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MAPPING THE CORTICOSPINAL MOTOR TRACTS USING DIFFUSION MRI AND PROBABILISTIC TRACTOGRAPHY

By

DEREK BRADLEY ARCHER

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2016

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© 2016 Derek Bradley Archer

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To my Dad

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ACKNOWLEDGMENTS

I would first like to thank Justin for his never ending love and support during my

graduate education. His constant encouragement has been vital in my success as a

graduate student. Thanks Bud!

I would also like to thank my mom and my aunt for their endless support during

my academic career. I thank my mentor Dr. Stephen Coombes for his dedication to me

as a PhD student. He put much effort into my education and I am forever indebted to

the experiences he has given me. I also thank my committee members, Dr. David

Vaillancourt, Dr. Evangelos Christou, and Dr. Carolynn Patten for their contribution to

the set-up and design of my dissertation project. Finally, I would like to give a special

thanks to Bentley and Spencer, who have made my time as a student very enjoyable.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

LIST OF FIGURES .......................................................................................................... 8

ABSTRACT ..................................................................................................................... 9

CHAPTER

1 MICROSTRUCTURAL PROPERTIES OF PREMOTOR PATHWAYS PREDICT VISUOMOTOR PERFORMANCE IN CHRONIC STROKE ..................................... 11

1.1 Chapter Summary ........................................................................................ 11 1.2 Introduction .................................................................................................. 12

1.3 Methods ....................................................................................................... 15 1.3.1 Subjects ................................................................................................. 15 1.3.2 Clinical Evaluations ................................................................................ 15

1.3.3 Force Data Acquisition ........................................................................... 16 1.3.4 Force Task ............................................................................................. 16

1.3.5 Visual Gain ............................................................................................ 18 1.3.6 Force Data Analysis ............................................................................... 19 1.3.7 MRI Acquisition ...................................................................................... 19

1.3.8 MRI Preprocessing ................................................................................ 20

1.3.9 Lesion Characterization ......................................................................... 20

1.3.10 Probabilistic Tractography ..................................................................... 21 1.3.11 Multiple Regression ............................................................................... 23

1.4 Results ......................................................................................................... 24 1.4.1 MVC ....................................................................................................... 24 1.4.2 Force Data Analysis ............................................................................... 24

1.4.3 MRI Analysis .......................................................................................... 26 1.5 Discussion .................................................................................................... 31

1.5.1 Increases in Visual Gain Lead to Decreases in Force Variability ........... 31 1.5.2 Tract Specific Microstructure Predicts Force Variability in Individuals

Post-Stroke ............................................................................................ 33

1.5.3 Role of Premotor Cortex in Visuomotor Processing............................... 34 1.5.4 Role of Premotor Cortex in Chronic Phase After Stroke ........................ 35

1.5.5 Conclusions ........................................................................................... 38

2 A PROBABILISTIC ATLAS OF THE CORTICOSPINAL MOTOR TRACTS ........... 47

2.1 Chapter Summary ........................................................................................ 47 2.2 Introduction .................................................................................................. 47 2.3 Methods ....................................................................................................... 51

2.3.1 Human Connectome Subjects ............................................................... 51

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2.3.2 University of Florida Subjects ................................................................ 52

2.3.3 Probabilistic Tractography ..................................................................... 52 2.3.4 Slice Level Thresholding ........................................................................ 53

2.3.5 Maximizing Tract Specificity: An Example ............................................ 55 2.3.6 Assembly of Corticospinal Tract Template ............................................ 57 2.3.7 Creating a Probabilistic Atlas ................................................................. 57 2.3.8 Application of Template to Quantify Microstructure ............................... 58

2.4 Results ......................................................................................................... 59

2.4.1 Corticospinal Tract Template Assembly ................................................ 60 2.4.2 Probabilistic Corticospinal Tract Atlas .................................................... 61 2.4.3 Quantifying Microstructure with the Corticospinal Tract Template ......... 61

2.5 Discussion .................................................................................................... 63

3 CONCLUSIONS ..................................................................................................... 80

LIST OF REFERENCES ............................................................................................... 81

BIOGRAPHICAL SKETCH ............................................................................................ 90

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LIST OF TABLES

Table page 1-1 Group Demographics and Relevant Clinical Information. ................................... 39

1-2 Mean Force Amplitude and Force Variability.. .................................................... 43

1-3 Multivariate Regression Coefficients and Statistics. ........................................... 45

2-1 Subject Characteristics. ...................................................................................... 73

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LIST OF FIGURES

Figure page 1-1 Experimental Setup. ........................................................................................... 40

1-2 Lesion Conjunction Map. .................................................................................... 41

1-3 Force Amplitude and Force Variability ................................................................ 42

1-4 Probabilistic Tractography in Motor and Visual Tracts ........................................ 44

1-5 Contribution to Force Variability.......................................................................... 46

2-1 Probabilistic Tractography Inputs. ...................................................................... 70

2-2 Slice Level Thresholding Rationale .................................................................... 71

2-3 Example of Slice Level Thresholding. ................................................................. 72

2-4 Slice Level Thresholding of CST Data ................................................................ 74

2-5 The Corticospinal Template ................................................................................ 75

2-6 Probabilistic Corticospinal Tract Atlas ................................................................ 76

2-7 Handedness Comparisons ................................................................................. 77

2-8 Gender Comparisons ......................................................................................... 78

2-9 Human Connectome Project Data Comparison with University of Florida Data. ................................................................................................................... 79

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

MAPPING THE CORTICOSPINAL MOTOR TRACTS USING DIFFUSION MRI AND PROBABILISTIC TRACTOGRAPHY

By

Derek Bradley Archer

August 2016

Chair: Stephen A. Coombes Major: Health and Human Performance

The corticospinal tract is a descending motor tract specific to mammalian

species, and is crucial for the performance of dexterous motor execution. Precise

measurement of CST structure is fundamental to our understanding of diseases and

disorders that can impact the CST such as stroke, upper motor neuron syndrome,

multiple sclerosis, traumatic brain injury, and spinal cord injury. Current approaches to

studying the CST in humans are focused on the CST descending from the primary

motor cortex. The goal in this dissertation was to examine the relation between brain

microstructure in multiple corticospinal tracts and visuomotor processing after stroke,

and to develop a high resolution corticospinal tract template which segmented the CST

based on six cortical regions including the primary motor cortex, dorsal premotor cortex,

ventral premotor cortex, supplemental motor area, pre-supplemental motor area, and

somatosensory cortex. Sophisticated neuroimaging methods and novel algorithms were

used to refine probabilistic tractography analyses to address these two goals. First, our

findings in Chapter 1 show that the microstructural properties of the premotor

corticospinal tract predict visual gain-related changes in force variability in individuals

post-stroke. Second, our findings in Chapter 2 offer a new corticospinal tract template

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and probabilistic atlas that segment and label the corticospinal tracts at a spatial

resolution not previously available. The current template provides a new tool that can be

used to localize tract specific damage, and to quantify microstructure in specific

corticospinal tracts that are associated with distinct cortical regions. Increases in spatial

localization may improve diagnostic and prognostic evaluations across a range of

diseases and disorders.

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CHAPTER 1

MICROSTRUCTURAL PROPERTIES OF PREMOTOR PATHWAYS PREDICT VISUOMOTOR PERFORMANCE IN CHRONIC STROKE1

1.1 Chapter Summary

Microstructural properties of the corticospinal tract descending from the motor

cortex predict strength and motor skill in the chronic phase after stroke. Much less is

known about the relation between brain microstructure and visuomotor processing after

stroke. In the current study, individual’s post-stroke and age-matched controls

performed a unimanual force task separately with each hand at three levels of visual

gain. We collected diffusion MRI data and used probabilistic tractography algorithms to

identify the primary and premotor corticospinal tracts. Fractional anisotropy within each

tract was used to predict changes in force variability across different levels of visual

gain. Our observations revealed that individuals post-stroke reduced force variability

with an increase in visual gain, performed the force task with greater variability as

compared with controls across all gain levels, and had lower fractional anisotropy in the

primary motor and premotor corticospinal tracts. Our results also demonstrated that the

corticospinal tract descending from the premotor cortex, rather than the primary motor

cortex, best predicted force variability. Together, these findings demonstrate that the

microstructural properties of the premotor corticospinal tract predict visual gain-related

changes in force variability in individuals post-stroke.

1 Published in Human Brain Mapping. Archer, D.B., Misra, G., Patten, C., Coombes, S.A. (2016)

Microstructural properties of premotor pathways predict visuomotor performance in chronic stroke. Hum Brain Mapp. Reprinted with permission from John Wiley and Sons.

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1.2 Introduction

Neuroimaging evidence points to the integrity of the corticospinal tract (CST) in

the prediction of many of the motor deficits experienced by post-stroke individuals

(Perez and Cohen, 2009; Schulz et al., 2012; Stinear et al., 2007). Fractional

anisotropy (FA) is reduced post-stroke in CST regions such as the cerebral peduncle

and the posterior limb of the internal capsule (PLIC) (Park et al., 2013; Schaechter et

al., 2008), and FA in these regions correlates positively with measures of motor skill,

grip strength, and clinical tests of motor function post-stroke (Lindenberg et al., 2010;

Lindenberg et al., 2012; Schaechter et al., 2008). These studies have been crucial in

advancing our understanding of CST microstructure and motor function post-stroke.

While many previous studies have focused on the CST originating in the primary motor

cortex (Bagce et al., 2012), the CST also projects from the premotor cortex, and the

premotor cortex plays an important role in motor function following stroke (Johansen-

Berg et al., 2002; Plow et al., 2015). The goal in the current study is to determine

whether the microstructural properties of the primary motor CST and the premotor CST

predict visuomotor control in individuals post-stroke.

The primary motor cortex (M1) is often directly altered following stroke, given its

intricate connections with the branches of the middle cerebral artery, which is the most

commonly damaged artery in stroke (Cramer et al., 2000). The premotor areas,

however, are less likely to be directly affected following stroke because they are

supplied by the anterior cerebral artery (Dum and Strick, 1991; Plow et al., 2015).

Premotor areas were thought to be part of the reticulospinal pathways outputting to the

axial and proximal muscles, but retrograde labeling in non-human primates has

demonstrated that 40% of the corticospinal tract descending to the hand originates from

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the premotor areas (Dum and Strick, 1991). Therefore, the CSTs descending from

premotor regions could serve as alternatives to the CST descending from M1. The

dorsal premotor cortex (PMd) receives visual and somatosensory information from the

medial intraparietal area, and is involved in the planning and execution of movement

(Kantak et al., 2012). The ventral premotor cortex (PMv) is engaged during grasping

tasks, and receives sensory information from the anterior intraparietal area (Kantak et

al., 2012). These properties of PMd and PMv point to their importance in rehabilitation,

as one common characteristic shared by many therapeutic strategies for stroke

rehabilitation is performing movement guided by vision (Bagce et al., 2012; Krebs et al.,

1998; Patten et al., 2013; Volpe et al., 2000). Indeed, the premotor areas are key

components of the visuomotor network, play a critical role in feedback control

(Coombes et al., 2010; Vaillancourt et al., 2006b), and their role in rehabilitation after

stroke continues to gain traction (Kantak et al., 2012; Plow et al., 2015). In non-human

primates, neuronal activity in premotor cortex increases during the planning phase of

arm movements (Hoshi and Tanji, 2000), and perturbing premotor cortex in humans via

transcranial magnetic stimulation (TMS) disrupts upper-extremity movement (Mochizuki

et al., 2005; Schluter et al., 1998). Other evidence for involvement of premotor areas in

visuomotor control comes from a visual gain experiment in healthy adults, which

demonstrated that increases in visual gain led to decreases in force variability and

increases in functional activity in PMd and PMv (Coombes et al., 2010). Hence,

premotor areas are more likely to be intact following stroke, play a major role in the

planning and execution of movements that are guided by visual feedback, and exhibit

increased activity when visuomotor processing demands are increased. Together,

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these findings lead to the hypothesis that FA of the descending motor pathways

projecting from both PMd and PMv may predict visuomotor processing in post-stroke

individuals, but there is currently no direct evidence that this is the case.

Visuomotor processing can be manipulated by changing visual gain during a

force control task (Coombes et al., 2010; Lee Hong and Newell, 2008; Vaillancourt et

al., 2006a). Visual gain is calculated by determining the performance error, in relation to

a target, in real-time, and then magnifying or minimizing this error before it is presented

to the subject as feedback (Newell and McDonald, 1994). Increases in visual gain

reduce variability in force production in healthy adults (Coombes et al., 2010; Lee Hong

and Newell, 2008; Vaillancourt et al., 2006a), and lead to small, but significant

improvements in motor control and upper-extremity function in individuals in the chronic

phase after stroke (Abdollahi et al., 2013; Patton et al., 2006). While a recent study

shows that baseline measures of CST microstructure predict changes in clinical scores

following a two week intervention (Lindenberg et al., 2012), it remains unclear if CST

microstructure projecting from M1 alone can predict visual gain induced changes in

force performance after stroke, or whether projections from PMd and PMv also

contribute.

