streamline algorithms and the need for operator

intervention when setting the tracking parameters (seed VOI’s, angular

deflection, minimum fiber length, seed density, and more), it poses one of

the biggest challenges in WM-tractography nowadays to obtain anatomical

correct as well as comparable results, for example, in clinical practice

(Burgel et al., 2009). Additionally to its potentials for pitfalls and

anatomical misinterpretation (book—Diffusion MRI, Jones 2010), the

major disadvantage of deterministic tractography is that the errors or

confidences of estimated tracts are generally unknown. This issue can be

more thoroughfully addressed using probabilistic methods.

Probabilistic methods try to overcome the limitation of deterministic

tracking methods and their lag of information regarding errors in the tracking

results by explicit characterization of the confidence with which connections

may be established through the diffusion data itself. The main sources of un-

certainty while constructing tractograms from diffusion data come from noise

in the data itself (noise generated from subject, involuntary movements, tissue

susceptibility) and from the data acquisition systems (scanner hardware,

RF-chain, gradient amplifiers, eddy currents). Further factors are imperfect

models of the diffusion process in the CNS, partial volume effects, limited spa-

tial resolution, andmultiple fiber populations per voxel. For more explicit and

detailed information, we must refer the reader to literature about this subject,

for example, Johansen-Berg and Behrens (2009), Jones (2011).


The unique advantage of probabilistically tract data is that the output of

connection probability values has a specific meaning—an observed value of

probability is the statistical probability that a connection from one start point

to a specific other voxel in the brain exists on the basis of our diffusion model

of connections. Although very valuable and tempting to draw further con-

clusions, this information does not provide us with clear evidence of a gen-

uine anatomical connection existing in the CNS. Nevertheless, we can

safely assume that there is a useful and direct link between microstructural

geometry and organization with the results of tractography. Currently,

we can only provide empirical evidence that the results of tractographic

methods, deterministic or probabilistic, are closely related to true anatomical

connections. From gathering knowledge by using more sophisticated track-

ing algorithms and comparisons with histological or tracing data, we will be

more and more able to establish where such portrayals of diffusion connec-

tivity relate to true anatomical connections but also identify scenarios where

this link might be broken or unjustifiable.

5. ACCURACY OF DIFFUSION TENSOR IMAGINGFIBERTRACKING AND ITS USE IN NEUROSURGERY

With growing experience, it became clear that there might be inac-

curacies associated with the DTI FT depiction of even large robust and very

well discernible fiber tracts—like the pyramidal tract—that among other fac-

tors arise at the software level (Burgel et al., 2009). In principal, limitations

that the deterministic FT algorithm poses are the termination of tracking in

areas of reduced fractional anisotropy (because of increased intermingling of

fibers, approach of the subcortical boundaries, and partial volume effects)

and the ambiguity in following the correct connection pathways in dense

areas of crossing, kissing, or branching fibers (limitation of single diffusion

tensor model) combined with a relatively low spatial resolution (�2 mm

isotropic voxel dimension) (Pai, Soltanian-Zadeh, & Hua, 2010;

Staempfli et al., 2007; Wakana et al., 2004). We will not go into further

detail about limitations of the DTI technique.

Fiber portrayal with MRI was first introduced for planning and per-

forming neurosurgical interventions in the early 2000 utilizing diffusion-

weighted imaging and DTT to project fiber tract anatomy into the operation

field with neuronavigation systems (Coenen et al., 2001, 2003, 2005;

Holodny, Schwartz, Ollenschleger, Liu, & Schulder, 2001; Nimsky,

Ganslandt, & Fahlbusch, 2007; Nimsky, Grummich, Sorensen, Fahlbusch, &


Ganslandt, 2005). Despite its value in glioma surgery (Wu et al., 2007), it soon

became apparent that there would be implicit errors or inaccuracies associated

with this technique (see above) which for neurosurgical applications were first

reported by Kinoshita et al. (2005) and were quantified by Nimsky and

coworkers to be as high as 5 mm in the anterior–posterior (y-) direction in

neocortical regions (Nimsky et al., 2007). In a first effort to determine the

accuracy of the DWI technique in combination with stereotactic surgery,

our own group applied visualization of the pyramidal tract in six patients

undergoing subthalamic nucleus DBS. In this experimental setup, we

determined electrophysiological distance by applying meta-analysis data

from Ranck (1975) and DWI determined distance between the electrode

position and the medial border of the internal capsule. We found an

accuracy of 3 mm in the z-direction and 1.3 mm in the “in plane”

directions (x,y), clearly better than what had been reported for the

neocortical regions, but now in the midbrain level (Coenen et al., 2006).

