A Template and Probabilistic Atlas of the Human Sensorimotor Tracts using Diffusion MRI

Abstract

The purpose of this study was to develop a high-resolution sensorimotor area tract template (SMATT) which segments corticofugal tracts based on 6 cortical regions in primary motor cortex, dorsal premotor cortex, ventral premotor cortex, supplementary motor area (SMA), pre-supplementary motor area (preSMA), and primary somatosensory cortex using diffusion tensor imaging. 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 preserving tract volume. Consistent with tracing studies in monkey and rodent, our observations show that cortical topography is generally preserved through the internal capsule, with the preSMA tract remaining most anterior and the primary somatosensory tract remaining most posterior. We combine our results into a freely available white matter template named the SMATT. We also provide a probabilistic SMATT that quantifies the extent of overlap between tracts. Finally, we assess how the SMATT operates at the individual subject level in another independent data set, and in an individual after stroke. The SMATT and probabilistic SMATT provide new tools that segment and label sensorimotor tracts at a spatial resolution not previously available.

Full text

Cerebral Cortex, 2017; 1–15
doi: 10.1093/cercor/bhx066
Original Article
ORIGINAL ARTICLE
A Template and Probabilistic Atlas of the Human
Sensorimotor Tracts using Diffusion MRI
Derek B. Archer
1
, David E. Vaillancourt
1,2,3
and Stephen A. Coombes
1
1
Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University
of Florida, Gainesville, FL 32611, USA,
2
Department of Neurology, College of Medicine, University of Florida,
Gainesville, FL 32611, USA and
3
Department of Biomedical Engineering, University of Florida, Gainesville,
FL 32611, USA
Address correspondence to Stephen A. Coombes, Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology,
University of Florida, PO Box 118206, Gainesville, FL 32611, USA. Email: scoombes@ufl.edu
Abstract
The purpose of this study was to develop a high-resolution sensorimotor area tract template (SMATT) which segments
corticofugal tracts based on 6 cortical regions in primary motor cortex, dorsal premotor cortex, ventral premotor cortex,
supplementary motor area (SMA), pre-supplementary motor area (preSMA), and primary somatosensory cortex using
diffusion tensor imaging. 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 preserving tract volume. Consistent with tracing studies
in monkey and rodent, our observations show that cortical topography is generally preserved through the internal capsule,
with the preSMA tract remaining most anterior and the primary somatosensory tract remaining most posterior. We
combine our results into a freely available white matter template named the SMATT. We also provide a probabilistic SMATT
that quantifies the extent of overlap between tracts. Finally, we assess how the SMATT operates at the individual subject
level in another independent data set, and in an individual after stroke. The SMATT and probabilistic SMATT provide new
tools that segment and label sensorimotor tracts at a spatial resolution not previously available.
Key words: diffusion, movement, sensorimotor, tractography, white matter template
Introduction
The corticospinal tract and the corticobulbar tract are crucial
for the performance of dexterous motor execution (Heffner and
Masterton 1983;Nudo and Masterton 1990a,1990b;Lemon and
Griffiths 2005;Jang 2009). Structural neuroimaging experiments
in humans converge with lesion studies in nonhuman primates
to show that damage to the descending motor tracts lead to
profound deficits in fine and gross motor function (Travis 1955;
Rouiller et al. 1998;Schaechter et al. 2008,2009;Schulz et al.
2012;Groisser et al. 2014;Morecraft et al. 2015). The extent of
damage to these tracts predicts deficits in function as well as
recovery potential after injuries such as stroke (Stinear et al.
2007,2012;Lindenberg et al. 2012;Schulz et al. 2012). Precise
measurement of descending motor tract microstructure is also
fundamental to our understanding of diseases and disorders
that impact motor function such as upper motor neuron syn-
drome (Sach et al. 2004), multiple sclerosis (Tovar-Moll et al.
2015), traumatic brain injury (Caeyenberghs et al. 2010;Jang
and Kim 2016), spinal cord injury (Hou et al. 2016), and cerebral
palsy (Jaspers et al. 2015). Current approaches to studying the
microstructure of the descending motor tracts in humans focus
on the corticospinal tract descending from the primary motor
cortex (M1) (Schaechter et al. 2008,2009;Lindenberg et al. 2012;
Groisser et al. 2014). However, corticospinal and corticobulbar
tracts originate in areas beyond the M1. Tracing studies in
monkey estimate that 50% of corticospinal projections originate
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from M1, with the remainder made up of projections that ori-
ginate in the premotor areas and parietal lobe (Dum and Strick
1991;Galea and Darian-Smith 1994). Functional imaging data
show that premotor and higher motor areas are important for
motor planning and motor execution (Mayka et al. 2006;
Coombes et al. 2010,2011,2012;Plow et al. 2015) and have the
capacity to influence motor function based on their distinct con-
nections to subcortical areas (Lehericy et al. 2004a,2004b)and
their direct projections to the spinal cord (Dum and Strick 1991).
Tracing studies in rodents and nonhuman primates show that
projections from different cortical regions are topographically
organized through the internal capsule and begin to overlap in
the cerebral peduncle (CP) (Barnard and Woolsey 1956;Fries et al.
1993;Coleman et al. 1997). Together these findings suggest that
the segregation between tracts associated with different regions
in sensorimotor cortex may be preserved in humans. However,
there is currently no tract template or probabilistic atlas available
to identify different segments of these tracts based on their topo-
graphical organization in sensorimotor cortex.
The only noninvasive in vivo method to study the 3D archi-
tecture of the sensorimotor tracts in humans is diffusion-
weighted imaging (Johansen-Berg and Rushworth 2009;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 (Behrens
et al. 2003a,2003b;Jbabdi et al. 2015;Archer et al. 2016;van
Baarsen et al. 2016). Each voxel in the likelihood map is assigned
a value based on the number of streamlines that traverse that
particular voxel. Voxels in which the number of streamlines is
near zero have a low probability of being part of the tract,
whereas voxels with a high number of streamlines have a high-
er probability of being part of the tract. Three important meth-
odological 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 that deter-
mines which voxels are included in the tract.
Studies that focus on the corticospinal tract typically iden-
tify a seed region in M1, waypoints in the posterior limb of the
internal capsule (PLIC) and CP, and a single threshold value
based on a percentage of the maximum probability value of all
voxels identified in the tract (Newton et al. 2006;Lindenberg
et al. 2012;Park et al. 2013;Archer et al. 2016). Probability values
are high in the PLIC where more streamlines follow the same
trajectory through fewer voxels. Probability values are relatively
lower in cortical areas where fewer streamlines follow the
same trajectory through each voxel. Conventional approaches
use a single threshold level for the entire tract. This threshold
value has to be kept low to avoid false negatives that occur
when eliminating voxels in the cortex (Newton et al. 2006;
Schaechter et al. 2009;Lindenberg et al. 2012;Park et al. 2013;
Archer et al. 2016;Potter-Baker et al. 2016). However, the low
threshold increases the probability of false positives in the PLIC
where fewer voxels are eliminated. As a result, much of the
PLIC is included in the tract, despite evidence that tracts from
different cortical regions are topographically organized through
the internal capsule (Barnard and Woolsey 1956;Fries et al.
1993;Coleman et al. 1997). A low threshold value can work well
when tracking a single pathway from the cortex as long as tract
segmentation in subcortical regions is not essential. However,
segmenting tracts into multiple compartments through the
internal capsule based on different regions of the sensorimotor
cortex requires a new approach that minimizes false negatives in
the cortex to maintain tract volume, while also minimizing false
positives in the internal capsule to maintain tract segmentation.
The purpose of this study is to create a high-resolution tem-
plate that segments the sensorimotor tracts into multiple com-
partments based on 6 cortical seeds extracted from the human
motor area template (HMAT: M1; dorsal premotor cortex (PMd);
ventral premotor cortex (PMv); supplementary motor area
(SMA) proper; pre-supplementary motor area (preSMA); and pri-
mary somatosensory cortex (S1)) (Mayka et al. 2006). First, for
each seed in each hemisphere, we conducted probabilistic trac-
tography analyses at the individual level in 100 subjects using
high-resolution Human Connectome Project data (Van Essen
et al. 2013;Sotiropoulos et al. 2013a). Planar waypoints were
positioned in the PLIC and the CP. Second, we implemented a
novel thresholding approach such that each slice was thre-
sholded independently at 9 different thresholds (10–50% in
increments of 5%). Third, tract overlap, tract volume, and coef-
ficient of variation of fractional anisotropy (CV
FA
) 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 deter-
mined threshold percentiles were then applied to each slice
and the slices were merged together to form the sensorimotor
area tract template (SMATT). Finally, we quantified the amount
of overlap between the tracts in each hemisphere to determine
the probability that a single voxel is unique to a specific tract.
Probability values for all voxels included in the template are
provided in the sensorimotor area tract probabilistic atlas.
Materials and Methods
Human Connectome Subjects
Diffusion-weighted imaging of 100 healthy individuals was
obtained from the Human Connectome Project website (http://
www.humanconnectomeproject.org)(Feinberg et al. 2010;
Moeller et al. 2010;Setsompop et al. 2012;Van Essen et al. 2013;
Sotiropoulos et al. 2013b). All subjects (54 females, 46 males)
were within the age range of 21–35. Diffusion images (reso-
lution: 1.25 mm ×1.25 mm ×1.25 mm isotropic; slices: 111; field
of view: 210 ×180; flip angle: 78°;b-values: 1000, 2000, and
3000 s/mm
2
) were collected using a customized Siemens 3 T
scanner (Connectome Skyra). Each individual’s diffusion mag-
netic resonance imaging (MRI) session consisted of 6 separate
scans, each lasting approximately 10 min (Van Essen et al.