In the current study, individuals post-stroke and controls performed a unimanual

force task separately with each hand at three levels of visual gain. We collected

diffusion MRI from each subject and used probabilistic tractography algorithms to

identify the primary and premotor CSTs. Measures of FA within each tract were then

used to predict changes in force variability across different levels of visual gain. We

tested the hypothesis that parametrically increasing visual gain would improve motor

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performance (i.e., reduce force variability) in both groups, and that the stroke group

would display greater variability at each gain level. Second, in the stroke group, we

tested the hypothesis that FA in M1, PMd, and PMv CSTs would predict force

performance at each gain level.

1.3 Methods

1.3.1 Subjects

Fourteen individuals post-stroke and fourteen healthy control subjects were

enrolled. Group demographics and clinical information are shown in Table 1-1.

Inclusion criteria for post-stroke individuals were as follows: (1) at least six months

following a single ischemic stroke affecting motor function in the contralateral hand, (2)

able to apply force to a force transducer in the pinch grip configuration, (3) have intact

sensation to light touch in the contralateral hand, and (4) able to provide informed

consent. Each subject provided informed consent before testing, which was approved

by the local Institutional Review Board and was in accord with the Declaration of

Helsinki.

1.3.2 Clinical Evaluations

Motor impairments of the upper extremities of stroke subjects were assessed

with the upper-extremity section of the Fugl-Meyer Assessment (UE FMA) (Fugl-Meyer

et al., 1975; Gladstone et al., 2002). Hemiparetic severity (mean UE FMA 36.8/66

points) and stroke chronicity (mean 8.4 years) captured a wide range of representative

individuals with upper-extremity motor impairments following stroke. Age was not

significantly different between groups [t(26)=1.05; p=0.30] and control subjects (range

31-74 years) were well matched to the individuals post-stroke (range 31-79) for both

age and sex. Healthy control subjects were not evaluated with the UE FMA due to its

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well-recognized ceiling effect, even among individuals post-stroke (i.e., maximum score

of 66 points does not represent normal or unimpaired motor function). Hypertonicity

was measured using the Modified Ashworth Scale (Bohannon and Smith, 1987; Haas et

al., 1996). Cognitive status was screened using the Mini-Mental State Examination

(Folstein et al., 1975). Stroke subjects self-reported pre-morbid hand dominance. Hand

dominance in controls was determined using the Edinburgh Handedness Inventory

(Oldfield, 1971).

1.3.3 Force Data Acquisition

Subjects laid in the supine position, with their forearms resting on their lower

trunk and one force transducer held in each hand at all times. Subjects produced force

against a custom fiber-optic force transducer with a resolution of 0.025 N (Neuroimaging

Solutions LLC, Gainesville, FL). The force produced by the subject was transmitted via

fiber-optic cable to a SM130 Optical Sensing Interrogator (Micron Optics, Atlanta,

Georgia). The interrogator digitized the analog force data at 125 Hz. Customized

software written in LabVIEW (National Instruments, Austin, TX) collected the force data

and then converted the data to Newtons (N). The output from the force transducer was

presented to the subject using a visual display at a refresh rate of 60 Hz. Force data

were low-pass filtered before analysis (Butterworth, 20 Hz 4th-order dual-pass).

1.3.4 Force Task

Each subject’s maximum voluntary contraction (MVC) was measured from each

hand during a practice session. Subjects were asked to maintain a contraction of

maximum force for three consecutive 5 second trials. Each trial was separated by a 60

second period of rest. The MVC was calculated as the average force during the

sustained maximum force contraction. During the task, subjects produced unimanual

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force to a target force of 15% of their MVC by gripping the force transducer between

their thumb and index finger (Figure 1-1A). Blocks of trials were performed unimanually

with the impaired and less-impaired hand for stroke subjects, and the dominant and

non-dominant hand for controls. Herein, the impaired hand of the stroke group and the

non-dominant hand of the control group will be referred to as the impaired hand. The

less-impaired hand of the stroke and the dominant hand of the control group will be

referred to as the unimpaired hand. Three unimanual tasks were completed by each

hand: low visual gain, medium visual gain, and high visual gain. Subjects completed a

total of six tasks (two hands, three gain levels) while lying in the supine position in the

MRI scanner (Figure 1-1B).

There were two conditions within each task: Rest and Force. The visual display

(Figure 1-1C) consisted of two bars, one white and one red/green as shown in Figure 1-

1D. The white target bar was set at 15% of each subject’s maximum voluntary

contraction (MVC). The red/green bar was used to cue the subject to rest or to produce

force. During the Rest condition subjects were instructed to fixate on the red bar.

When the bar turned green, the subjects began producing isometric force and the bar

fluctuated in real-time to reflect the amount of force being produced. Subjects were

instructed to be as accurate as possible and cover the white target bar with the green

force bar. Rest and Force conditions each lasted 30 seconds and were alternated within

tasks. Each task began and ended with a Rest period. Blocks within each task followed

the same sequence, were displayed on the visual display (Figure 1-1D), and lasted 270

seconds total. The independent variable that was manipulated between scans was

visual gain.

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1.3.5 Visual Gain

Visual feedback was altered between tasks by changing the visual gain of the

real-time feedback. The difference between the amount of force produced by the

subject and the target force was calculated. This difference was then multiplied by a

visual gain factor (low, medium, high), which manipulated the spatial amplitude of visual

feedback by altering the height of force fluctuations on the visual display using the

following formula:

𝐶𝑢𝑟𝑠𝑜𝑟 𝑃𝑜𝑠𝑖𝑡𝑖𝑜𝑛 = (𝐹𝑝 − 𝐹𝑡) ∗ 𝐺 + 𝐹𝑡 (1-1)

in which Fp is the force produced by the subject, Ft is the target force, and G is the gain

level used to manipulate the spatial amplitude of visual feedback.

Based on prior work (Vaillancourt, et al., 2006a), we calculated the visual angle

for each visual gain level by assuming a set force output standard deviation of 0.3 N.

This estimate was derived from previous studies (Laidlaw, et al., 2000; Slifkin and

Newell, 1999). The value of the standard deviation was multiplied by 6 to approximate

the full range (±3 standard deviations) of estimated variance for the height of the force

fluctuations. The visual angle for each gain level was then calculated using the

following formula:

∝= 2 ∗ tan−1 (𝐻1

𝐷) (1-2)

in which α is the visual angle, D is the distance to the display, and H1 is the height of

the total range of motion in the top half of the visual field. Because previous evidence

has shown that performance error approaches an asymptote at approximately 0.5

degrees, we ensured that low gain was well below 0.5 degrees and that high gain was

well above 0.5 degrees (Coombes, et al., 2010). The low, medium, and high visual gain

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levels corresponded to visual angles of 0.039º, 0.39º, and 2.39º, respectively. The task

was performed unimanually by each hand at the low, medium, and high gain level.

Example force output is shown in Figure 1-1E, which shows that low gain resulted in

greater fluctuations in force output around the target force level. Increases in visual gain

at the medium and high gain levels show a reduction in the fluctuation of force output

around the target force level. To control for potential order effects, both hand order and

visual gain order for each hand were counterbalanced across subjects.

1.3.6 Force Data Analysis

Force data were analyzed using custom algorithms in LabVIEW. Two measures

were calculated: mean force and force variability (standard deviation). Force measures

were calculated using an 18 second portion of each contraction starting 7 seconds after

contraction onset and ending 5 seconds before the end of the contraction. We excluded

beginning and end effects of force production as they are likely independent of

visuomotor processing (Coombes, et al., 2010; Coombes, et al., 2011; Lodha, et al.,

2012a; Lodha, et al., 2012b; Naik, et al., 2011). Mean force and force variability were

calculated separately for each hand at each gain level. In addition, an asymmetry value

was calculated for each force measure [(unimpaired measure – impaired

measure)/(unimpaired measure + impaired measure)]. Lower asymmetry scores reflect

greater mean force and greater force variability in the impaired hand.

1.3.7 MRI Acquisition

Magnetic resonance images were collected using a 32 channel head coil inside a

3 Tesla magnetic resonance scanner (Achieva, Best, the Netherlands). T1-weighted

images (resolution: 1 mm isotropic, TR=6.8 ms, TE=3.3 ms, flip angle=8°) and diffusion

MRI images (resolution: 2 mm isotropic, 64 non-collinear diffusion directions, b-value of

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1,000 s/mm2 and one with a b-value of 0 s/mm2, 75 axial slices covered the cortex and

brainstem) were collected from each subject.

1.3.8 MRI Preprocessing

Prior to analyses, the diffusion MRI volumes and T1 images of 5 subjects with

lesions in the right hemisphere were flipped along the mid-sagittal plane, so that the

impaired hemisphere had the same coordinates for all subjects (Lindenberg, et al.,

2012; Wang, et al., 2012). The lesioned hemisphere in the stroke group and the

hemisphere contralateral to the non-dominant hand in the control group will be referred

to as the impaired hemisphere. The non-lesioned hemisphere in the stroke group and

the hemisphere contralateral to the dominant hand in the control group will be referred

to as the unimpaired hemisphere. FSL (fsl.fmrib.ox.ac.uk) was used for all diffusion MRI

(dMRI) data analyses (Jenkinson, et al., 2012a; Smith, et al., 2004; Woolrich, et al.,

2009). The dMRI data were corrected for eddy currents and head motion using a 3-D

affine registration, and the brain was extracted (Smith, 2002). FA values were obtained

from the dMRI data. The FA map was normalized into standard space using a linear

transformation (FLIRT) (Jenkinson, et al., 2002; Jenkinson and Smith, 2001) followed by

a nonlinear transformation (FNIRT) (Jenkinson, et al., 2012a; Smith, et al., 2004;

Woolrich, et al., 2009). Lesions were masked out during both transformations to

prevent lesion related distortions and inaccuracies of transformations. Goodness of fit

to the standard template was confirmed visually by evaluating the position of the corpus

callosum for each subject.

1.3.9 Lesion Characterization

Structural T1-weighted images were transformed using the same dMRI

normalization technique outlined above. These images were used to assess lesion

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characteristics in the stroke group. Lesions were manually drawn on the T1 image by

two raters for each stroke subject. Manual lesion drawing is comparable in accuracy to

semi-automatic lesion identification (Clas, et al., 2012; de Haan, et al., 2015; Wilke, et

al., 2011). Lesions were then transformed into standard space using the diffusion

nonlinear warp field. Inter-rater reliability of manual lesion drawing was examined using

intraclass correlation coefficients (ICC) between the two drawers. The ICC was high for

all variables, including FA (ICC=0.81), center of mass (ICC=0.99, 0.88, and 0.97 for x,

y, and z, respectively), and volume (ICC=0.83). As FA was our variable of interest, we

performed t-tests between FA values in each lesion drawn by the two drawers, which

did not identify any significant differences [t(26)=1.3, p=0.20]. To prevent the effect of

lesions within the FA maps from confounding analyses, voxels within the lesion were

excluded from all FA analyses. Figure 1-2 shows the lesion conjunction map for all

stroke subjects throughout the whole brain. The maximum overlap within the entire

brain did not exceed 7/14, and the maximum overlap occurred within the external

capsule at x=-31, y=-3, z=-1.

1.3.10 Probabilistic Tractography

Probabilistic tractography was conducted to identify primary and premotor CST

projections. First, a probability distribution was estimated for each voxel modelling all

possible fiber directions by using the eddy current corrected images. To identify both

the primary and premotor CSTs, the cerebral peduncle was used as a seed (obtained

from Johns Hopkins University white-matter labels atlas). Next, waypoints were

selected to partition each tract appropriately: a spherical mask of 8mm was placed in

M1 at x=24, y=-28, and z=53 to identify the primary CST projections, and spherical

masks of 8mm diameter were placed in PMd at x=33, y=-7,and z=51, and PMv at

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x=53,y=2, and z=36 to identify the secondary CST projections (Coombes, et al., 2010).

To obtain specific tracts, one mask was used as a waypoint while the other masks were

used as exclusion masks. For instance, when identifying the CST projections between

the cerebral peduncle and PMv, spheres in M1 and PMd were used as exclusion

masks. An exclusion mask was also placed at the midline to eliminate transcallosal

fibers.