Hypothetically, this would allow the use of the DWI technique for

neuronavigation with its implicit localization accuracy of 3–5 mm

depending on the imaging modality (MRI, computed tomography) used.

In order to further determine the accuracy when using 3T MRI, we

utilized a similar setup but now utilizing DTI and a 12-direction encoding

sequence and extending it with a second electrophysiological approach

based on the implanted neuromodulation device and a finite element

method to simulate the electric field as reported by Butson and coworkers

(Butson et al., 2007; Butson, Cooper, Henderson & McIntyre, 2006). In

this—yet unpublished—study, we found accuracies of 1.5–2 mm for the

DTI technique when applied with stereotactic methods, 3T MRI and back

fusion of postoperative computed tomographic data (Pinsker et al., 2008)

in the midbrain level that was implicitly applied and proved correct in later

studies (Coenen et al., 2009; Coenen, Allert, & Madler, 2011; Coenen,

Madler, et al., 2011)(Madler & Coenen, 2012; Sajonz, Madler,

Herberhold, Paus, & Coenen, 2011).

6. ELECTRIC FIELD SIMULATION

DBS is a technique that delivers electrical pulses, variable in amplitude,

pulse width, and frequency, through permanently implanted electrodes,

which are connected to an implantable pulse generator (IPG). The elec-

trodes are usually placed by using stereotactic procedures based on imaging

studies (CT, MR imaging) combined with real-time intraoperative X-ray


and electrophysiology. Depending on the diagnosis and patient-specific

symptoms, different anatomic targets have been proposed.

Experimental measurements of the electromagnetic field distribution gen-

erated byDBSelectrodes are still lacking.Therefore, computationalmodels are

an invaluable tool for thedescriptionof electric field patterns generatedbyDBS

electrodes and are essential to understand, optimize, and predict stimulation ef-

fects for certain DBS treatments. Substantial efforts have beenmade in design-

ing conceptional volume conductor electric field models of clinical

DBS (Butson et al., 2006; Kuncel & Grill, 2004; McIntyre et al., 2004;

McIntyre & Thakor, 2002; Yousif, Bayford, & Liu, 2008; Yousif, Bayford,

Wang, & Liu, 2008) and coupling them to multicompartmental neuronal

cable models to develop theoretical predictions of neural activation as a

function of therapeutic stimulation parameters (McIntyre et al., 2004;

Miocinovic et al., 2006, 2009). Recently, we introduced a robust and

simple numerical model based on multivariate polynomial fitting of

previously obtained data from a finite element model that provides sufficient

and practically relevant feedback as to the patient’s individual VAT

depending on specific generator-stimulation settings and patient-dependent

electrode-tissue properties. The model primarily relies on a patient’s

individual impedance measures obtained during postoperative IPG

programming with monopolar DBS electrode settings and variable voltages.

It generates VAT distance isolines around the DBS contact for specific IPG

voltages by means of a spherical model (Madler & Coenen, 2012).

7. APPLICATIONS

7.1. Identification of “true” target structures
In order to understand the merit of a new technique like DTI in conjunc-
tion with DBS, one has to closely look at what has been done historically

(Hariz, Blomstedt, & Zrinzo, 2010). In the past, efforts had been made to

more precisely target regions in the brain. Spiegel and Wycis introduced

their stereotactic apparatus (Hariz et al., 2010; Spiegel, Wycis, Marks, &

Lee, 1947) in order to improve crude techniques of psychosurgery by

directly targeting small areas in the thalamus (dorsomedial thalamotomy).

