CLINICAL CASE STUDY
published: 25 June 2013
doi: 10.3389/fnhum.2013.00290

HUMAN NEUROSCIENCE

Extensive neurological recovery from a complete spinal
cord injury: a case report and hypothesis on the role ofcortical plasticity
Ann S. Choe 1,2,3† , Visar Belegu 1,2† , Shoko Yoshida 4 , Suresh Joel 3,4 , Cristina L. Sadowsky 2,5 ,Seth A. Smith 6 , Peter C. M. van Zijl 3,4 , James J. Pekar 3,4 and John W. McDonald 1,2,5*
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
International Center for Spinal Cord Injury, Hugo W. Moser Research Institute at Kennedy Krieger, Inc., Baltimore, MD, USA
3
F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
4
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
5
Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA
6
Department of Radiology and Radiological Science, Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
1
2

Edited by:Sven Bestmann, University College
London, UK
Reviewed by:
Patrick Freund, University College
London Institute of Neurology, UK
Antonio Oliviero, Hospital Nacionalde Paraplejicos, Spain*Correspondence:John W. McDonald, Kennedy
Krieger Institute, Johns Hopkins
University School of Medicine, 707
N. Broadway, Baltimore, MD 21205,
USA
e-mail: mcdonaldj@
kennedykrieger.org† These authors have contributed
equally to this work.

Neurological recovery in patients with severe spinal cord injury (SCI) is extremely
rare. We have identified a patient with chronic cervical traumatic SCI, who suffered
a complete loss of motor and sensory function below the injury for 6 weeks after
the injury, but experienced a progressive neurological recovery that continued for 17
years. The extent of the patient’s recovery from the severe trauma-induced paralysis
is rare and remarkable. A detailed study of this patient using diffusion tensor imaging(DTI), magnetization transfer imaging (MTI), and resting state fMRI (rs-fMRI) revealed
structural and functional changes in the central nervous system that may be associatedwith the neurological recovery. Sixty-two percent cervical cord white matter atrophy wasobserved. DTI-derived quantities, more sensitive to axons, demonstrated focal changes,
while MTI-derived quantity, more sensitive to myelin, showed a diffuse change. No
significant cortical structural changes were observed, while rs-fMRI revealed increased
brain functional connectivity between sensorimotor and visual networks. The study
provides comprehensive description of the structural and functional changes in the patient
using advanced MR imaging technique. This multimodal MR imaging study also shows
the potential of rs-fMRI to measure the extent of cortical plasticity.
Keywords: spinal cord injury, trauma, diffusion tensor imaging, magnetization transfer imaging, resting state fMRI,
plasticity

CASE PRESENTATION
The patient, a 58-year-old male, endured vertebral fractures atcervical levels C3–C6 in a motor vehicle accident at the ageof 22. The patient did not have any respiratory problem andhis stay in the ICU was limited to 5 days. Following the injury,
the patient underwent manual motor testing of lower and upper
body muscles groups as well as sensory testing. The motor testing involved examination of flexion and extension of upper body(shoulder, elbow, and wrist), and lower body joints (hip, knee,
and ankle), and sensory testing involved examination of sensation to pinprick and light touch on both hands and feet. Initially,
the patient had no motor or sensory function below the levelof injury. Motor and sensory function at the sacral level was
also absent, which was tested during the bladder evacuation andcatheter placement procedure. This injury is equivalent to an
ASIA A (ASIA: American Spinal Injury Association), or a “complete” injury, according to today’s ASIA impairment scale (AIS)(Maynard et al., 1997), which did not exist at the time of thepatient’s injury.
Spinal cord stabilization was accomplished with a halo.
His rehabilitation regimen consisted of active-assisted range ofFrontiers in Human Neurosciencemotion (ROM) exercises of the lower and upper limb performed
at home. Lower limb ROM exercise was performed for 30 min,three times a day, and the upper limb ROM exercise was performed for 30 min, two times a day. The exercise was performed
at home, assisted by his family members.
He experienced slow and progressive neurological recovery
that continued for 17 years after the injury. The first neurologicalrecovery, movement of the left big toe (a grade 1 motor function), occurred 6 weeks after the injury. The first recovery of
sensation occurred 6 months after the injury in the form of apainful dysesthetic sensation in the pelvis. Eleven months after
the injury, the patient was able to perform complex upper bodymotor functions such as sitting up without assistance. Complex
lower body motor functions such as ability to walk unassisted
were recovered 15 months after the injury. Concomitantly, thepatient recovered autonomic nervous system functions (i.e., ahigh degree of bladder and bowel functions were recovered up
to 64 months after the injury) although many remain abnormal(Table 1). Currently, his injury is categorized as ASIA D. His latest AIS evaluation (Figure 1) showed that he has regained 94% of
motor function in the upper body and 100% in the lower body.

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 1

Choe et al.

Neurological recovery from complete SCI

Table 1 | Detailed timeline of the patient’s recovery.
Date

Description0

Sep-74

Accident

1

Nov-74Flicker of contraction present at left big toe. No sensory
recovery2Mar-75Flicker of contraction present at left quadriceps

3

Mar-75Dysesthetic (abnormal) sensation at pelvis

4Mar-75Flick of sensation present at left bicep

5

Mar-75Flick of sensation present at left fingers

6

Apr-75Flicker of contraction present at right big toe

7

May-75

Begin to gain trunk movement8

May-75

Begin working on trunk balance9

Jun-75Sit with 50% assistance10

Jun-75Flicker of contraction present at left triceps

11

Oct-75Sit without assistance12

Dec-75

Active movement of left arm against gravity

13

Jan-76

Stand with 50% assistance14

Feb-76

Regain ability to grip with hands

15

May-76

Regained ability to ejaculate

16

Jun-76

Stand without assistance17

Sep-76First swim

18

Jan-77

Begin to gain control of bladder movement (incomplete)

19

Jan-78First walk 40 feet without assistance20

Jun-78

Could walk in community

21

Jan-79Begin to gain control of bowel movements (incomplete)22

1978–1980

Functional recovery rate begins to plateau23

Apr-91First run

The 1974 motor accident [0] resulted in a complete loss of motor and sensory
function below the injury epicenter that lasted 6 weeks after the injury.
Initial regaining of motor and sensory function [1, 3] was then followed by a long
but steady recovery process that continued for 17 years after the injury, whereat the end of the 17 years, he was able to run without assistance [23].

The patient manifests incomplete and asymmetric motor recovery in his hands—i.e., his left hand is more functional then theright one. He has also regained 23% of sensory function in theupper body (above the level of T7), but only 10% in the lower
body. The patient reports that his walking and functions he performs with his feet depend heavily on visual feedback; he also
manifests autonomic dysreflexia. It should be noted that the AIS
evaluation of the motor function is based on muscle strength in
the extremities, and that sensory function is evaluated based on
conscious sensation of light touch and pin prick on the skin, anddoes not include proprioception.

BACKGROUND
The spinal cord is a conduit for the exchange of information
between the brain and the body, and damage to it disrupts conduction of sensory and motor signals across the lesion epicenter(Belegu et al., 2007). The extent of the neurological impairment
following spinal cord injury (SCI) varies, and the severity of the
injury limits the subsequent neurological recovery (McDonald
and Sadowsky, 2002; Kirshblum et al., 2004). Severely injured
SCI patients, classified as ASIA A on the AIS (Maynard et al.,1997), experience limited neurological recovery that often does

Frontiers in Human Neuroscience

not translate to functional improvements. Patients with lesssevere injuries, classified as ASIA B-D, experience greater recovery(Kirshblum et al., 2004). This is further corroborated by a retrospective study of 173 traumatic SCI patients with 5-year follow
up (Vazquez et al., 2008), which reported relatively good recovery results for patients with incomplete injury; at the time of
their discharge, 33.3% the ASIA B patients, 76.4% of the ASIA C
patients, and 100% of the ASIA D patients showed some degreeof neurological recovery. The same study also reported, however,that 94% of the ASIA A patients remained complete upon discharge without any neurological recovery and none of them werefunctional at the time of their discharge (Vazquez et al., 2008).
Chances of neurological recovery for ASIA A patients become
even smaller in chronic phase of SCI (McDonald and Sadowsky,2002).
Two variables that contribute to the capacity for neurologicalrecovery are well understood. First, preservation of longitudinallyoriented axonal tracts in the white matter is required (Blight,
1983; Nathan, 1994). Second, proper conductance through thespared white matter axons and hence proper myelination isrequired. However, only 5% of intact axons in the white matter
tracts are needed to procure locomotion following SCI in cats
(Blight, 1983), while in humans, locomotion can recover after
resection of 25% of the spinal cord (Nathan, 1994), indicatingthat preservations of axons and myelination are not proportional
to the degree of neurological recovery. Task-activated functional
MRI (fMRI) studies show different activation pattern between
patients who experience neurological recovery and those who
do not (Jurkiewicz et al., 2007, 2010), suggesting that corticalplasticity may be a third variable. While the occurrence of cortical plasticity subsequent to SCI has been observed (Endo et al.,2007; Jurkiewicz et al., 2007, 2010), it is unclear how it leads toneurological recovery.
Here, we present a report of a patient with a chronic cervicaltraumatic SCI, who suffered a complete loss of motor and sensory
function below the injury epicenter for 6 weeks after the injury,and experienced a slow and progressive neurological recovery that
continued for 17 years. The extent of the recovery is so remarkable that it represents a pragmatic cure from trauma induced
complete paralysis. We utilized advanced MRI technologies suchas diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), and resting state fMRI (rs-fMRI) to investigate thepatient’s late recovery. While DTI and MTI showed abnormalities
in the spinal cord that indicate axonal and myelin damage, but are
not associated with the patient’s late recovery, rs-fMRI provided
a picture of cortical plasticity that is associated with the patient’s
late recovery.

MATERIALS AND METHODSPARTICIPANTS

Spinal cord DTI, brain DTI, and brain rs-fMRI data of healthy
controls were obtained from previously acquired and separate
databases that exist in the International Center for Spinal Cord
Injury (ICSCI) and F.M. Kirby center. Spinal cord imaging was
performed in ten healthy volunteers (21–49 years, mean 33,
M/F ratio: 6/4). For brain imaging, DTI was performed in six
age matched healthy volunteers (55–61 years, mean 59, M/F

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 2

Choe et al.