2013;Sotiropoulos et al. 2013a). The Human Connectome
Project data were preprocessed, which included eddy current
distortion correction and head motion correction (Andersson
and Sotiropoulos 2015,2016). Following download, fiber orienta-
tions were estimated with BEDPOSTX, in which 3 fibers were
modeled per voxel (Jbabdi et al. 2012). The FA map for each
individual’s data was created via DTIFIT (Jenkinson et al. 2012).
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 (1 ×1×1 mm) by an
affine transformation with 12 degrees of freedom and trilinear
interpolation using FLIRT (Jenkinson and Smith 2001;Jenkinson
et al. 2002). This resulted in a linear transformed FA map and
its corresponding transformation matrix. The linear transform-
ation was followed by a nonlinear transformation (Smith et al.
2004;Woolrich et al. 2009;Jenkinson et al. 2012), 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
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coefficient file. All 100 subjects were used to create the tracto-
graphy template.
University of Florida Subjects
Diffusion-weighted imaging of 20 healthy individuals was col-
lected at the University of Florida (11 males, 9 females; age and
handedness are shown in Table 1). 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. MRIs were collected using a 32 channel
head coil inside a 3 Tesla magnetic resonance scanner
(Achieva, Best, the Netherlands). Diffusion MRI images (reso-
lution: 2 mm isotropic, 64 noncollinear diffusion directions, b-
value of 1000 s/mm
2
and one with a b-value of 0 s/mm
2
, 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 outlined above.
Probabilistic Tractography
Probabilistic tractography was conducted using the probtrackx2
program in FSL (default setting—curvature threshold of 80°,
5000 streamlines per voxel, step length of 0.5 mm) (Behrens
et al. 2003b,2007). The HMAT regions were used as seeds
(Fig. 1A) to generate tracts from specific sensorimotor regions
(Mayka et al. 2006). The HMAT contains 6 separate sensori-
motor areas: M1, PMd, PMv, SMA, preSMA, and S1 (Fig. 1B).
Additional regions of interest in the tractography analysis were
planar waypoints at the level of the PLIC (z=7 to 9) and CP (z=
−31 to −29). A planar point was placed at the midline (x=−1to
1) to exclude transcallosal fibers. 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 spa-
tial normalization procedure outlined above. Tracking was
conducted separately for each hemisphere. The output of this
step resulted in 6 sensorimotor tracts in the left hemisphere
(M1, PMd, PMv, SMA, preSMA, and S1) and 6 sensorimotor tracts
in the right hemisphere (M1, PMd, PMv, SMA, preSMA, and S1)
for each individual subject. Each voxel in each tract was
assigned a probability value corresponding to the number of
streamlines that traversed that voxel in that individual. Each
subject’s tracts were warped to standardized space using the
nonlinear coefficient file from the FA map normalization pro-
cedure outlined above.
Tract Level Thresholding Versus Slice Level
Thresholding in the M1 Tract
Thresholding is conducted at the individual level. Conventional
probabilistic tractography studies implement a threshold
approach that takes the maximum probability value (based on
the number of streamlines) in any voxel in the identified tract
and sets a threshold value based on a percentage of this max-
imum probability value. A single value is, therefore, used to
threshold the entire tract. This conventional thresholding
approach is sensitive to differences in the number of stream-
lines throughout the tract which can be driven by differences
in fiber proximity in different regions of the brain. In the cur-
rent study, we implemented a novel approach for thresholding
probabilistic tractography data. Instead of identifying and
calculating a single value for the whole tract based on a per-
centage of the voxel with the maximum number of stream-
lines, we used a segmented approach. First, we split the tract
into individual slices using fslsplit. When using fslsplit in the
z-direction, the image was split into 181 axial 1 mm slices.
When using fslsplit in the x-direction, the image was split into
181 sagittal 1 mm slices. Next, the maximum value within any
voxel of each slice was identified and threshold levels were cal-
culated based on that maximum value for that slice independ-
ently. We first assess volume of the M1 tract in a single subject
using a conventional approach and our slice level thresholding
approach. For the conventional approach, the entire M1 sen-
sorimotor tract was thresholded based on values equal to 10%,
25%, and 50% of the maximum probability value found in the
entire tract. For our proposed approach, each slice was thre-
sholded based on values equal to 10%, 25%, and 50% of the
maximum probability value within each slice. Following
thresholding, tracts were binarized and the volume of the tract
at each threshold level was compared between approaches.
Slice Level Thresholding Including all Sensorimotor
Tracts
The proposed experimental approach will characterize 6 tracts
in each hemisphere (M1, PMd, PMv, SMA, preSMA, and S1)
using 9 percentile thresholds (10–50% in increments of 5%) in
100 subjects. The majority of the sensorimotor tracts can be
split into axial slices because their primary direction of travel is
along the z-axis. However, the PMv sensorimotor tract moves
medially from the PMv toward the PLIC, and then travels infer-
ior to the CP. Slice level thresholds for PMv were, therefore, cal-
culated for axial and sagittal slices. PMv was included with all
other tracts in axial slices from z=−35 to 20. Lateral regions of
the PMv tract in the right hemisphere were identified using the
slice level threshold approach in sagittal slices from x>30.
Portions of the tract between z>20 and x<30 were identified
using a conjunction analysis of the axial and sagittal PMv sen-
sorimotor tracts, in which voxels common to both tracts were
Table 1 Age, sex, and hand dominance for University of Florida
subjects used in the slice-by-slice profile analysis in Figure 7
Subject Human connectome project University of Florida
Age Gender Handedness Age Gender Handedness
1 22M R 22M R
2 32M R 32M R
3 36M R 37M R
4 34M R 34M R
5 34M R 34M R
6 30M R 30M R
7 23M R 25M R
8 23M R 21M R
9 23M R 21M R
10 30 M R 30 M R
11 24 M R 23 M R
12 26 F R 26 F R
13 31 F R 31 F R
14 22 F R 22 F R
15 26 F R 26 F R
16 22 F R 22 F R
17 25 F R 25 F R
18 23 F R 21 F R
19 22 F R 20 F R
20 24 F R 21 F R
Sensorimotor Area Tract Template (SMATT) Archer et al.
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included in the final PMv tract. The same logic was used for the
left hemisphere using negative values in the x-direction.
Individual tracts were thresholded at the 9 threshold levels,
and then binarized for each subject. Binarized tracts were then
submitted to a group level conjunction analysis such that
within each tract, voxels were retained if the voxel was com-
mon to at least 10 individuals. We used a liberal threshold
here, so that tracts could be refined objectively as described
below. The output of this stage of the analysis was 9 binarized
tracts (one at each percentile threshold) for each of the 6 cor-
tical seed regions (M1, PMd, PMv, SMA, preSMA, and S1), for the
left and the right hemisphere.
Overlap
Binarized conjunction maps for each tract were used to deter-
mine the extent of overlap between tracts at each threshold
level. We counted the number of voxels within each slice,
within each tract, that were also common to other tracts. For
example, voxels in the M1 tract with no overlap with any other
tracts were assigned a value of 0. Voxels in the M1 tract that
were common to one or more other tracts were assigned a
value of 1. Every voxel in the M1 tract was assigned a value of 0
or 1 following this logic. Overlap values were then summed to
create a single overlap value for the M1 tract for a single slice.
The same procedure was completed for each tract, in each
slice, at each threshold, in each hemisphere.
Coefficient of Variation of FA
To constrain tracts primarily to white matter, we calculated a
normalized variability measure using FA. The CV
FA
was calcu-
lated for each tract at each slice for each threshold percentile
for each individual. The binarized tracts from the group level
conjunction analysis were used as masks at the individual level
to calculate CV
FA
(SD of mean FA/mean FA). CV
FA
values were
averaged across individuals for each tract, at each slice, for
each threshold percentile. At lower thresholds, we expected
relatively higher CV
FA
values because greater tract volume
should increase the chances that voxels containing gray matter
or cerebrospinal fluid will be included in the tract.
Volume
To determine the extent to which threshold levels influence
the number of voxels retained within each tract, we calculated
tract volume. Tract volume was calculated at the group level
for each of the binarized tracts identified in the conjunction
analysis. Volume was calculated by summing the number of
voxels included in each tract at each threshold percentile at
each slice.
Threshold Selection
After overlap, CV
FA
, and volume were calculated, the 3 values
were multiplied together for each tract, for each threshold, and
for each slice. Note that when overlap was equal to zero, it was
eliminated from the calculation. We expected this summary
score to have high values at low percentiles and relatively low-
er values at higher percentiles for all tracts. A single summary
score was then calculated for each slice at each percentile by
summing values across tracts. As a result, 9 values were asso-
ciated with each slice; one at each threshold percentile.
Segmented regression analyses were then conducted (segmen-
ted package in R version 3.2.1) at each slice to fit 2 simple
regression models to the 9 data points. The breakpoint analysis
was used to identify the point of intersection of the 2 linear
functions of different slopes. The goal was to identify the point
at which increases in threshold no longer led to large decreases
in overlap or volume. Hence, we wanted to identify the point at
which overlap is best minimized while volume is maximized.