Only later, these techniques were used for pain and movement disorders

and even later than this also applied for the chronic DBS techniques we

use today with implantable neurostimulators although stimulation

techniques had been used earlier to verify lesion sites. In the same way

as Spiegel and Wycis in their time applied the stereoencephalotome, new


techniques like DTI FT can be used to identify true strategic target sites to

perform neuromodulation (Coenen, Allert, & Madler, 2011; Coenen,

Madler, et al., 2011; Henderson, 2012). Additionally, this technique helps

to understand the effect spectrum of DBS (Barkhoudarian et al., 2010;

Sillery et al., 2005) and its side effects (Coenen et al., 2009; Madler &

Coenen, 2012). We further elaborate on this in the following section,

where the DTI technique is actually used to identify a supposedly more

efficacious target region for a psychiatric intervention (Coenen et al., 2010;

Schoene-Bake et al., 2010). In this section, however, we try to explain

with an example from the movement disorders—especially Parkinson’s

disease and tremor—how the effectiveness of current established target

regions for DBS surgery can be appreciated with the DTI technique and be

attributed to the stimulation of fiber tracts.

7.2. Tremor and the dentatorubrothalamic tractFor the past 60 years, functional neurosurgeons used lesion surgery in the

ventral thalamus to achieve dramatic alleviation of tremor for patients

(Hassler, Mundinger, & Riechert, 1979). The ventralis intermedius nucleus

of the thalamus is the typical target structure among a group of previous tar-

gets for tremor surgery in the ventral thalamus (Pilitsis et al., 2008), but there

is an efficacy signal for the benefit of the posterior subthalamic region and the

caudal zona incerta as probably superior target regions for certain distinct

tremor forms (Krack, Pollak, Limousin, Benazzouz, & Benabid, 1997;

Plaha, Khan, & Gill, 2008; Plaha, Patel, & Gill, 2004). With the advent

of DBS, lesion strategies were replaced with the less invasive, adjustable,

and reproducible chronic stimulation technique (Benabid et al., 1996).

Hassler et al. (1979) had speculated that lesion techniques would involve

the fiber projections that reach up from the cerebellum to the

contralateral thalamus. This idea was later repeated by Plaha et al. (2004,

2008). With the DTI FT technique and the combination with DBS and

EFS, we were able to present clinical evidence for the identification of

the dentatorubrothalamic (DRT) (cf. Fig. 10.3) as the key target structure

for tremor suppression (Coenen, Allert, & Madler, 2011; Coenen,

Madler, et al., 2011; Sajonz et al., 2011).

To show an example for the significance of individual depiction of the

DRT with DTI, we present the case of a 73-year-old woman with essential

tremor (essential tremor rating scale (ETRS)¼63). Bilateral thalamic DBS

in the Vim target with conventional targeting methods and intraoperative

MC R LMC

A B

PTV. lat. V. lat.

iii

STR

DRTthal

CI

Vimz = 0

z = −4HDPSTNcZI

RNSNr

SCP

DN

Figure 10.3 (A) Schematic representation of the bilateral dentatorubrothalamic tracts(DRT) in coronal section. (B) Probabilistic DTI atlas depiction in the MNI152 space. Leg-end: MC, motor cortex; V.lat, lateral ventricles; iii, 3rd ventricle; PT, pyramidal tract; STR,superior thalamic peduncle; thal, thalamus; CI, internal capsule; Vim, ventralis interme-diate nucleus; HDP, hyperdirect pathway; STN, subthalamic nucleus; cZI, caudal zonaincerta; RN, red nucleus; SNr, substantia nigra; SCP, superior cerebellar peduncle; DN,dentate nucleus.


test stimulation was performed for bilateral essential tremor. Immediately

after surgery stimulation was started and tremor remained well controlled

for a couple of weeks. Tremor control was lost for her left body side and

monopolar stimulation had to be increased. Because of unbearable side

effects (tingling), stimulation was changed to a bipolar stimulation mode

with 4.5 V, 90 ms, 180 Hz (cf. Figs. 10.4–10.6, red sphere). In this

situation that represented moderate tremor control, she started to develop

stimulation induced gait instability and ambulated with a walker due to a

tendency to fall. After evaluation of preoperative DTI (cf. Fig. 10.4), an

asymmetry in her DRT was seen. A second surgery was offered this time

directly aiming for the right DRT with additional electrode (R2,

Fig. 10.4). A combination of stimulation of both right-sided electrodes

(cf. Figs. 10.5 and 10.6) leads to sufficient tremor control. A follow-up

test after 1.5 years after the second additional surgery showed that in the

OFF condition she has an ETRS score of 62, in the ON condition (three

electrodes, cf. Figs. 10.3 and 10.4, blue spheres) of 22. This is an

improvement of 65%.

