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Neurolmage: Clinical 2 (2013) 440-447 




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Neurolmage: Clinical 



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Diffuse reduction of white matter connectivity in cerebral palsy with (BScrossMark 
specific vulnerability of long range fiber tracts^ 

Zoe A. Englander Carolyn E. Pizoli Anastasiya Batrachenko ^.Jessica Sun Gordon Worley ^, 
Mohamad A. Mikati Joanne Kurtzberg "" ^ Allen W. Song 

^ Brain Imaging and Analysis Center, Duke University Medical Center, USA 
^ Department of Pediatrics, Duke University Medical Center, USA 
The Robertson Cell and Translational Therapy Center, Duke University Medical Center, USA 



ARTICLE INFO 



ABSTRACT 



Article history: 

Received 31 January 2013 

Received in revised form 7 March 2013 

Accepted 11 March 2013 

Available online 22 March 2013 



Keywords: 
Cerebral palsy 
Diffusion tensor imaging 
Tractography 
Structural connectome 
Network disruption 



Cerebral palsy (CP) is a heterogeneous group of non-progressive motor disorders caused by injury to the de- 
veloping fetal or infant brain. Although the defining feature of CP is motor impairment, numerous other 
neurodevelopmental disabilities are associated with CP and contribute greatly to its morbidity. The relation- 
ship between brain structure and neurodevelopmental outcomes in CP is complex, and current evidence sug- 
gests that motor and developmental outcomes are related to the spatial pattern and extent of brain injury. 
Given that multiple disabilities are frequently associated with CP, and that there is increasing burden of 
neurodevelopmental disability with increasing motor severity, global white matter (WM) connectivity was 
examined in a cohort of 17 children with bilateral CP to test the hypothesis that increased global WM damage 
will be seen in the group of severely affected (Gross Motor Function Classification Scale (GMFCS) level of IV) 
as compared to moderately affected (GMFCS of II or III) individuals. Diffusion tensor tractography was 
performed and the resulting fibers between anatomically defined brain regions were quantified and analyzed 
in relation to GMFCS levels. Overall, a reduction in total WM connectivity throughout the brain in severe ver- 
sus moderate CP was observed, including but not limited to regions associated with the sensorimotor system. 
Our results also show a diffuse and significant reduction in global inter-regional connectivity between sever- 
ity groups, represented by inter-regional fiber count, throughout the brain. Furthermore, it was also observed 
that there is a significant difference (p = 0.02) in long-range connectivity in patients with severe CP as com- 
pared to those with moderate CP, whereas short-range connectivity was similar between groups. This new 
finding, which has not been previously reported in the CP literature, demonstrates that CP may involve dis- 
tributed, network-level structural disruptions. 

© 2013 The Authors. Published by Elsevier Inc. All rights reserved. 



1. Introduction 

Cerebral palsy (CP) refers to a group of heterogeneous disorders 
characterized by the impairment of movement and posture with resul- 
tant limitation of activity (Bax et al, 2005). CP remains the most preva- 
lent motor disorder affecting children (Clark and Hankins, 2003), and 
the common co-morbid deficits in sensation, cognition, communica- 
tion, and behavior contribute immensely to the burden of the disorder 
in these patients (Bax et al, 2005; Volpe, 2008). A variety of distur- 
bances in the developing fetal or infant brain may lead to CP with an 



^ This is an open-access article distributed under the terms of the Creative Commons 
Attribution-NonCommercial-ShareAlike License, which permits non-commercial use, 
distribution, and reproduction in any medium, provided the original author and source 
are credited. 

* Corresponding author at: Brain Imaging and Analysis Center, Duke University Medical 
Center, Durham, NC 27710, USA. Tel.: + 1 919 684 1215; fax: + 1 919 681 7033. 
E-mail address: allen.song@duke.edu (A.W. Song). 



individual's clinical presentation likely determined by the spatial pat- 
tern and extent of gray matter (CM) and white matter (WM) involve- 
ment (Folkerth, 2005; Volpe, 2008). Standard clinical neuroimaging 
identifies gross patterns of injury in 70-90% of cases and provides 
some useful prognostic information; however, tremendous clinical het- 
erogeneity still exists that limits the utility of this neuroimaging data on 
an individual basis (Benini et al, 2013; Korzeniewski et al, 2008; 
Krageloh-Mann and Horber, 2007; Martinez-Biarge et al, 2011; 
Towsley et al, 2011 ; Woodward et al, 2006). 

Multi-modal and advanced neuroimaging techniques such as diffu- 
sion tensor imaging (DTI) show great promise for providing increased 
sensitivity and specificity of the underlying structure-function relation- 
ships related to the neurobehavioral deficits in CP. These advanced neu- 
roimaging techniques are also being explored for the more immediate 
clinical needs of subgroup classification, prognostication, targeting 
treatment strategies and treatment monitoring (Benini et al, 2013; 
Hoon, 2005; Shimony et al, 2008). The latter has become increasingly 
necessary as more neuroprotective and rehabilitative treatment trials 



2213-1582/$ - see front matter © 2013 The Authors. Published by Elsevier Inc. All rights reserved. 
http://dx.d0i.0rg/l 0.1 01 6/j.nicl.201 3.03.006 



ZA. Englander et al. / Neurolmage: Clinical 2 (2013) 440-447 



441 



are underway. Thus far, neuroimaging data have shown early promise 
as treatment response biomarkers in neuroprotective therapeutic hypo- 
thermia (Porter et al, 2010; Rutherford et al, 2010), stem cell therapies 
(Lee et al, 2012; Min et al, 2013) and rehabilitative trials within this 
population (Sutcliffe et al, 2009; Trivedi et al, 2008). 

Diffusion weighted imaging techniques, including DTI and diffu- 
sion tensor tractography (DTT), are particularly well-suited for the 
detection of WM microstructural changes (Gerig et al., 2004; Mori 
and Zhang, 2006) that are thought to contribute heavily to the clinical 
manifestations of CP. In the last several years, many groups have 
reported DTI-based ultra-structural WM differences in this popula- 
tion. The majority of studies have predominately focused on regions 
of the brain associated with putative sensorimotor function and dem- 
onstrate variable deficits (Chang et al., 2012; Hoon et al, 2002; 
Ludeman et al., 2008; Murakami et al, 2008; Rose et al, 2007; 
Scheck et al., 2012; Trivedi et al., 2010) (for systematic review see 
Scheck et al. (2012)). For the most part, these studies used similar ap- 
proaches and typically measure diffusivity and anisotropy within a 
priori selected WM ROIs related to sensorimotor function. Additional 
studies utilizing tractography methods were likewise focused on the 
identification of a priori sensorimotor tracts as ROIs for quantification 
of diffusivity measurements (Rha et al, 2012; Rose et al, 2011; 
Thomas et al, 2005; Yoshida et al., 2010). 