CV was included as a control variable to ensure that the tract is
restricted to white matter. The segmented regression analysis
method fits 2 different linear regression models to the data, in
which the models are separated where the linear relationship
changes (called the breakpoint) (Muggeo 2003). The threshold
closest to this breakpoint was then used for its corresponding
slice. Threshold levels were held constant across tracts for the
Figure 1. Probabilistic tractography inputs. (A) The regions within the HMAT were used as seed regions in the probabilistic tractography analyses. (B) A separate prob-
abilistic tractography analysis was conducted for M1 (green), PMd (dark yellow), PMv (light yellow), SMA (orange), preSMA (red), and S1 (blue), in which a planar way-
point was placed at the level of the PLIC (z=7 to 9) and the CP (z=−31 to −29). Additionally, transcallosal streamlines were excluded by including a planar exclusion
mask at the midline (x=−1 to 1).
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Cerebral Cortex

same slice, but were able to vary across slices based on slice
level breakpoint analyses. The resulting tracts identified in
each of the 181 slices were then merged with the results from
all other slices to create a group level whole-brain template for
each sensorimotor tract.
Sensorimotor Area Tract Probabilistic Atlas
Once the sensorimotor tract template was compiled, we next
determined the probability that each voxel within the template
was unique to a specific tract. For example, if a voxel was com-
mon to the M1 and the S1 tracts, there was a 50% chance that
the voxel was unique to the M1 tract and a 50% chance that the
voxel was unique to the S1 tract. Since there are 6 tracts in
each hemisphere, each voxel could be assigned a value ranging
from one-sixth (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 expected voxels in the most inferior portions
of the tracts to have low probabilities due to an increase in fiber
density in subcortical brain regions, and probabilities to
increase in cortical regions as fiber density decreased.
Evaluation of the SMATT in an Independent Data Set
To evaluate how the SMATT operates in an independent data
set, we overlaid the template on diffusion scans of 20 individ-
ual subjects from an independent data set collected at the
University Florida. We overlaid the template onto the scans of
20 individual subjects from the Human Connectome Project
(HCP) data set. These 20 subjects were a subset of the 100 sub-
jects used to create the template and were selected to match
the University of Florida data set for age (HCP: 26.15 ±5.31
years; UF: 26.6 ±4.72 years), sex, and handedness (see Table 1
for subject details). We also calculated FA values for each slice
of each tract for each individual. As the 2 data sets were col-
lected on different scanners using different pulse sequences, we
first normalized each individuals FA map by calculating their
mean whole-brain FA and dividing their FA map by this value.
Evaluation of the SMATT in an Individual after Stroke
Diffusion-weighted imaging of one chronic stroke individual
was obtained at the University of Florida. This subject provided
informed consent and scanning was approved by the local
Institutional Review Board and was in accord with the
Declaration of Helsinki. Diffusion parameters were identical to
the parameters for the other University of Florida subjects
noted above. The subject was a 65-year-old male with a single
subcortical ischemic stroke in the left hemisphere, who was
impaired on the right hand (right-hand grip strength: 30.8 N;
left-hand grip strength: 70.4 N). Time since stroke was 5.59
year, the Upper Extremity Fugl-Meyer Assessment score was
30, and the Mini Mental State Examination score was 29. The
SMATT was used to characterize tract microstructure and
stroke location.
Results
Figure 2shows probabilistic tractography data from one subject
for the M1 sensorimotor tract in the right hemisphere using a
conventional thresholding approach (Fig. 2A–D) and our pro-
posed slice level thresholding approach (Fig. 2E–H). The solid
blue line in Figure 2Ashows the maximum number of stream-
lines from any single voxel within each axial slice along the M1
tract between the CP (z=−36) and M1 (z=80). The maximum
number of streamlines is 120 which is located at z=−5 within
the inferior PLIC. Conventional thresholding approaches take
this value and then multiply it by an arbitrary percentage. For
example, a 10% threshold level would correspond with a
streamline value of 12, a 25% threshold level would lead to a
value of 30, and a 50% threshold level would lead to a value of
60. Slices in which all voxels have streamline values below this
value are then eliminated from the tract. For instance, the red
dotted horizontal line in Figure 2Ashows that at 10%, the most
superior cortical slices would be eliminated (z>40), and the
resulting 3D tract is shown in Figure 2B. At 25%, more of the
tract is eliminated (z>8) as shown by the orange horizontal
dotted line in Figure 2Aand the decrease in tract volume in the
3D representation of the tract in Figure 2C. At 50% (yellow hori-
zontal line in Fig. 2A), most of the tract is eliminated (z<−16
and z>0) as shown in Figure 2D, with only slices adjacent with
the slice with the maximum voxel being retained in the final
output. When tracking the entire sensorimotor tracts using this
conventional approach, low threshold levels must, therefore,
be used to retain volume in cortical regions of the tract.
Figure 2Eshows the data analyzed using our novel
approach, based on the same blue streamline profile shown in
Figure 2A. Using the same threshold percentages, but applying
them independently to each slice leads to clear differences in
tract volume. The dashed lines in Figure 2Eshow that at the
10% level the threshold changes along the trajectory of the
tract. The corresponding red 3D tract in Figure 2Fshows that
tract volume is retained in every slice, and overall volume is
greater than the corresponding conventional approach shown
in Figure 2B. As the threshold increases to 25% (orange dashed
line in Fig. 2Eand orange 3D tract in Fig. 2G) and 50% (yellow
dashed line in Fig. 2Eand yellow 3D tract in Fig. 2H) it is import-
ant to note that the overall volume of the tract is reduced with-
out eliminating entire slices from the tract. Previous studies
that use a conventional approach often decide on an arbitrary
threshold level that ensures that volume is retained across the
whole sensorimotor tract. Although implementing a slice level
thresholding approach addresses the volume issue, threshold
levels are still arbitrary and are not based on an objective data-
driven metric. To address this issue, we propose a novel
approach which identifies a threshold at each individual slice
based on a combined measure that includes tract overlap,
CV
FA
, and tract volume.
Figure 3Ashows the 6 binarized tracts in the right hemi-
sphere derived from individual probabilistic tractography
results thresholded at 10%. The box in Figure 3Ahighlights one
slice positioned at z=10. All remaining panels (Fig. 3B–G) show
data calculated for this slice. Figure 3Bshows an axial view of
the 6 tracts within the right PLIC thresholded at 10%, 25%, and
50%. The tracts descending from M1 (green), PMd (dark yellow),
PMv (light yellow), SMA (orange), preSMA (red), and S1 (blue)
are shown. At 10%, there is high volume for each tract and high
overlap between tracts making tract segregation difficult. As
thresholding increases to 25% and then 50%, both volume and
overlap decrease making tract segregation more viable.
Figure 3Cshows overlap values for each tract for each thresh-
old from 10% to 50% in 5% increments. At 10%, 102 voxels
within the M1 sensorimotor tract were also common to one or
more other tracts. At the same 10% threshold level, overlap in
preSMA was 57 voxels. As thresholding increases there is a
decrease in overlap, with thresholds set at 50% leading to over-
lap values of 16 for the M1 tract and 5 for the preSMA tract.
Figure 3Dshows CV
FA
at each threshold. The CV
FA
for M1 was
0.281 at 10%, and decreased to 0.208 at 50%. Figure 3Eshows
Sensorimotor Area Tract Template (SMATT) Archer et al.
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5

tract volume at each threshold. At 10%, the M1 tract has a vol-
ume of 105 voxels, whereas preSMA has a volume of 78 voxels.
As thresholding increases to 50%, we see a reduction in volume
in all tracts, with voxel count in the M1 tract decreasing to 31
voxels and voxel count in the preSMA tract decreasing to 16
voxels. Measures of overlap, CV
FA
, and volume were then
multiplied together and these data are shown in Figure 3F.At
10%, the value is high for all tracts, and this value approaches
zero as thresholding increases to 50%. Values across tracts
were then summed to create one overall score for each thresh-
old and these scores are represented by the yellow dots shown
in Figure 3G. Summed values were then used as dependent
variables in a segmented regression analysis. Two lines were fit
to the data (red lines in Fig. 3G), and the breakpoint was calcu-
lated to be at 17.24%. Threshold levels were rounded to the
nearest multiple of 5 within the 10–50% range. Therefore, the
threshold for this slice was selected as 15% (blue dashed line in
Fig. 3G). The same procedure was conducted independently for
each slice in each hemisphere.
Sensorimotor Area Tract Template
The mean threshold for the left hemisphere was 16.43% (SD =
2.45, range: 10–25). The mean threshold for the right hemi-
sphere was 19.83% (SD =3.11, range: 15–25). We also averaged
across slices to calculate regional thresholds. For cortical
regions between z=84 and z=19 the average threshold across
the left and the right hemisphere was 17.6%. For the PLIC
between z=18 and z=−4 the average threshold was 20.0%.