In addition to the motor tracts, we used probabilistic tractography to track the

ventral and dorsal visual streams, as previous studies have also associated these tracts

with visuomotor processing (Caeyenberghs, et al., 2010; Jager, 2005; Wolter and

Preda, 2006; Yoshida, et al., 2010). To identify the ventral stream, the primary visual

cortex (V1) (x=20.9,y=-57.9,z=-6.1) was used as a seed (Haar, et al., 2015). Waypoints

for the ventral stream were a planar waypoint which was placed inferior to V1 (z=-11),

and an additional axial planar waypoint that was placed at the level of the fusiform gyrus

(z=-19). A planar exclusion mask was placed superior to V1 to exclude dorsal stream

fibers (z=-2). The dorsal stream was identified by placing a seed in the superior

longitudinal fasciculus, and a 16 mm sphere in the extrastriate visual area (V3) (x=-

43.8,y=-68,z=-5.7) was used as a waypoint (Coombes, et al., 2010). A midline

exclusion mask (x=0) was used to exclude any transcallosal connections for both tracts,

and the thalamus was used as an exclusion mask to exclude any connections to the

primary visual pathway.

Tracking was performed with default parameters (number of samples = 5,000,

curvature threshold = 0.2, minimum FA = 0.2). Tracking results were then thresholded

so that only the top 5% of probable voxels was included. For each tract, a group

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template was calculated in standard space by overlaying tracts from all control subjects

to build a conjunction map. Areas of each tract with high overlap between subjects

were binarized to create a group level template for each tract. Region specific

differences of FA in each group level tract template were calculated using a slice-by-

slice approach which allowed us to determine mean FA in each slice of the tract along

its primary axis of travel for each individual. A custom linux shell-script computed the

average FA of the tract for each individual at each slice. We then compared the

average FA within each slice between groups by conducting FDR corrected

independent samples t-test.

An average FA profile was then calculated for each group for each hemisphere.

FA asymmetry profiles were created by calculating the asymmetry at each slice for each

tract [(unimpaired tract FA – impaired tract FA)/(unimpaired tract FA + impaired tract

FA)]. This asymmetry approach controls for within subject variability in FA, and is

consistent with studies that have associated FA asymmetry with behavioral measures

(Lindenberg, et al., 2010; Park, et al., 2013; Stinear, et al., 2007; Wang, et al., 2012).

Higher asymmetry scores indicate lower FA in the impaired hemisphere compared to

the unimpaired hemisphere. Asymmetry scores were compared between groups at

each slice using FDR corrected independent samples t-test.

1.3.11 Multiple Regression

For each tract, a region of interest (ROI) was created by evaluating the FA

asymmetry profile. Each ROI included 3 slices. The slice which contained the highest

asymmetry value was used as the center of the ROI. For the motor tracts, a slice

superior to the center and a slice inferior to the center were also used in the ROI. For

the visual tracts, a slice posterior to the center and anterior to the center were included

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in the ROI. FA within each ROI (M1, PMd, PMv, ventral, dorsal), subject age, lesion

age, lesion size, and UE FMA were used as independent variables in a multivariate

multiple regression analysis to predict the variables (force amplitude and/or force

variability) with significant differences between groups, at each gain level. A

bidirectional stepwise regression analysis was used to determine which independent

variables best described variance in the dependent variables. The model with the

highest Radj2 was selected as the best fit model, and the contribution of each

independent variable to this best fit model was calculated (Gromping, 2006). Significant

multivariate multiple regression analyses were followed up with multiple regression

analyses to determine the contribution of each independent variable at each level of

visual gain. Statistical analyses were performed using the R statistical analysis

package (Version 3.0.2, www.r-project.org).

1.4 Results

1.4.1 MVC

The average MVC of the impaired hand for the stroke group (36.44 ± 5.66 N)

was significantly less [t(26)=4.22; p<0.001] than the average MVC of the impaired hand

for the control group (73.11 ± 6.60 N). The average MVC of the unimpaired hand for

the stroke group (70.19 ± 5.40 N) was not significantly different than the average MVC

of the unimpaired hand for the control group (79.33 ± 6.95 N).

1.4.2 Force Data Analysis

Force amplitude. Mean force produced by the unimpaired hand is shown in

Figure 1-3A and the impaired hand in Figure 1-3B. Mean force across all task

conditions and groups ranged from 12.83-14.86% MVC. Consistent with the data shown

in Figure 1-3A, the group x gain ANOVA model to assess mean force produced by the

25

unimpaired hand revealed no significant effect of group [F(1,26)=0.03; p=0.88], gain

[F(1.01,26.22)=0.33; p=0.574], or group x gain interaction [F(1.01, 26.22)=0.02; p=0.90].

As shown in Figure 1-3B, mean force produced by the impaired hand at the low gain

level for the stroke group was 12.83% MVC and 14.38% MVC for the control group,

respectively. At medium and high gain levels, mean force was within 1% of the target

force level for both groups. Similar to findings in the non-impaired hand, the two-way

(group (2) x gain (3)) ANOVA of mean force for the impaired hand revealed no

significant effect of gain [F(1.05,27.25)=1.79; p=0.19], group [F(1,26)=1.75; p=0.20], or

group x gain interaction [F(1.05,27.25)=0.58; p=0.46]. Consistent with these findings,

Figure 1-3C shows asymmetry scores for mean force, and shows that there was no

group effect, gain effect, or group x gain interaction.

Force variability. Figure 1-3D shows mean force variability for the stroke group

and the control group at each gain level for the unimpaired hand. A two-way (group (2)

x gain (3)) ANOVA on the unimpaired hand revealed a main effect of gain

[F(1.28,33.37)=59.94; p<0.01; η2=0.70], showing a reduction in variability with an

increase in visual gain (low>medium>high). There was no significant effect of group

[F(1,26)=0.32; p=0.58], and no group x gain interaction [F(1.28,33.37)=0.13; p=0.78].

Figure 1-3E shows mean force variability for the stroke group and the control

group at each level of visual gain for the impaired hand. Force variability was greater in

the stroke group as compared to the control group at all gain levels. Force variability

was higher at low visual gain and decreased with increases in visual gain for both

groups. The data shown in Figure 1-3E are consistent with findings from the two-way

ANOVA on force variability which revealed significant main effects of group

26

[F(1,26)=18.38; p<0.01; η2=0.41] and gain [F(1.13,29.48)=8.37; p<0.01; η2=0.24], but no

group x gain interaction [F(1.13,29.48)=1.55; p=0.23]. The stroke group showed

increases in variability compared to the control group (see: Table 1-2). In the entire

cohort, force variability was reduced as visual gain was increased (low>medium>high;

all p<0.05, FDR corrected). Figure 1-3F shows that asymmetry scores for force

variability were similar across gain levels within each group, but were lower for the

stroke group compared to the control group (Table 1-2). One sample t-tests revealed

that asymmetry in the stroke group differed from 0 at the low (p=0.01), medium

(p<0.001), and high (p<0.001) gain levels. In contrast, asymmetry in the control group

did not differ from 0 at any gain level. These findings demonstrate that stroke-related

motor impairment, rather than hand dominance, was the primary factor driving task

performance.

1.4.3 MRI Analysis

Probabilistic tractography. Figure 1-4 shows the probabilistic tractography

results for each tract (column 1) and its corresponding slice-by-slice FA profile in the

unimpaired hemisphere (column 2), impaired hemisphere (column 3), and asymmetry

profile (column 4). Figure 1-4A shows the conjunction of the tract between the cerebral

peduncle and M1 (M1-CST, blue tract). The tract began at z=-21 at the level of the

cerebral peduncle and terminated at the precentral gyrus at z=68. To characterize the

FA within the tract, we first determined the average FA of each slice within the tract for

both groups in the unimpaired hemisphere. Data from the stroke group are represented

by black lines (average) and light gray shading (±SEM). Data from the control group

are represented by black lines and dark gray shading. The tracts showed a similar

pattern for each group, with relatively high FA found between the cerebral peduncle and

27

the posterior limb of the internal capsule (z=-21 to z=21). At the level of the centrum

semiovale (z=21), FA begins to decrease, consistent with the increased number of

crossing fibers in this region. Once the tract leaves the centrum semiovale, there is a

relative increase in FA which peaks at z=50, followed by a subsequent decrease where

the tract enters gray matter. Comparing groups within the unimpaired hemisphere,

there were some significantly different slices in which the stroke group had decreased

FA. Significant differences between slices are marked by horizontal black lines above

each plot. In the impaired hemisphere, the control group displayed a tract similar to the

unimpaired hemisphere, whereas the stroke FA profile was lower and more variable

than the control group, especially between z=-1 and z=19, which is within the PLIC.

Column 4 in Figure 1-4A shows the FA asymmetry profile for each slice within the M1-

CST. The control group had asymmetry near 0 across the entire tract. In contrast, the

stroke group had greater tract asymmetry, which was significantly higher than the

control group and greatest at the level of the PLIC. The highest value of asymmetry

within the tract is shown in red in Figure 1-4A, and this illustrates the slices within the

tract that correspond to the ROI used in the multiple regression analysis. For example,

for the M1-CST, the highest asymmetry in the stroke group was located at z=1.

Therefore, the ROI was centered a z=1 and a slice superior and inferior were also used

within the ROI; thus, the ROI encompassed the M1-CST between z=0 and z=2. This

ROI is represented in red in Figure 4A, and an additional illustration of this region is

shown in Figure 1-5B.

The same probabilistic tractography analyses were performed for the other four

tracts of interest. These tracts are shown in Figure 1-4B-E. The tract connecting the

28

cerebral peduncle to PMd (PMd-CST; Figure 1-4B, green) followed a similar pattern to

the M1-CST. The stroke group had decreased FA compared to controls within some of

the unimpaired hemisphere tract and much of the impaired hemisphere tract.

Additionally, the stroke group had greater FA asymmetry across much of the tract, with

the highest asymmetry located at z=6 within the PLIC. This slice, in addition to one

superior and one inferior slice, was used as an ROI for the multiple regression analysis;

thus, the ROI encompassed the PMd-CST between z=5 and z=7. This ROI is shown in

red in Figure 1-4B, and an additional illustration of this ROI is shown in Figure 1-5B.

The tract connecting the cerebral peduncle to PMv (PMv-CST) is shown in Figure 1-4C

in yellow. Within the unimpaired hemisphere, there were some differences between

groups in which the stroke group had decreased FA; however, there were no

differences in FA in the impaired hemisphere, which could be a result of high tract

volume as the tract moves laterally to PMv. Moreover, there were no differences in

asymmetry values between groups within this tract. The highest asymmetry within this

tract for the stroke group was located at z=6 within the PLIC. This slice, in addition to

one superior and one inferior slice, was used as an ROI for the multiple regression

analysis; thus, the ROI encompassed the PMv-CST between z=5 and z=7. This ROI is

shown in red in Figure 1-4C, and an additional illustration of this ROI is shown in Figure

1-5B.

The ventral stream is shown in Figure 1-4D in magenta, which shows the tract

progressing from V1 to the inferior fusiform gyrus. The FA profiles looked similar

between groups in the unimpaired hemisphere, with between group differences found in

a small section of the tract between y=-48 to y=-58, which corresponds with the middle

29

temporal gyrus. Between group differences were more pronounced in the impaired

hemisphere, with the stroke group showing reduced FA across much of the tract.

However, negligible between group differences were found in FA asymmetry scores,

with significant differences only found in the most anterior portions of the tract. The

highest asymmetry score in the stroke group was found at y=-20 within the fusiform

gyrus. Finally, the dorsal stream is shown in Figure 1-4E in orange and extends from

V3 to the inferior frontal gyrus. Extensive between group differences in the dorsal

stream were not evidenced in either the impaired or unimpaired hemisphere and this

was ultimately reflected in non-significant between group asymmetry findings. The

highest asymmetry value within the stroke group was located at y=-16 within the

precentral gyrus.

Multiple regression. The slices demonstrating the largest asymmetry within

each tract (shown in red, Figure 1-4) were used to position an ROI from which FA

asymmetry values were calculated for each individual. As an example, Figure 1-5A

displays the three motor tracts, and 1-5B displays the portions of the tract containing the

highest asymmetry values. FA asymmetry values were used in a multivariate multiple

regression analysis, which was conducted with bidirectional elimination, to determine

which tracts describe the variance in force variability within the visual gain task. For this

analysis, FA asymmetry in M1, PMd, PMv, and ventral and dorsal ROIs were used as

independent variables. Age, lesion age, lesion size, and UE FMA were also entered

into the model as behavioral independent variables. Dependent variables were low,

medium, and high force variability. The final model contained FA asymmetry in M1,

PMd, and PMv ROIs, as well as age and lesion age. Together these variables predicted

30

88.9% of the variance in force variability (p=0.01). The overall contribution of each

region (M1=9.08%, PMd=31.58%, PMv=35.56%, Age=14.92%, Lesion Age=8.86%) is

shown in Figure 5C. Model coefficients and other statistics are shown in Table1-3.

Note that the ventral and dorsal visual tracts did not contribute to the model that best

predicted force variability. This finding highlights the specificity of our findings in primary

and premotor CSTs.