R1

R1

R2 L1

L1D

BSR

oblique

P

L

DB

S DB

S

DRT DRT

PC

ACR2

Figure 10.4 Seventy-three-year-old female with essential tremor. Transverse section atthe level of the ACPC line. After bilateral nucleus ventralis intermedius deep brain stim-ulation (VIM DBS, electrodes R1 and L1 implanted stereotactically), her left sided tremorwas only insufficiently controlled. According to DTI fiber tracking, acquired prior to herfirst surgery, her individual dentatorubrothalamic tracts (DRT, yellow and orange) areslightly asymmetric. Additional right electrode (R2) was implanted aiming for herDRT. Combined stimulation of both right electrodes control tremor sufficiently(ETRS¼23).


This case report illustrates the direct application of DTI in conjunction

with DBS. DTI together with EFS helped to identify anatomical variation

of the DRT that led to insufficient tremor control in almost symmetric

electrode position. Furthermore, a side effect (loss of proprioception due to

involvement of the medial lemniscus) (cf. Fig. 10.5) could be assigned to

an anatomical structure as has been demonstrated before (Coenen et al.,

2009; Madler & Coenen, 2012). Direct use of DTI for targeting (Coenen,

Allert, & Madler, 2011) allowed to directly place a second electrode.

Our results regarding direct visualization and targeting of the DRT being

the target structure in tremor suppression have recently been replicated by

another group (Henderson, 2012).

8. DEVELOPMENT OF A NEW TARGET REGION UTILIZINGTHE DTI TECHNIQUE: A NEW DBS TARGET TO TREAT
MAJOR DEPRESSION
Before the DBS era, otherwise untreatable psychiatric conditions had

been treated with lesion techniques that later were applied using a stereotactic

technique (Coenen & Honey, 2009; Hariz et al., 2010; Spiegel et al., 1947).

A B C

Figure 10.5 Same case as Fig. 10.4. Two stimulation situations are shown (A–C). Clini-cally beneficial (blue spheres) is a complete coverage of DRT fiber cross section(dentatorubrothalamic tract, yellow) without the electric fields reaching into the mediallemniscus (green). Electric field simulations of the combined stimulation over two DBSelectrodes (blue spheres, not touching the medial lemniscus, green). Stimulation withhigher field strength to suppress tremor with only one electrode (red sphere) clearlyreaches into the medial lemniscus (green). Red sphere (R1: Cþ, 8�, 4.5 V, 180 Hz,90 ms) tremor moderately controlled, gait unsteady, patient falls due to loss of propri-oception. Blue spheres (R1: 10þ, 11�, 3.5 V, 180 Hz, 60 ms; R2: Cþ, 0�, 3.0 V, 180 Hz,60 ms): tremor well controlled, no gait instability.


DBS has been used for the treatment of major depression in the recent years in

principle with three target regions: subgenual cingulum (previous cg25)

(Hamani et al., 2009; Lozano et al., 2008; Mayberg et al., 2005), anterior

limb of the internal capsule (ALIC, vc/vs) (Malone et al., 2009), and

nucleus accumbens (Schlaepfer et al., 2008; Schlaepfer & Lieb, 2005). Of

particular interest are certain facts these different target regions share: (1) all