Below the internal capsule from z=−5to−35 the average
threshold was 17.9%. The important point here is that although
the threshold percentages are relatively similar, the maximum
probability value that they are applied to is different between
regions (see Fig. 2). 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 common to both
tracts were included in the final template. Figure 4Ashows the
assembled SMATT in the right hemisphere. Volume was re-
tained in all slices of each tract. Figure 4Bshows an axial view
of the SMATT overlaid on an anatomical image at slice z=55,
z=10, and z=−30. At z=55, Figure 4Bshows that cortical top-
ography was relatively well maintained in the SMATT, and that
the SMATT is constrained to white matter. Overlap is increased
at z=10, as compared to the cortex, but this is to be expected
given the increase in fiber proximity in the PLIC. Cortical topog-
raphy was generally well maintained in PLIC, with the preSMA
tract most anterior and the S1 tract most posterior with no
overlap between these 2 tracts. Highest overlap was found
between tracts in the most inferior portions of the descending
tracts, such as in the CP which is shown at z=−30. To show
the utility of slice level thresholding, we took the average slice
level thresholding value (18%), and used this percentage to per-
form a tract-wide threshold (Fig. 4C). Figure 4Cshows that with
a tract-wide threshold of 18%, cortical volume is abolished in
all tracts. Figure 4Dshows the Johns–Hopkins template of the
corticospinal tract thresholded at 3 different levels (Hua et al.
2008). When using the Johns–Hopkins template with a 0%
threshold, it is clear that white and gray matter will be included
in the template. At the 25% threshold, the template is restricted
to a small area in the medial portion of M1, and at the 50%
threshold there is no volume in cortical areas of the tract.
Similar patterns are shown at z=10 and z=−30. The SMATT
overcomes each of these issues while also identifying tracts
that are specific to distinct cortical regions.
Probabilistic SMATT
The overlap of each tract in the SMATT was quantified by sum-
ming the number of tracts that were common to each voxel in
the SMATT. Each voxel in the probabilistic SMATT can be repre-
sented by a value ranging from one-sixth (or 0.167) to 1, in
Figure 2. Tract level thresholding versus slice level thresholding in the M1 tract. (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. Conventional tract level thresholding calculates the maximum num-
ber of streamlines within the profile and bases the threshold on a percentage of this value. Thresholds can be arbitrarily set at 10% (red line), 25% (orange line), or
50% (yellow line) of the peak value. 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 some 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 PLIC (z=−16 to 0 remain). (E) By splitting the tract into individual slices, each slice
can be thresholded independently. A benefit of this method is that it does not result in any excluded slices within the tract. At 10%, tract volume is high. At 25% and
50% the volume of the tract decreases but volume is maintained in every slice of the tract (F–H).
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which higher values indicate more certainty that a particular
voxel is unique to a particular sensorimotor tract. Figure 5A
shows the probabilistic M1 tract atlas in the right hemisphere.
Red colors represent voxels that have a low probability of resid-
ing in a single tract. As the colors get brighter voxels have a
higher probability of being unique to the M1 tract, as shown by
the increase in number of yellow voxels starting at the level of
the PLIC. A probability profile for the M1 tract is shown on the
right side of Figure 5A. The red line represents the mean prob-
ability value for each axial slice in the M1 tract. The yellow
shading represents the corresponding ±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 tract increases to approximately 0.6.
Superior to the PLIC, there is a progressive increase in the prob-
ability of voxels being within M1 tract until z=40. At z=40,
there is a sharp increase in probability, which reaches 1 at z=
55. 3D images and probability profiles for the remaining tracts
in the right hemisphere are shown in Figures 5B–F. All tracts
show similar probability profiles to the M1 tract, with probabil-
ity generally increasing in more superior regions of the tract.
Based on the conjunction analyses performed on the SMATT,
probability values in the left hemisphere are the same as in the
right hemisphere. The probabilistic SMATT includes values for
both hemispheres for all tracts.
Evaluation of SMATT in an Independent Data Set
To assess how the SMATT operates in individual subjects, we
first visually inspected alignment of the SMATT within the PLIC
at the individual level using a subset of 20 subjects from the
Human Connectome Project data set as well as individual sub-
jects collected at the University of Florida. Figure 6Ashows the
SMATT overlaid on an axial slice of the FMRIB anatomical tem-
plate at the level of the PLIC (z=10). Figure 6Bshows the
SMATT overlaid on individual subject anatomical images in
standard space. Individual data from 20 subjects collected as
part of the Human Connectome Project and individual data
from 20 subjects collected at the University of Florida are
shown. The SMATT is well aligned in both groups for all tracts,
even though the data from the University of Florida has a lower
resolution than the Human Connectome Project. Next, we cre-
ated mean normalized FA profiles (average SMATT FA within
each slice divided by whole brain mean FA) for each tract for
each data set. Figure 7Ashows the M1 tract in the right hemi-
sphere. Figure 7B,Cshow the slice-by-slice profiles of normal-
ized FA values for the left and right hemisphere. HCP data are
represented with black lines and red shading (mean ±SEM), and
University of Florida data are represented with black lines and
green shading (mean ±SEM). Figure 7Bshows that the M1 tract
begins at z=−35 at the level of the CP and terminates in M1 at
z=75. HCP and University of Florida profiles show a similar pat-
tern. Normalized FA was approximately 1.1 within the CP and
increased to approximately 1.6 within the PLIC (z=−20 to 20).
From z=20 to 40, there is a reduction in normalized FA, which
is consistent with the associated crossing fibers within the cen-
trum semiovale. At z=40, there is a steady increase in normal-
ized FA which peaks at z=55, followed by a slow decrease in
normalized FA in cortical regions of the M1 tract. Figure 7C
shows that a similar pattern was evidenced in the right hemi-
sphere. Profiles were created for each of the 5 remaining tracts
in both hemispheres and are shown in Figure 7D–R.
To determine whether subject age influenced the develop-
ment of the template, we calculated area under the curve (AUC)
for each individuals FA profile for each tract. Figure 8shows
correlations between age and AUC in all tracts in the left hemi-
sphere (A–F) and the right hemisphere (G–L). No significant cor-
relations were found between FA and age in any tract.
Evaluation of SMATT in a Stroke Subject
An axial view of the chronic stroke subject’s FA map is shown
in Figure 9Aat z=15 at the level of the PLIC. The lesion merges
with the ventricle in the left hemisphere. Anterior regions of
the PLIC show extensive damage. Figure 9Bshows the SMATT
Figure 3. Slice level thresholding of sensorimotor tract data. (A) The probabilis-
tic tractography results for all 6 sensorimotor tracts in the right hemisphere at
the 10% threshold level. At this low threshold, aberrant voxels are still present.
For instance, red voxels that are associated with the preSMA tract can be seen
above the corpus callosum, and blue and yellow voxels can be been in the ven-
tral temporal lobe. The presence of these aberrant voxels support our position
that thresholding the entire tract at one relatively low percentile can lead to
the inclusion of lowly probably voxels. The gray box identifies the individual
slice (z=10) that is analyzed in Figure 3B–G.(B) Axial slices of the PLIC in the
right hemisphere with the sensorimotor tracts thresholded at 10%, 25%, and
50%. Increases in threshold lead to a decrease in volume and a decrease in
overlap between the tracts. (C) Overlap of each sensorimotor tract with every
other sensorimotor tract was calculated at each percentile. The 6 sensorimotor
tracts were thresholded at 9 percentiles. (D) The CV
FA
of each sensorimotor
tract was calculated for each percentile threshold. (E) The volume of each sen-
sorimotor tract was calculated for each percentile threshold. (F) The overlap,
CV
FA
, and volume were incorporated into one variable and calculated for each
tract at each percentile threshold. (G) Values shown in 3Fwere summed across
tracts for each percentile (yellow dots) and a segmented regression analysis
was conducted. Two lines were fit to the data (red lines), and the breakpoint
was calculated to be at 17.24%. Threshold levels were rounded to the nearest
multiple of 5 within the 10–50% range. The threshold for this slice was selected
as 15% (blue dashed line).
Sensorimotor Area Tract Template (SMATT) Archer et al.
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7

overlaid on top of the same structural scan shown in Figure 9A.
Here, we show successful normalization of the lesioned brain
to MNI space, which allows the SMATT to overlay well.
Anterior regions of the template in the left hemisphere overlap
the lesion. Lesion overlap in the template was calculated
within each tract at z=15 for the left hemisphere, and is
shown in Figure 9C. The x-axis is sorted from posterior to anter-
ior, and shows that S1 and M1 have 0% lesion overlap whereas
lesion overlap for PMd and preSMA is 100%. FA was quantified
for each tract at z=15 (Fig. 9D), and shows an inverse relation-
ship with lesion overlap. Regions with high lesion overlap have
lower FA. We also quantified lesion overlap in the entire tract
(Fig. 9E), and show that the more posterior tracts have low
lesion overlap (<2.5%), while the more anterior tracts had high-
er lesion overlap (>7.5%). Lesion overlap provides an overall
assessment of lesion impact, but it does not provide informa-
tion on lesion location. We next calculated FA profiles for each
tract in the z-direction (Fig. 9F). Profiles for S1 and M1 tracts fol-
low a similar pattern to the profiles of healthy controls which
are shown in Figure 7B. Anterior tracts including PMv, PMd,
SMA, and preSMA which had higher lesion overlap show large
reductions in FA from z=5 to 40. Our observations suggest that
the SMATT can be useful to locate and characterize lesions that
impact the sensorimotor tracts.