There are a few DTI studies investigating more global WM deficits 
in CP, also demonstrating changes in mean diffusivity (MD) and frac- 
tional anisotropy (FA) (Lee et al, 2011; Rai et al, 2013). In a 
tractography study, Nagae et al. found multiple and wide-spread dif- 
ferences in a qualitative assessment of 26 manually defined fiber 
tracts (Nagae et al., 2007). These studies suggest a more diffuse pat- 
tern of white matter injury, and indicate the need for a more compre- 
hensive whole-brain characterization of white matter damage in CP. 

Thus far, there have been no DTI studies in CP aimed at quantifying 
WM connectivity throughout the whole brain using a comprehensive 
and standardized set of ROIs. Given the neuropathologic evidence of dif- 
fuse gross and ultra-structural WM pathology in CP patients (Folkerth, 
2005; Hoon, 2005; Volpe, 2003) we investigated the spatial pattern 
and extent of WM tract differences using whole-brain connectivity 
analysis in children with CP. Specifically, WM connectivity measures 
were compared between groups of individuals with severe versus mod- 
erate CP (grouped by GMFCS level), in order to test the hypothesis that 
WM connectivity is affected throughout the brain in proportion to the 
severity of the disorder. 



2. Methods 

2.1. Subjects 

Participants included children, ages 1 to 5 years, with a clinical diag- 
nosis of bilateral CP and Gross Motor Function Classification System 
(GMFCS) level of II, III or IV. Participants were part of a randomized, pla- 
cebo controlled trial of autologous cord blood infusions in children with 
CP. As part of the study, baseline MRI and functional assessments were 
performed. The data presented here are the results from baseline imag- 
ing findings in a subset of children enrolled in the clinical trial. The subset 
was restricted to children with predominantly spastic bilateral CP in 
order to minimize clinical heterogeneity in an effort to identify brain 
pathologies specific to this discrete population and relatable to function- 
al deficits. Thus, patients were excluded if they had hemiparetic CP (uni- 
lateral involvement), predominant dystonic CP, seizure disorder, brain 
dysmorphogenesis, or known genetic disease. The main structural MRI 
findings in this cohort are enlarged ventricles accompanied by gross 
white matter atrophy, and none of these individuals showed evidence 
of perinatal stroke as the primary etiology. Patients underwent neurolog- 
ical testing of motor control, muscle tone and spasticity, overall flexibility 
and reflexes, as well as cognitive assessment if able (see below). The 
physical examinations were performed by two senior clinicians experi- 
enced with clinical care of patients with CP. Demographic information 
is presented in Table 1. 

The cohort in this report consists of 17 children (age = 2.4 years ± 
1.18). Individuals were divided into two groups based on GMFCS level, 
a severe group (GMFCS of IV, n = 9, mean age = 1.83 years ± 0.77), 
and a moderate group (GMFCS of II or III, n = 8, mean age = 
3.10 years ± 1.23). The moderate and severe group distinction used 
here separates the participants according to the precursors of their even- 
tual levels of self-mobility. According to the GMFCS levels, individuals at 
levels II or III will be able to walk with limitations (possibly with a hand 
held assistance or mobility device) after age 4, however individuals clas- 
sified at level IV have more limited self-mobility. GMFCS assessment can 
be performed on children prior to age 4 and is predictive and reliable of 
functional abilities throughout development (Palisano et al., 1997, 2007). 

Of the 17 children in this cohort, 13 were between 0 and 3 years of 
age and qualified for neuropsychometric testing with the Bayley 
Scales of Infant and Toddler Development, Third Edition (BSID-III) 
(Bayley, 2005). The other 4 children were older than 3 years of age 
at the time of testing; of these, two were too impaired to be tested. 



Table 1 

Demographic information of the CP patient cohort. 



Subject 


GMFCS level 


Age (at scan, years) 


BSID-III cognitive composite 


Abnormal movements 


Typography 


Gestational age (weeks) 


1 


IV 


1.83 


55 


Spastic predominant with dystonia 


Q 


38 


2 


II 


2.5 


105 


Spastic 


D 


35 


3 


IV 


1.25 


55 


Spastic predominant with dystonia 


Q 


30 


4 


III 


4.42 


N/J^ 


Spastic 


D 


27 


5 


III 


2.08 


90 


Spastic predominant with dystonia 


Q 


31 


6 


IV 


1.5 


NfT^ 


Spastic predominant with dystonia 


Q 


31 


7 


IV 


3.75 


N/T^ 


Spastic 


Q 


31 


8 


II 


2.08 


100 


Spastic 


D 


41 


9 


II 


4.83 


N/T^ 


Spastic 


D 


38 


10 


II 


2.83 


100 


Spastic 


D 


32 


11 


IV 


1.42 


90 


Spastic 


Q 


32 


12 


IV 


1.92 


55 


Spastic predominant with dystonia 


Q 


32 


13 


II 


4.3 


69^ 


Spastic predominant with dystonia 


D 


38 


14 


IV 


1.17 


55 


Spastic predominant with dystonia 


D 


39 


15 


IV 


1.7 


N/J^ 


Spastic 


Q 


40 


16 


IV 


1.95 


55 


Spastic 


Q 


30 


17 


II 


1.73 


80 


Spastic predominant, mixed unspecified 


Q 


35 



^ Not tested due to language barrier. 

^ Too severely disabled to complete testing. 

^ Tested using WlSC-111. 



442 



ZA. Englander et al. / Neurolmage: Clinical 2 (2013) 440-447 



one was not tested due to a language barrier, and one was tested 
using the WISC-III with a full scale IQof 69 (Table 1). Of the 13 sub- 
jects tested with BSID-III, two children were too severely disabled to 
complete testing; therefore 11 children in this report (mean age = 
1.9 years ± 0.50) were tested with cognitive composite scores 
shown in Table 1. The GMFCS levels and cognitive scores were corre- 
lated using a Kendall-tau rank correlation test and were found to be 
significantly inversely correlated (t = —0.76, p = 0.01). Therefore, 
most individuals within the severe CP group (GMFCS = IV) also 
scored lowest on the cognitive assessment, indicating that most 
cases impaired motor function was accompanied by impaired cogni- 
tive function in our cohort. 

All patients were sedated for the MRI scans to limit subject discomfort 
and motion artifacts. Written informed consent was obtained from the 
parents of each participant and all study related procedures were ap- 
proved by the Duke University Medical Center Institutional Review Board. 