Neurological recovery from complete SCI

FIGURE 1 | ASIA classification of the patient after recovery, performed according to the International Standards for Neurological Classification.

ratio: 2:4), and rs-fMRI was performed in 21 (22–61 years,
mean 32, M/F ratio: 11/10; available on web: www.nitrc.org/
projects/multimodal) healthy volunteers (Landman et al., 2011).
Only the six healthy participants from the brain DTI databasewere age matched due to the limited number of older healthy participants in the spinal cord DTI and brain rs-fMRI databases. All
participants gave their informed consent, and the study protocol
was approved by the institutional review board at Johns Hopkins
University.
IMAGE ACQUISITION

Participants were scanned on a Philips 3T scanner. The scanroom was lit during the scan, but the lights were kept off inside
the scanner bore. For spinal cord imaging, DTI was performed
using a multi-slice, pulsed gradient spin echo (PGSE) sequence(b = 0, 500 s/mm2 , 16 diffusion weighting directions, TR/TE =6300/63 ms, SENSE factor = 2, 1.5 × 1.5 × 3 mm3 original resolution (axial sections of 3 mm thickness), 0.57 × 0.57 × 3 mm3
final resolution (after zero-fill), scan time = 2 min, 16-channel
neurovascular coil) (Figures 2D,H). MT-weighted images were

Frontiers in Human Neuroscienceacquired using a three dimensional (3D) spoiled gradient-echo
sequence with an EPI readout (EPI factor = 3, TR/TE =102/13 ms, α = 9◦ , and SENSE factor = 2, 0.61 × 0.69 × 3 mm3resolution, scan time = 8 min, 16-channel neurovascular coil)(Figures 2C,G). A sagittal T2 weighted (T2-w) was also acquired(Figures 2A,B,E,F). The total scan time for the spinal cord imaging was 28 min.
Brain DTI was performed using a multi-slice PGSE sequence(b = 0, 700 s/mm2 , 32 directions, TR/TE = 7270/71 ms, SENSE
factor = 2.5, 2.2 × 2.2 × 2.2 mm3 original resolution, 0.83 ×
0.83 × 2.2 mm3 final resolution, scan time = 5 min, 16-channel
neurovascular coil). A high resolution T1 weighted (T1-w)
MPRAGE image (TR/TE = 10/6 ms, SENSE factor = 2, 1.10 ×
1.10 × 1.17 mm3 original resolution, 0.83 × 0.83 × 1.17 mm3
final resolution, scan time = 5 min, 16-channel neurovascularcoil) was also obtained.
Each rs-fMRI scan of the controls (Landman et al., 2011) was
acquired using a single-shot SENSE-EPI (TR/TE = 2000/30 ms,
SENSE factor = 2, 3 × 3 × 3 mm3 resolution, 1 mm gap, scantime = 420 s, 32-channel head coil). Identical imaging parameters

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 3

Choe et al.

Neurological recovery from complete SCI

FIGURE 2 | Structural MR images acquired from the patient (top row)
and a control (bottom row). (A,E) Sagittal T2-w images. Bracket in (A)highlights injury epicenter of the patient. (B,F) Axial T2-w images. (C,G) Axial
MTCSF images. (D,H) Axial FA color maps. The FA maps are color-codedusing a standard diffusion color-encoding scheme (r/l: red, a/p: green, s/i:
blue). Each row of (B–D) shows a section from: (1) above the injury epicenter,(2) injury epicenter, and (3) below the injury epicenter. Images from thecorresponding cervical levels in the control are shown in each row of (F–H).

were used to acquire the rs-fMRI data of the SCI patient, withthese differences: (1) time of each run: 360 s, (2) number of runs
acquired: 8. For the controls, the first 360 s of the data was used
for further analysis. Total scan time for the brain imaging was
30 min. The rs-fMRI scans were always acquired after the brain
T1-w image acquisition to allow the participants to get acclimatedto the noise and the new environment inside the scanner. The participants were instructed to stay as still as possible with their eyes
closed during the entire scan. A dataset from one of the healthy
controls was excluded due to excessive motion.

The registration of the MTI dataset to the mean b0 volume
was performed in two steps. First, the MTI dataset was registered to the b0 volume using a 3D, six degrees of freedom, rigid
body transformation using a similar method proposed by Maes
et al. (1999). This 3D registration procedure was then followed
by a two-dimensional (2D) registration, using a three degrees of
freedom rigid body transformation that involved two in-planetranslations and one rotation. The second coregistration procedure was implemented for added accuracy of the result (Smithet al., 2010).

IMAGE REGISTRATION FOR SPINAL CORD IMAGING

MEASUREMENT OF WHOLE CORD AND WHITE MATTERCROSS-SECTIONAL AREA

Basic eddy current correction was performed on the spinal
cord and brain DTI datasets using CATNAP (Coregistration,
Adjustment, and Tensor-solving, a Nicely Automated Program)(Landman et al., 2007), which performs volume-wise coregistration using a method based on FLIRT (FMRIB’s Linear Imaging
Registration Tool, Oxford, UK) (Jenkinson et al., 2002). Each diffusion weighted image was registered to an initial b = 0 s/mm2(b0, non-diffusion weighted image) volume using a six degrees of
freedom, rigid-body registration. Diffusion gradient tables were
updated to account for any rotation.

Frontiers in Human Neuroscience

The whole cord and white matter atrophy of the patient’s spinal
cord was measured using fractional anisotropy (FA) maps. We
first separated the spinal cord and the surrounding cerebrospinal
fluid (CSF) area from the rest of the neck region by performing manual segmentation. This was done by placing ellipsoidal
binary masks around the spinal cord and the surrounding CSF
area. Once the segmentation of the spinal cord and CSF wasdone, the whole cord cross-sectional area (wca) of each slice ofthe controls and the patient was measured automatically usingwww.frontiersin.org

June 2013 | Volume 7 | Article 290 | 4

Choe et al.

Neurological recovery from complete SCI

the Otsu’s thresholding method, therefore reducing rater bias.
The segmented FA maps of the cords were then thresholded at
0.2—the same threshold used during the subsequent fiber tractograophy for the column-specific data analysis—to create themasks of the white matter only area (wma). We have previously
shown that FA measures performed in this manner have acceptable inter-rater and test-retest reliability (Smith et al., 2010), andthis is expected to extend to the volume measures based on thisparameter.
DIFFUSION FIBER TRACTOGRAPHY OF SPINAL COLUMNS AND
CREATION OF COLUMN PROFILES

Patient
Mean
Below

Epicenter

Above

Total

0.67

Left lateral
FA

0.62

0.67

0.69

MD (µm2 /ms)

1.2

1.02

0.83

1

λ∥ (µm2 /ms)

2.08

1.86

1.58

1.83

0.76

0.62

0.47

0.6

0.69

0.72

0.62

0.65

FA

0.59

0.49

0.72

0.61

MD (µm2 /ms)

1.03

1.40

0.79

1.14

λ∥ (µm2 /ms)

1.76

2.18

1.53

1.93

0.66

1.01

0.43

0.76

MTCSF

0.78

0.68

0.63

0.68

FA

0.71

0.55

0.73

0.67

MD (µm2 /ms)

1.13

1.40

0.97

1.14

λ∥ (µm2 /ms)

2.19

2.33

1.96

2.1

0.6

0.94

0.48

0.66

0.65

0.72

0.67

0.65

FA

0.55

0.46

0.67

0.55

MD (µm2 /ms)

1.27

1.79

0.91

1.33

λ∥ (µm2 /ms)

2.06

2.61

1.67

2.12

0.87

1.38

0.54

0.94

0.73

0.71

0.63

0.66

λ⊥ (µm2 /ms)
MTCSF

After the registration of DTI and MTI images and the calculation of DTI- and MTI-derived quantities, diffusion fiber tractography was performed using DTIStudio (Jiang et al., 2006).
Higher contrast MTCSF (MT-weighted signal intensity relative
to cerebrospinal fluid) images—compared to T2-w images andFA maps—were used to manually draw regions of interest (ROIs)
within left and right lateral, dorsal, and ventral columns of the
spinal cords. This manual ROI selection was performed on every
third section along the entire cervical cord, to be used as seedregions for the fiber tractography to follow. Note that the registered MTCSF images are in the same image space as that of the
DTI images. FA threshold of 0.2 and a maximum tract turning
angle of 60◦ were used as the fiber tractography stopping criterion. Spurious fibers, such as those that cross from one spinal
column to the next or move out of the spinal cord, were manually
excluded.
Once each spinal column was reconstructed, vertebrae level ofC2 and C6 were identified and the corresponding column profiles (Smith et al., 2010; Cohen-Adad et al., 2013) were created.
In our previous study (Smith et al., 2010), typical neck length of
study participants was found to be 75 mm, which corresponds to25, 3 mm thickness image sections. Using this information, eachcolumn profile was spatially normalized to 25 points spanning
C2–C6.

Right lateralλ⊥ (µm2 /ms)
Dorsalλ⊥ (µm2 /ms)
MTCSF

Ventralλ⊥ (µm2 /ms)
MTCSF

Imaging parameters (FA, MD, λ∥ , λ⊥ , and MTCSF) were measured for theregions of above the injury, injury epicenter, and below the injury.