Discussion
The purpose of this study was to develop a high-resolution
template of the sensorimotor tracts which segmented the
tracts based on 6 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 are
consistent with tracing studies in monkey and show that cor-
tical topography is generally preserved as the tracts descend
through the brain, with the preSMA tract remaining most
anterior and the S1 tract remaining most posterior through the
internal capsule. We have combined the 6 individual tracts into
a white matter template named the SMATT. We also provide a
probabilistic SMATT that quantifies the extent of overlap at the
voxel level between tracts. The probabilistic SMATT showed
that overlap between adjacent tracts increased as tract proxim-
ity increased through the internal capsule and CP. We also
show that the SMATT operates well in an independent data set
based on slice-by-slice normalized FA profiles. Finally, we infer
tract-specific and slice-specific damage to the sensorimotor
tracts in a stroke patient by using the template to extract FA
profiles. The SMATT and probabilistic SMATT provide new tools
that segment and label sensorimotor tracts at a spatial reso-
lution not previously available.
Sophisticated tracing techniques have been used in rodents
and monkeys to identify the axons that link the motor cortex
with the reticular formation and spinal cord (Barnard and
Woolsey 1956;Dum and Strick 1991;Fries et al. 1993;He et al.
1993,1995;Coleman et al. 1997;Maier et al. 2002). Over the last
several decades important advances in structural neuroima-
ging have allowed us to start characterizing the sensorimotor
pathways in vivo in humans. However, as compared with tra-
cing studies in animal models, when using diffusion MRI and
probabilistic tractography to identify these pathways, tract vol-
ume and tract overlap are more variable and can be influenced
by factors that include seed masks, waypoint and exclusion
masks, and threshold values.
The selection of seed masks is a large source of variability
between probabilistic tractography studies. One approach is to
Figure 4. The SMATT. (A) The assembled SMATT (M1—green; PMd—dark yellow;
PMv—light yellow; SMA—orange; preSMA—red; S1—blue). Note that non-
uniformity in the lateral portion of the PMv tract is driven by the tract traveling
around gray matter. (B) Axial slices of the SMATT in the cortex (z=55), PLIC (z=
10), and CP (z=−30). (C) To compare independent slice level thresholding and
tract level thresholding, we used the average slice level threshold (18%) to
threshold each entire tract. At z=55 there is no volume in any cortical tracts.
At z=10 and z=−30 tract location follows a similar pattern as the SMATT. (D)
Axial slices of the Johns–Hopkins corticospinal template thresholded at 0%,
25%, and 50% in the cortex (z=55), PLIC (z=10), and CP (z=−30).
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Cerebral Cortex

Figure 5. Probabilistic SMATT. Each tract was overlaid with one another to quantify the amount of overlap between tracts so that a probabilistic value could be calcu-
lated for each voxel in the template. The mean probability of each slice is shown with a red line, with the yellow shading representing the mean ±SEM. A view of
each tract (A—M1; B—PMd; C—PMv; D—SMA; E—preSMA; F—S1) is shown, in which darker red colors indicate higher overlap with other tracts (i.e., low probability
voxel is only part of one tract), whereas brighter yellow colors identify 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. As the tracts reach more superior regions, the probability of voxels being unique to a particular sensorimotor tract
increases.
Figure 6. SMATT overlaid on individual subject data. (A) SMATT overlaid on an axial slice of the FMRIB anatomical template at the level of the PLIC (z=10). (B) The
SMATT overlaid on individual subject anatomical images in standard space. Individual data from 20 subjects collected as part of the Human Connectome Project and
individual data from 20 subjects collected at the University of Florida are shown. The SMATT is well aligned in both groups for all tracts. Axial slices are shown at z=
10 which is within the PLIC. M1—green; PMd—dark yellow; PMv—light yellow; SMA—orange; preSMA—red; S1—blue.
Sensorimotor Area Tract Template (SMATT) Archer et al.
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9

generate seed masks by extracting regions from templates such
as the Johns–Hopkins DTI-based white matter atlas (Wakana
et al. 2007;Hua et al. 2008). A second approach is to hand draw
seeds in the precentral gyrus (Schaechter et al. 2008;
Lindenberg et al. 2010,2012), and the hand bump region in
particular (Stinear et al. 2007;Schaechter et al. 2009), based
on anatomical landmarks in structural scans and fiber direction
in FA maps (Newton et al. 2006;Schaechter et al. 2008;
Lindenberg et al. 2012). However, the landmarks used to iden-
tify seeds are different between studies, and hand drawing
regions for large data sets can be time consuming. A third
approach is to generate seed masks based on clusters of
voxels identified in task-based functional MRI experiments or
tract-based spatial statistics (TBSS) analyses that identify corre-
lations between microstructure and behavior (Stinear et al.
2007;Schaechter et al. 2008,2009;Lindenberg et al. 2010,2012;
Schulz et al. 2012;Leunissen et al. 2013;Archer et al. 2016).
Identifying seeds based on individual task-based studies means
that the seed locations are constrained by the specific task
used and by the specific subjects tested in the study. In add-
ition, 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 HMAT (Mayka et al. 2006). The tem-
plate identifies the borders of 6 sensorimotor areas (M1, PMd,
PMv, SMA, preSMA, and S1) based on activation-likelihood esti-
mation from 126 functional neuroimaging studies that manipu-
lated upper and lower limb motor control. Generating seeds
from the HMAT ensured that the SMATT is based on segregated
brain function in the cortex, and is generalizable to the upper
and lower limbs.
Following seed generation, the use of waypoint masks and
exclusion masks can have a profound impact on probabilistic
tractography results. Tract volume generally decreases when
using small waypoint masks and when the total number of
waypoint masks and exclusion masks increases. In the case of
the tract descending from M1, the PLIC and the CP are often
used as waypoints (Schaechter et al. 2008,2009;Lindenberg
et al. 2010,2012;Archer et al. 2016;Potter-Baker et al. 2016) and
a planar sagittal slice at the midline can be used as an exclu-
sion mask to eliminate transcallosal fibers. As with seed gener-
ation, waypoint masks can be hand drawn, extracted from
white matter templates, or obtained from TBSS analyses
(Stinear et al. 2007;Schaechter et al. 2008,2009;Lindenberg
et al. 2010,2012;Archer et al. 2016;Potter-Baker et al. 2016).
However, there is currently no established way of identifying
segregated subcortical waypoints that map to our 6 cortical
regions. As a result, we used an unconstrained approach by
placing axial planar waypoints at the level of the PLIC (z=7–9)
and at the level of the CP (z=−31 to −29). Additionally, we
placed an exclusion mask at the midline (x=−1 to 1) to exclude
transcallosal fibers. Minimally constrained 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
(Schaechter et al. 2009;Lindenberg et al. 2012;Schulz et al.
2012). This approach is sensitive to peaks in probability values
within the tract, and these values vary according to tract length
Figure 7. Mean normalized FA profiles for each tract for each hemisphere for data collected as part of the Human Connectome Project and data collected at the
University of Florida (UF). (A) M1 tract in the right hemisphere. (B) Slice-by-slice profile of normalized FA values in M1 tract in the left hemisphere. (C) Slice-by-slice
profile of normalized FA values in M1 tract in the right hemisphere. HCP data are represented with black lines and red shading (mean ±SEM). UF data are represented
with black lines and green shading (mean ±SEM). HCP and UF profiles show a similar pattern in the M1 tract across group and hemisphere. Profiles were created for
each of the 5 remaining sensorimotor tracts in both hemispheres and are shown in Figures (D–R).
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Cerebral Cortex

and tract location. For instance, a recently developed white
matter tract template for the cerebellum successfully used a
threshold of 50th percentile (van Baarsen et al. 2016), whereas
tractography studies focused on the sensorimotor tract from
M1 have used lower thresholds that range from 0% to 2% of the
maximum to preserve volume across the whole tract and min-
imize false negatives in the cortex (Newton et al. 2006;
Schaechter et al. 2009;Lindenberg et al. 2012;Park et al. 2013).
Here, we aimed to circumvent this issue by developing a meth-
od that independently thresholded each slice at 9 different
levels (10–50% in increments of 5%) and then objectively identi-
fied the threshold to use for each slice using a data-driven
approach. Our slice level approach was designed to minimize
false negatives in cortical regions and minimize false positives
in subcortical regions.
A single threshold level was independently determined for
each slice based on 3 factors: overlap between the different
tracts, the CV
FA
, and the volume of each different tract. Overlap
was incorporated into the analysis to quantify tract segrega-
tion. Although segregation between cortical regions is well
established based on anatomy and function (Dum and Strick
2005;Behrens et al. 2006;Mayka et al. 2006), segmentation of
sensorimotor tracts in vivo in humans is not as well developed.
Tracing studies in rodents show that axons from spatially dis-
tinct cortical areas occupy different regions of the cross-
sectional area of the internal capsule (Coleman et al. 1997).
Similar topography has been demonstrated in monkey
(Barnard and Woolsey 1956;Fries et al. 1993) and human
(Newton et al. 2006), where axons of M1 pass through the mid-
dle third of the PLIC, and axons from SMA course through more
anterior regions of the PLIC. More caudally within the rodent
brain, axons in the CP and the longitudinal pontine fasciculus
from distant cortical areas remain largely separate, but those
from adjacent cortical areas begin to overlap. Our observations
in the CP are consistent with these findings and show that tract
separation is most pronounced for cortical regions that are fur-
thest from each other. By the medullary pyramid, the pyram-
idal decussation, and the dorsal column of the spinal cord, the
representations of all the cortical regions overlap as axons
become intermingled (Coleman et al. 1997). Similar conclusions
have been drawn from studies in monkey which show that dif-
ferent cortical regions project onto the same motoneurons in
the cervical spinal cord (Barnard and Woolsey 1956;Maier et al.