2.2. Image acquisition 

Diffusion weighted images were acquired on a 3 Tesla GE HD scan- 
ner (Waukesha, WI) using a 25-direction gradient encoding scheme at 
b = 1 000 s/mm^ with 3 non-diffusion-weighted images to ensure a ro- 
bust baseline. An echo time (TE) of 70.5 ms and a repetition time (TR) of 
1 2,000 ms were used. Isotropic resolution of 2 mm^ was achieved using 
the 96 X 96 acquisition matrix in a field of view (FOV) of 192 x 
192 mm^. Tl -weighted images were obtained with an inversion- 
prepared 3D fast spoiled-gradient-recalled (FSPGR) pulse sequence 
with a TE of 2.5 ms, an inversion time (TI) of 450 ms, TR of 6.5 ms, 
and flip angle of 12°, at 1 mm^ isotropic resolution. 



2.3. Region of interest parcellation 

Region of interest (ROI) parcellation was performed using the 
JHU-DTI-MNI "Eve" atlas template (Oishi et al., 2009), which was 
warped into each subject's DTI image space via the Large Deformation 
Diffeomorphic Metric Mapping (LDDMM) algorithm (Faria et al, 
2011; Miller et al, 2005). Each individual patient's warping result was 
visually inspected to confirm anatomical consistency. ROIs were refined 
by subtracting WM masks obtained from FAST (FMRIB's Automated 
Segmentation Tool) to restrict the ROIs to only GM voxels (Zhang et 
al., 2001 ). Regions of the atlas associated with the brainstem were com- 
bined to create a single WM ROI, which was added back to the ROI set 
after WM masking was performed. As a result, a total of 63 regions 
were defined for each individual, 31 GM regions per hemisphere, plus 
a single region encompassing the brainstem. 



2.4. White matter tractography and filtering 

Following visual inspection of the data for motion artifacts, diffusion 
tensors were derived from our 25-direction DTI dataset. Tractography 
was then performed in the whole brain using a streamline fiber- 
tracking algorithm (Mori and Zhang, 2006; Mori et al, 1999). Fig. 1 illus- 
trates primary data containing a T2 (baseline bO) image, diffusion weight- 
ed image (b = 1000), colored FA map, and tractography for a 
representative subject (GMFCS II, age = 1.73). Inter-regional streamlines 
(fibers) were defined as those that began and terminated within a pair 
ROIs, while passing through part of the WM mask. Additionally, a length 
threshold of 40 mm was applied to the whole brain tractography results 
in order to classify fibers as either short or long range. To confirm the con- 
sistency of our findings, we also used length thresholds ranging from 
30 mm to 60 mm to classify fibers. All tractographies and parcellations 
were performed via a standardized pipeline for each patient without re- 
gard for the patients' clinical classification. 



2.5. Whole-brain connectome analysis 

The whole-brain connectome analysis was carried out to investi- 
gate the relationship between regional and inter-regional structural 
connectedness in the severe versus moderate CP groups, using a pipe- 
line based on the Connectome Mapper Toolkit (http://www.cmtk. 
org) (Gerhard et al., 2011). The amount of all fibers beginning or 
ending in a region reflected the total connectivity associated with 
that region, while the amount of fibers between each pair of ROIs 
was used to indicate mutual connectivity. Connections and regions 
that showed decreased connectivity in the severe group as compared 
to the moderate group were identified. The probabilities of differ- 
ences in connectivity measures between groups were obtained 
using a Kruskal-Wallis non-parametric ANOVA. The directions of 
the differences were obtained by comparing the means of each 
group. A matrix of p-values demonstrating significantly different con- 
nections was obtained for the group, and corrected for multiple com- 
parisons using false discovery rate correction as implemented by FSL's 
FDR algorithm (Genovese et al, 2002). This analysis was performed 
for both the total fibers associated with each ROI (total connectivity) 
and inter-regional fibers (mutual connectivity). To adjust for WM 
change across development and volume loss associated with injury, 
each set of fibers was normalized by total WM volume in each indi- 
vidual. In addition, to examine possible GM ROI volume bias in the 
connectivity measures, we identified the ROIs that demonstrated sig- 
nificant differences between groups. Finally, the mean FA and MD of 
the WM in the whole brain were compared between groups. Signifi- 
cance levels of all group differences were obtained using the 
Kruskal-Wallis test. 

3. Results 

Whole-brain connectivity analysis revealed several significant 
findings. Specifically, it was found that in addition to the expected wide- 
spread reduction in WM and GM volumes, there was a reduction in total 
connectivity associated with individual regions as well as inter-regional 
mutual connectivity reductions between severity groups throughout 
the brain. It should be noted that, as expected, there was a marked reduc- 
tion in connectivity involving regions associated with the sensorimotor 
network, which was detected using the comprehensive and unbiased 
analysis performed here. Additionally, there was a selective reduction 
of long-range fibers throughout the brain in severe versus moderate CP 
that was consistently demonstrated using progressively increasing 
length thresholds. These findings are discussed in detail as follows. 

3.1. Total and mutual connectivities are reduced throughout the brain, 
including but not limited to putative sensori-motor regions 

The amount of fibers associated with each of the 63 anatomical re- 
gions were assessed to determine how total regional connectivity was 
altered in relation to CP severity. Nodes throughout the brain showed 
a marked reduction in total connectivity in the group of individuals 
with severe CP as compared to moderate CP. In Fig. 2, the red-yellow 
spheres represent the regions that showed a significant decrease in 
total connectivity in severe CP versus moderate CP (p < 0.05, corrected 
for multiple comparisons, with the yellow-red color spectrum indicat- 
ing the progressing significance levels, and the radii of the nodes 
proportional to the absolute number of connections on average associ- 
ated with that region). Regions showing connectivity deficits across 
groups included regions associated with the putative sensorimotor net- 
work, as well as anatomic regions associated with other functionalities. 
In addition to the bilateral pre- and post-central gyri and brainstem 
within the sensorimotor network, the most significant total connectiv- 
ity deficits between groups were seen in the bilateral middle occipital 
gyri, right inferior and superior occipital gyri, bilateral middle and supe- 
rior frontal gyri, right lingual gyrus, right inferior temporal gyrus, right 



ZA. Englander et al. / Neurolmage: Clinical 2 (2013) 440-447 



443 



A)bO B)DWI, b=1000 





Fig. 1. Primary data images containing (A) T2 (baseline bO), (B) diffusion weighted (b : 
age = 1.73). 



1000), (C) colored FA, and (D) tractography from a representative subject (GMFCS 1 



cuneus, left putamen and left superior parietal gyrus. These anatomic 
regions have been associated with a variety of functional networks, in- 
cluding visual, auditory, and language processing, as well as other 
higher level cognitive functions such as memory, emotion, and atten- 
tion. All nodes that demonstrated insignificant changes in total connec- 
tivity are shown as gray spheres in Fig. 2. 