Values reported are mean of each injury region. Examining the cervical cord in an
injury region specific way provides better insight into the actual changes occurring along the patient’s cervical spinal cord, as opposed to looking at a single

INJURY REGION SPECIFIC DATA ANALYSIS

Tables 2, 3 report the column profile values of the patient (mean)
and the controls (mean ± SD) for three spinal cord regions: (1)above the injury epicenter, (2) injury epicenter, and (3) below
the injury epicenter. The injury epicenter was identified using thepatient’s T2-w images (Figure 2B). One vertebrae level (approximately 15 mm) above the beginning of the lesion site was definedas the region “above” the injury epicenter, and one vertebrae
level down from the end of the lesion site was defined as the“below” the injury epicenter. The three spinal cord regions of
the controls were defined to match the location of the patient’s
regions.
Each cell in Table 2 is color coded according to the degree ofdifference between the values of the controls and patient: (1) cells
without color indicate difference less than one SD, (2) light graycells indicate difference of more than one SD but less than twice
the SD, (3) dark gray cells indicate difference of bigger than twice
the SD. When DTI and MTI derived parameters were averaged
along the whole cord, only FA of the right lateral column and
MTCSF of the dorsal column were found to have changes of more

Frontiers in Human Neuroscience

Table 2 | Injury region specific data comparison of the patient.

average values of an entire spinal column.
Difference compared to the controls.
Within normal range
Difference is bigger than 1 × SD
Difference is bigger than 2 × SD

than twice the SD (Table 2). A detailed look at the Tables 2, 3,
however, reveals that there are substantial changes of DTI- andMTI-derived parameters above, at, and below the injury epicenter of different spinal columns. FA, MD, and λ⊥ altered more than
two SD at the injury epicenter of right lateral, dorsal, and ventral
columns. λ∥ altered more than two SD at the injury epicenter ofventral column. MTCSF changed more than two SD at the injury
epicenter of the left lateral, dorsal, and ventral column, below the
injury epicenter at the right lateral, dorsal, and ventral column,
and above the injury epicenter at the dorsal column. All DTIderived quantities values for the left lateral column were within
the normal range, suggesting the left lateral column is the mostwell preserved spinal column. This corresponds with the patient’s

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 5

Choe et al.

Neurological recovery from complete SCI

Table 3 | Injury region specific data comparison of controls.
Controls
Mean ± SD
Below

Epicenter

Above

Total

0.68 ± 0.10

0.74 ± 0.10

0.79 ± 0.09

0.75 ± 0.10

1.89 ± 0.23

2.01 ± 0.34

1.98 ± 0.34

1.98 ± 0.29

0.48 ± 0.08

0.48 ± 0.11

0.74 ± 0.09

0.79 ± 0.07

0.78 ± 0.09

0.78 ± 0.08

1.99 ± 0.22

1.90 ± 0.26

1.97 ± 0.32

1.98 ± 0.30

0.48 ± 0.10

0.48 ± 0.10

0.48 ± 0.08

0.78 ± 0.09

0.77 ± 0.08

0.76 ± 0.10

2.10 ± 0.22

2.17 ± 0.20

2.16 ± 0.28

0.48 ± 0.10

0.46 ± 0.09

0.47 ± 0.09

0.59 ± 0.07

0.61 ± 0.07

0.66 ± 0.08

0.61 ± 0.08

1.92 ± 0.28

2.03 ± 0.36

2.03 ± 0.29

2.05 ± 0.35

0.52 ± 0.08

0.51 ± 0.09

Left lateralFA
MD (µm2 /ms)λ∥ (µm2 /ms)λ⊥ (µm2 /ms)
MTCSF

0.99 ± 0.16

0.54 ± 0.18

1.00 ± 0.24

0.93 ± 0.25

0.51 ± 0.23

0.42 ± 0.21
0.48 ± 0.09

0.97 ± 0.22
0.47 ± 0.22
0.49 ± 0.10

Right lateral

into 130 anatomical structures (Oishi et al., 2008, 2011). Some ofthe structures were then further segmented into gray and whitematter using an FA threshold of 0.25 in each subject, followedby minimum manual adjustment. This resulted in 46 additional
parcellations, yielding a total of 176 regions. When the quantitative values were obtained, the cerebrospinal fluid spaces were
excluded by an MD threshold at 0.0020 mm2 /s. The final resultsof the anatomical quantification included volume, FA and MD
for each subject. The non-linear image transformation and theatlas-based parcellation were performed using DiffeoMap and
RoiEditor (http://www.MriStudio.org) (Oishi et al., 2008).
BRAIN RESTING STATE fMRI PREPROCESSING AND ANALYSIS

clinical presentation, who presents higher motor score for the left
side of the body than the right.

The preprocessing of rs-fMRI data was performed using SPM8
(www.fil.ion.ucl.ac.uk/spm) (Friston et al., 1994) and Matlab(Natick, MA). The preprocessing pipeline involved: (1) slice
timing correction, (2) motion correction, (3) co-registration,(4) unified segmentation-normalization (Ashburner and Friston,2005), (5) high pass filtering with 0.005 Hz cutoff, and (6)a spatial smoothing using 6 mm full-width at half-maximum
Gaussian kernel. Finally, principal component analysis was performed, followed by group independent component analysis(GICA) (Calhoun et al., 2001) of all runs from the healthy controls and the subject using the GIFT software (http://mialab.mrn.
org/software/gift). An order selection using minimum description length (MDL) criterion (Li et al., 2007) was performed anda total of 39 independent components were initially estimated.
After GICA, three were eliminated due to low similarity measures obtained using ICASSO toolbox; (Himberg et al., 2004) 24
were rejected due to artifacts (e.g., motion) and physiology (e.g.,
cardiac pulsation).
For each remaining IC, the magnitude of temporal fluctuations was computed to provide a measure of within-network
connectivity (WNC). This was done by first converting the backreconstructed time courses to percent signal change measurements for normalization purpose, and computing the root meansquare (RMS) of the normalized time course values. The between
network connectivity (BNC) was computed for every pair of functional networks as well, as the Pearson’s correlation of the network
time courses.

BRAIN DTI DATA ANALYSISRESULTS

The structural integrity of the patient’s brain was investigatedthrough T1-w imaging and DTI, using an atlas-based analysis(ABA) method (Faria et al., 2010, 2011). The diffusion weighted
images were first co-registered to one of the b0 images using a
12-mode affine transformation of Automated Image Registration(AIR) (Woods et al., 1998). The six elements of the diffusion
tensor, FA, and mean diffusivity (MD) were calculated. Afterskull-stripping, the images were first normalized to the JHU-DTIMNI “Eve” template (Jiang et al., 2006) with a nine-parameter
affine transformation of AIR using b0 image. Then, a non-linear
transformation using dual-contrast large deformation diffeomorphic metric mapping (LDDMM) (Beg et al., 2005) was applied,
using FA and MD images. These procedures are reciprocal, so theinverse-transformed brain parcellation map was superimposed
onto the original MRI images, and led to parcellation of the brain

MRI-BASED STRUCTURAL EVALUATION OF A CHRONICALLY INJUREDSPINAL CORDFA
MD (µm2 /ms)λ∥ (µm2 /ms)λ⊥ (µm2 /ms)
MTCSF

0.98 ± 0.18
0.48 ± 0.19

0.89 ± 0.16

0.39 ± 0.13

0.94 ± 0.23
0.43 ± 0.21

0.94 ± 0.21
0.43 ± 0.19
0.49 ± 0.10

DorsalFA
MD (µm2 /ms)λ∥ (µm2 /ms)λ⊥ (µm2 /ms)
MTCSF

0.70 ± 0.14
1.11 ± 0.31

0.98 ± 0.17

0.59 ± 0.32

0.42 ± 0.17

2.14 ± 0.36

0.47 ± 0.08

1.02 ± 0.15
0.45 ± 0.16

1.04 ± 0.22
0.49 ± 0.22

VentralFA
MD (µm2 /ms)λ∥ (µm2 /ms)λ⊥ (µm2 /ms)
MTCSF

1.10 ± 0.17

0.69 ± 0.14
0.52 ± 0.11

1.15 ± 0.25

0.71 ± 0.22

1.09 ± 0.20

0.62 ± 0.18

1.16 ± 0.25

0.71 ± 0.22

0.54 ± 0.11

Imaging parameters (FA, MD, λ∥ , λ⊥ , and MTCSF) were measured for cervical spinal cord regions that correspond with the subject’s above the injury,
injury epicenter, and below the injury region of the subject. Values reported
are mean ± SD of each injury region.

Frontiers in Human Neuroscience

In the T2-weighted MR images, a lesion is visible in C4–C5 of the
injured spinal cord (Figures 2A,B), location of which agrees withthe known location of the injury epicenter. No lesion is observedin a healthy spinal cord (Figures 2E,F).
The degree of preservation of longitudinally oriented axonal
tracts in white matter was investigated by assessing the whole cord
and white matter atrophy of the patient’s spinal cord. We measured wca (Figure 3A) and wma (Figure 3B) of each slice in thecervical cord for the controls (mean ± SD; SD: standard deviation; blue line) and the patient (mean; red line) using FA maps
(Figures 2D,H). Gray boxes in each profile represent the injury
epicenter. The wca and wma are reduced throughout the cervical
cord (more than one SD decrease) and maximally reduced in the

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 6

Choe et al.