2002). These findings suggest that topographically organized
corticospinal tracts in the cortex converge at some level of the
neuroaxis (Dum and Strick 1991;He et al. 1993,1995).
In monkeys, we know that the preSMA projects weakly, if at
all, to the spinal cord (Dum and Strick 1991;Luppino et al.
1994). In addition to the striatum and prefrontal cortex, preSMA
projects to the reticular formation (Keizer and Kuypers 1989)by
way of the CP. Here, we map this descending tract, and show
that the preSMA tract is most anterior in PLIC and most medial
in the CP. In contrast, the S1 tract coursed through posterior
regions of PLIC and was located more laterally in the CP. These
findings are in good agreement with other human tractography
studies (Newton et al. 2006;Park et al. 2008;Schulz et al. 2012;
Archer et al. 2016), and suggest good preservation of cortical
topography at the level of the internal capsule in humans.
Probabilistic tractography has previously been used to iden-
tify descending tracts from multiple cortical areas, but overlap
between tracts has not been reported, sample sizes are gener-
ally small, and thresholding has been set at a single value for
Figure 8. Correlations of SMATT FA with Age. FA profiles were created for each individual. FA values for each tract were summed to create an area under the curve
measure. This measure was correlated with age for all tracts in the left (A–F) and the right (G–L) hemisphere. No significant correlations were found between FA and
age.
Sensorimotor Area Tract Template (SMATT) Archer et al.
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11

the entire tract (Newton et al. 2006;Schulz et al. 2012;Jang and
Seo 2015;Archer et al. 2016;Potter-Baker et al. 2016). Our find-
ings suggest that the low threshold values used in previous
studies (0–2%) likely lead to high volume and high overlap
between tracts, which further diminishes one’s ability to track
topography through the internal capsule and CP. Without
knowing the extent of overlap between tracts, it is difficult to
make a firm conclusion about the characteristics of different
tracts.
The probabilistic SMATT advances the current literature by
offering greater spatial localization in the sensorimotor tracts.
White matter atlases such as the Johns–Hopkins white matter
template (Wakana et al. 2007;Hua et al. 2008) and the Harvard–
Oxford cortical and subcortical structural atlas (Frazier et al.
2005;Desikan et al. 2006;Makris et al. 2006;Goldstein et al.
2007) can be used to generate masks of entire tracts and their
corresponding regions (i.e., PLIC and CP), but specific informa-
tion within these regions corresponding to distinct cortical tar-
gets is not currently available. The probabilistic SMATT extends
the literature by providing probability values that a voxel is
contained within a specific sensorimotor tract, and has the
potential to localize lesions with greater precision. For instance,
we used the template to extract the FA measure for one stroke
subject and showed that lesion overlap was greatest and FA
values were lowest for the SMA, preSMA, PMd, and PMv tracts.
Slice level FA profiles revealed tract-specific and slice-specific
decreases in FA within these same tracts (Fig. 9). The use of the
template in future studies will provide greater specificity when
examining the association between brain structure and behav-
ior (Lindenberg et al. 2010,2012;Zhu et al. 2010;Stinear et al.
2012). To evaluate whether the SMATT worked in an independ-
ent data set, we overlaid the template onto individual FA maps
from 20 healthy adults in standard space. Alignment of the
SMATT to white matter was good for all individual subjects
(Fig. 7), suggesting that the SMATT translates well to independ-
ent data sets. Consistency across individuals was also demon-
strated by similar normalized FA profiles across the HCP group
data and the independent data set (Fig. 7). One advantage of
using the SMATT in future studies is that probabilistic tracto-
graphy and slice level threshold analyses will not have to be
completed. For instance, in the clinical setting, any structural
scan can be collected, normalized, and then the SMATT can be
used to extract tract-specific profiles from the data. In principle,
any lesion or tumor that directly impacts an SMATT tract
should be evident within the corresponding profiles. This pro-
cedure takes approximately 5 min to complete on a standard
desktop computer with a single core. In instances where tracto-
graphy analyses do need to be completed, our observations
suggest that region specific, rather than slice specific, thresh-
olds may be a viable approach that is computationally less
intensive.
A well known limitation of tractography is its ability to track
through areas of the brain which contain crossing fibers
(Wedeen et al. 2008). This limitation is mitigated to some
extent by the increased spatial resolution of the Human
Connectome Project data, and advanced tractography algo-
rithms that allow for the tracking of 3 fibers per voxel (Jbabdi
et al. 2012), rather than conventional approaches which allow
for the tracking of 1 or 2 fibers per voxel. Diffusion MRI images
are susceptible to partial volume effects and noise. Partial vol-
ume effects are smaller; however, when fiber bundles are larger
and curvature is lower, as is the case for the sensorimotor
tracts (Vos et al. 2011). We have avoided using “corticospinal
tract”and “corticobulbar tract”when labeling the template.
Without collecting data from the cortex to the cervical spinal
cord and then placing a waypoint in the cervical spinal cord, it
is impossible to differentiate the corticospinal tract from the
corticobulbar tract. It is, therefore, probable that many of the
individual tracts in the SMATT include both corticospinal and
corticobulbar fibers (Dum and Strick 1991;Galea and Darian-
Smith 1994).
The SMATT was created using diffusion MRI data from
healthy adults ranging in age from 21 to 35. Correlation ana-
lyses did not reveal any association between tract microstruc-
ture and age, and we show that the template can be useful in
identifying tract- and slice-specific changes in FA after stroke
in an individual who is 65 years old. Nevertheless, caution
should be exercised when using this template to study older
individuals and disease states that are associated with known
changes in brain anatomy. Although we have demonstrated
that the SMATT works well at the individual subject level, by
necessity this requires that the data to be assayed is accurately
warped to standard space. The SMATT was created using data
from 87 right handers and 13 left handers. Future studies will
be necessary to determine whether tract volume and tract
orientation varies as a function of handedness and hemi-
sphere. Although the thresholds used to create the SMATT
were objectively determined, the measures that contributed to
the calculation were subjectively determined. As the field
Figure 9. Using the SMATT to assess tract-specific damage after stroke. (A)
Diffusion-weighted image at z=15 showing lesion damage to the internal cap-
sule. (B) SMATT overlaid on the diffusion-weighted image. (C) Lesion overlap
was calculated for each tract in the left hemisphere at z=15. No lesion overlap
was found for S1 or M1. Extensive overlap was found for SMA, preSMA, PMv,
and PMd. (D) FA was calculated for each tract at z=15. FA was greatest in more
posterior regions of the tract (S1, M1) as compared to anterior regions (SMA,
preSMA, PMd, and PMv). (E) Tract-wide lesion overlap in all left hemisphere
tracts. (F) Tract-specific FA profiles were calculated for each slice for each sen-
sorimotor tract. Profiles directly impacted by the stroke (SMA, preSMA, PMd,
and PMd) can be identified based on the distinct reductions in FA between z=5
and z=40.
12
|
Cerebral Cortex

progresses, new variables may be identified that help contrib-
ute to the optimal slice level threshold value. It is also import-
ant to note that the current template in no way provides a
quantitative estimate of the actual strength of direct anatom-
ical connectivity between the cortex and CP, a measure that
cannot be derived from any form of structural imaging (Jones
et al. 2013).
Precise measurements of the tracts that link the sensori-
motor cortex with the CP are fundamental to our understand-
ing of diseases and disorders that can impact the human motor
system such as stroke (Newton et al. 2006;Schaechter et al.
2008,2009;Lindenberg et al. 2012;Schulz et al. 2012;Groisser
et al. 2014;Archer et al. 2016), upper motor neuron syndrome
(Sach et al. 2004), multiple sclerosis (Tovar-Moll et al. 2015),
traumatic brain injury (Jang and Kim 2016), spinal cord injury
(Hou et al. 2016), pain (Misra and Coombes 2015;Misra et al.
2016), and cerebral palsy (Jaspers et al. 2015). Conventional
approaches to studying the microstructural properties of the
sensorimotor tracts in humans are focused on the M1 tract and
likely include voxels that are also common to the corticospinal
and corticobulbar tracts that descend from regions beyond M1
(Schaechter et al. 2008,2009;Lindenberg et al. 2012;Groisser
et al. 2014). The SMATT provides a new tool that can be used
to localize tract-specific damage, and to quantify microstruc-
ture in specific tracts that are associated with distinct cortical
regions. Increases in spatial localization may improve diagnos-
tic and prognostic evaluations across a range of diseases
and disorders. The SMATT and probabilistic SMATT are freely
available at www.lrnlab.org (Laboratory for Rehabilitation
Neuroscience).
Funding
The American Heart Association (contract grant number
15GRNT25700431) and the National Institutes of Health (con-
tract grant number R01 NS058487).
Notes
MRI data collection was supported through the National High
Magnetic Field Laboratory and obtained at the Advanced
Magnetic Resonance Imaging and Spectroscopy facility in the
McKnight Brain Institute of the University of Florida. Data were
provided [in part] by the Human Connectome Project, WU-
Minn Consortium (Principal Investigators: David Van Essen and
Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes
and Centers that support the NIH Blueprint for Neuroscience
Research; and by the McDonnell Center for Systems
Neuroscience at Washington University. Conflict of Interest:
None declared.
References
Andersson JL, Sotiropoulos SN. 2015. Non-parametric represen-
tation and prediction of single- and multi-shell diffusion-
weighted MRI data using Gaussian processes. Neuroimage.
122:166–176.