Separate follow-up multiple regression analyses were then conducted for low,

medium, and high force variability using the independent variables identified in the

multivariate multiple regression. Significant models were found for force variability at

each gain level (Low: Radj2=75.18, p=0.01; Medium: Radj

2=64.38, p=0.03; High:

Radj2=60.54, p=0.04). We found that PMd and PMv contributed most to the model that

best predicted force variability, while M1, age, and lesion age contributed much less. At

the low level of visual gain, PMd contributed 43% and PMv contributed 39%. The

remaining independent variables contributed between 2% and 9%. At the medium level

of visual gain, PMd contributed 13% and PMv contributed 32%. The remaining

independent variables contributed between 0% and 24%. At the high level of visual

gain, PMd contributed 37% and PMv contributed 23%. The remaining independent

variables contributed between 2% and 18% across all gain levels.

Secondary control analyses were completed to determine if lesion load within

M1, PMd, or PMv was predictive of force variability at each gain level. Lesion load was

calculated by overlaying each individual’s lesion map with M1, PMd, and PMv regions of

the Human Motor Area Template (Mayka, et al., 2006), and counting the number of

overlapping voxels within each region. Lesion load for M1, PMd, and PMv, as well as

31

age, lesion age, lesion size, and UE FMA were all entered into a multivariate model.

There were no significant models, indicating that lesion load of M1, PMd, and PMv were

not significant predictors of force variability at any gain level.

1.5 Discussion

This study examined the relationship between force variability during a

visuomotor force task at three levels of visual gain and microstructural properties of

motor, premotor, and visual tracts in post-stroke individuals. First, we found that

individuals post-stroke reduced force variability with an increase in visual gain, despite

extensive microstructural damage in the primary motor CST, premotor CSTs, and visual

tracts. Second, microstructure in primary motor and premotor CSTs predicted force

variability across all gain levels, whereas microstructure of visual tracts did not. Third,

CSTs projecting from the premotor cortex, as compared to the primary motor cortex,

contributed the greatest amount to the model that best predicted force variability in post-

stroke individuals. These findings provide novel evidence that the segregation of the

CST into motor and premotor components can be a useful tool in the prediction of

visuomotor processing in post-stroke individuals.

1.5.1 Increases in Visual Gain Lead to Decreases in Force Variability

Between group differences in force variability revealed that individuals post-

stroke had higher force variability as compared to controls. It is important to note that

both groups reduced force variability with an increase in visual gain, and that changes in

variability were not driven by changes in mean force, as mean force did not vary as a

function of group or gain level. Demonstrating that alterations in visual gain can

influence motor task performance is consistent with previous studies in chronic stroke

that have manipulated visual feedback to alter force amplitude and range of motion

32

(Brewer, et al., 2005; Brewer, et al., 2008), joint excursion and trajectory smoothness of

finger movements, and CST excitability (Bagce, et al., 2012). Improvement in the

movement trajectory produced by individuals post-stroke has also been shown following

training paradigms with forces that amplify movement error (Patton, et al., 2006).

Combining visual and haptic distortions to amplify upper-extremity tracking error by a

factor of 1.5 has also been found to reduce motor impairment and improve motor

function following a two week treatment intervention in a cohort of chronic stroke

patients (Abdollahi, et al., 2013). However, since both visual and haptic distortions were

used, it is unclear if visual distortion alone can lead to alterations in force production in

post-stroke individuals. In the current study, we provide evidence that manipulating the

properties of visual feedback alone attenuates variability during a visuomotor force task.

Reducing performance error is a basic principle of motor learning, and learning

progresses more quickly when errors are large (Rumelhart, et al., 1988). In the

presence of errors, the sensorimotor control system engages visuomotor feedback

pathways to process this information until the feedforward controller learns the

appropriate dynamics (Franklin, et al., 2012). Given the continuous characteristic of the

force control task we used here, it is difficult to differentiate feedforward and feedback

control, although previous neuroimaging findings suggest that increases in visual gain

more strongly engage a feedback control network that includes premotor cortex, inferior

parietal cortex, and extrastriate visual cortex as compared to the cerebellar circuits that

are implicated in feedforward control (Bastian, 2006; Coombes, et al., 2010; Therrien

and Bastian, 2015; Vaillancourt, et al., 2006b). Augmented error has been studied in the

context of motor learning using force field adaptation and perturbation paradigms during

33

upper and lower limb tasks (Emken and Reinkensmeyer, 2005; Patton, et al., 2013).

Although the current findings do not inform learning models associated with error

augmentation directly, they demonstrate that changes in visual gain during a continuous

force control task can be used to drive acute changes in force variability, even following

stroke.

1.5.2 Tract Specific Microstructure Predicts Force Variability in Individuals Post-Stroke

The multivariate multiple regression analysis revealed an association between

grip force variability and FA in primary motor and premotor CSTs. This finding is

consistent with evidence that the microstructural properties of the primary motor and

premotor CSTs predict upper-extremity function in individuals post-stroke (Bagce, et al.,

2012; Perez and Cohen, 2009; Schulz, et al., 2012; Stinear, et al., 2007), and with

evidence that FA in the posterior limb of the internal capsule correlates positively with

motor skill and grip strength in individuals post-stroke (Lindenberg, et al., 2010;

Schaechter, et al., 2008). While our data support the notion that the fractional

anisotropy of the primary motor CST is associated with upper-extremity visuomotor

processing, we found that its contribution to the model that best predicted force

variability was 9%, while the contribution of premotor CST microstructure contributed

66%. Hence, the novel finding of this study is that microstructure in the premotor CSTs

most strongly predicted visual gain-induced changes in force variability in individuals

post-stroke. The role of premotor cortex function for visuomotor processing and the

impact of stroke on premotor cortex function are two possible explanations for our

findings.

34

1.5.3 Role of Premotor Cortex in Visuomotor Processing

The association between grip force variability and FA in the premotor CST links

well with evidence that PMd and PMv are involved in visuomotor processing. Both PMd

and PMv are key components of the visuomotor network, and PMd, in particular, has

been consistently identified in neuroimaging studies that assess visuomotor processing

and feedback control (Coombes, et al., 2010; Vaillancourt, et al., 2006b). In non-human

primates, neural activity in PMd increases during spatial cues that instruct direction

specific motor responses (di Pellegrino and Wise, 1993; Weinrich and Wise, 1982). In

humans, studies show that the BOLD signal in PMd scales with force amplitude when

an individual is guided by visual cues (Chouinard, et al., 2005), and TMS-elicited virtual

lesions disrupt PMd function and lead to errors in visuomotor control (Davare, et al.,

2006). PMv is also important for visuomotor processing, is a critical part of the grasping

network, and controls the preshaping of the hand to objects (Kantak, et al., 2012).

Inactivation of PMv in non-human primates results in inaccurate preshaping of the hand

(Fogassi, et al., 2001), and TMS over PMv in humans disrupts finger position on an

object (Davare, et al., 2006). These studies suggest that PMd and PMv play a pivotal

role in the integration of visual information into motor commands and grasping objects.

Using a similar visual gain paradigm to that used in the current study, we have

previously shown that acute changes in visual gain alter functional activity in the

visuomotor network in healthy adults, including M1, PMd, and PMv (Coombes, et al.,

2010). Small increases in visual gain were associated with acute increases in functional

activity within M1, and were accompanied by a significant reduction in force error. In

contrast, large increases in functional activity within PMd were only evident with large

changes in visual gain from moderate to high visual gain levels; PMv, however,

35

exhibited large changes in functional activity with both small and large changes in visual

gain. This finding demonstrated that acute changes in visual gain correspond with

acute changes in brain activity in the cortical motor system. In the current study, we

extend these findings by associating brain structure in key visuomotor pathways with

performance in a visual gain task in the chronic phase after stroke. This is a key

advance in the literature because we show for the first time that visuomotor processing

can be predicted by a relatively stable measurement of brain microstructure in specific

CSTs.

1.5.4 Role of Premotor Cortex in Chronic Phase After Stroke

Lesions that directly impact M1 are common because M1 is intricately connected

to branches of the middle cerebral artery (Cramer, et al., 2000). Following damage to

the primary motor CST, the central nervous system takes advantage of alternate

premotor pathways to control upper-extremity movement (Dum and Strick, 1991; Plow,

et al., 2015). For instance, injecting muscimol into PMd in the lesioned hemisphere in

non-human primates inhibits recovery from weakness following a focal experimental

lesion in M1 (Fridman, et al., 2004; Liu and Rouiller, 1999), and human stimulation

studies have consistently linked premotor cortex activation with the trajectory of stroke

recovery (Fridman, et al., 2004; Johansen-Berg, et al., 2002; Plow, et al., 2015). These

and other findings have led Kantak et al. (2012) to propose the premotor reorganization

hypothesis, which suggests that lesion load of the primary motor CST influences the

extent to which premotor cortex is engaged during voluntary movement. Much of the

evidence supporting a role for premotor cortex comes from functional neuroimaging

studies and TMS studies (Johansen-Berg, et al., 2002; Kang and Cauraugh, 2015;

O'Shea, et al., 2007; Plow, et al., 2015; Ward, et al., 2006), although evidence has

36

identified a significant relationship between microstructural properties of the premotor

CST and grip force strength (Newton, et al., 2006; Schulz, et al., 2012), Here we

compliment these findings to show that FA asymmetry of the premotor CST predicts

force variability during a visuomotor task.

Predicting motor function and motor recovery with measures of brain

microstructure is attractive because diffusion MRI scans are task and severity

independent, meaning that stroke severity is not a limiting factor for data collection.

Methodological advances over the last decade have improved our ability to identify

specific tracts in the brain. For instance, previous studies have used hand drawn ROI

analyses to characterize the association between FA in the PLIC with motor control and

recovery in individuals post-stroke (Park, et al., 2013; Stinear, et al., 2007), but these

hand drawn ROIs likely included portions of the primary motor and premotor CST.

Other studies have used tractography algorithms to calculate mean FA in the entire

CST, but these studies have either restricted the tracking algorithm to the primary motor

CST, (Lindenberg, et al., 2012; Park, et al., 2013; Schaechter, et al., 2009), or when

also tracking from premotor areas, have only examined the relation between entire tract

FA and grip strength (Schulz, et al., 2012). Here, we used a novel approach where we

first used probabilistic tractography algorithms in controls to identify the CST projecting

from M1, PMd, and PMv. We then used this template to analyze FA of each slice within

each tract for both control and stroke subjects. Next, we selected ROIs based on slices

within each tract that exhibited the highest asymmetry in the stroke subjects. Highest

asymmetry in each motor and premotor CST was located within the PLIC, which

corroborates previous evidence that PLIC microstructure predicts upper-extremity

37

function after stroke (Park, et al., 2013; Stinear, et al., 2007). However, our observation

that premotor PLIC microstructure contributed more to the prediction of force variability

in post-stroke individuals as compared to regions of the PLIC associated with the

primary motor CST demonstrates the importance of segregating PLIC regions by

seeding distinct cortical motor areas. Our findings suggest that microstructure of

premotor CSTs may be helpful in predicting intervention outcomes associated with

visuomotor paradigms for stroke motor recovery (Abdollahi, et al., 2013; Patton, et al.,

2006).

Four alternative explanations can be offered for our findings. First, although we

screened for intact sensation to light touch and proprioception in the impaired arm/hand,

we did not perform detailed sensory discrimination testing nor screen for sensory

deficits specific to the task. However, the task was designed to drive feedback control

rather than feedforward control and proprioception. Second, our groups were

predominantly male. Sex differences are an unlikely explanation for our findings,

however, because force amplitude was normalized across all subjects, and stroke and

control groups were well matched for sex. Hand dominance is a third potential

explanation for our findings, but symmetry in force variability scores in controls and

asymmetric force variability scores in the stroke group suggest that motor impairment,

rather than hand dominance, was the primary factor driving differences in task

performance following stroke. Fourth, hemispheric laterality of stroke is an important

variable when examining motor function after stroke (Schaefer, et al., 2012). Although

stroke laterality was not controlled in the current study, regardless of group all subjects

showed reductions in force variability with increases in visual gain. Moreover, laterality

38

data showed that task performance in controls was not sensitive to differences in

hemispheric specialization for motor control.

1.5.5 Conclusions

Individuals post-stroke reduced force variability as visual gain levels increased

despite widespread microstructural deficits in motor and visual tracts. FA within the

descending motor pathways predicted force variability in post-stroke individuals, with

premotor areas (PMd and PMv) of PLIC contributing more than primary motor (M1) area

of PLIC, to the statistical model that best predicted force variability. Although our

findings are limited to the prediction of acute changes in force variability via changes in

visual feedback, they provide a foundation for future studies to explore the role of

premotor CSTs in visuomotor processing.