Neurological recovery from complete SCI

increased by more than one SD at the injury epicenter in all spinal
columns, and its changes were diffuse and extended throughout
the cervical cord. Unlike MTCSF, FA decreased by more than one
SD primarily at the injury epicenter along all spinal columns, withthe greatest decrease observed in the dorsal column. The decreasein FA was more pronounced in the right lateral column than in
the left lateral column (Figure 4). These observations are consistent with the patient’s neurological manifestation, as the patient’s
sensory deficit is worse than motor deficit and motor deficit on
the right side of the body is worse than left side of the body. MD,
λ∥ , and λ⊥ increased focally at the injury epicenter. However, thepattern of changes of these quantities is different from that of FA,
with changes being more pronounced in the right versus left lateral column and the ventral column being the most pronounced(Figure 4, Tables 2, 3).
MRI-BASED EVALUATION OF THE BRAIN IN A CHRONIC SPINAL CORD
INJURY PATIENT

Structural imaging

FIGURE 3 | Quantification of whole cord and white matter atrophy, asa measure of white matter tissue sparing throughout the cervicalspinal cord. (A) Degree of whole cord atrophy, as measured by the whole
cord area, (B) white matter area, and (C) the ratio of wma over wca, wasmeasured between C2–C6. Values of the controls (blue line)
are mean ± SD. Plot of wca (A) and wma (B) demonstrate atrophy of thepatient’s spinal cord not only at the injury epicenter (dark area) but
throughout the cervical cord. Plot of ratio of wma over wca (C) shows
marked decrease of white matter around the injury epicenter.

injury epicenter (55 and 62%, respectively). These profiles showthat atrophy spans the entire measured area within the cervical
cord but is more pronounced in the injury epicenter. In addition,a measure of the area of spared white matter tissue was provided
by the wma to wca ratio (Figure 3C). A sharp decrease of the ratio(23%) shows that the focal atrophy at the epicenter of the injury
is due to white matter atrophy.
We aimed to assess the axonal integrity and myelination within
major spinal cord columns (dorsal, lateral, and ventral columns)of the spared white matter tissue, where dorsal columns convey
sensory function while the lateral and ventral columns convey
primarily motor functions. Segmentation of each of the columns
was performed using DTI fiber tractography, and column profiles for each of the DTI- and MTI-derived quantities were created(Figure 4). Quantities derived from DTI [FA, MD, axial diffusivity (λ∥ ), and radial diffusivity (λ⊥ )] and MTI (MTCSF) have beenpreviously shown to be sensitive to axonal and myelin integrity(Beaulieu, 2002; Song et al., 2002, 2005; Smith et al., 2005; Farrell
et al., 2010; Landman et al., 2010; Levesque et al., 2010; Choe
et al., 2012), with DTI-derived quantities more sensitive to axonand MTI-derived quantities more sensitive to myelin. Next, weused MTCSF images to place ROIs for DTI tractography. MTCSF

Frontiers in Human Neuroscience

Considering the extensive structural changes we observed in the
spinal cord of the patient, we wanted to assess if such changes
extend to the brain. The structural integrity of the patient’s brain
was investigated through T1-w imaging and DTI, using an atlas
based whole brain analysis with 176 parcellations. Structure volume, FA, and MD measurements of appropriate controls and thepatient were compared using a Mann-Whitney U-test. No significant differences between the controls and the patient wereobserved (Table 4). The lack of alterations of these quantities
indicates the SCI has not produced significant structural changes
in the patient’s brain.
Resting state fMRI (rs-fMRI)

In order to investigate cortical plasticity, we used rs-fMRI, whichmeasures spontaneous fluctuations in BOLD (blood oxygenation level dependent) signal in the absence of externally-cuedtask. This allows for direct comparison with controls regardlessof a patient’s motor and sensory deficits. Functional networks
were identified using GICA, which uses higher-order statistics
to recover independent sources from multivariate data (Calhoun
et al., 2001). It separates rs-fMRI data into components reporting on spontaneous fluctuations in intrinsic brain networks,
and components reporting on nuisances such as head motion
and cardiac pulsations. The procedure yielded 12 independent components (IC) representing different functional networks(Figure 5).
Within-network connectivity: For each IC, the magnitudeof temporal fluctuations—the root mean square (RMS) of thecomponent time course—provides a measure of within-network
connectivity. The RMS % BOLD changes for each IC of appropriate controls and the patient are shown as series of box plots in
Figure 5. The outliers in the plot were determined using a variable w (maximum whisker length), which was set to 1.5. Points
were drawn as outliers if they are larger than q3 + w(q3 − q1) or
smaller than q1 − w(q3 − q1), where q1 and q3 are the 25th and75th percentiles, respectively. The plotted whisker extends to theadjacent value, which is the most extreme data value that is not an
outlier. The median temporal modulations of the controls’ ICs arewww.frontiersin.org

June 2013 | Volume 7 | Article 290 | 7

Choe et al.

Neurological recovery from complete SCI

FIGURE 4 | Column-specific data comparison of DTI- and MTI-derived
quantities of the controls (blue line) and the patient (red line). FA, MD,
λ∥ , and λ⊥ , and MTCSF values of each spinal column were measured
between C2–C6. The resulting column profiles are shown. Darker area

within the plots indicates the patient’s injury epicenter. Values reported forthe controls are mean ± SD. FA value decreased, while MD, λ∥ , and λ⊥increased at the injury epicenter. MTCSF value increased throughout thecervical cord.

between 0.3 and 0.5% of original BOLD MRI signal. The median
temporal modulations of the patient’s ICs are between 0.4 and1.4% of average BOLD MRI signal, except for the dorsal attention
and left executive networks with higher temporal modulation at
1.6 and 1.9%, respectively. The range of temporal modulationsof the patient’s ICs were bigger for auditory, dorsal somatosensory, secondary visual, and dorsal attention networks, comparedto those of the controls. In general, magnitude of the network
temporal modulation of the patient was bigger than that of thecontrols.
Between-network connectivity: Synchrony between network
time courses is referred to as between-network connectivity(BNC) (Joel et al., 2011). Figures 6A,B show a combined correlation matrix of the controls and the patient, and a combined
matrix of the standard deviation of the corresponding BNC measurements, respectively. Figure 6C shows the difference between
the controls and the patient’s BNC measurements. Figure 6D
shows box plots of the controls’ BNC measurements, with themean BNC value of corresponding functional networks of thepatient indicated using red dots. Ten functional networks withthe biggest mean BNC value difference between the controls andthe patient are shown. Compared to controls, five functional

networks of the patient, namely the sensorimotor, visual, DMN,
attention, and executive networks, show increased synchrony withother networks. A decrease of functional connectivity is also
observed between the left and right executive networks. The visual
networks show a marked increase of connectivity with other functional networks such as the DMN, with the largest increase in
the connectivity being observed between sensorimotor and visual
networks. The patient has not regained sensory function in thelower body and relies heavily on vision to guide his lower bodymotor tasks. The increased connectivity between the sensorimotor and the visual networks is consistent with this clinicalpresentation and suggests compensatory functional changes in
the patient’s brain.

Frontiers in Human Neuroscience

DISCUSSION
We used advanced MRI technologies to understand how a patientwith a complete SCI achieved a pragmatic cure, defined as sufficient recovery of motor, sensory, and autonomic function to
enable the patient to live and function independently. The patientachieved the recovery despite a significant atrophy, a focal FA
decrease, and a diffuse increase of MTCSF in the residual whitematter. This indicates that the patient’s residual spinal cord tissue

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 8

Volume

0.286
0.857
0.286
0.857
0.571
1.000
0.286
0.286
1.000
0.857
1.000
0.857
0.571
1.000
0.286
0.857
0.286
0.857
0.857
0.286
0.571
0.286
0.571
0.571
0.286
0.571
0.571
0.571
0.571
1.000
0.286
0.571
0.286
0.286
0.286
0.286
0.286
0.857
0.286
0.857

Column ID

SPG_L
CingG_L
SFG_L
MFG_L
IFG_L
PrCG_L
PoCG_L
AG_L
PrCu_L
Cu_L
LG_L
Fu_L
PHG_L
SOG_L
IOG_L
MOG_L
ENT_L
STG_L
ITG_L
MTG_L
LFOG_L
MFOG_L
SMG_L
RG_L
Ins_L
Amyg_L
Hippo_L
cerebrellum_L
CST_L
ICP_L
ML_L
SCP_L
CP_L
ALIC_L
PLIC_L
PTR_L
ACR_L
SCR_L
PCR_L
CGC_L

0.857
0.857
1.000
1.000
1.000
1.000
0.857
0.571
1.000
1.000
0.571
1.000
0.857
1.000
0.857
0.857
0.571
1.000
0.571
1.000
0.571
1.000
0.857
0.857
0.857
0.857
0.286
0.857
0.286
0.571
0.571
0.571
0.286
0.286
0.286
1.000
0.571
0.857
0.286
0.571

FA

p-value

0.829
0.654
0.745
0.680
0.697
0.673
0.640
0.875
0.581
0.773
0.958
0.817
0.974
0.677
0.881
0.760
0.779
0.794
0.947
0.796
0.763
0.611
0.877
0.532
0.385
0.765
0.859
0.805
0.534
0.994
0.810
0.655
0.144
0.449
0.633
0.650
0.948
0.737
0.997
0.980

Trace
SS_L
EC_L
UNC_L
PCT_L
MCP_L
Fx_L
GCC_L
BCC_L
SCC_L
RLIC_LRedNc_L
Snigra_R
TAP_L
Caud_L
Put_L
Thal_LGP_L
Midbrain_L
Pons_L
Medulla_L
SPWM_L
Cingwm
SFWM_L
MFWM_LIFWM_L
PrCWM_L
PoCWM_L
AWM_L
PrCuWM_L
CuWM_L
LWM_L
Fu_WM_L
SOWM_L
IOWM_L
MOWM_LSTwm_LITWM_LMTWM_LLFOWM_L
MFOWM_L

Column ID
0.571
0.857
0.571
0.286
1.000
0.286
0.286
0.857
0.286
0.857
1.000
0.286
0.857
0.286
0.857
0.286
0.286
0.857
0.571
0.571
0.667
0.333
0.333
0.667
1.000
1.000
0.333
0.333
1.000
0.333
0.667
1.000
0.667
0.667
1.000
0.667
0.333
0.333
1.000
0.333

Volume
1.000
0.571
0.857
0.857
1.000
0.571
0.571
0.571
0.286
0.571
0.857
0.571
0.286
0.571
0.857
0.571
0.857
0.286
0.857
0.286
0.667
1.000
0.667
0.667
0.667
0.667
0.667
1.000
0.667
1.000
1.000
0.667
1.000
0.667
0.667
0.667
0.667
0.667
1.000
0.667

FA

p-value

0.757
0.894
0.687
0.690
0.768
0.785
0.321
0.220
0.938
0.585
0.973
0.987
0.225
0.764
0.591
0.911
0.560
0.492
0.798
0.746
0.984
0.948
0.990
0.958
0.855
0.794
0.848
0.915
0.892
0.629
0.767
0.926
0.823
0.890
0.987
0.994
0.856
0.927
0.732
0.888