targets show very similar antianhedonic effects, (2) all target regions use

relatively high voltages (5–10 V) as compared to the movement disorder

targets, and (3) all targets have a very similar effectiveness (50–60%). DTI

has been used to investigate connectivity of these target regions (Gutman

et al., 2009; Johansen-Berg et al., 2007) and in one study also the

connectivity of lesions sites (Schoene-Bake et al., 2010). One explanation

for the shared characteristics is that all target regions for therapy refractory

depression are interconnected by the medial forebrain bundle (MFB). This

hypothesis was tested with simulated implantations at three target points in

R1

R1

DB

S

DB

S

DRT

DRT

dn

pg

ML

PC

MCP

1/4

1/3

R2

R2

ACthal

amus

rnrn

AB

Figure 10.6 Same case as Figs. 10.4 and 10.5. (A) CT superimposed reconstruction. (B)Schematic presentation. Right-sided deep brain stimulation electrodes R1/R2 withrespect to the patient's individual dentatorubrothalamic tract (DRT) and mediallemniscus (ML). Stimulation of both electrodes (blue spheres) covers DRT and doesnot interfere with ML. Stimulation on singel electrode (R1, red sphere) leads totremor suppression and coverage of DRT but also involvement of ML. Patientclinically is unsteady and has falls. Legend: rn, red nucleus; pg, precentral gyrus; dn,dentate nucleus; PC, posterior commissure; AC, anterior commissure; MCP,midcommissural point.


the MNI152 brain at the same time using a probabilistic model of the MFB

(Coenen et al., 2012) together with EFS (Madler & Coenen, 2012). As a

resultant, it was shown that the MFB is partially included in the electric

field at the different depression target sites in up to 92% (Coenen et al.,

2010). Figures 10.7 and 10.8 exemplarily show the topographic

relationship of the MFB with the distinct target sites.

The humanMFBwas only recently described by our group (Coenen et al.,

2009, 2010, 2012) using the DTI technique. Obviously, there is a good

indication that the MFB represents parts of the mesolimbic dopaminergic

system and as such has an important function in the reward system. Affective

Neuroscience has conceptualized the function of the MFB as being

confluent with the SEEKING system: a system of appetitive motivation and

reward (capitalization is an affective neuroscience nomenclature convention

reflecting on the ability of primary process affective systems to directly

Hab

ALIC

NAcc

sIMFB

Accumbens

Caudate

Thalamus

Figure 10.7 Right medial forebrain bundle (green). This figure exemplifies how typicaltarget sites for depression (red spheres) are related to this large fiber structure. Legend:slMFB, superolateral branch of the medial forebrain bundle; Hab, habenula; NAcc, nu-cleus accumbens; ALIC, anterior limb of the internal capsule.


respond to electrical stimulation).Withhelpof theEFS technique, a new target

site was identified that would reach the maximum of fibers of the MFB at

its “bottle neck” just lateral to the ventral tegmental area (cf.

Figs. 10.7–10.9). This target region is currently under investigation under a

local ethics committee protocol for the treatment of refractory depression

with the DBS technique (www.clinicaltrials.gov, NCT01095263,

“FORESEE-trial”).

9. DTI FT AND THE CONCEPT OF NETWORK BALANCES

With increasing knowledge about the mechanisms of high-frequency

stimulation, it becomes apparent that one main factor for the effectiveness of

DBS is the modulation of the white matter environment surrounding the

targeted nuclei. This very fiber environment can now be individually

visualized with DTI studies and EFS, leading to a better comprehension

http://www.clinicaltrials.gov

RN RN

x

y

VTA

LHLH

VTASNrSNr

STN

STN

mtt

VIII

fx

imMFB

SCG SCG

antidysphoric/antidepressive

antidysphoric& anti-OCD

antidysphoric/& anti-OCD

sIMFBantidysphoric/antidepressive

anti-OCD

PFC

DB

S

imMFBsIMFBsIMFB

NAcc

ALIC

NA

ALI

C

PTPT

PAG

A.

B.

C.

D.

E.

Figure 10.8 Medial forebrain bundle (green), schematic depiction. Note how DBS sitesfor depression group around this large fiber bundle (colored spheres). Legend: slMFB,superolateral branch of the medial forebrain bundle; imMFB, inferomedial branch of themedial forebrain bundle; PFC, prefrontal cortex; SCG, subgenual cingulate gyrus; fx,fornic; viii, third ventricle; mtt, mamillothalamic tract; LH, lateral hypothalamus; VTA,ventral tegmental area; SNr, substantia nigra; STN, subthalamic nucleus; PT, pyramidaltract; RN, red nucleus; NAcc, nucleus accumbens; ALIC, anterior limb of the internalcapsule.