39

Table 1-1. Group Demographics and Relevant Clinical Information. Mean values are reported ± standard deviation. Abbreviations: yrs, years; M, Male; F, Female; L, Left; R, Right; C, Cortical Stroke; SC, Subcortical Stroke; FMA, Fugl-Meyer Assessment; MAS, Modified Ashworth Scale; MMSE, Mini Mental State Exam. The motor (66 points) and sensory (12 points) portions of the upper-extremity FMA are reported. MAS scores are reported as median of: shoulder flexion, abduction, external rotation; elbow flexion, extension; wrist flexion, extension, by individual. Lesion conjunction is illustrated in Figure 1-2

Subject Age (yrs) Sex Time Since Stroke (yrs)

Stroke Location

Affected Hemisphere

FMA Motor Score

FMA Sensation

MAS Median

MMSE

1 46 M 4.6 C/SC L 9 11 2 28

2 63 M 12.4 C/SC L 49 12 1 27

3 52 M 0.73 SC L 45 8 1 30

4 79 M 10.21 C/SC L 62 9 0 29

5 76 F 2.67 SC L 64 12 0 30

6 56 M 24.45 SC L 30 7 0 24

7 77 M 12.48 C L 10 12 0 27

8 59 M 17.65 C/SC L 51 12 0 27

9 65 M 5.59 SC L 28 12 3 29

10 56 M 4.3 C/SC R 30 4 0 30

11 57 M 9.61 C R 26 10 1 30

12 67 F 1.52 C R 62 12 1 30

13 31 M 5.53 C R 19 10 3 29.5

14 75 F 4.67 SC R 30 12 1 27

Mean 61.36 ± 13.39 11M/3F 8.32 ± 6.69 9L/5R 36.79 ± 18.75 10.20 ± 2.5 0.9 ±1.1 28.39 ± 1.80

Control 56.57 ± 10.45 10M/4F n/a n/a n/a 29.9 ± 0.27

40

Figure 1-1. Experimental Setup. The force transducer was held between the thumb and

the index finger by the subject during the MRI session (A), and the subject laid in the supine position in which the hand and transducer rested at the lower trunk (B). Above the field of view of the subject was a mirror, which reflected the visual display (C). The visual display instructed the subject when to produce force. The subject initially saw two bars (one red, one white) on the black screen, which indicated the “Rest” condition. The white bar was set at 15% of each subject’s MVC. Following the 30 second Rest condition, the red bar would turn green which indicated the “Force” condition. Subjects were instructed which hand to use before the task began, and to produce no force with their other hand. During the force condition, the goal was to produce 15% MVC, which would cover the white bar so that it was not visible. Following the Force condition, the trial was repeated. This was repeated four times for each gain level, and for each hand. (E) Example data is shown for each gain level. Force data corresponds with the visual display instructions shown in D. The green line represents 15% MVC, the black line represents force data for the hand used in the task, and the gray line represents force data for the hand not used in the task.

41

Figure 1-2. Lesion Conjunction Map. The lesion conjunction across subjects shown on

a series of axial T1 slices. The color bar represents the number of individuals with a lesion in each voxel. Dark colors (red) indicate fewer subjects with a lesion in the same voxel, whereas brighter colors (yellow) indicate higher lesion overlap in the same voxel.

42

Figure 1-3. Force Amplitude and Force Variability. Mean force amplitude is shown for

the unimpaired (3A) and impaired hand (3B) for both the control (dark gray) and stroke (light gray) groups. Figure 3C shows the asymmetry of mean force amplitude for both groups. Force variability is shown for the unimpaired (3D) and impaired hand (3E) for both groups. Corresponding asymmetry values are shown in 3F. Each data point represents the group mean at each level of visual gain, and error bars represent ± SEM.

43

Table 1-2. Mean Force Amplitude and Force Variability. Mean and standard deviation values are shown for the unimpaired hand, impaired hand, and asymmetry for both mean force amplitude and mean force variability.

Low Gain Medium Gain High Gain

Control Stroke Control Stroke Control Stroke

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

Force (%MVC)

Unimpaired 14.70 3.15 14.56 2.27 14.87 0.44 14.82 0.30 14.94 0.34 14.93 0.14

Impaired 14.38 2.07 12.83 5.74 14.83 0.60 14.12 1.09 14.86 0.10 14.70 0.41

Asymmetry 0.01 0.10 0.12 0.38 0.00 0.02 0.05 0.07 0.00 0.02 0.02 0.02

Variability (% MVC)

Unimpaired 5.39 2.41 5.57 2.65 2.62 1.65 2.85 0.74 1.79 0.89 2.28 1.07

Impaired 4.65 1.81 11.48 10.87 2.32 0.78 6.89 3.02 1.90 0.72 4.39 1.57

Asymmetry 0.07 0.19 -0.24 0.30 0.03 0.18 -0.35 0.28 -0.04 0.14 -0.32 0.20

44

Figure 1-4. Probabilistic Tractography in Motor and Visual Tracts. Fractional anisotropy (FA) profiles of all motor and visual tracts. The mean FA for each group is displayed with a black line, and the dark gray (control) and light gray (stroke) shaded areas represent ± 1 SEM for each group. Comparisons were made in the unimpaired hemisphere and impaired hemisphere, with FDR corrected p<0.05 represented with horizontal black lines in each plot. Asymmetry of each slice within each tract was also calculated, and is displayed in the final column. The slice with the highest asymmetry is highlighted in red, and represents the region used to extract data for the multiple regression analyses.

45

Table 1-3. Multivariate Regression Coefficients and Statistics. Regression statistics are shown for the best fit multivariate multiple regression model, as well as the best fit multiple regression model at each gain level for variability.

Coefficients Model Statistics

Model Intercept M1 PMd PMv Age Lesion Age

Degrees of Freedom

F p Corrected p

Radj2

Overall Variability -2.07 -1.29 -3.45 4.53 0.03 -0.04 (5,7) 20.28 0.00 0.00 88.93

Low Gain Variability -0.54 -0.56 -1.59 2.01 0.01 -0.01 (5,7) 8.27 0.01 0.01 75.18

Medium Gain Variability -0.88 -0.76 -0.85 1.55 0.01 -0.01 (5,7) 5.34 0.02 0.03 64.38

High Gain Variability -0.65 0.03 -1.01 0.97 0.01 -0.02 (5,7) 4.68 0.03 0.04 60.54

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Figure 1-5. Contribution to Force Variability. (A) The three CSTs overlaid on a T1 template (M1 – blue, PMd – green, PMv – yellow). (B) The largest between group differences in FA-asymmetry were found in the posterior limb of the internal capsule for each motor CST. The black box magnifies the PLIC region of the tract and shows these regions in solid colors superimposed over the corresponding transparent CSTs. (C) The contribution of each ROI and behavioral variable to the model that best predicted force variability across all levels of the visual gain task.

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CHAPTER 2 A PROBABILISTIC ATLAS OF THE CORTICOSPINAL MOTOR TRACTS

2.1 Chapter Summary

The purpose of this study was to develop a high resolution corticospinal tract

(CST) template which segments the CST based on six cortical regions in M1, PMd,

PMv, SMA, preSMA, and S1. Individual probabilistic tractography analyses were

conducted in 100 subjects using the highest resolution data currently available.

Tractography results were refined using a novel algorithm to objectively determine slice

level thresholds that best minimized overlap between tracts while simultaneously

preserving tract volume. Our observations show that cortical topography is generally

preserved as the tracts descend through the brain, with preSMA tracts remaining most

anterior and the S1-CST remaining most posterior. We also provide a probabilistic atlas

that quantifies the extent of overlap between tracts. Overlap between adjacent tracts

increases as tracts converge in subcortical brain regions. Finally, we demonstrate the

generalizability of the CST template across handedness and sex in Human

Connectome Project subjects and across two independent data sets collected from

different scanners. This CST template and CST probabilistic atlas provide new tools that

segment and label CSTs at a spatial resolution not previously available. We hope these

tools will be helpful to researchers who study the CST.

2.2 Introduction

The corticospinal tract (CST) is a descending motor tract specific to mammalian

species, and is crucial for the performance of dexterous motor execution (Heffner and

Masterton, 1983; Jang, 2009; Lemon and Griffiths, 2005; Nudo and Masterton, 1990a;

Nudo and Masterton, 1990b). Structural neuroimaging experiments in humans

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converge with lesion studies in non-human primates to show that damage to the CST

leads to profound deficits in both fine and gross motor function (Archer, et al., 2016;

Dum and Strick, 1991; Groisser, et al., 2014; He, et al., 1993; He, et al., 1995;

Schaechter, et al., 2009; Schaechter, et al., 2008; Schulz, et al., 2012), with the extent

of the damage predicting deficits in function as well as recovery potential after injury

(Lindenberg, et al., 2012; Schulz, et al., 2012; Stinear, et al., 2012; Stinear, et al., 2007).

Precise measurement of CST structure is therefore fundamental to our understanding of

diseases and disorders that impact motor function such as stroke (Archer, et al., 2016;

Groisser, et al., 2014; Lindenberg, et al., 2012; Newton, et al., 2006; Schaechter, et al.,

2009; Schaechter, et al., 2008; Schulz, et al., 2012), upper motor neuron syndrome

(Sach, et al., 2004), multiple sclerosis (Tovar-Moll, et al., 2015), traumatic brain injury

(Jang and Kim, 2016), and spinal cord injury (Hou, et al., 2016). Current approaches to

studying the microstructural properties of the CST in humans are focused on the CST

descending from the primary motor cortex (M1) (Groisser, et al., 2014; Lindenberg, et

al., 2012; Schaechter, et al., 2009; Schaechter, et al., 2008). However, it is estimated

that only 50% of corticospinal projections originate from M1, with the remainder made

up of projections that originate in the premotor cortex and higher motor areas (Dum and

Strick, 1991). Functional imaging data show that premotor and higher motor areas are

important for motor planning and motor execution (Mayka, et al., 2006; Plow, et al.,

2015) and have the capacity to directly influence motor function independently of M1

based on their direct projections to the spinal cord. Together these findings suggest

that the segregation between different cortical regions may be preserved in the

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descending CST. However, there is currently no CST atlas available to identify different

segments of the CST based on their cortical origin.

The only non-invasive in vivo method to study the three dimensional architecture

of the CST in humans is diffusion weighted imaging (Jones, et al., 2013). Probabilistic

tractography is one approach that has gained traction because it uses diffusion

weighted images to generate a likelihood map of connectivity between different brain

regions which are termed seeds and waypoints (Archer, et al., 2016; Behrens, et al.,

2003a; Behrens, et al., 2003b; Jbabdi, et al., 2015; van Baarsen, et al., 2016). Each

voxel in the likelihood map is assigned a value based on the number of streamlines that

traversed that particular voxel. Voxels in which the number of streamlines is zero have

a low probability of being part of the tract, whereas voxels with a high number of

streamlines have a higher probability of being part of the tract. Three important

methodological decisions can shape how a tract is identified: (1) seed location and seed

size from which the tracking algorithm will begin, (2) location and size of waypoints

through which the tract must pass, and (3) the probability threshold level which

determines which voxels are included in the tract.

Studies that track the CST typically identify a seed region in M1, waypoints in the

posterior limb of the internal capsule (PLIC) and cerebral peduncle (CP), and a single

threshold value based on a percentage of the maximum probability value of all voxels

identified in the tract (Archer, et al., 2016; Lindenberg, et al., 2012; Newton, et al., 2006;

Park, et al., 2013). Probability values are high in the PLIC where streamlines converge.

Probability values are relatively lower in cortical areas where fewer streamlines

converge. Conventional approaches use a single threshold level for the entire tract, so

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thresholds are set at around 0-2% of the maximum probability value so that voxels in

the cortex, which have much lower probability values, are not eliminated from the tract

due to the high probability values in the PLIC (Archer, et al., 2016; Lindenberg, et al.,

2012; Newton, et al., 2006; Park, et al., 2013; Potter-Baker, et al., 2016; Schaechter, et

al., 2009). Low threshold values work well when tracking the CST from one cortical

region because volume in cortical regions is preserved and segmentation in subcortical

regions is not necessary. However, segmenting the entire CST into multiple

compartments based on different cortical regions requires a new approach that

optimizes tract segmentation while preserving tract volume.

The purpose of this study is to create a high resolution CST template that

segments the CST into multiple compartments based on 6 cortical seeds extracted from

the Human Motor Area Template (HMAT: Primary motor cortex (M1); dorsal premotor

cortex (PMd); ventral premotor cortex (PMv); supplementary motor area proper (SMA);

pre-supplementary motor area (preSMA); primary somatosensory cortex (S1) (Mayka,

et al., 2006). First, for each seed in each hemisphere, we conducted probabilistic

tractography analyses at the individual level in 100 subjects using high resolution

Human Connectome Project data (Sotiropoulos, et al., 2013; Van Essen, et al., 2013).