Mutual connectivities were also analyzed to assess the significance of 
structural network impairment between CP severity groups. Analogous 
to the topography of total regional connectivity deficit, our data demon- 
strated reduced inter-regional fibers throughout the brain with increased 
CP severity. For all connections between the 63 nodes (ROIs), 75 were sig- 
nificantly reduced in relation to CP severity. Fig. 2 displays the spatial pat- 
tern of the significantly reduced mutual connectivity as cool-colored 
edges (p < 0.05, corrected for multiple comparisons). The color gradient 
of the edges reflects the level of significance, with dark blue indicating the 
most significant differences in connectivity between severity groups. As 
expected, significant deficits in mutual connectivity involved regions of 
the sensorimotor network, including connections between the brainstem 
and bilateral pre- and post-central gyri, as well as homotopic and 
interhemispheric edges between the pre- and post-central gyri. Addition- 
al significant mutual connectivity reductions in severe versus moderate 
CP were observed between the right thalamus and right middle occipital 
gyrus, right superior temporal gyrus and right inferior occipital gyrus, left 



putamen and superior parietal gyrus, and left putamen and left inferior 
temporal gyrus, among others. These connections are involved in many 
functional networks related to perception and cognition, and are repre- 
sentative of the diffuse and global nature of the connectivity deficit be- 
tween groups seen in this cohort. 

Given the diffuse nature of the observed connectivity reductions, we 
further characterized factors that contributed to this finding. It was 
observed that there is a significant group difference in whole-brain 
WM volume between the moderate and severe groups (p = 0.01) 
(Fig. 3A). As WM volume likely changes over the course of brain devel- 
opment, we tested the relationship between WM volume and age using 
the Kendall-tau rank correlation test, which is appropriate for 
comparing the continuous variables age and WM volume. As expected, 
there was a significant relationship (t = — 0.4, p = 0.02) between age 
and total WM volume, justifying the need for using total WM volume as 
a normalization measure. Subsequently, all connectivity measures were 
normalized by total WM volume in each subject. As a confirmation of 
the effectiveness of this control measure, the relationship between 
age and total fiber volume after correction was investigated using the 
same correlation test and found to be insignificant (t = 0.28, p = 
0.18). In contrast, there was a highly significant group difference in 
corrected fiber volume (p = 0.01), as shown in Fig. 3B, indicating that 
this group difference is due to CP severity and not age. Additionally, a 



444 



ZA. Englander et al. / Neurolmage: Clinical 2 (2013) 440-447 



total connectivity 
p<0.03^^B p<0 05 

mutual connectivity 



RG_R RG_L 
LOFG_R S'Sr'^ / \ SFG_L LOFG_L 



p<0.0005l 



ifg.rO 




SMG_L 



p<0.05 



SPG 
SPG_R 
PrCu 
PrCu 



SFG_R 




lOG 
LG R 



T<- o ' 1 ^LOFG L 

TG_R MFOG.R [.opG r " 



LG_L |tg_r' o 

Brainstem 



AG Angular Gyrus 
Caud Caudate 
CingG Cingulate Gyrus 



SOG R 



Cu 

Fu 

IFG 

lOG 

Ins 



Cuneus 
Fusiform Gyrus 
Inferior Frontal Gyrus 
Inferior Occipital Gyrus 
Insula 



LFCX3 Lateral Fronto-orbrtal Gyrus 

LG Lingual Gyrus 

MFG Middle Frontal Gyrus 

MFCX3 Middle Fronto-orbital Gyrus 

MCX3 Middle Occipital Gyrus 

MTG Middle Temporal Gyrus 

PHG Parahippocampal Gyrus 

PoCG Postcentral Gyrus 



PrCu 

Put 

RG 

SFG 

SMG 

SPG 

SOG 

STG 



ITG Inferior Temporal Gyrus PrCG Precentral Gyrus 



Precuneus 
Putamen 
Gyrus Rectus 
Superior Frontal Gyrus 
Supramarginal Gyrus 
Superior Parietal Lobule 
Superior Occipital Gyrus 
Superior Temporal Gyrus 



Thai Thalamus 



Fig. 2. Inter-regional whole-brain connectivity analysis reveals a diffuse pattern of nodes and edges with significantly reduced connectivity in severe versus moderate CP. A total of 
63 nodes (ROls) are depicted as spheres. The sphere is placed at the center of mass of the ROl, with a radius proportional to the average number of connections across individuals 
associated with that region. Red-yellow nodes show significantly reduced total connectivity to all other brain regions in severe versus moderate CP (p < 0.05, corrected for multiple 
comparisons). The color gradient of the nodes denotes the significance level of the difference in total connectivity between groups; red nodes indicating the most significantly dif- 
ferent between groups. Gray nodes show insignificant differences in total connectivity between groups. Cool-colored edges (connections) between nodes indicate significantly re- 
duced mutual connectivities in the severe CP group as compared to moderate CP group (p < 0.05, corrected for multiple comparisons). The color gradient of the edges denotes the 
significance level of the difference in connectivity between groups; dark blue edges being the most significantly different between groups. Nodes marked with an asterisk (*) in- 
dicate ROls with significant GM volume reduction in severe versus moderate CP. All connectivity measures are normalized by total WM volume in each individual. 



decreasing trend was also found in whole-brain WM FA with increasing 
CP severity (data not shown), as it has already been reported in previ- 
ous studies (Lee et al, 2011; Rai et al, 2013). 

To examine possible GM volume biases, an analysis of ROI volume 
differences between groups was carried out. Regions that showed a sig- 
nificant (p < 0.05, corrected for multiple comparisons) volume reduc- 
tion between severity groups are denoted with an asterisk in Fig. 2. 
Although there was some overlap between GM volume and connectiv- 
ity differences, there was a discrepancy between regions showing re- 
duced GM volume and reduced total and/or mutual connectivity. This 



indicates that differences in GM volume alone were not sufficient to ex- 
plain the diffuse structural connectivity changes reported here. 

3.2. Specific reduction in long-range connections 

Further analysis of our inter-regional connectivity measures, likewise 
after normalization by total WM volume, demonstrated a length- 
dependent reduction of mutual connectivities between severity groups. 
Specifically, a significant difference (p = 0.02) in long-range con- 
nectivities (>40 mm in length) was found between severity 



3.0 



E 
E 

0) 

E 
s 

o 
> 



2.0 



1.0 



0.0 
x10« 



*p =0.01 



GMFCS ll/lll 



GMFCS IV 



B 

0) 

E 

D 
O 
> 



0.40 



0) 

E 

3 
O 
> 

il 



0.30 



0.20 



0.10 



0.0 



*p = 0.01 



GMFCS ll/lll GMFCS IV 



Fig. 3. A: Global WM volume is reduced in severe versus moderate CP (p = 0.01); B: Corrected fiber volume (normalized by global WM volume) is reduced in severe vs. moderate 
CP (p = 0.01). Error bars represent standard errors. 