of the complex physiological interactions of motor control and affective and

cognitive regulation. In this respect, an appealing interpretation for psycho-

tropic effects is the inadvertent coactivation of a by-passing affective

circuitry due to an electric field that spreads beyond the electrode contact

into this fiber environment (Coenen et al., 2009, Fig. 10.1). Obviously,

DBS-induced emotional dysregulation is not reactive but merely has a neu-

roanatomical and neurophysiological substrate. This effect points toward the

true mechanism of action in DBS for OCD and depression: a (gradual) ac-

tivation of the reward circuitry. Conceptualizations of structures identified

with the DTI technique like the MFB as being confluent with the SEEK-

ING system and the anterior thalamic radiation confluent with the PANIC

system in conjunction with the DBS technique help us to understand func-

tion and connectivity (Coenen et al., 2012; Schoene-Bake et al., 2010). We

have elaborated on the idea of dynamic balances that are used to reach an

emotional equilibrium in the prefrontal cortex under physiological and

1 sIMFB

PFC

23

41 11

10

8

612

57 13

9

2 imMFB3 Fornix4 Mamillothalamic tract5 STN6 SNr7 RN8 Pyramidal tract9 Hippocampus

10 GPi11 GPe12 Occulomotor nerve fibers13 Lateral geniculate body

sIMFB DBS site

Figure 10.9 slMFB DBS target site. Artist's depiction at the midbrain level. Legend:slMFB, superolateral branch of the medial forebrain bundle (MFB); imMFB, inferomedialMFB; STN, subthalamic nucleus; SNr, substantia nigra; RN, red nucleus; GPi, globuspallidus internus; GPe, globus pallidus externus.


pathological circumstances (Coenen et al., 2010, 2012). A further

interpretation is depicted in Fig. 10.10: A deficient but still connected

depression system (PANIC) can be counter-regulated by activation of the

SEEKING system. Under physiological conditions, this would be done

by the PFC itself. Under pathological conditions, DBS of the slMFB can

be used to activate the SEEKING system.

Probing the human affective circuitry will help neuroscientists to gain

insights into the complex regulatory systems and the neuroanatomy of hu-

man affect and cognition if they rigorously analyze the sites that they truly

modulate. We have established and reported the techniques that fulfill these

purposes.

10. SUMMARY

DTI has tremendously influenced our understanding of themechanisms

of DBS. It helps us to understand which parts of the brain tissue are connected

to others and which ones are truly influenced with neuromodulation. The

PFCemotionalequilibrium

PFCemotionalequilibrium

emotionalDISequilibrium

SEEKINGPANIC

Normal state

Depression

Antidepressive effect of DBS

(motivation)(loss)

SEEKING

DB

S

PANIC(motivation)

sIMFBNAccALIC

(SCG)

(loss)

SEEKINGPANIC(motivation)(loss)

depression sadness euphoria maniaeuthymiadepression sadness euphoria maniaeuthymia

depression sadness euphoria maniaeuthymia

PFC

A B

C

Figure 10.10 Dynamic network balances in affect regulation. Conceptualizationaccording to principles of affective neuroscience. In this concept, deep brain stimulationof the SEEKING system counterbalances a supposedly highly (pathologically) active PANICsystem in depression leading to euthymia as detected by the prefrontal cortex (PFC). Leg-end: slMFB, superolateral branch of the medial forebrain bundle; NAcc, nucleusaccumbens; ALIC, anterior limb of the internal capsule; scg, subgenual cingulate gyrus.


complex interaction of DBS with the surrounding tissues—scrutinized with

DTI—allows to create testable hypotheses that can explain network interac-

tions. Those interactions are vital for our understanding of the net effects of

neuromodulation. We have shown that how the experience gained in tremor

surgery could be extrapolated to the evaluation of established target sites for

DBS in depression. This understanding together with electric field simulation

techniques might help to develop operation strategies that will allow neuro-

surgeon to place electrodes under general anesthesia without the help of the

awake patient. First results have been shown by our and other groups and are

promising. Future research in DTI FT together with DBS will focus on affect

regulating network balances and their therapeutic alterations with the

neuromodulation technology.


ACKNOWLEDGMENTSDoctors Coenen and Schlapfer were sponsored with a grant from HDRS and ISAN.

Dr.Coenenoccasionally received honoraries and travel support fromMedronic (USA,Europe).

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Diffusion Tensor Imaging and Neuromodulation: DTI as Key Technology for Deep Brain Stimulation

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