Planar waypoints were positioned in the PLIC and the CP. Second, we implemented a

novel thresholding approach such that each slice was thresholded independently at nine

different thresholds (10%-50% in increments of 5%). Third, tract overlap, tract volume,

and coefficient of variation within each tract were calculated at each threshold percentile

for each slice. Segmented regression analyses were then used to minimize tract overlap

while preserving tract volume. Individually determined threshold percentiles were then

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applied to each slice and the slices were merged together to form the CST template.

Finally, we quantified the amount of overlap between the merged tracts to determine

confidence of tract segmentation. These values are provided in the CST probabilistic

atlas.

2.3 Methods

2.3.1 Human Connectome Subjects

Diffusion weighted imaging of 100 healthy individuals was obtained from the

Human Connectome Project website (http://www.humanconnectomeproject.org) (Van

Essen, et al., 2013). All subjects (54 females, 46 males) were within the age range of

21-35. Diffusion images (resolution: 1.25mm x 1.25mm x 1.25mm isotropic; slices: 111;

FOV: 210 x 180; flip angle: 78°; b-values: 1000, 2000, and 3000 s/mm2) were collected

via a customized Siemens 3T scanner (“Connectome Skyra”). Each individual’s

diffusion MRI session consisted of 6 separate scans, each lasting approximately 10

minutes (Sotiropoulos, et al., 2013; Van Essen, et al., 2013). The Human Connectome

Project data was preprocessed, which included eddy-current distortion correction and

head motion correction (Andersson and Sotiropoulos, 2015; Andersson and

Sotiropoulos, 2016). Following download, fiber orientations were estimated via

BEDPOSTX, in which 3 fibers were modelled per voxel (Jbabdi, et al., 2012). The

fractional anisotropy (FA) map for each individual’s data was created via DTIFIT

(Jenkinson, et al., 2012b). To obtain a standardized space representation of the FA

map, the original FA map for each individual was registered to the FMRIB FA template

in standard space (1x1x1 mm) by an affine transformation with 12 degrees of freedom

and trilinear interpolation using FLIRT (Jenkinson, et al., 2002; Jenkinson and Smith,

2001). This resulted in a linear transformed FA map and its corresponding

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transformation matrix. The linear transformation was followed by a nonlinear

transformation (FNIRT) (Jenkinson, et al., 2012a; Smith, et al., 2004; Woolrich, et al.,

2009), in which the input was the original FA map and the FLIRT transformation matrix.

The output of this step was the standardized space representation of the FA map and

the corresponding nonlinear coefficient file.

2.3.2 University of Florida Subjects

Diffusion weighted imaging of 13 healthy individuals was collected at the

University of Florida. Each subject provided informed consent before testing. Scanning

was approved by the local Institutional Review Board and was in accord with the

Declaration of Helsinki. Magnetic resonance images were collected using a 32 channel

head coil inside a 3 Tesla magnetic resonance scanner (Achieva, Best, the

Netherlands). Diffusion MRI images (resolution: 2 mm isotropic, 64 non-collinear

diffusion directions, b-value of 1,000 s/mm2 and one with a b-value of 0 s/mm2, 75 axial

slices covered the cortex and brainstem) were collected from each subject. FA images

were obtained for each subject and normalized to standard space, using the protocol

identical to the Human Connectome Project subjects.

2.3.3 Probabilistic Tractography

Probabilistic tractography was conducted using the probtrackx2 program in FSL

(default settings – curvature threshold of 80 degrees, 5000 streamlines per voxel, step

length of 0.5 mm) (Behrens, et al., 2007; Behrens, et al., 2003b). The Human Motor

Area Template regions were used as seeds (Figure 2-1A) to generate tracts from

specific sensorimotor regions (Mayka, et al., 2006). The HMAT contains six separate

sensorimotor areas: M1, PMd, PMv, SMA, preSMA, and S1 (Figure 2-1B). Additional

regions of interest in the tractography analysis were planar waypoints at the level of the

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PLIC (z=7 to 9) and CP (z=-31 to -29). Finally, transcallosal fibers were excluded by

including a planar exclusion mask at the midline (x=-1 to 1). Tracking was completed at

the individual subject level in native subject space. Seeds from the HMAT were

transformed to each subject’s native space using the inverse of the nonlinear coefficient

file from the spatial normalization procedure outlined above. Tracking was conducted in

both hemispheres independently. The output of this step resulted in 6 CSTs in the left

hemisphere and 6 CSTs in the right hemisphere for each individual subject. Each

subject’s tracts were warped to standardized space using the nonlinear coefficient file

from the FA map normalization procedure outlined above. Individual tracts from each

subject were then combined and a conjunctional analysis was performed in which

voxels were retained if the voxel was common to at least 10 individuals. We used a

liberal threshold here, so that tracts can be refined using our novel data driven

approach.

2.3.4 Slice Level Thresholding

Conventional probabilistic tractography studies implement a threshold approach

that takes the maximum probability value (based on number of streamlines) in any voxel

in the identified tract and sets a threshold based on a percentage of this maximum

value. However, this approach is sensitive to differences in the number of streamlines

throughout the tract which can be driven by differences in fiber convergence in different

regions of the brain. Figure 2-2 shows probabilistic tractography data from one subject.

Figure 2-2A shows the maximum number of streamlines at each axial slice (blue line)

along the CST between the CP (z=-32) and M1 (z=80). The maximum number of

streamlines within any voxel within any slice is around 120 which is located at z=-5.

This value is then multiplied by an arbitrary percentage, such as 10%, 25%, or 50%

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giving the product of 12, 30, and 60 streamlines, respectively. The dotted red horizontal

line in Figure 2-2A shows that at 10%, the most superior cortical slices would be

eliminated (z>40), and the resulting tract is shown in Figure 2-2B. At 25%, more of the

tract is eliminated (z>8) as shown by the orange horizontal dashed line in Figure 2-2A

and the decrease in tract volume in the 3D representation of the tract in Figure 2-2C. At

50% (yellow horizontal line in Figure 2-2A), most of the tract has been eliminated (z<-16

and z>0) as shown in Figure 2-2D, with only slices adjacent with the slice with the

maximum voxel being retained in the final output. When tracking the entire CST using

this conventional approach, low threshold levels must therefore be used to retain

volume in cortical regions of the tract.

In the current study, we implement a novel approach for thresholding probabilistic

tractography data. Instead of identifying and calculating a single value for the whole

tract based on a percentage of the voxel with the maximum number of streamlines, we

use a segmented approach. First, we split the tract into individual axial slices using

fslsplit. Next, the maximum value within any voxel of each slice is identified and

threshold levels are calculated based on that maximum value for that slice

independently. Figure 2-2E illustrates our novel approach, based on the same blue

streamline profile shown in Figure 2-2A. The dashed lines in Figure 2-2E show that

even at the 10% level (red dashed line in Figure 2-2A and red 3D tract in Figure 2-2B),

the threshold changes along the trajectory of the tract. As the thresholds increase to

25% (orange dashed line in Figure 2-2E and orange 3D tract in Figure 2-2G) and 50%

(yellow dashed line in Figure 2-2E and yellow 3D tract in Figure 2-2H) the volume of the

tract is reduced without completely eliminating entire slices from the tract.

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2.3.5 Maximizing Tract Specificity: An Example

Figure 2-3 shows a cartoon of the workflow of our experimental approach using

dummy data. Figure 2-3A represents the group conjunction (n>10) of three

independent tracts (Tract 1 (red), Tract 2 (orange), Tract 3 (yellow)) without any percent

thresholding. As this experiment implements a slice level thresholding, the analysis

herein will focus on the tracts in one slice (gray box). Figure 2-3B shows an axial view

of the three tracts within this slice, in which the first column represents the 10%

threshold, the second column represents the 25% threshold, and the third column

represents the 50% threshold. Qualitatively, it is noticeable that at 10% there is high

volume in the tracts, which is accompanied with high overlap between the tracts. In

contrast, the 50% threshold has comparatively low volume and low overlap between the

tracts.

Overlap calculations. After the original tracts are thresholded at 10%, 25%,

and 50%, the overlap between tracts is calculated for each tract. This is completed by

taking a tract (for example, Tract 1) and counting the number of voxels within the tract

that are also common to other tracts (i.e., Tracts 2 and 3). This is calculated for each

tract independently, and results in an overlap value for each tract at each threshold

percentile. Figure 2-3C shows that as the threshold is increased from 10% to 50%,

there is a progressive decrease in the amount of overlap between tracts.

Coefficient of variation of FA (CVFA) calculations. The coefficient of variation

of FA (CVFA) is then calculated for each tract at each threshold percentile. The mean

and standard deviation of FA of all voxels within each tract for each individual slice are

first calculated. Next, the standard deviation is divided by the mean to produce a CVFA

metric for each tract at each slice (Figure 2-3D). At 10%, we expect relatively high CVFA

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values because voxels may include gray matter or CSF. Increases in threshold should

lead to a progressive decrease in the CVFA of each tract as the tract becomes fully

constrained to white matter.

Volume calculations. Volume is then calculated by counting the number of

voxels in each tract at each individual slice. At 10%, we predict relatively high volume in

all tracts, which is expected to decrease as thresholds increase (Figure 2-3E).

Selection of Optimal Threshold. After overlap, CVFA, and volume are

calculated, the three values are multiplied together for each tract for each slice. This

summary value is shown in Figure 2-3F. Note that when overlap is 0, it is eliminated

from the equation. We expect this summary score to have high values at 10%, and we

expect it to reduce as values of overlap, CVFA, and volume decrease with a progressive

increase in percentile threshold. Furthermore, we predict that once threshold reaches a

certain level, additional increases in threshold will lead to reductions in volume but no

meaningful decreases in overlap or CVFA.

Summary scores are then summed across tracts at each percentile (yellow dots,

Figure 2-3G), and a segmented regression analysis is conducted to fit two simple

regression models to the data (red lines, Figure 2-3G). The segmented regression

analysis objectively determines the optimal percentile threshold for each slice. In this

example, the optimal threshold is 25% (blue line, Figure 2-3G), which means that 25%

will be used to threshold all tracts in this slice. The resulting tracts in this axial slice will

then be merged with the independent results from all other axial slices to create a

whole-brain template for the CSTs.

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2.3.6 Assembly of Corticospinal Tract Template

The above example provides the framework for the creation of the CST template

using three tracts and three percentiles. The proposed experimental approach will

characterize six tracts (M1-CST, PMd-CST, PMv-CST, SMA-CST, preSMA-CST, S1-

CST) using nine percentile thresholds (10%-50% in increments of 5%), which will allow

us to be more precise in identifying optimal threshold values. The PMv-CST travels

superiorly from the cerebral peduncle through the posterior limb of the internal capsule,

then takes a sharp curve towards the ventral regions of the premotor cortex (Archer, et

al., 2016). For this reason, PMv-CST will be included in the axial analysis with all other

tracts from z=-35 to z=20, but will be excluded in axial slices from z>20. At axial slices

z>20, the PMv-CST will be evaluated in the sagittal plane. The PMv-CST analysis in

the axial and sagittal planes will then be merged to create one PMv-CST tract in both

hemispheres.

2.3.7 Creating a Probabilistic Atlas

While the purpose of this experiment is to minimize overlap while maximizing

volume between the CSTs, overlap will not be completely eliminated; therefore, the

amount of overlap between tracts can be quantified for each voxel, and a probability of

being within a particular CST for each voxel can be calculated. For example, if a voxel

is common to the M1-CST and S1-CST, there is 50% chance that the voxel is unique to

the M1-CST. Since there are six tracts, each voxel can be assigned a value ranging

from 1/6 (i.e.,16% chance that the voxel is unique to one specific tract) to 1 (i.e., 100%

probable that the voxel is unique to one tract). We expect voxels in the most inferior

portions of the CSTs to have low probabilities due to an increase in fiber convergence in

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subcortical brain regions, and probabilities to decrease in cortical regions as fiber

convergence decreases.

2.3.8 Application of Template to Quantify Microstructure

To demonstrate generalizability of the template, we will use the template to

calculate tract specific FA profiles across individuals and across data sets. The first

analysis will compare FA profiles of 13 left and 13 right handed age- and sex-matched

subjects (see Table 2-1 for subject details), taken from the Human Connectome Project

(HCP) data set. The analysis is restricted to 13 individuals per group because the HCP

dataset only contains 13 left handers. Second, we will compare FA profiles of 20 male

and 20 female age-matched individuals taken from the HCP data set (see Table 2-1 for

subject details). Region specific differences of FA in each group will be calculated at

the slice level, allowing us to examine FA along each CST. A custom linux shell-script

will calculate the FA at each slice for each CST and merge the results together to build

6 CST FA profiles for each hemisphere. We will then compare average FA between left

and right handers and between males and females by conducting independent samples

t-tests. A p-value (p<0.01) was considered a significant difference between groups.