ZA. Englander et al. / Neurolmage: Clinical 2 (2013) 440-447 



445 



groups, with short range connectivities (<40 mm in length) 
remaining similar between groups, as illustrated in Fig. 4. Addition- 
al analyses using different length thresholds were carried out, 
which continued to show the same pattern of significant group dif- 
ferences. Table 2 summarizes the p-values of the group differences 
in normalized connectivities of short versus long fibers at system- 
atically progressing length thresholds from 30 mm to 60 mm. 
These data show that there is a significant reduction in long range 
connectivities from moderate to severe CP. As expected, the level 
of significance of this group difference increases with the 
progressing length threshold, further confirming the long-range 
connectivity deficit between severity groups. 

In a secondary and exploratory analysis, the length dependent reduc- 
tion in mutual connectivity was examined in relation to cognitive scores. 
For this analysis, 1 1 subjects with cognitive composite scores available 
from the BSID-III were included and grouped into low functioning 
(n = 5, composite scores <55) and high functioning (n = 6, composite 
scores > 80) groups. The mean long-range mutual connectivities (with 
respect to the total WM volume) were determined to be 0.04 ± 0.04 in 
the low functioning group and 0.10 i 0.05 in the high functioning 
group. Similar to our previous finding when grouped by motor scores, 
there appears to be a trend toward the low functioning group having a 
greater deficit in long-range connectivity than the high functioning 
group (p = 0.15). However, given the noted correlation between the 
motor and cognitive scores in this cohort, the long range connectivity def- 
icit cannot be yet attributed to either motor or cognitive deficit alone. 



4. Discussion 

Our results describe differential WM connectivity in severe versus 
moderate CP in regions associated with the putative sensorimotor 
network, a finding that is consistent with prior studies (Scheck et 
al, 2012) and likely relating to the common trait in these patients 
being motor disturbance. However, the distribution of the observed 
differences in connectivity was not limited to the sensorimotor net- 
work, providing additional support for the role of more widespread 
WM structural deficits in the pathophysiology of CP. 

Indeed, the most salient findings in our whole-brain connectome 
analysis are the diffuse and length-dependent nature of the WM con- 
nectivity deficits in severe versus moderate CP. In particular, we dem- 
onstrate a reduction in total and mutual WM connectivities associated 
with regions throughout the brain between CP severity groups. Notably, 
the additional length-dependent deficit in connectivity shown here is 



Table 2 

Short versus long fiber results at different length designations. 



0.20 



5* 
> 

I 

c 

§ 
o 

3 



0.10 



0.00 



I <40 mm 
^40 mm 



p = 0.02 * 



3_ 



GMFCS ll/lll 



GMFCS IV 



Fig. 4. Long-range mutual connectivity is reduced in severe versus moderate CP. A sig- 
nificant difference (p = 0.02) long-range connectivities (>40 mm) is observed in se- 
vere versus moderate CP, while the short-range connectivities (<40 mm in length) 
remain similar between groups. Connectivity measures were normalized by total 
WM volume in each individual. Error bars represent standard errors. 



Length 

threshold 

(mm) 


Group difference in normalized 
mutual connectivity < threshold 


Group difference in normalized 
mutual connectivity > threshold 


30 


p = 0.22 


p = 0.02* 


40 


p = 0.11 


p = 0.02* 


50 


p = 0.06 


p = 0.01* 


60 


p = 0.03* 


p = 0.01* 



Significant difference (p < 0.05). 



particularly intriguing given the implications of structural network dis- 
ruption during development and the variability of motor and cognitive 
deficits frequently observed in children with CP. 

4.1. Diffuse reduction in total and mutual connectivities 

Within the more common perinatally-acquired injuries resulting 
in CP, different patterns of injury have a preponderance of either 
WM, diffuse GM, deep gray nuclear or deep gray nuclear/brainstem 
involvement that relate to the timing and severity of insult that pro- 
vide etiologically useful distinctions (Hoon, 2005; Krageloh-Mann 
and Horber, 2007; Volpe, 2008). However, how these multiple and 
overlapping patterns of injury ultimately manifest symptomatically 
is not well understood. The variability in symptomatic outcome be- 
tween and even within any given injury pattern precludes accurate 
clinical classification, prognostication and treatment targeting. Ad- 
vances in neuroimaging show promise in providing additional evi- 
dence to improve the sensitivity and specificity of linking injury 
patterns to clinical phenotypes. 

To achieve a better understanding of how these injury patterns relate 
to clinical phenotypes, we used an unbiased, standardized and compre- 
hensive approach to assess WM connectivity, in an effort to explore 
structural network disruptions throughout the brain in relation to 
neurodevelopmental outcomes in CP. Our data demonstrate total con- 
nectivity deficits involving diffuse anatomic regions (red-yellow nodes. 
Fig. 2), including but not limited to regions associated with the sensori- 
motor network. The diffuse nature of the total connectivity deficits ob- 
served here is in line with structural neuroimaging data that has 
identified a wide range of GM and WM abnormalities and varied neuro- 
pathology in CP (Folkerth, 2005; Volpe, 2008) and is possibly related to 
the additional co-morbid deficits seen in this cohort. 

Our data also demonstrate diffuse reductions in mutual connectiv- 
ity in severe versus moderate CP involving regions associated with 
many different functional networks. While the majority of DTI studies 
in the CP literature restricted their analysis to a priori selected WM 
regions or tracts within putative sensorimotor networks, based on 
the inter-disciplinary and multi-modal evidence of wide-spread 
WM damage in CP and the varying results in studies of a priori 
selected regions, whole-brain WM connectivity analysis may be ap- 
propriate in the investigation of network deficits in CP. Reduced 
WM integrity in the corticospinal tract (Murakami et al., 2008) or 
the thalamocortical projections (Hoon et al, 2002, 2009) has been re- 
peatedly shown, and is similarly demonstrated in our data, along with 
a more widespread connectivity deficit, using an unbiased and com- 
prehensive whole brain analysis. 