Trace
SFG_R
MFG_R
IFG_R
PrCG_R
PoCG_R
AG_R
PrCu_R
Cu_R
LG_R
FuG_R
PHG_R
SOG_R
IOG_R
MOG_R
ENT_R
STG_R
ITG_R
MTG_R
LFOG_R
MFOG_R
SMG_R
RG_R
Ins_R
Amyg_R
Hippo_R
cerebellum_R
CST_R
ICP_R
ML_R
SCP_R
CP_R
ALIC_R
PLIC_R
PTR_R
ACR_R
SCR_R
PCR_R
CGC_R
CGH_R
Fx/ST_R

Column ID
0.857
0.857
0.286
1.000
1.000
0.857
0.571
1.000
0.857
0.571
0.571
0.286
0.571
0.857
0.286
0.571
0.571
0.286
0.857
0.857
0.571
0.571
0.286
0.857
0.571
1.000
0.857
1.000
0.286
0.286
0.286
0.571
0.286
0.286
0.286
0.571
0.286
1.000
0.571
0.571

Volume
1.000
1.000
1.000
1.000
0.857
0.857
0.857
1.000
0.857
1.000
1.000
0.857
0.857
0.857
0.857
1.000
0.857
1.000
0.571
1.000
0.857
1.000
1.000
0.571
0.571
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.857
0.571
0.571
0.857
0.857
0.571
0.571

FA

p-value

0.808
0.706
0.463
0.518
0.550
0.458
0.712
0.680
0.943
0.501
0.818
0.957
0.541
0.777
0.911
0.480
0.537
0.431
0.408
0.482
0.551
0.479
0.282
0.622
0.912
0.627
0.324
0.858
0.673
0.621
0.279
0.301
0.448
0.914
0.958
0.646
0.900
0.921
0.929
0.570

Trace
UNC_R
PCT_R
MCP_R
Fx_R
GCC_R
BCC_R
SCC_R
RLIC_R
RedNc_R
Snigra_R
TAP_R
Caud_R
Put_R
Thal_R
GP_R
Midbrain_R
Pons_R
Medulla_R
SPwm_R
Cingwm_R
SFWM_R
MFWM_R
IFWM_R
PrCWM_R
PoCWM_R
AWM_R
PreCuWM_R
CuWM_R
LWM_R
Fuwm_R
SOWM_R
IOWM_R
MOWM_R
STWM_R
ITWM_R
MTWM_RLFOWM_R
MFOWM_R
SMWM_R
RGWM_R

Column ID

Table 4 | Comparison of the DTI-derived quantities (structure volume, FA, and MD) between the controls and the patient using Mann–Whitney U test.

0.571
0.857
0.857
0.286
0.286
0.857
0.286
1.000
0.857
0.286
0.857
1.000
0.286
0.286
0.857
0.857
0.857
1.000
0.667
0.667
0.667
1.000
0.333
1.000
0.667
0.333
0.667
0.667
0.667
0.667
0.667
1.000
0.667
0.667
0.333
0.667
0.333
0.333
0.333
0.333

VolumeFrontiers in Human Neuroscience

www.frontiersin.org

0.471
0.557
0.753
0.094
0.329
0.310
0.702
0.991
0.969
0.692
0.496
0.657
0.271
0.889
0.283
0.607
0.750
0.836
0.983
0.896
0.936
0.871
0.502
0.682
0.842
0.909
0.911
0.482
0.642
0.893
0.867
0.490
0.739
0.705
0.800
0.671
0.681
0.801
0.483
0.953

Trace

(Continued)

0.571
0.571
0.286
0.286
0.286
0.857
0.571
0.857
0.286
0.857
0.857
0.857
0.571
0.571
0.857
0.571
1.000
0.286
1.000
1.000
0.667
0.667
0.667
1.000
0.667
0.667
0.667
1.000
0.333
0.667
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.667
0.667
0.667

FA

p-value

Choe et al.
Neurological recovery from complete SCI

June 2013 | Volume 7 | Article 290 | 9

Frontiers in Human Neurosciencehas sufficient integrity to enable function. The rs-fMRI showed
increased functional connectivity between the patient’s sensorimotor and the visual networks, indicating cortical plasticity; thiscortical plasticity was not accompanied by structural changes inthe brain.
MRI-BASED STRUCTURAL EVALUATION OF A CHRONICALLY INJUREDSPINAL CORD

supramarginalwm; RGWM, rectuswm; cerebrellumwm, cerebellum wm.

wm; MOWM, middle occipital wm; STwm, superior temporalwm; ITWM, inferior temporalwm; MTWM, middle temporalwm; LFOWM, lateral fronto-orbitalwm; MFOWM, middle fronto-orbitalwm; SMWM,

precentralwm; PoCWM, postcentralwm; AWM, angularwm; PrCuWM, pre-cuneuswm; CuWM, cuneus wm; LWM, lingual wm; Fu_WM, fusiform wm; SOWM, superior occipital wm; IOWM, inferior occipital

SCC, splenium of corpus callosum; RLIC, retrolenticular part of internal capsule; RedNc, red nucleus; Snigra_R, substancia nigra; TAP, tapatum; Caud, caudate nucleus; Put, putamen; Thal, thalamus; GP, globus

pallidus; Midbrain, midbrain; Pons, pons; Medulla, medulla; SPWM, superior parietalwm; Cingwm, cingulum wm; SFWM, superior frontal wm; IFWM, inferior frontalwm; MFWM, middle frontalwm; PrCWM,

SS, sagittal stratum; EC, external capsule; UNC, uncinate fasciculus; PCT, pontine crossing tract; MCP, middle cerebellar peduncle; Fx, fornix; GCC, genu of corpus callosum; BCC, body of corpus callosum;

capsule; PLIC, posterior limb of internal capsule; PTR, posterior thalamic radiation; ACR, anterior corona radiata; SCR, superior corona radiata; PCR, posterior corona radiata; CGC, cingulum (cingulate gyrus);

CGH, cingulum (hippocampus); Fx/ST, fornix (cres)/stria terminalis; SLF, superior longitudinal fasciculus; SFO, superior fronto-occipital fasciculus; SMG, supramarginal gyrus; IFO, inferior fronto-occipital fasciculus;

hippocampus; cerebrellum, cerebellum; CST, corticospinal tract; ICP, inferior cerebellar peduncle; ML, medial lemniscus; SCP, superior cerebellar peduncle; CP, cerebral peduncle; ALIC, anterior limb of internal

superior temporal gyrus; ITG, inferior temporal gyrus; MTG, middle temporal gyrus; LFOG, lateral fronto-orbital gyrus; MFOG, middle fronto-orbital gyrus; RG, gyrus rectus; Ins, insular; Amyg, amygdala; Hippo,

pre-cuneus; Cu, cuneus; LG, lingual gyrus; Fu, fusiform gyrus; PHG, parahippocampal gyrus; SOG, superior occipital gyrus; IOG, inferior occipital gyrus; MOG, middle occipital gyrus; ENT, entorhinal area; STG,

SPG, superior parietal lobule; CingG, cingulate gyrus; SFG, superior frontal gyrus; MFG, middle frontal gyrus; IFG, inferior frontal gyrus; PrCG, precentral gyrus; PoCG, postcentral gyrus; AG, angular gyrus; PrCu,

0.587
0.750
0.706
0.608
0.667
0.571
1.000
0.571
cerebrellumwm_R
IFO_L
CingG_R
EC_R
0.725
0.712
0.399
0.966
0.857
0.571
0.857
0.857
0.286
1.000
0.286
0.571
SLF_R
SFO_R
IFO_R
SS_R
0.709
0.737
0.632
0.769
0.667
0.667
0.667
1.000
0.333
0.333
0.667
0.571
SMWM_LRGWM_L
cerebrellumwm_L
SPG_R
0.877
0.952
0.972
0.936
0.857
0.857
0.286
1.000
CGH_L
Fx/ST_L
SLF_L
SFO_L

0.857
0.286
0.857
0.571

Column ID
Trace
FA
VolumeColumn ID

FA

Trace

Column ID

VolumeFA

Trace

Column ID

Volumep-valuep-valuep-value
Table 4 | Continued

0.333
0.286
0.857
0.857

Trace
FA

Neurological recovery from complete SCI

Volumep-value

Choe et al.

Whole cord cross-sectional area was shown to correlate withthe motor and sensory scores of SCI patients (Lundell et al.,2011). However, the atrophy of the whole cord is accompanied
by increased scar tissues in the cord and the degree of whitematter atrophy can be greater than that of the whole cord, as
our data shows (Figure 3). We used FA images, which can differentiate white matter from gray matter and scar tissues, tosegment white matter from the injured cord. The wca and wma
values of the healthy spinal cords measured using this approachwere comparable to those values measured using higher resolution structural images acquired through 7T MRI (Sigmund et al.,2011), CT (Fountas et al., 1998), cadaver anatomy (Ko et al.,2004), as well as other previous histological methods (Kameyama
et al., 1996; White et al., 1997; Gilmore et al., 2005). Our
results showed that the patient’s neurological recovery is achieved
despite a 62% atrophy of the white mater tissue at the injury
epicenter.
Structural integrity of the major columns directly affects
neurological recovery of specific motor and sensory functions.
Column-specific data analysis (Smith et al., 2010) enabledincorporation of manual and tractography-based ROI selection
approach, allowing more time efficient data analysis, and comparison of DTI- and MTI-derived quantities of the controls
and the patient in the residual tissue from the major columns(Figure 4). DTI and MTI are advanced MR imaging modalities that are sensitive to tissue microstructures. DTI provides anindirect measure of tissue structure on a microscopic scale byprobing water diffusion (Basser et al., 1994), while MTI does so bymeasuring the magnetization interaction between bulk water protons and semi-solid macromolecular protons (Wolff and Balaban,
1989; Smith et al., 2005). FA, λ∥ , and MTCSF are sensitive tochanges in both axonal and myelin integrity, but previous studies have shown that λ∥ is more sensitive to axonal damages (Songet al., 2002, 2005; Budde et al., 2009) while FA is not specific(Farrell et al., 2010; Landman et al., 2010). In our patient, focal
changes of DTI-derived quantities (Figure 4) were found thatindicate mixed damage types over the cervical cord—i.e., thereduction in FA is caused mainly by increases in λ∥ that could
be due to axonal and myelin damage. MTCSF is more sensitive
to myelination and the diffuse increase of MTCSF throughout
the cervical cord (Figure 4) suggests diffuse demyelination ordysmyelination throughout the cervical cord. Overall, the DTI
parameters describe the extensive damage but not how it affected
the recovery.
A high correlation between FA and total AIS scores in chronic
SCI patients has been described (Cohen-Adad et al., 2011). Fromthat data we extrapolate a FA value (0.47) for chronic SCI patientswith the same AIS score as our patient (124). The FA value of thepatient (0.63) is higher than the extrapolated FA value (0.47). The

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 10

Choe et al.