Andersson JL, Sotiropoulos SN. 2016. An integrated approach to
correction for off-resonance effects and subject movement
in diffusion MR imaging. Neuroimage. 125:1063–1078.
Archer DB, Misra G, Patten C, Coombes SA. 2016.
Microstructural properties of premotor pathways predict
visuomotor performance in chronic stroke. Hum Brain
Mapp. 37:2039–2054.
Barnard JW, Woolsey CN. 1956. A study of localization in the
corticospinal tracts of monkey and rat. J Comp Neurol. 105
(1):25–50.
Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW.
2007. Probabilistic diffusion tractography with multiple fibre
orientations: what can we gain? Neuroimage. 34(1):144–155.
Behrens TE, Jenkinson M, Robson MD, Smith SM, Johansen-Berg
H. 2006. A consistent relationship between local white mat-
ter architecture and functional specialisation in medial
frontal cortex. Neuroimage. 30(1):220–227.
Behrens TE, Johansen-Berg H, Woolrich MW, Smith SM,
Wheeler-Kingshott CA, Boulby PA, Barker GJ, Sillery EL,
Sheehan K, Ciccarelli O, et al. 2003a. Non-invasive mapping
of connections between human thalamus and cortex using
diffusion imaging. Nat Neurosci. 6(7):750–757.
Behrens TE, Woolrich MW, Jenkinson M, Johansen-Berg H,
Nunes RG, Clare S, Matthews PM, Brady JM, Smith SM.
2003b. Characterization and propagation of uncertainty in
diffusion-weighted MR imaging. Magn Reson Med. 50(5):
1077–1088.
Caeyenberghs K, Leemans A, Geurts M, Taymans T, Linden CV,
Smits-Engelsman BC, Sunaert S, Swinnen SP. 2010. Brain-
behavior relationships in young traumatic brain injury
patients: DTI metrics are highly correlated with postural
control. Hum Brain Mapp. 31(7):992–1002.
Clatworthy PL, Williams GB, Acosta-Cabronero J, Jones SP,
Harding SG, Johansen-Berg H, Baron JC. 2010. Probabilistic
tractography of the optic radiations—an automated method
and anatomical validation. Neuroimage. 49(3):2001–2012.
Coleman KA, Baker GE, Mitrofanis J. 1997. Topography of fibre
organisation in the corticofugal pathways of rats. J Comp
Neurol. 381(2):143–157.
Coombes SA, Corcos DM, Pavuluri MN, Vaillancourt DE. 2012.
Maintaining force control despite changes in emotional con-
text engages dorsomedial prefrontal and premotor cortex.
Cereb Cortex. 22(3):616–627.
Coombes SA, Corcos DM, Sprute L, Vaillancourt DE. 2010.
Selective regions of the visuomotor system are related to
gain-induced changes in force error. J Neurophysiol. 103(4):
2114–2123.
Coombes SA, Corcos DM, Vaillancourt DE. 2011. Spatiotemporal
tuning of brain activity and force performance. Neuroimage.
54(3):2226–2236.
Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC,
Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT,
et al. 2006. An automated labeling system for subdividing
the human cerebral cortex on MRI scans into gyral based
regions of interest. Neuroimage. 31(3):968–980.
Dum RP, Strick PL. 1991. The origin of corticospinal projections
from the premotor areas in the frontal lobe. J Neurosci. 11
(3):667–689.
Dum RP, Strick PL. 2005. Frontal lobe inputs to the digit repre-
sentations of the motor areas on the lateral surface of the
hemisphere. J Neurosci. 25(6):1375–1386.
Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S,
Gunther M, Glasser MF, Miller KL, Ugurbil K, Yacoub E. 2010.
Multiplexed echo planar imaging for sub-second whole
brain FMRI and fast diffusion imaging. PLoS One. 5(12):
e15710.
Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN,
Herbert MR, Bent EK, Koneru VK, Dieterich ME, et al. 2005.
Structural brain magnetic resonance imaging of limbic and
thalamic volumes in pediatric bipolar disorder. Am J
Psychiatry. 162(7):1256–1265.
Sensorimotor Area Tract Template (SMATT) Archer et al.
|
13

Fries W, Danek A, Scheidtmann K, Hamburger C. 1993. Motor
recovery following capsular stroke. Role of descending path-
ways from multiple motor areas. Brain. 116(Pt 2):369–382.
Galea MP, Darian-Smith I. 1994. Multiple corticospinal neuron
populations in the macaque monkey are specified by their
unique cortical origins, spinal terminations, and connec-
tions. Cereb Cortex. 4(2):166–194.
Goldstein JM, Seidman LJ, Makris N, Ahern T, O’Brien LM,
Caviness VS Jr, Kennedy DN, Faraone SV, Tsuang MT. 2007.
Hypothalamic abnormalities in schizophrenia: sex effects
and genetic vulnerability. Biol Psychiatry. 61(8):935–945.
Groisser BN, Copen WA, Singhal AB, Hirai KK, Schaechter JD.
2014. Corticospinal tract diffusion abnormalities early after
stroke predict motor outcome. Neurorehabil Neural Repair.
28(8):751–760.
He SQ, Dum RP, Strick PL. 1993. Topographic organization of
corticospinal projections from the frontal lobe: motor areas
on the lateral surface of the hemisphere. J Neurosci. 13(3):
952–980.
He SQ, Dum RP, Strick PL. 1995. Topographic organization of
corticospinal projections from the frontal lobe: motor areas
on the medial surface of the hemisphere. J Neurosci. 15(5 Pt
1):3284–3306.
Heffner RS, Masterton RB. 1983. The role of the corticospinal
tract in the evolution of human digital dexterity. Brain
Behav Evol. 23(3–4):165–183.
Hou J, Xiang Z, Yan R, Zhao M, Wu Y, Zhong J, Guo L, Li H,
Wang J, Wu J, et al. 2016. Motor recovery at 6 months after
admission is related to structural and functional reorganiza-
tion of the spine and brain in patients with spinal cord
injury. Hum Brain Mapp. 37(6):2195–2209.
Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, Calabresi PA,
Pekar JJ, van Zijl PC, Mori S. 2008. Tract probability maps in
stereotaxic spaces: analyses of white matter anatomy and
tract-specific quantification. Neuroimage. 39(1):336–347.
Jang SH. 2009. The role of the corticospinal tract in motor recov-
ery in patients with a stroke: a review. NeuroRehabilitation.
24(3):285–290.
Jang SH, Kim SY. 2016. Injury of the corticospinal tract in
patients with mild traumatic brain injury: a diffusion tensor
tractography study. J Neurotrauma. 33:1790–1795.
Jang SH, Seo JP. 2015. Aging of corticospinal tract fibers accord-
ing to the cerebral origin in the human brain: a diffusion
tensor imaging study. Neurosci Lett. 585:77–81.
Jaspers E, Byblow WD, Feys H, Wenderoth N. 2015. The corti-
cospinal tract: a biomarker to categorize upper limb func-
tional potential in unilateral cerebral palsy. Front Pediatr. 3:
112.
Jbabdi S, Sotiropoulos SN, Haber SN, Van Essen DC, Behrens TE.
2015. Measuring macroscopic brain connections in vivo. Nat
Neurosci. 18(11):1546–1555.
Jbabdi S, Sotiropoulos SN, Savio AM, Grana M, Behrens TE. 2012.
Model-based analysis of multishell diffusion MR data for
tractography: how to get over fitting problems. Magn Reson
Med. 68(6):1846–1855.
Jenkinson M, Bannister P, Brady M, Smith S. 2002. Improved
optimization for the robust and accurate linear registration
and motion correction of brain images. Neuroimage. 17(2):
825–841.
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith
SM. 2012. Fsl. Neuroimage. 62(2):782–790.
Jenkinson M, Smith S. 2001. A global optimisation method for
robust affine registration of brain images. Med Image Anal.
5(2):143–156.
Johansen-Berg H, Rushworth MF. 2009. Using diffusion imaging
to study human connectional anatomy. Annu Rev Neurosci.
32:75–94.
Jones DK, Knosche TR, Turner R. 2013. White matter integrity,
fiber count, and other fallacies: the do’s and don’ts of diffu-
sion MRI. Neuroimage. 73:239–254.
Keizer K, Kuypers HG. 1989. Distribution of corticospinal neu-
rons with collaterals to the lower brain stem reticular form-
ation in monkey (Macaca fascicularis). Exp Brain Res. 74(2):
311–318.
Lehericy S, Ducros M, Krainik A, Francois C, Van de Moortele PF,
Ugurbil K, Kim DS. 2004a. 3-D diffusion tensor axonal track-
ing shows distinct SMA and pre-SMA projections to the
human striatum. Cereb Cortex. 14(12):1302–1309.
Lehericy S, Ducros M, Van de Moortele PF, Francois C, Thivard
L, Poupon C, Swindale N, Ugurbil K, Kim DS. 2004b. Diffusion
tensor fiber tracking shows distinct corticostriatal circuits in
humans. Ann Neurol. 55(4):522–529.
Lemon RN, Griffiths J. 2005. Comparing the function of the cor-
ticospinal system in different species: organizational differ-
ences for motor specialization? Muscle Nerve. 32(3):261–279.
Leunissen I, Coxon JP, Geurts M, Caeyenberghs K, Michiels K,
Sunaert S, Swinnen SP. 2013. Disturbed cortico-subcortical
interactions during motor task switching in traumatic brain
injury. Hum Brain Mapp. 34(6):1254–1271.