On a related note, such global effects in WM properties and connec- 
tivity have been demonstrated in the neuroimaging literature of other 
acquired brain injuries (Gratton et al, 2012; Kraus et al., 2007). As in 
other acquired brain injuries such as stroke and TBI, connectivity 
based neuroimaging approaches may be ideally suited to investigate 
the impaired brain networks in children with CP, in particular because 
CP is a disorder that originates from early brain injury and is often dif- 
fuse in nature. 



446 



ZA. Englander et al. / Neurolmage: Clinical 2 (2013) 440-447 



42. Specific reduction in long-range connectivities 

The most novel and significant finding in the present study is the 
specific reduction in long-range WM connections in children with se- 
vere versus moderate CP (Fig. 4). This finding was present throughout 
the brain and not limited to sensorimotor networks (Fig. 2). Our confir- 
matory analysis using a range of length thresholds from 30 mm to 
60 mm also yielded consistent results on this finding (see Table 2). Spe- 
cifically, here we note that this finding becomes increasingly more sig- 
nificant with the increasing length threshold, further confirming the 
presence of long-range connectivity reduction in severe versus moder- 
ate CP. 

It is possible that the selective vulnerability of long range connections 
is due to the length of tissue vulnerable to multifocal acute ischemic and 
inflammatory insults, or related to the developmental establishment and 
maintenance of tracts that are more likely to traverse areas of prior ische- 
mia and inflammation. The most common cause of brain injury resulting 
in CP is hypoperfusion in the perinatal period leading to selective neuro- 
nal and pre-mylenating oligdendroglial cell death with the timing, sever- 
ity, and duration of ischemia influencing the injury pattern (Folkerth, 
2005; Volpe, 2008). The role of the pre-mylenating oligodendrocyte in 
the pathophysiology of CP is becoming increasingly apparent, and may 
relate to the reduction in the long range WM connectivity with increas- 
ing CP severity demonstrated here. 

The length-dependence of the diffuse connectivity deficits seen here 
suggests possible structural network dysfunction. Because networks in- 
volved in processes such as memory, executive function, attention, and 
even motor activity involve spatially distant brain regions, they rely the 
integrity of long range connections to function. Thus, our data 1 ) support 
the diffuse nature of pathology in CP and 2) suggest that the pattern of 
long-range vulnerability may result in network level dysfunction. The 
common co-morbid neurodevelopmental disabilities associated with 
CP overlap with other pediatric neurodevelopmental disorders that are 
being increasingly thought of at a network-level of pathology, suggesting 
a similar network-level of dysfunction in children with CP. In fact, Lee et 
al. demonstrate complicated changes in the functional connectivity of 
motor and thalamocortical networks in children with CP. Their cohort 
also demonstrated large-scale deficits in WM integrity, but the study 
was not designed to couple the observed structural and functional defi- 
cits directly (Lee et al, 2011). Indeed, previous studies have suggested 
significant correlations between cognitive deficit and brain injury sever- 
ity in CP (Rai et al, 2013; Schatz et al, 1997). 

In our cohort, the most severely affected showed both motor and 
cognitive deficits and the greatest reduction in long-range connectivity. 
This would be consistent with the hypothesis that the neurocognitive 
impairments may be related to a network of connectivity deficits. How- 
ever, it is not yet possible to attribute either cognitive or motor deficits 
to decreases in long range connectivity based on this data alone. Never- 
theless, our data does suggest that increased diffuse injury and long 
range connectivity deficit may affect structural and functional net- 
works, and may contribute differentially to neurocognitive disability 
and motor disability in children with CP, which can be further explored 
in future analyses. 

Additional shortcomings in our study have also been considered. For 
example, in our analysis we proceeded under the concept that the 
amount of fibers linking two anatomical regions is representative of 
WM tract connectivity and that greater tract volume is indicative of 
connection integrity. Streamline volume does not completely describe 
the integrity of WM connectivity; however this metric has been used 
widely in the literature to reflect the presence and strength of structural 
connections between distant ROIs (Gerig et al, 2004; Yoshida et al, 
2010). We also did not include an age-matched healthy control group 
for this very young age group due to ethics considerations with sedation 
and technical challenges without sedation. However, to make up for this 
design constraint, here we have adopted the strategy of examining con- 
nectivity that is scaled with disease severity. The analysis presented 



here is related to functional outcomes in CP, using DTT to define pat- 
terns of structural changes within the spectrum of the disorder. 

Because CP by definition results from injury or abnormality to the 
developing brain, the network "dysfunction" may involve long-term 
network development in addition to acute injury and future studies 
can be designed to investigate this further. In a study of preterm in- 
fants, Smyser et al. reported altered functional network development 
in several resting state networks, including the sensorimotor net- 
work, supporting this point (Smyser et al, 2010). Thus, structural 
and functional connectome analyses may provide increased sensitiv- 
ity to relevant pathology and descriptive patterns of network connec- 
tivity that relate to disorder classification, prognosis, and treatment 
targets in children with CP. 

5. Conclusions 

In summary, our data emphasize the feasibility and suggest the po- 
tential importance of including network-based whole brain analyses 
when studying CP. We found global impairments associated with regions 
including, but not restricted to, those that are putatively involved with 
sensori-motor functionality. Additionally, it was demonstrated for the 
first time, a specific reduction in long-range connections in severe versus 
moderate CP, which provides potentially provocative insight into the 
patho-mechanistic disruption of network development that may relate 
to motor impairment and other neuropsychological comorbidities of CP. 

Acknowledgments 

The authors acknowledge the support from the NIH (NS 07501 7 and 
EB 009483 to AWS) and the Robertson Foundation (to JK). The authors 
thank Kathryn Gustafson, PhD for the behavioral assessments, Susan 
Music for the patient scans, Laura Case DPT, and Stephen Raymond for 
contributions to the statistical analysis, and Chris Petty for technical as- 
sistance. The authors also thank the patients and their families for their 
contributions to the project. 

References 

Bax, M., Goldstein, M., Rosenbaum, P., Leviton, A., Paneth, N., Dan, B., Jacobsson, B., 
Damiano, D., Executive Committee for the Definition of Cerebral, P., 2005. Proposed 
definition and classification of cerebral palsy, April 2005. Developmental Medicine 
and Child Neurology 47, 571-576. 

Bayley, N., 2005. Bayley Scales of Infant and Toddler Development, 3rd edition. Pearson 
Assessments, San Antonio, TX. 

Benini, R., Dagenais, L, Shevell, M.I., 2013. Normal imaging in patients with cerebral 
palsy: what does it tell us? Journal of Pediatrics 162, 369-374. 

Chang, M.C., Jang, S.H., Yoe, S.S., Lee, E., Kim, S., Lee, D.G., Son, S.M., 2012. Diffusion ten- 
sor imaging demonstrated radiologic differences between diplegic and quadriple- 
gic cerebral palsy. Neuroscience Letters 512, 53-58. 