Neurological recovery from complete SCI

FIGURE 5 | Functional networks of the controls and the patient
estimated using GICA. GICA was performed to delineate same functional
networks in the controls and the patient. ICs are overlaid on the Montreal
Neurological Institute (MNI) template. (A–L) First column corresponds to thesagittal, coronal, and axial view of the controls’ functional networks. Second
column corresponds to the patient’s functional networks. Coordinates (inmm) for each view are indicated on top of the subfigures, along with the IC
numbers assigned during GICA. WNC of the controls (in blue) and the patient(in red) for each network are shown as a series of box plots. On each box,
central mark is the median and edges of the box are the 25th and 75th
percentiles. Aud, auditory; Smot, seonsorimotor; Vis, visual; DMN, default
mode network; Attn, attention; Exec, executive; Sal, salience.

higher FA of the patient is driven by the higher λ∥ . The higher λ∥value can be explained in part by the previously reported trend of
MD and λ∥ change in different stages of traumatic injuries, withinitial decrease during the acute phase of injury (Loy et al., 2007)
and increased values during the chronic phase of injury (Naismith
et al., 2009; Klawiter et al., 2011). This trend alone, however,does not fully explain the higher λ∥ observed in the patient, as
the patient cohort observed in Cohen-Adad et al. (2011) are alsochronic SCI patients with comparable delay after injury as thepatient in this report. The higher λ∥ of the patient therefore may
suggest higher axonal integrity. The DTI- and MTI-derived quantities of the controls were comparable to those reported in similar
studies (Zackowski et al., 2009; Smith et al., 2010).

The small size of a spinal cord presents major imaging challenges. High resolution images are often desired, and the size
makes the images more vulnerable to partial volume effect andvarious motions artifacts. While longer scan time and gating may
be used to alleviate these issues to an extent, keeping patientswith movement difficulties in the scanner for a long time is undesirable. Issue of misregistration cannot be ignored either, as theconsequence of misregistration is greater in the spinal cord due
to its size. One source of such misregistration is the use of an
EPI read out during DTI acquisition, which can result in substantial eddy current effects. In order to minimize the effect ofeddy current and misregistration, a robust registration scheme
utilizing CATNAP (Farrell et al., 2007; Landman et al., 2007) was

Frontiers in Human Neuroscience

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 11

Choe et al.

Neurological recovery from complete SCI

FIGURE 6 | Between-network connectivity. (A) BNC correlation matrix
shows synchrony between pairs of functional networks. (B) Combined matrixof the standard deviation of the corresponding BNC measurements. (A,B) In
reference to the top–left to bottom–right diagonal axis, bottom–left portion
corresponds to the controls and top–right portion corresponds to the patient.

Diagonal elements have been zeroed for display purposes. (C) Difference
between the controls and the patient’s BNC measurements. (D) Box plots ofcontrols’ BNC measurements, with the patient’s mean BNC values indicatedby red dots. Shown are the 10 functional networks with the largest
differences between the patient and controls.

used to correct artifacts in DTI, using optimized input parameters (Farrell et al., 2008). While a more sophisticated eddy current
correction scheme that uses field maps may improve the registration accuracy further, the tract-specific data comparison ofthis study showed good correlation between structural and functional information provided by MRI and clinical evaluation of thepatient.

atrophy in the sensorimotor cortex (Crawley et al., 2004;
Jurkiewicz et al., 2006; Wrigley et al., 2009; Freund et al., 2011;
Henderson et al., 2011), our initial hypothesis was that we would
observe similar gray matter atrophy in the patient’s brain. Our
result indicates, however, that the injury did not lead to significant
structural changes in the patient’s brain. One possible explanationis that unlike other SCI patients, the patient’s structural changes
were small and intracortical, and we did not have enough sample
size and statistical power to detect the subtle changes. One study(Henderson et al., 2011), for example, shows that an extensivefunctional reorganization such as the one the patient demonstrates, may work to minimize the anatomical atrophy in the

MRI-BASED EVALUATION OF THE BRAIN IN A CHRONIC SPINAL CORD
INJURY PATIENT

Considering the extensive structural changes of the patient’s
spinal cord, and based on previous studies that report marked

Frontiers in Human Neuroscience

www.frontiersin.org

June 2013 | Volume 7 | Article 290 | 12

Choe et al.

Neurological recovery from complete SCI

sensorimotor cortex. This hypothesis is also in part supported by
the initial observations of chronic deafferentiation and its effecton cortical anatomy (Florence et al., 1998) where major functional reorganization following limb amputation of a macaquemonkey was accompanied by significant increase of intracoritcal connections but not by any significant changes of major
thalamocortical fiber tracts.
In order to differentiate functional networks, GICA (Calhoun
et al., 2001) was performed using all eight runs of the subject and
one run from 20 healthy controls. This enabled us to identify similar functional networks in both the control group and the subject,effectively eliminating the necessity to perform ICA separately
on each subject and allowing differentiation of the same functional networks with different activation patterns. While there
have been some concerns that the identification of aggregate functional networks using a single dataset of multiple groups may
bias the results, a simulation study (Schmithorst and Holland,2004) showed that GICA can identify a functional network that ispresent in only 10–15% of the total population. Another advantage of GICA is that the method is model-free and does not makeany assumptions about the nature of BNC.
A novel finding of this study is the increased connectivity
between sensorimotor and visual networks. The patient regained
only 10% of sensory function in the lower body and relies heavily on vision to guide his movements. In light of this clinicalpresentation, our observation suggests plastic changes in the sensorimotor and visual networks of the patient. Studies on stroke
patients have shown that visual guidance of movement contributes to the recovery of hand motor function (Kalra et al.,
1997; Seitz et al., 1999), while lack of it may be detrimental
for the recovery of sensorimotor function (Kalra et al., 1997).
The increased functional connectivity between the sensorimotorand visual networks suggests that deprivation of sensory inputthat resulted from the SCI may have led to compensatory functional changes in the patient’s brain. Another possibility is thatthe patient had increased connectivity between the two networks
prior to the injury and this contributed to his recovery.
We also observed increased connectivity of the left executive
network to other brain networks. The executive network (Seeley
et al., 2007) links dorsolateral frontal and parietal cortex and isa task positive network that is related to working memory andcontrol processes (Dosenbach et al., 2007; Stevens, 2009). The
asymmetrical nature of the increased connectivity (left more thanright) may be related to the asymmetric recovery of motor andsensory function of the patient. It should be noted that the magnitude of the asymmetry of the motor and sensory function is
not fully appreciated through the AIS score, which is well-suited
to describe the neurological level of the injury but ill-suited todescribe the degree of functional loss.
The study presented here is a case report; therefore we are limited in our ability to draw statistically significant conclusions—for
spatial differences of ICs as well as changes in BNC. This isbecause we are comparing single run data from multiple controls,
subject to an inter-subject variability, to multiple run data fromthe subject, subject to an intra-subject variability. A larger cohortof SCI population is necessary in order to draw statistically significant conclusion about the changes in ICs and BNC of SCI
patients. Nevertheless, the study showed good agreement between
the rs-fMRI results and the clinical presentation of the subject andpresents the possibility that rs-fMRI may aid in better SCI diagnosis and prognosis, as well as better monitoring and optimizing
therapies for SCI subjects.

REFERENCES

Blight, A. R. (1983). Cellular morphology of chronic spinal cord
injury in the cat: analysis of myelinated axons by line-sampling.
Neuroscience 10, 521–543. doi:
10.1016/0306-4522(83)90150-1
Budde, M. D., Xie, M., Cross, A. H.,
and Song, S. K. (2009). Axial diffusivity is the primary correlate ofaxonal injury in the experimental
autoimmune
encephalomyelitis
spinal cord: a quantitative pixelwise
analysis. J. Neurosci. 29, 2805–2813.
doi:
10.1523/JNEUROSCI.460508.2009

Ashburner, J., and Friston, K. J.
(2005). Unified segmentation.
Neuroimage 26, 839–851. doi:
10.1016/j.neuroimage.2005.02.018
Basser, P. J., Mattiello, J., and LeBihan,
D. (1994). MR diffusion tensor
spectroscopy and imaging. Biophys.
J. 66, 259–267. doi: 10.1016/S00063495(94)80775-1
Beaulieu, C. (2002). The basis of
anisotropic water diffusion in the
nervous system – a technical review.
NMR Biomed. 15, 435–455. doi:
10.1002/nbm.782

Frontiers in Human Neuroscience

Beg, M., Miller, M., Trouve, A., andYounes, L. (2005). Computing large
deformation metric mappings via
goodesic flows of diffeomorphisms.
Int. J. Comput. Vis. 61, 139–157.
doi: 10.1023/B:VISI.0000043755.
93987.aa
Belegu, V., Oudega, M., Gary, D. S., andMcDonald, J. W. (2007). Restoring
function after spinal cord injury:
promoting spontaneous regeneration with stem cells and activitybased therapies. Neurosurg. Clin.
North Am. 18, 143–168, xi. doi:
10.1016/j.nec.2006.10.012

CONCLUDING REMARKS
In conclusion, the current study provides a detailed analysis of
structural and functional changes in the central nervous system of a patient with chronic SCI who underwent a remarkableneurological recovery that continued for 17 years. To the bestof our knowledge, this is the first study which used rs-fMRI to
investigate cortical functional changes in SCI patients, alongside
the investigation of structural changes using spinal cord imaging. Furthermore, the study has theoretical significance because
it may identify variables—i.e., sufficient structural integrity forproper signal conduction in the spinal cord and compensatory
plastic changes in the brain—that are necessary for neurologicaland functional improvements. Additionally, this work shows thepotential of rs-fMRI to evaluate the extent of cortical plasticity
required for functional recovery in patients whose neurologicaldeficits prohibit completion of externally cued motor or sensory
tasks.