The final analysis in this experiment compared FA profiles of data collected as

part of the HCP with data collected at the University of Florida. The goal of this is to

determine whether scanner type, pulse sequence, and spatial resolution lead to

differences in FA profiles within specific tracts within the template. FA profiles of M1-

CST of 13 individuals from the HCP were compared to the FA profiles of M1-CST of 13

individuals whose data were collected at University of Florida. Subjects between sites

were matched for age, sex, and handedness (see Table 2-1 for subject details). As the

two datasets were collected in different locations on different scanners, using different

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pulse sequences, we normalized each individuals FA map by calculating their mean

whole-brain FA and dividing their FA map by this value. After comparing mean

normalized FA profiles with independent samples t-tests (p<0.01), we also calculated

the change in FA between consecutive axial slices for each tract to determine how FA

changes from slice to slice. In other words, we calculated the slope of the mean FA

profile for each group. We then conducted an independent samples t-test on these

slope profiles to determine if there were any differences (p<0.01) between groups.

2.4 Results

CST tracking was conducted using the HMAT seeds shown in Figure 2-1, and

the tracts were required to pass through a planar waypoint at the level of the PLIC and

the CP to be included in the final output. A summary of the probabilistic tractography

results for one slice (z=10) is shown in Figure 2-4. A coronal view of the identified tracts

is shown in Figure 2-4A and it is immediately apparent that there is high overlap

between tracts. Figure 2-4B shows an axial view of the 6 CSTs within the right PLIC.

The CST descending from M1 (green), PMd (dark yellow), PMv (light yellow), SMA

(orange), preSMA (red), S1 (blue) are shown.

Results were then thresholded from 10% to 50% in 5% increments. Figure 2-4B

shows axial slices of the tracts at thresholds of 10%, 25%, and 50%. At 10%, there is

high volume for each tract and high overlap between tracts making tract segregation

difficult. As thresholding increases to 25%, both volume and overlap decrease. At

50%, there is little volume remaining in the tracts, resulting in low overlap and high

segregation. The amount of overlap for each tract at each threshold was quantified and

is shown in Figure 2-4C. At 10%, M1 has extensive overlap (100 voxels), whereas

overlap in preSMA is lower (50 voxels). As thresholding increases to 50%, we see a

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steady decrease in overlap – M1 (15 voxels) and preSMA (5 voxels). The CVFA was

calculated at each threshold and is shown in Figure 2-4D. The CVFA for M1 was

approximately 0.28 at 10%, and decreased to 0.22 at 50%. The volume of each tract

was calculated and is shown in Figure 2-4E. At 10%, M1 has a volume of

approximately 100 voxels, whereas preSMA has a volume of approximately 75 voxels.

As thresholding increases to 50%, we see a reduction in volume in all tracts – M1 (30

voxels) and preSMA (15 voxels). The overlap, CVFA, and volume were then multiplied

together to create a variable sensitive to all variables and is shown in Figure 2-4F. At

10%, this value is high for all tracts, and this value approaches zero as thresholding

increases to 50%. These values were then summed at each threshold and used as

dependent variables in a segmented regression analysis (Figure 2-4G). Two lines were

fitted to the data (red lines), and the breakpoint (blue) was calculated to be at 17.24%.

Threshold levels were rounded to the nearest multiple of 5 within the 10-50% range.

Therefore, the optimal threshold for this slice was selected as 15%.

2.4.1 Corticospinal Tract Template Assembly

Tractography was successfully conducted in all subjects, and the optimal

threshold for each slice was selected. The threshold was consistent between

hemispheres (left hemisphere: 16.43 ± 2.45%; right hemisphere: 19.83 ± 3.11%). To

control for tract specific differences in volume between hemispheres, we used a

conjunction analysis by overlaying tracts from the left hemisphere onto the tracts in the

right hemisphere. Only voxels that were included in both tracts were included in the final

template. As shown in Figure 2-5, the tracts can be distinguished in the cortex (Figure

2-5A), while the overlap in the PLIC is higher (Figure 2-5B). The highest overlap

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between tracts is in the more inferior portions of the CSTs, such as in the CP (Figure 2-

5C).

2.4.2 Probabilistic Corticospinal Tract Atlas

The overlap of each tract was quantified by summing the number of tracts

contained in each voxel. Each voxel in the probabilistic tract template can be

represented by a value ranging from 1/6 (or 0.167) to 1, in which higher values indicate

more certainty that a particular voxel is unique to a particular CST. Figure 2-6A

illustrates the probabilistic M1-CST atlas. In Figure 2-6A, red colors represent voxels

that were identified in many different CSTs. As the colors get brighter (starting at the

level of the PLIC), voxels have a higher probability of being unique to the M1-CST. A

probability profile for the M1-CST is shown in Figure 2-6A, in which the red line is the

mean probability for each axial slice. The yellow shading represents ±SEM. At the more

inferior portions of the tract, probability is approximately 0.35. As the tract travels

through the PLIC (z=10), probability of being unique to the M1-CST increases to

approximately 0.6. Superior to the PLIC, there is a progressive increase in the

probability of voxels being within M1-CST until z=40. At z=40, there is a sharp increase

in probability, which reaches 1 at z=55. The probabilistic atlas for the other 5 CSTs is

shown in Figure 2-6 (B-F). All tracts show similar probability profiles to M1-CST, in

which probability increases in more superior regions of the tract.

2.4.3 Quantifying Microstructure with the Corticospinal Tract Template

Figure 2-7 (column 1 and 4) shows the template for each of the six CSTs in the

right hemisphere. The FA profiles for the left and right hemisphere are shown to the

right of each 3D tract template. Figure 2-7A shows the 3D tract and the FA profile for

the M1-CST in the left and right hemisphere. To characterize the FA within the tract, we

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first determined the average FA of each slice within the M1-CST in the left hemisphere.

Data from the left handers are represented with black lines and light gray shading

(±SEM), and data from the right handers are represented with black lines and dark gray

shading. First, we evaluated the M1-CST in the left hemisphere. M1-CST began at z=-

35 at the level of the CP and terminated in the precentral gyrus at z=75. The tracts

showed a similar pattern between left and right handers, in which FA was approximately

0.4 within the CP and increased to 0.8 within the PLIC (z=-20 to z=20). From z=20 to

z=33, there is a reduction in FA, which is consistent with the associated crossing fibers

within the centrum semiovale. At z=33, there is a steady increase in FA which peaks at

z=51, followed by a slow decrease in FA in cortical regions of the M1-CST. Comparing

left handers and right handers in the left hemisphere, there was one slice demonstrating

a significant difference (z=57, p<0.01). In the right hemisphere, similar profiles were

found for the M1-CST, and no slices demonstrated significant differences between

groups. Significant differences between groups are marked by horizontal lines above

the plot. Similar analyses were conducted for each of the five remaining CSTs in both

hemispheres and are shown in Figure 2-7 (D-R). Very few significant between group

differences were found in any of the CSTs, suggesting that the template is generalizable

to left and right handers.

Next, we compared sex differences within all of the tracts. These results are

shown in Figure 2-8. Significant differences between groups are marked by horizontal

lines above the plots. We found no strong evidence of sex differences in FA in either

hemisphere in any of the CSTs, suggesting that the template is generalizable to males

and females.

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We next compared HCP data with University of Florida data. First, we visually

inspected alignment of the CST template at the individual level. Figure 2-9A shows the

template overlaid on each individual’s anatomical image in standardized space at the

level of the PLIC. As shown in Figure 2-9A, the template is well aligned in both groups

for all CSTs. Next, we created normalized FA profiles by calculating the average FA

profile for each subject and dividing the profile by the average whole-brain FA. This

allowed us to normalize FA values across sites. As shown in Figure 2-9B, FA profiles

were similar between the HCP (red) and University of Florida (blue) datasets for the M1-

CST. Slices with significant differences (p<0.01) between groups are marked with

horizontal green lines.

Next, we were interested if the slope of the normalized FA profile was similar

between groups. We implemented an approach in which we calculated the difference in

FA between respective slices (i.e., slope of FA). The slope of the M1-CST FA curve for

both groups is shown in Figure 2-9C. Only three slices demonstrated differences in

slope of FA between groups (z=-25 to -24, z=-10).

2.5 Discussion

The purpose of this study was to develop a high resolution CST template which

segmented the CST based on six cortical regions in M1, PMd, PMv, SMA, preSMA, and

S1. Individual probabilistic tractography analyses were conducted in 100 subjects using

the highest resolution data currently available. Tractography results were refined using

a novel algorithm to objectively determine slice level thresholds that best minimized

overlap between tracts while simultaneously preserving tract volume. Our observations

show that cortical topography is generally preserved as the tracts descend through the

brain, with preSMA tracts remaining most anterior and the S1-CST remaining most

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posterior. We also provide a probabilistic atlas that quantifies the extent of overlap

between tracts. Overlap between adjacent tracts increased as tracts converged in

subcortical brain regions. Finally, we demonstrate the generalizability of the CST

template across handedness and sex and across two independent data sets collected

from different scanners. The current CST template and CST probabilistic atlas provide

new tools that segment and label CSTs at a spatial resolution not previously available.

We hope these tools will be helpful to researchers who study the CST.

The selection of seed masks is a large source of variability between probabilistic

tractography studies that focus on the CST. One approach is to generate seed masks

by extracting regions from templates such as the Johns Hopkins DTI-based white

matter atlas (Hua, et al., 2008; Wakana, et al., 2007). A second approach is to hand

draw seeds in the precentral gyrus (Lindenberg, et al., 2010; Lindenberg, et al., 2012;

Schaechter, et al., 2008), and the hand bump region in particular (Schaechter, et al.,

2009; Stinear, et al., 2007), based on anatomical landmarks in structural scans and fiber

direction in fractional anisotropy maps (Lindenberg, et al., 2012; Schaechter, et al.,

2008). However, the landmarks used to identify CST seeds are different between

studies, and hand drawing regions for large datasets can be time-consuming. A third

approach is to generate seed masks based on clusters of voxels identified in task-based

fMRI experiments or TBSS analyses that identify correlations between microstructure

and behavior (Archer, et al., 2016; Leunissen, et al., 2013; Lindenberg, et al., 2010;

Lindenberg, et al., 2012; Newton, et al., 2006; Schaechter, et al., 2009; Schaechter, et

al., 2008; Schulz, et al., 2012; Stinear, et al., 2007). However, identifying seeds based

on individual task-based studies means that the seed locations are constrained by the

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specific task used and by the specific subjects tested in the study. In addition, the

volume of each seed region is variable across studies. In the current study, we

overcame these issues by using seed regions derived from the Human Motor Area

Template (HMAT) (Mayka, et al., 2006). The template identifies the borders of six

sensorimotor areas (M1, PMd, PMv, SMA, preSMA, S1) based on activation likelihood

estimation from 126 functional neuroimaging studies that manipulated upper and lower

limb motor control. Generating seeds from the HMAT ensured that the proposed CST

template is based on segregated brain function in the cortex and is generalizable to the

upper and lower limb.

Following seed generation, the use of waypoint masks and exclusion masks can

have a profound impact on probabilistic tractography results. Tract volume decreases

when using small waypoint masks and when the total number of waypoint masks and

exclusion masks increases. In the case of the CST, the PLIC and the CP are often used

as waypoints (Archer, et al., 2016; Lindenberg, et al., 2010; Lindenberg, et al., 2012;

Potter-Baker, et al., 2016; Schaechter, et al., 2009; Schaechter, et al., 2008; Stinear, et

al., 2007), and a planar sagittal slice at the midline can be used as an exclusion mask to

eliminate transcallosal fibers (Archer, et al., 2016). As with seed generation, waypoint

masks can be hand drawn, extracted from white matter templates, or obtained from

TBSS analyses (Archer, et al., 2016; Lindenberg, et al., 2010; Lindenberg, et al., 2012;

Potter-Baker, et al., 2016; Schaechter, et al., 2009; Schaechter, et al., 2008; Stinear, et

al., 2007). However, there is currently no established way of identifying segregated

subcortical waypoints that map to our six cortical regions. As a result, we used an

unconstrained approach by placing axial planar waypoints at the level of the PLIC (z=7

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to 9) and at the level of the CP (z=-31 to -29). The CP waypoint covered the basis

pontis portion (i.e. anterior CP) of the CP, as the tegmentum pontis portion (i.e.

posterior CP) has been suggested to be connected to the alternate corticofugal

projections to the spinal cord (Lindenberg, et al., 2012). Additionally, we placed a

sagittal exclusion mask at the midline (x=-1 to 1) to exclude transcallosal fibers.

Minimally restrained tractography results were then refined using a novel thresholding

approach.