Clark, S.L, Hankins, G.D., 2003. Temporal and demographic trends in cerebral palsy- 
fact and fiction. American Journal of Obstetrics and Gynecology 188, 628-633. 

Faria, A.V., Hoon, A., Stashinko, E., Li, X., Jiang, H., Mashayekh, A., Akhter, K., Hsu, J., 
Oishi, K., Zhang, J., Miller, M.I., van Zijl, P.C., Mori, S., 2011. Quantitative analysis 
of brain pathology based on MRI and brain atlases— applications for cerebral 
palsy. Neurolmage 54, 1854-1861. 

Folkerth, R.D., 2005. Neuropathologic substrate of cerebral palsy. Journal of Child Neu- 
rology 20, 940-949. 

Genovese, C.R., Lazar, N.A., Nichols, T., 2002. Thresholding of statistical maps in func- 
tional neuroimaging using the false discovery rate. Neurolmage 15, 870-878. 

Gerhard, S., Daducci, A., Lemkaddem, A., Meuli, R., Thiran, J.P., Hagmann, P., 2011. The 
connectome viewer toolkit: an open source framework to manage, analyze, and vi- 
sualize connectomes. Frontiers in Neuroinformatics 5, 3. 

Gerig, G., Gouttard, S., Corouge, I., 2004. Analysis of brain white matter via fiber tract 
modeling. Conference Proceedings: Annual International Conference of the IEEE 
Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Bi- 
ology Society. Conference 6, 4421-4424. 

Gratton, C, Nomura, E.M., Perez, F., D'Esposito, M., 2012. Focal brain lesions to critical 
locations cause widespread disruption of the modular organization of the brain. 
Journal of Cognitive Neuroscience 24, 1275-1285. 

Hoon Jr., A.H., 2005. Neuroimaging in cerebral palsy: patterns of brain dysgenesis and 
injury. Journal of Child Neurology 20, 936-939. 

Hoon Jr., A.H., Lawrie Jr., W.T., Melhem, E.R., Reinhardt, E.M., Van Zijl, P.C, Solaiyappan, M., 
Jiang, H., Johnston, M.V., Mori, S., 2002. Diffusion tensor imaging of periventricular 
leukomalacia shows affected sensory cortex white matter pathways. Neurology 59, 
752-756. 



ZA. Englander et al. / Neurolmage: Clinical 2 (2013) 440-447 



447 



Hoon Jr., A.H., Stashinko, E.E., Nagae, LM., Lin, D.D., Keller, J., Bastian, A., Campbell, M.L, 
Levey, E., Mori, S., Johnston, M.V., 2009. Sensory and motor deficits in children with ce- 
rebral palsy born preterm correlate with diffusion tensor imaging abnormalities in 
thalamocortical pathways. Developmental Medicine and Child Neurology 51, 697-704. 

Korzeniewski, SJ., Birbeck, G., DeLano, M.C., Potchen, M.J., Paneth, N., 2008. A systematic 
review of neuroimaging for cerebral palsy. Journal of Child Neurology 23, 216-227. 

Krageloh-Mann, L, Horber, V., 2007. The role of magnetic resonance imaging in eluci- 
dating the pathogenesis of cerebral palsy: a systematic review. Developmental 
Medicine and Child Neurology 49, 144-151. 

Kraus, M.F., Susmaras, T., Caughlin, B.P., Walker, C.J., Sweeney, J.A., Little, D.M., 2007. 
White matter integrity and cognition in chronic traumatic brain injury: a diffusion 
tensor imaging study. Brain 130, 2508-2519. 

Lee, J.D., Park, H.J., Park, E.S., Oh, M.K., Park, B., Rha, D.W., Cho, S.R., Kim, E.Y., Park, J.Y., 
Kim, C.H., Kim, D.G., Park, C.I., 2011. Motor pathway injury in patients with 
periventricular leucomalacia and spastic diplegia. Brain 134, 1199-1210. 

Lee, Y.H., Choi, K.V., Moon, J.H., Jun, H.J., Kang, H.R., Oh, S.L, Kim, H.S., Um, J.S., Kim, M.J., 
Choi, Y.Y., Lee, Y.J., Kim, H.J., Lee, J.H., Son, S.M., Choi, S.J., Oh, W., Yang, Y.S., 2012. Safety 
and feasibility of countering neurological impairment by intravenous administration 
of autologous cord blood in cerebral palsy. Journal of Trans lational Medicine 10, 58. 

Ludeman, N.A., Berman, J.I., Wu, Y.W., Jeremy, R.J., Kornak, J., Bartha, A.L, Barkovich, A.J., 
Ferriero, D.M., Henry, R.G., Glenn, O.A., 2008. Diffusion tensor imaging of the pyra- 
midal tracts in infants with motor dysfunction. Neurology 71, 1676-1682. 

Martinez-Biarge, M., Diez-Sebastian, J., Kapellou, 0., Gindner, D., Allsop, J.M., 
Rutherford, M.A., Cowan, F.M., 2011. Predicting motor outcome and death in 
term hypoxic-ischemic encephalopathy. Neurology 76, 2055-2061. 

Miller, M.L, Beg, M.F., Ceritoglu, C, Stark, C, 2005. Increasing the power of functional 
maps of the medial temporal lobe by using large deformation diffeomorphic metric 
mapping. Proceedings of the National Academy of Sciences of the United States of 
America 102, 9685-9690. 

Min, K., Song, J., Kang, J.Y., Pt, J.K., Ryu, J.S., Kang, M.S., Jang, S., Kim, S.H., Oh, D., Kim, 
M.K., Soo, K.S., Kim, M., 2013. Umbilical cord blood therapy potentiated with eryth- 
ropoietin for children with cerebral palsy: a double-blind, randomized, placebo- 
controlled trial. Stem Cells 31, 581-591. 

Mori, S., Zhang, J., 2006. Principles of diffusion tensor imaging and its applications to 
basic neuroscience research. Neuron 51, 527-539. 

Mori, S., Grain, B.J., Chacko, V.P., van Zijl, P.C., 1999. Three-dimensional tracking of axonal 
projections in the brain by magnetic resonance imaging. Annals of Neurology 45, 
265-269. 

Murakami, A., Morimoto, M., Yamada, K., Kizu, 0., Nishimura, A., Nishimura, T., 
Sugimoto, T., 2008. Fiber-tracking techniques can predict the degree of neurologic 
impairment for periventricular leukomalacia. Pediatrics 122, 500-506. 