Lindenberg R, Renga V, Zhu LL, Betzler F, Alsop D, Schlaug G.
2010. Structural integrity of corticospinal motor fibers pre-
dicts motor impairment in chronic stroke. Neurology. 74(4):
280–287.
Lindenberg R, Zhu LL, Ruber T, Schlaug G. 2012. Predicting func-
tional motor potential in chronic stroke patients using diffu-
sion tensor imaging. Hum Brain Mapp. 33(5):1040–1051.
Luppino G, Matelli M, Camarda R, Rizzolatti G. 1994.
Corticospinal projections from mesial frontal and cingulate
areas in the monkey. Neuroreport. 5(18):2545–2548.
Maier MA, Armand J, Kirkwood PA, Yang HW, Davis JN, Lemon
RN. 2002. Differences in the corticospinal projection from
primary motor cortex and supplementary motor area to
macaque upper limb motoneurons: an anatomical and elec-
trophysiological study. Cereb Cortex. 12(3):281–296.
Makris N, Goldstein JM, Kennedy D, Hodge SM, Caviness VS,
Faraone SV, Tsuang MT, Seidman LJ. 2006. Decreased vol-
ume of left and total anterior insular lobule in schizophre-
nia. Schizophr Res. 83(2–3):155–171.
Mayka MA, Corcos DM, Leurgans SE, Vaillancourt DE. 2006.
Three-dimensional locations and boundaries of motor and
premotor cortices as defined by functional brain imaging: a
meta-analysis. Neuroimage. 31(4):1453–1474.
Misra G, Coombes SA. 2015. Neuroimaging evidence of motor
control and pain processing in the human midcingulate cor-
tex. Cereb Cortex. 25(7):1906–1919.
Misra G, Ofori E, Chung JW, Coombes SA. 2016. Pain-related
suppression of beta oscillations facilitates voluntary move-
ment. Cereb Cortex. (in press).
Moeller S, Yacoub E, Olman CA, Auerbach E, Strupp J, Harel N,
Ugurbil K. 2010. Multiband multislice GE-EPI at 7 tesla, with
16-fold acceleration using partial parallel imaging with
application to high spatial and temporal whole-brain fMRI.
Magn Reson Med. 63(5):1144–1153.
Morecraft RJ, Ge J, Stilwell-Morecraft KS, McNeal DW, Hynes
SM, Pizzimenti MA, Rotella DL, Darling WG. 2015.
Vulnerability of the medial frontal corticospinal projection
accompanies combined lateral frontal and parietal cortex
injury in rhesus monkey. J Comp Neurol. 523(4):669–697.
14
|
Cerebral Cortex

Muggeo VM. 2003. Estimating regression models with unknown
break-points. Stat Med. 22(19):3055–3071.
Newton JM, Ward NS, Parker GJ, Deichmann R, Alexander DC,
Friston KJ, Frackowiak RS. 2006. Non-invasive mapping of
corticofugal fibres from multiple motor areas—relevance to
stroke recovery. Brain. 129(Pt 7):1844–1858.
Nudo RJ, Masterton RB. 1990a. Descending pathways to the
spinal cord, III: sites of origin of the corticospinal tract.
J Comp Neurol. 296(4):559–583.
Nudo RJ, Masterton RB. 1990b. Descending pathways to the
spinal cord, IV: some factors related to the amount of cortex
devoted to the corticospinal tract. J Comp Neurol. 296(4):
584–597.
Park CH, Kou N, Boudrias MH, Playford ED, Ward NS. 2013.
Assessing a standardised approach to measuring corticosp-
inal integrity after stroke with DTI. Neuroimage Clin. 2:
521–533.
Park JK, Kim BS, Choi G, Kim SH, Choi JC, Khang H. 2008.
Evaluation of the somatotopic organization of corticospinal
tracts in the internal capsule and cerebral peduncle: results
of diffusion-tensor MR tractography. Korean J Radiol. 9(3):
191–195.
Plow EB, Cunningham DA, Varnerin N, Machado A. 2015.
Rethinking stimulation of the brain in stroke rehabilitation:
why higher motor areas might be better alternatives for
patients with greater impairments. Neuroscientist. 21(3):
225–240.
Potter-Baker KA, Varnerin NM, Cunningham DA, Roelle SM,
Sankarasubramanian V, Bonnett CE, Machado AG, Conforto
AB, Sakaie K, Plow EB. 2016. Influence of corticospinal tracts
from higher order motor cortices on recruitment curve prop-
erties in stroke. Front Neurosci. 10:79.
Rouiller EM, Yu XH, Moret V, Tempini A, Wiesendanger M,
Liang F. 1998. Dexterity in adult monkeys following early
lesion of the motor cortical hand area: the role of cortex
adjacent to the lesion. Eur J Neurosci. 10(2):729–740.
Sach M, Winkler G, Glauche V, Liepert J, Heimbach B, Koch MA,
Buchel C, Weiller C. 2004. Diffusion tensor MRI of early
upper motor neuron involvement in amyotrophic lateral
sclerosis. Brain. 127(Pt 2):340–350.
Schaechter JD, Fricker ZP, Perdue KL, Helmer KG, Vangel MG,
Greve DN, Makris N. 2009. Microstructural status of ipsile-
sional and contralesional corticospinal tract correlates with
motor skill in chronic stroke patients. Hum Brain Mapp. 30
(11):3461–3474.
Schaechter JD, Perdue KL, Wang R. 2008. Structural damage to
the corticospinal tract correlates with bilateral sensorimotor
cortex reorganization in stroke patients. Neuroimage. 39(3):
1370–1382.
Schulz R, Park CH, Boudrias MH, Gerloff C, Hummel FC, Ward
NS. 2012. Assessing the integrity of corticospinal pathways
from primary and secondary cortical motor areas after
stroke. Stroke; a journal of cerebral circulation. 43(8):
2248–2251.
Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ,
Wald LL. 2012. Blipped-controlled aliasing in parallel
imaging for simultaneous multislice echo planar imaging
with reduced g-factor penalty. Magn Reson Med. 67(5):
1210–1224.
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens
TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I,
Flitney DE, et al. 2004. Advances in functional and structural
MR image analysis and implementation as FSL.
Neuroimage. 23(Suppl 1):S208–S219.
Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S,
Auerbach EJ, Glasser MF, Hernandez M, Sapiro G, Jenkinson
M, et al. 2013a. Advances in diffusion MRI acquisition and
processing in the human connectome project. Neuroimage.
80:125–143.
Sotiropoulos SN, Moeller S, Jbabdi S, Xu J, Andersson JL,
Auerbach EJ, Yacoub E, Feinberg D, Setsompop K, Wald LL,
et al. 2013b. Effects of image reconstruction on fiber orienta-
tion mapping from multichannel diffusion MRI: reducing
the noise floor using SENSE. Magn Reson Med. 70(6):
1682–1689.
Stinear CM, Barber PA, Petoe M, Anwar S, Byblow WD. 2012. The
PREP algorithm predicts potential for upper limb recovery
after stroke. Brain. 135(Pt 8):2527–2535.
Stinear CM, Barber PA, Smale PR, Coxon JP, Fleming MK, Byblow
WD. 2007. Functional potential in chronic stroke patients
depends on corticospinal tract integrity. Brain. 130(Pt 1):
170–180.
Tovar-Moll F, Evangelou IE, Chiu AW, Auh S, Chen C,
Ehrmantraut M, Ohayon JM, Richert N, Bagnato F. 2015.
Diffuse and focal corticospinal tract disease and its impact
on patient disability in multiple sclerosis. J Neuroimaging.
25(2):200–206.
Travis AM. 1955. Neurological deficiencies after ablation of the
precentral motor area in Macaca mulatta. Brain. 78(2):
155–173.
van Baarsen KM, Kleinnijenhuis M, Jbabdi S, Sotiropoulos SN,
Grotenhuis JA, van Cappellen van Walsum AM. 2016. A
probabilistic atlas of the cerebellar white matter.
Neuroimage. 124(Pt A):724–732.
Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E,
Ugurbil K. 2013. The WU-Minn human connectome project:
an overview. Neuroimage. 80:62–79.
Vos SB, Jones DK, Viergever MA, Leemans A. 2011. Partial vol-
ume effect as a hidden covariate in DTI analyses.
Neuroimage. 55(4):1566–1576.
Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M,
Gollub RL, Hua K, Zhang J, Jiang H, Dubey P, et al. 2007.
Reproducibility of quantitative tractography methods
applied to cerebral white matter. Neuroimage. 36(3):630–644.
Wedeen VJ, Wang RP, Schmahmann JD, Benner T, Tseng WY,
Dai G, Pandya DN, Hagmann P, D’Arceuil H, de Crespigny AJ.
2008. Diffusion spectrum magnetic resonance imaging (DSI)
tractography of crossing fibers. Neuroimage. 41(4):
1267–1277.
Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S,
Behrens T, Beckmann C, Jenkinson M, Smith SM. 2009.
Bayesian analysis of neuroimaging data in FSL. Neuroimage.
45(Suppl 1):S173–S186.
Zhu LL, Lindenberg R, Alexander MP, Schlaug G. 2010. Lesion
load of the corticospinal tract predicts motor impairment in
chronic stroke. Stroke; a journal of cerebral circulation. 41
(5):910–915.
Sensorimotor Area Tract Template (SMATT) Archer et al.
|
15