Determining the appropriate threshold to use when refining tractography results

has received little attention and is often arbitrary and unjustified (Clatworthy, et al.,

2010). The most common method is to threshold based on a percentage of the

maximum probability value within the tractography results (Lindenberg, et al., 2012;

Schaechter, et al., 2009; Schulz, et al., 2012). This approach is sensitive to peaks in

probability values within the tract, and these values vary according to tract length and

tract location. For instance, a recently developed white matter tract template for the

cerebellum successfully used a threshold of 50% (van Baarsen, et al., 2016), whereas

tractography studies focused on the CST have used lower thresholds that range from

0% to 2% to preserve volume across the whole tract (Lindenberg, et al., 2012; Newton,

et al., 2006; Park, et al., 2013; Schaechter, et al., 2009). Here, we aimed to circumvent

this issue by developing a method that independently thresholded each slice at nine

different levels (10% to 50% in increments of 5%) and then objectively identified the

optimal threshold to use for each slice using a data driven approach.

A single threshold level was independently determined for each slice based on

three factors: overlap between the different tracts, the coefficient of variation of FA, and

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the volume of each different tract. Overlap was incorporated into the analysis so that

we could optimally segregate tracts. Although segregation between cortical regions is

well established based on anatomy and function (Dum and Strick, 2005; Mayka, et al.,

2006) and 50% of the CST is connected to the premotor areas (Dum and Strick, 1991),

studies in non-human primates show that different cortical regions project onto the

same motoneurons in the cervical spinal cord (Maier, et al., 2002). These findings

suggest that the CST tracts converge at some level of the neuroaxis. (Dum and Strick,

1991; He, et al., 1993; He, et al., 1995). Consistent with previous human neuroimaging

evidence, our observations did reveal overlap between the six CSTs, which reduced

with an increase in threshold. Other studies have used probabilistic tractography to

identify tracts from multiple cortical areas, but overlap between tracts has not been

reported (Archer, et al., 2016; Jang and Seo, 2015; Newton, et al., 2006; Potter-Baker,

et al., 2016; Schulz, et al., 2012). Our findings suggest that the low threshold values

used in previous studies (0-2%) likely lead to high volume and high overlap between

tracts, making conclusions about the microstructure of distinct CSTs difficult to interpret.

Thresholds in the current study were higher than those previously used and ranged

from 10% to 25% (16.42 ± 2.45%). Nevertheless, our findings are consistent with

previous studies that show a general preservation of cortical topography as the tracts

descend through the brain (Archer, et al., 2016; Newton, et al., 2006; Schulz, et al.,

2012), with preSMA tracts more anterior in regions superior to the PLIC and more

medial in regions inferior to the PLIC. In contrast, the S1-CST remains posterior in

regions superior to the PLIC and more lateral in regions inferior to the PLIC (Park, et al.,

2008). Coefficient of variation of FA was included in the analysis to minimize gray

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matter and cerebrospinal fluid from the CST template. As expected, the pattern in the

CVFA approached an asymptote at lower thresholds as compared to overlap and volume

and small changes in CVFA were evidenced at thresholds between 10-25%. This was

likely driven by the tracts already being well constrained to white matter based on the

minimum FA parameter that was set at 0.2 in the probabilistic tracking algorithm.

Hence, the current CST template offers an alternative to other diffusion based

templates, such as the Johns Hopkins white-matter tractography atlas which includes

gray matter and cerebrospinal fluid and does not segregate tracts based on cortical

regions (Hua, et al., 2008; Wakana, et al., 2007). Tract volume was included in the

calculation to control for instances where overlap was low, as in the cortex and in the

lateral portions of the PMv tract. Including volume in the algorithm ensured that the

slice level thresholding approach is easily generalizable to other tracts where overlap is

not an issue.

The probabilistic CST atlas advances the current literature by offering greater

spatial localization in the CST. White matter atlases such as the Johns-Hopkins white

matter template (Hua, et al., 2008; Wakana, et al., 2007) and the Harvard-Oxford

cortical and subcortical structural atlas (Desikan, et al., 2006; Frazier, et al., 2005;

Goldstein, et al., 2007; Makris, et al., 2006) can be used to generate masks of the entire

CST and its corresponding regions (i.e., PLIC, CP), but specific information within these

regions corresponding to distinct cortical targets is not currently available. Our

proposed template extends the literature by providing probability values that a voxel is

contained within a specific corticospinal tract (e.g., 50% chance to be in M1-CST, 50%

chance to be in PMd-CST).

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Generalizability of the tract template was assessed by overlaying the template

onto individual FA maps in standard space from two different data sets collected on

different scanners using different pulse sequences. Alignment of the template to white

matter was good for all individual subjects (Figure 2-9), suggesting that the template is

generalizable across site and pulse sequence. Consistency across individuals was also

demonstrated by similar FA profiles for males and females and for left and right

handers.

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Figure 2-1. Probabilistic Tractography Inputs. A) The regions within the Human Motor Area Template (HMAT) were used as seed regions in the probabilistic tractography analyses. B) A separate probabilistic tractography analysis was conducted for M1 (green), PMd (dark yellow), PMv (light yellow), SMA (orange), preSMA (red), and S1 (blue), in which a planar waypoint was placed at the level of the PLIC (z=7 to 9) and the CP (z=-31 to -29). Additionally, a planar exclusion mask was placed at the midline (x=-1 to 1) to exclude transcallosal streamlines.

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Figure 2-2. Slice Level Thresholding Rationale. A) When performing probabilistic

tractography from M1 to the CP, the number of streamlines per slice varies (blue line), in which there is a peak number of streamlines at z=-5. A) Conventional approaches calculate the maximum number of streamlines within this profile and base the threshold on a percentage of this value. Individuals could arbitrarily threshold at 10% (red line), 25% (orange line), or 50% (yellow line). Higher thresholds lead to a reduction in tract volume (B-D). Blue lines that fall below the threshold line would be excluded from the final results. Therefore, a threshold of 10% results in a loss of cortical volume (z>40 eliminated), while a 25% threshold results in additional loss of volume in the cortex (z>8 eliminated). At 50%, the only slices which remain are within the posterior limb of the internal capsule (z=-16 to 0 remain). E) By splitting the tract into individual slices, each slice can be thresholding independently. A benefit of this method is that it does not result in any excluded slices within the tract. At 10%, there is a large volume of the tract; however, at 25% and 50% the volume of the tract decreases while maintaining data in every slice of the tract (F-H).

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Figure 2-3. Example of Slice Level Thresholding. A) The resulting tracts from three separate probabilistic tractography analyses (Tract 1 (red), Tract 2 (orange), Tract 3 (yellow)), in which a gray box highlights the slice which was analyzed within this analysis. B) An axial view of the slice of interest, in which the example tracts were thresholding at 10%, 25%, and 50%. C) Overlap at 10%, 25%, and 50% are shown for all tracts. D) CVFA at 10%, 25%, and 50% for all tracts. E) Volume at 10%, 25%, and 50% for all tracts. F) The overlap*CVFA*volume metric at 10%, 25%, and 50% for all tracts. G) The segmented regression analysis was performed using one data point (yellow) for each threshold, in which the two linear regressions (red lines) were fitted to the data. The breakpoint analysis calculated the breakpoint in the threshold (blue line).

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Table 2-1. Subject Characteristics. Subjects were obtained from the Human Connectome Project and from the University of Florida.

Handedness Analysis Gender Analysis HCP - UF Analysis

Left

Handed Right

Handed Male Female HCP UF

Subject Sex Age Sex Age Handedness Age Handedness Age Sex Handedness Age Sex Handedness Age

1 F 35 F 35 R 27 R 27 M R 22 M R 22

2 F 28 F 28 R 33 R 33 M R 32 M R 32

3 F 34 F 34 R 22 R 22 F R 26 F R 26

4 F 27 F 27 R 29 R 29 F R 31 F R 31

5 M 26 M 26 R 34 R 35 F R 22 F R 22

6 M 25 M 26 R 30 R 30 M R 36 M R 37

7 F 25 F 25 R 26 R 25 M R 34 M R 34

8 F 34 F 34 R 32 R 32 F R 26 F R 26

9 F 30 F 30 R 29 R 29 F R 22 F R 22

10 M 29 M 29 R 31 R 31 M R 34 M R 34

11 M 25 M 24 R 33 R 33 M R 30 M R 30

12 M 22 M 22 R 32 R 32 M R 23 M R 25

13 F 26 F 26 R 31 R 31 F R 25 F R 25

14 R 31 R 31

15 R 32 R 32

16 R 34 R 34

17 R 32 R 32

18 R 30 R 30

19 R 34 R 34

20 R 27 R 27

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Figure 2-4. Slice Level Thresholding of CST Data. A) The probabilistic tractography results for all six CSTs, in which the gray box illustrates the slice (z=10) analyzed in this figure. B) The six CSTs were thresholded at nine levels of percentile threshold. This figure illustrates the threshold at 10%, 25%, and 50%. C) Overlap of each CST with every other CST was calculated at each percentile. D) The CVFA of each CST was calculated for each percentile. E) The volume of each CST was calculated for each percentile. F) The overlap, CVFA, and volume were incorporated into one variable and calculated for each tract at all thresholds. G) A segmented regression analysis was conducted using the summed metric at each threshold. The blue line represents the threshold selected for this slice (15%).

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Figure 2-5. The Corticospinal Template. The assembled CST template (M1 – green; PMd – dark yellow; PMv – light yellow; SMA – orange; preSMA – red; S1 – blue). A) An axial slice of the template in the cortex (z=55). B) An axial slice of the template within the posterior limb of the internal capsule (z=10). C) An axial slice of the template within the cerebral peduncle (z=-30).

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Figure 2-6. Probabilistic Corticospinal Tract Atlas. Each tract was overlaid with one

another to quantify the amount of overlap between tracts so that a probabilistic value could be applied to each voxel within the template. A view of each tract (A – M1-CST; B – PMd-CST; C – PMv-CST; D – SMA-CST; E – preSMA-CST; F – S1-CST) is shown, in which darker colors indicate higher overlap with other tracts (i.e., low probability voxel is only part of one tract), whereas brighter colors are indicative of voxels with low overlap with other tracts (i.e., high probability voxel is only part of one tract). Probability profiles are shown for each tract, which demonstrate as the tracts reach more superior regions, the probability of voxels being unique to a particular CST increases.

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Figure 2-7. Handedness Comparisons. FA profiles for all 6 CSTs comparing

handedness. The mean FA for each group is displayed with a black line, and the light gray (left handers) and dark gray (right handers) shaded areas represent ±SEM for each group. Comparisons were made in the left and right hemispheres between groups at each slice. Significantly different slices (p<0.01) are represented with horizontal lines in each plot.

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Figure 2-8. Gender Comparisons. FA profiles for all 6 CSTs comparing gender. The mean FA for each group is displayed with a black line, and the light gray (male) and dark gray (female) shaded areas represent ±SEM for each group. Comparisons were made in the left and right hemispheres between groups at each slice. Significantly different slices (p<0.01) are represented with horizontal lines in each plot.

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Figure 2-9. Human Connectome Project Data Comparison with University of Florida

Data. A) A visual inspection was done for each Human Connectome Project subject and each University of Florida subject to ensure proper alignment of the template. B) The average normalized fractional anisotropy (FA) value was calculated for each axial slice for both groups. Independent samples t-tests were performed for each slice individually to determine differences between groups, in which green lines represent significant differences (p<0.01). C) The difference in FA between slices was then computed for each group and compared. Green lines represent significant (p<0.01) differences between groups.

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CHAPTER 3 CONCLUSIONS

Precise measurement of CST structure is fundamental to our understanding of

diseases and disorders that can impact the CST such as stroke (Archer, et al., 2016;

Groisser, et al., 2014; Lindenberg, et al., 2012; Newton, et al., 2006; Schaechter, et al.,

2009; Schaechter, et al., 2008; Schulz, et al., 2012), upper motor neuron syndrome

(Sach, et al., 2004), multiple sclerosis (Tovar-Moll, et al., 2015), traumatic brain injury

(Jang and Kim, 2016), and spinal cord injury (Hou, et al., 2016). Current approaches to

studying the microstructural properties of the CST in humans are focused on the CST

descending from M1 (Groisser, et al., 2014; Lindenberg, et al., 2012; Schaechter, et al.,

2009; Schaechter, et al., 2008). The current template provides a new tool that can be

used to localize tract specific damage, and to quantify microstructure in specific CSTs

that are associated with distinct cortical regions. Increases in spatial localization may

improve diagnostic and prognostic evaluations across a range of diseases and

disorders. The proposed CST template and CST probabilistic atlas are freely available

at www.lrnlab.org (Laboratory for Rehabilitation Neuroscience).

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BIOGRAPHICAL SKETCH

Derek Bradley Archer was born in Washington, Indiana. He received his

Bachelor in Science in biomedical engineering from Rose-Hulman Institute of

Technology in May 2012. Following Rose-Hulman, Derek began his doctoral studies at

the University of Florida in August 2012 in the Department of Applied Physiology and

Kinesiology majoring in health and human performance with a concentration in

biobehavioral science. Under the guidance of Dr. Stephen Coombes, he received his

PhD in August 2016.