Nagae, L.M., Hoon Jr., A.H., Stashinko, E., Lin, D., Zhang, W., Levey, E., Wakana, S., Jiang, 
H., Leite, C.C., Lucato, L.T., van Zijl, P.C., Johnston, M.V., Mori, S., 2007. Diffusion ten- 
sor imaging in children with periventricular leukomalacia: variability of injuries to 
white matter tracts. AJNR. American Journal of Neuroradiology 28, 1213-1222. 

Oishi, K., Faria, A., Jiang, H., Li, X., Akhter, K., Zhang, J., Hsu, J.T., Miller, M.L, van Zijl, P.C., 
Albert, M., Lyketsos, C.G., Woods, R., Toga, A.W., Pike, G.B., Rosa-Neto, P., Evans, A., 
Mazziotta, J., Mori, S., 2009. Atlas-based whole brain white matter analysis using 
large deformation diffeomorphic metric mapping: application to normal elderly 
and Alzheimer's disease participants. Neurolmage 46, 486-499. 

Palisano, R., Rosenbaum, P., Walter, S., Russel, D., Wood, E., Galuppi, B., 1997. Develop- 
ment and reliability of a system to classify gross motor function in children with 
cerebral palsy. Developmental Medicine and Child Neurology 39, 214-223. 

Palisano, R., Rosenbaum, P., Bartlett, D., Livingston, M., 2007. Gross Motor Function 
Classification System — Expanded and Revised. 

Porter, E.J., Counsell, S.J., Edwards, A.D., Allsop, J., Azzopardi, D., 2010. Tract-based spa- 
tial statistics of magnetic resonance images to assess disease and treatment effects 
in perinatal asphyxial encephalopathy. Pediatric Research 68, 205-209. 



Rai, Y., Chaturvedi, S., Paliwal, V.K., Goyal, P., Chourasia, A., Singh Rathore, R.K., Yadav, A., 
Pandey, CM., Lalla, R.S., Garg, R.K., Gupta, R.K., 2013. DTI correlates of cognition in 
term children with spastic diplegic cerebral palsy. European Journal of Paediatric 
Neurology (Epub ahead of print). 

Rha, D.W., Chang, W.H., Kim, J., Sim, E.G., Park, E.S., 2012. Comparing quantitative 
tractography metrics of motor and sensory pathways in children with 
periventricular leukomalacia and different levels of gross motor function. Neurora- 
diology 54, 615-621. 

Rose, J., Mirmiran, M., Butler, E.E., Lin, C.Y., Barnes, P.D., Kermoian, R., Stevenson, D.K., 
2007. Neonatal microstructural development of the internal capsule on diffusion 
tensor imaging correlates with severity of gait and motor deficits. Developmental 
Medicine and Child Neurology 49, 745-750. 

Rose, S., Guzzetta, A., Pannek, K., Boyd, R., 2011. MRl structural connectivity, disruption 
of primary sensorimotor pathways, and hand function in cerebral palsy. Brain Con- 
nectivity 1,309-316. 

Rutherford, M., Ramenghi, LA., Edwards, A.D., Brocklehurst, P., Halliday, H., Levene, M., 
Strohm, B., Thoresen, M., Whitelaw, A., Azzopardi, D., 2010. Assessment of brain 
tissue injury after moderate hypothermia in neonates with hypoxic-ischaemic en- 
cephalopathy: a nested substudy of a randomised controlled trial. Lancet Neurolo- 
gy 9, 39-45. 

Schatz, J., Craft, S., Koby, M., Park, T.S., 1997. Associative learning in children with perina- 
tal brain injury. Journal of the International Neuropsychological Society 3, 521-527. 

Scheck, S.M., Boyd, R.N., Rose, S.E., 2012. New insights into the pathology of white mat- 
ter tracts in cerebral palsy from diffusion magnetic resonance imaging: a systemat- 
ic review. Developmental Medicine and Child Neurology 54, 684-696. 

Shimony, J.S., Lawrence, R., Neil, J.J., Inder, T.E., 2008. Imaging for diagnosis and treat- 
ment of cerebral palsy. Clinical Obstetrics and Gynecology 51, 787-799. 

Smyser, CD., Inder, T.E., Shimony, J.S., Hill, J.E., Degnan, A.J., Snyder, A.Z., Neil, J.J., 2010. 
Longitudinal analysis of neural network development in preterm infants. Cerebral 
Cortex 20, 2852-2862. 

Sutcliffe, T.L, Logan, W.J., Fehlings, D.L, 2009. Pediatric constraint-induced movement 
therapy is associated with increased contralateral cortical activity on functional 
magnetic resonance imaging. Journal of Child Neurology 24, 1230-1235. 

Thomas, B., Eyssen, M., Peeters, R., Molenaers, G., Van Hecke, P., De Cock, P., Sunaert, S., 
2005. Quantitative diffusion tensor imaging in cerebral palsy due to periventricular 
white matter injury. Brain 128, 2562-2577. 

Towsley, K., Shevell, M.L, Dagenais, L, 2011. Population-based study of neuroimaging 
findings in children with cerebral palsy. European Journal of Paediatric Neurology 
15, 29-35. 

Trivedi, R., Gupta, R.K., Shah, V., Tripathi, M., Rathore, R.K., Kumar, M., Pandey, CM., 
Narayana, P.A., 2008. Treatment-induced plasticity in cerebral palsy: a diffusion 
tensor imaging study. Pediatric Neurology 39, 341-349. 

Trivedi, R., Agarwal, S., Shah, V., Goyel, P., Paliwal, V.K., Rathore, R.K., Gupta, R.K., 2010. 
Correlation of quantitative sensorimotor tractography with clinical grade of cere- 
bral palsy. Neuroradiology 52, 759-765. 

Volpe, J.J., 2003. Cerebral white matter injury of the premature infant-more common 
than you think. Pediatrics 112, 176-180. 

Volpe, J.J., 2008. Neurology of the Newborn, 5th edition. Elsevier, Philadelphia, PA. 

Woodward, L.J., Anderson, P.J., Austin, N.C, Howard, K., Inder, T.E., 2006. Neonatal MRI 
to predict neurodevelopmental outcomes in preterm infants. The New England 
Journal of Medicine 355, 685-694. 

Yoshida, S., Hayakawa, K., Yamamoto, A., Okano, S., Kanda, T., Yamori, Y., Yoshida, N., 
Hirota, H., 2010. Quantitative diffusion tensor tractography of the motor and sen- 
sory tract in children with cerebral palsy. Developmental Medicine and Child Neu- 
rology 52, 935-940. 

Zhang, Y., Brady, M., Smith, S., 2001. Segmentation of brain MR images through a hid- 
den Markov random field model and the expectation-maximization algorithm. 
IEEE Transactions on Medical Imaging 20, 45-57.