ACKNOWLEDGMENTS
The authors would like to thank Dr. Jeffrey D. Rothstein for
helpful discussion and advice. This work was supported in partby grants from the DOD (W81XWH-08-1-0192) and NIH (R21
EB006120-01A2 and P41 EB015909).

www.frontiersin.org

Calhoun, V. D., Adali, T., Pearlson, G.
D., and Pekar, J. J. (2001). A method
for making group inferences fromfunctional MRI data using independent component analysis. Hum.
Brain Mapp. 14, 140–151. doi:
10.1002/hbm.1048
Choe, A. S., Stepniewska, I., Colvin,
D. C., Ding, Z., and Anderson, A.
W. (2012). Validation of diffusion
tensor MRI in the central nervous
system using light microscopy:
quantitative comparison of fiber
properties. NMR Biomed. 25,
900–908. doi: 10.1002/nbm.1810

June 2013 | Volume 7 | Article 290 | 13

Choe et al.

Cohen-Adad, J., El Mendili, M. M.,
Lehericy, S., Pradat, P. F., Blancho,
S., Rossignol, S., et al. (2011).
Demyelination and degeneration in the injured human spinal
cord detected with diffusion andmagnetizationtransfer
MRI.
Neuroimage 55, 1024–1033. doi:
10.1016/j.neuroimage.2010.11.089
Cohen-Adad, J., El Mendili, M. M.,
Morizot-Koutlidis, R., Lehericy,
S., Meininger, V., Blancho, S.,et al. (2013). Involvement of
spinal sensory pathway in ALS
and specificity of cord atrophy
to lower motor neuron degeneration. Amyotroph. Lateral Scler.
Frontotemporal Degener. 14, 30–38.
doi: 10.3109/17482968.2012.701308
Crawley, A. P., Jurkiewicz, M. T.,
Yim, A., Heyn, S., Verrier, M.
C., Fehlings, M. G., et al. (2004).
Absence of localized grey mattervolume changes in the motor
cortex following spinal cord
injury. Brain Res. 1028, 19–25. doi:
10.1016/j.brainres.2004.08.060
Dosenbach, N. U., Fair, D. A., Miezin,
F. M., Cohen, A. L., Wenger, K.
K., Dosenbach, R. A., et al. (2007).
Distinct brain networks for adaptive and stable task control inhumans. Proc. Natl. Acad. Sci.
U.S.A. 104, 11073–11078. doi:
10.1073/pnas.0704320104
Endo, T., Spenger, C., Tominaga, T.,
Brene, S., and Olson, L. (2007).
Cortical sensory map rearrangement after spinal cord injury: FMRI
responses linked to nogo signalling.
Brain 130, 2951–2961. doi: 10.1093/
brain/awm237
Faria, A. V., Hoon, A., Stashinko, E.,
Li, X., Jiang, H., Mashayekh, A.,et al. (2011). Quantitative analysis of brain pathology basedon MRI and brain atlases–
applications for cerebral palsy.
Neuroimage 54, 1854–1861. doi:
10.1016/j.neuroimage.2010.09.061
Faria, A. V., Zhang, J., Oishi, K., Li,
X., Jiang, H., Akhter, K., et al.
(2010). Atlas-based analysis ofneurodevelopment from infancy
to adulthood using diffusion tensor imaging and applications forautomated abnormality detection.
Neuroimage 52, 415–428. doi:
10.1016/j.neuroimage.2010.04.238
Farrell, J. A., Landman, B. A., Jones, C.
K., Smith, S. A., Prince, J. L., vanZijl, P. C., et al. (2007). Effects ofsignal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity,and principal eigenvector measurements at 1.5 T. JMRI 26, 756–767.
doi: 10.1002/jmri.21053

Frontiers in Human Neuroscience

Neurological recovery from complete SCI

Farrell, J. A., Smith, S. A., GordonLipkin, E. M., Reich, D. S., Calabresi,
P. A., and van Zijl, P. C. (2008).
High b-value q-space diffusionweighted MRI of the human cervicalspinal cord in vivo: feasibility andapplication to multiple sclerosis.
Magn. Reson. Med. 59, 1079–1089.
doi: 10.1002/mrm.21563
Farrell, J. A., Zhang, J., Jones, M.
V., Deboy, C. A., Hoffman, P. N.,
Landman, B. A., et al. (2010).
Q-space and conventional diffusion
imaging of axon and myelin damage
in the rat spinal cord after axotomy.
Magn. Reson. Med. 63, 1323–1335.
doi: 10.1002/mrm.22389
Florence, S. L., Taub, H. B., and Kaas, J.
H. (1998). Large-scale sprouting ofcortical connections after peripheral
injury in adult macaque monkeys.
Science (N.Y.) 282, 1117–1121. doi:
10.1126/science.282.5391.1117
Fountas, K. N., Kapsalaki, E. Z., Jackson,
J., Vogel, R. L., and Robinson, J.
S. Jr. (1998). Cervical spinal cord–
smaller than considered? Spine 23,1513–1516. doi: 10.1097/00007632199807150-00001
Freund, P., Weiskopf, N., Ward, N. S.,
Hutton, C., Gall, A., Ciccarelli,O., et al. (2011). Disability,
atrophy and cortical reorganization following spinal cord
injury. Brain 134, 1610–1622. doi:
10.1093/brain/awr093
Friston, K. J., Holmes, A. P., Worsley,
K. J., Poline, J. P., Frith, C.
D., and Frackowiak, R. S. J.
(1994).
Statisticalparametricmaps in functional imaging: ageneral linear approach. Hum.
Brain Mapp. 2, 189–210. doi:
10.1002/hbm.460020402
Gilmore, C. P., DeLuca, G. C., Bo, L.,
Owens, T., Lowe, J., Esiri, et al.
(2005). Spinal cord atrophy inmultiple sclerosis caused by whitematter volume loss. Arch. Neurol.
62, 1859–1862. doi: 10.1001/archneur.62.12.1859
Henderson, L. A., Gustin, S. M.,
Macey, P. M., Wrigley, P. J., andSiddall, P. J. (2011). Functional
reorganization of the brain inhumans following spinal cord
injury: evidence for underlying
changes in cortical anatomy. J.
Neurosci. 31, 2630–2637. doi:
10.1523/JNEUROSCI.2717-10.2011
Himberg, J., Hyvarinen, A., andEsposito, F. (2004). Validating
theindependent
components
of neuroimaging time series
via clustering and visualization.
Neuroimage 22, 1214–1222. doi:
10.1016/j.neuroimage.2004.03.027
Jenkinson, M., Bannister, P., Brady,M., and Smith, S. (2002). Improved

optimization for the robust andaccurate linear registration andmotion correction of brain images.
Neuroimage 17, 825–841. doi:
10.1006/nimg.2002.1132
Jiang, H., van Zijl, P. C., Kim, J.,
Pearlson, G. D., and Mori, S.
(2006).
DtiStudio:
resource
program for diffusion tensor
computation and fiber bundle tracking. Comput. Methods
Progr. Biomed. 81, 106–116. doi:
10.1016/j.cmpb.2005.08.004
Joel, S. E., Caffo, B. S., van Zijl, P. C.,
and Pekar, J. J. (2011). On the relationship between seed-based andICA-based measures of functional
connectivity. Magn. Reson. Med. 66,
644–657. doi: 10.1002/mrm.22818
Jurkiewicz, M. T., Crawley, A. P.,
Verrier, M. C., Fehlings, M.
G., and Mikulis, D. J. (2006).
Somatosensory
cortical
atrophy after spinal cord injury:avoxel-based
morphometry
study. Neurology 66, 762–764.
doi: 10.1212/01.wnl.0000201276.
28141.40
Jurkiewicz, M. T., Mikulis, D. J.,
Fehlings, M. G., and Verrier, M.
C. (2010). Sensorimotor cortical activation in patients withcervical spinal cord injury withpersisting paralysis. Neurorehabil.
Neural Repair 24, 136–140. doi:
10.1177/1545968309347680
Jurkiewicz, M. T., Mikulis, D. J.,
McIlroy, W. E., Fehlings, M. G., andVerrier, M. C. (2007). Sensorimotor
cortical plasticity during recovery
following spinal cord injury: a longitudinal fMRI study. Neurorehabil.
Neural Repair 21, 527–538. doi:
10.1177/1545968307301872
Kalra, L., Perez, I., Gupta, S., andWittink, M. (1997). The influenceof visual neglect on stroke rehabilitation. Stroke 28, 1386–1391. doi:
10.1161/01.STR.28.7.1386
Kameyama, T., Hashizume, Y., andSobue, G. (1996). Morphologic
features of the normal humancadaveric spinal cord. Spine 21,
1285–1290. doi: 10.1097/00007632199606010-00001
Kirshblum, S., Millis, S., McKinley,W., and Tulsky, D. (2004). Lateneurologic recovery after traumatic
spinal cord injury. Arch. Phys.
Med. Rehab. 85, 1811–1817. doi:
10.1016/j.apmr.2004.03.015
Klawiter, E. C., Schmidt, R. E.,
Trinkaus, K., Liang, H. F., Budde,
M. D., Naismith, R. T., et al.
(2011). Radial diffusivity predicts demyelination in ex vivo
multiple sclerosis spinal cords.
Neuroimage 55, 1454–1460. doi:
10.1016/j.neuroimage.2011.01.007

www.frontiersin.org

Ko, H. Y., Park, J. H., Shin, Y. B., andBaek, S. Y. (2004). Gross quantitative measurements of spinal cordsegments in human. Spinal Cord 42,
35–40. doi: 10.1038/sj.sc.3101538
Landman, B. A., Farrell, J. A., Jones,
C. K., Smith, S. A., Prince, J.
L., and Mori, S. (2007). Effects
of diffusion weighting schemeson the reproducibility of DTIderived
fractional
anisotropy,
mean diffusivity, and principal
eigenvector measurements at 1.5T.
Neuroimage 36, 1123–1138. doi:
10.1016/j.neuroimage.2007.02.056
Landman, B. A., Farrell, J. A., Smith,
S. A., Reich, D. S., Calabresi, P. A.,
and van Zijl, P. C. (2010). Complex
geometric models of diffusion andrelaxation in healthy and damaged
white matter. NMR Biomed. 23,
152–162. doi: 10.1002/nbm.1437
Landman, B. A., Huang, A. J., Gifford,
A., Vikram, D. S., Lim, I. A., Farrell,
J. A., et al. (2011). Multi-parametricneuroimaging
reproducibility: A 3-T resource study.
Neuroimage 54, 2854–2866. doi:
10.1016/j.neuroimage.2010.11.047
Levesque, I. R., Giacomini, P. S.,
Narayanan, S., Ribeiro, L. T., Sled,
J. G., Arnold, D. L., et al. (2010).
Quantitative magnetization transfer
and myelin water imaging of theevolution of acute multiple sclerosis lesions. Magn. Reson. Med. 63,
633–640. doi: 10.1002/mrm.22244
Li, Y. O., Adali, T., and Calhoun,
V. D. (2007). Estimating thenumber of independent components for functional magnetic
resonance imaging data. Hum.
Brain Mapp. 28, 1251–1266. doi:
10.1002/hbm.20359
Loy, D. N., Kim, J. H., Xie, M.,
Schmidt, R. E., Trinkaus, K., andSong, S. K. (2007). Diffusion
tensor imaging predicts hyperacute spinal cord injury severity.
J. Neurotr. 24, 979–990. doi:
10.1089/neu.2006.0253
Lundell,
H.,
Barthelemy,D.,
Skimminge, A., Dyrby, T. B.,
Biering-Sorensen, F., and Nielsen, J.
B. (2011). Independent spinal cord
atrophy measures correlate to motorand sensory deficits in individuals
with spinal cord injury. Spinal
Cord 49, 70–75. doi: 10.1038/sc.
2010.87
Maes, F., Vandermeulen, D., andSuetens, P. (1999). Comparativeevaluationofmultiresolution
optimization
strategiesformultimodality
image
registration by maximization ofmutualinformation.
Med.
Image Anal. 3, 373–386. doi:
10.1016/S1361-8415(99)80030-9

June 2013 | Volume 7 | Article 290 | 14

Choe et al.

Maynard, F. M. Jr., Bracken, M. B.,
Creasey, G., Ditunno, J. F. Jr.,
Donovan, W. H., Ducker, T. B.,
et al. (1997). International standards for neurological and functional classification of spinal cord
injury. american spinal injury association. Spinal Cord 35, 266–274.
doi: 10.1038/sj.sc.3100432
McDonald, J. W., and Sadowsky, C.
(2002). Spinal-cord injury. Lancet
359, 417–425. doi: 10.1016/S01406736(02)07603-1
Naismith, R. T., Xu, J., Tutlam, N. T.,
Snyder, A., Benzinger, T., Shimony,J., et al. (2009). Disability in optic
neuritis correlates with diffusion
tensor-derived directional diffusivities. Neurology 72, 589–594.
doi: 10.1212/01.wnl.0000335766.
22758.cd
Nathan, P. W. (1994). Effects onmovement of surgical incisions
into the human spinal cord.
Brain 117(Pt 2), 337–346. doi:
10.1093/brain/117.2.337
Oishi, K., Mori, S., Donohue, P. K.,
Ernst, T., Anderson, L., Buchthal, S.,et al. (2011). Multi-contrast humanneonatal brain atlas: application tonormal neonate development analysis. Neuroimage 56, 8–20. doi:
10.1016/j.neuroimage.2011.01.051
Oishi, K., Zilles, K., Amunts, K., Faria,
A., Jiang, H., Li, X., et al. (2008).
Human brain white matter atlas:
identification and assignmentof common anatomical structures in superficial white matter.
Neuroimage 43, 447–457. doi:
10.1016/j.neuroimage.2008.07.009
Schmithorst, V. J., and Holland, S.
K. (2004). Comparison of threemethods for generating group
statistical inferences from independent component analysis offunctional magnetic resonanceimaging data. JMRI 19, 365–368.
doi: 10.1002/jmri.20009

Frontiers in Human Neuroscience

Neurological recovery from complete SCI

Seeley, W. W., Menon, V., Schatzberg,
A. F., Keller, J., Glover, G.
H., Kenna, H., et al. (2007).
Dissociable intrinsic connectivity
networks for salience processing and executive control. J.
Neurosci. 27, 2349–2356. doi:
10.1523/JNEUROSCI.5587-06.2007
Seitz, R. J., Knorr, U., Azari, N. P.,
Herzog, H., and Freund, H. J.
(1999). Visual network activation inrecovery from sensorimotor stroke.
Restor. Neurol. Neurosci. 14, 25–33.
Sigmund, E. E., Suero, G. A., Hu,
C., McGorty, K., Sodickson, D.
K., Wiggins, G. C., et al. (2011).
High-resolution human cervical spinal cord imaging at 7 T.
NMR Biomed. 25, 891–899. doi:
10.1002/nbm.1809
Smith, S. A., Golay, X., Fatemi, A.,
Jones, C. K., Raymond, G. V., Moser,
H. W., et al. (2005). Magnetizationtransfer weighted imaging in
the upper cervical spinal cordusing cerebrospinal fluid as intersubject normalization reference(MTCSF imaging). Magn. Reson.
Med. 54, 201–206. doi: 10.1002/
mrm.20553
Smith, S. A., Jones, C. K., Gifford, A.,
Belegu, V., Chodkowski, B., Farrell,
J. A., et al. (2010). Reproducibility oftract-specific magnetization transfer and diffusion tensor imaging
in the cervical spinal cord at 3
tesla. NMR Biomed. 23, 207–217.
10.1002/nbm.1447
Song, S. K., Sun, S. W., Ramsbottom,
M. J., Chang, C., Russell,J., and Cross, A. H. (2002).
Dysmyelination revealed through
MRI as increased radial (butunchanged axial) diffusion of water.
Neuroimage 17, 1429–1436. doi:
10.1006/nimg.2002.1267
Song, S. K., Yoshino, J., Le, T. Q.,
Lin, S. J., Sun, S. W., Cross, A.
H., et al. (2005). Demyelination

increases radial diffusivity in corpus callosum of mouse brain.
Neuroimage 26, 132–140. doi:
10.1016/j.neuroimage.2005.01.028
Stevens, M. C. (2009). The developmental cognitive neuroscienceoffunctional
connectivity.
Brain Cogn. 70, 1–12. doi:
10.1016/j.bandc.2008.12.009
Vazquez, X. M., Rodriguez, M. S.,
Penaranda, J. M., Concheiro, L.,
and Barus, J. I. (2008). Determining
prognosis after spinal cord injury.
J. Foren. Legal Med. 15, 20–23. doi:
10.1016/j.jflm.2007.06.003
White, L. E., Andrews, T. J., Hulette, C.,
Richards, A., Groelle, M., Paydarfar,J., et al. (1997). Structure of thehuman sensorimotor system. II: lateral symmetry. Cereb. Cortex (N.Y)
7, 31–47.
Wolff, S. D., and Balaban, R. S.
(1989). Magnetization transfer
contrast (MTC) and tissue water
proton relaxation in vivo. Magn.
Reson. Med. 10, 135–144. doi:
10.1002/mrm.1910100113
Woods, R. P., Grafton, S. T., Holmes, C.
J., Cherry, S. R., and Mazziotta, J. C.
(1998). Automated image registration: I. general methods and intrasubject, intramodality validation. J.
Comput. Ass. Tomogr. 22, 139–152.
doi: 10.1097/00004728-19980100000027
Wrigley, P. J., Gustin, S. M., Macey,
P. M., Nash, P. G., Gandevia, S.
C., Macefield, V. G., et al. (2009).
Anatomical changes in humanmotor cortex and motor pathways following complete thoracic
spinal cord injury. Cereb. Cortex(N.Y) 19, 224–232.
Zackowski, K. M., Smith, S. A.,
Reich, D. S., Gordon-Lipkin, E.,
Chodkowski, B. A., Sambandan,
D. R., et al. (2009). Sensorimotor
dysfunction in multiple sclerosis
andcolumn-specificwww.frontiersin.org

magnetizationtransfer-imaging
abnormalities in the spinal
cord. Brain 132, 1200–1209.
doi: 10.1093/brain/awp032
Conflict of Interest Statement: Dr.
Pekar serves as Manager of the F. M.
Kirby Research Center for Functional
Brain Imaging, which receives researchsupport from Philips HealthCare,
which manufactures the MRI scanners
used in this study. Dr. van Zijl has grantfunding from Philips Healthcare. In
addition, Dr. van Zijl is the inventor oftechnology that is licensed to Philips
and is a paid lecturer for Philips. This
arrangement has been approved by
Johns Hopkins University in accordance with its conflict of interest
policies. The other authors declarethat the research was conducted in
the absence of any commercial or
financial relationships that could beconstrued as a potential conflict ofinterest.
Received: 25 February 2013; accepted:
03 June 2013; published online: 25 June
2013.
Citation: Choe AS, Belegu V, Yoshida S,
Joel S, Sadowsky CL, Smith SA, van Zijl
PCM, Pekar JJ and McDonald JW (2013)
Extensive neurological recovery from acomplete spinal cord injury: a case reportand hypothesis on the role of corticalplasticity. Front. Hum. Neurosci. 7:290.
doi: 10.3389/fnhum.2013.00290
Copyright © 2013 Choe, Belegu,
Yoshida, Joel, Sadowsky, Smith, van Zijl,
Pekar and McDonald. This is an openaccess article distributed under the termsof the Creative Commons Attribution
License, which permits use, distribution
and reproduction in other forums,
provided the original authors and source
are credited and subject to any copyrightnotices concerning any third-party
graphics etc.

June 2013 | Volume 7 | Article 290 | 15