Epilepsy Research (2013) 107, 263—271

jo ur nal ho me p ag e: www.elsev ier .com/ locate /ep i lepsyres

Cerebral white matter integrity in childrenwith active versus remitted epilepsy 5years after diagnosis

Ishmael Amarreha,∗, Kevin Dabbsb, Daren C. Jacksonb,Jana E. Jonesb, Mary E. Meyeranda, Carl E. Stafstromb,David A. Hsub, Michael Seidenbergc, Bruce P. Hermannb

a Department of Medical Physics, University of Wisconsin School of Medicine and Public Health,Madison, WI, USAb Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USAc Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago,IL, USA

Received 7 March 2013 ; received in revised form 28 August 2013; accepted 17 September 2013Available online 28 September 2013

KEYWORDSDTI;Childhood epilepsy;Tract based spatialstatistics;Epilepsy remission

SummaryIntroduction: Diffusion tensor imaging (DTI) studies have reported white matter abnormalitiesin childhood-onset epilepsy, but the mechanisms and timing underlying these abnormalities,and their resolution, are not well understood. This study examined white matter integrity inchildren with active versus remitted epilepsy.Methods: Tract-based spatial statistics (TBSS) was used to examine whole-brain DTI indices offractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity(RD) in 20 children with epilepsy 5—6 years after diagnosis, compared to 29 healthy con-trols. To determine the status of white matter following cessation of seizures, participantswith epilepsy were classified as active versus remitted and comparisons included: (1) controlsversus all children with epilepsy, (2) controls versus children with remitted seizures, (3) controls

versus children with active seizures, and (4) children with active versus remitted epilepsy. Results: In the active compared to remitted epilepsy group, significantly higher FA and lower MD, AD and RD values were dispersed in the internal capsule, cingulum, body of the corpuscallosum, superior corona radiata and superior fronto-occipital fasciculus. Similar differenceswere found between the active epilepsy and the control group. There were no significantdifferences between the remitted epilepsy and control groups.

∗ Corresponding author at: Department of Medical Physics, University of Wisconsin, 1111 Highland Avenue, RM 1005, Madison, WI 53705,USA. Tel.: +1 614 404 1008; fax: +1 608 262 2413.

E-mail addresses: amarreh@wisc.edu, ismael.amarreh@gmail.com (I. Amarreh).

0920-1211/$ — see front matter © 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.eplepsyres.2013.09.012

264 I. Amarreh et al.

Conclusion: Children with active epilepsy differed in white matter integrity compared to childrenwith remitted epilepsy and healthy controls. It remains to be determined whether these findingsrepresent the outcomes of seizure remission versus an initial biomarker for those children whowill ultimately have intractable epilepsy.

s res

I

Dcifbfpifd

s(whweemm22ietcf

sptsfnw2tli2

fiacw2wsamhb

cmiatetDm

flrismwttSc

M

S

PaaaTyterermacsaphat(related epilepsies [LRE]) as well as narrow band diag-nostic classifications within the IGE (Juvenile Myoclonic

© 2013 Elsevier B.V. All right

ntroduction

iffusion tensor imaging (DTI) is a sensitive tool for assessingerebral white matter. Frequently utilized DTI-based indicesnclude fractional anisotropy (FA), a measure of the pre-erred directionality of diffusion within a voxel rangingetween 0 (isotropic diffusion) and 1 (unidirectional dif-usion), radial diffusivity (RD) which is a measure oferpendicular diffusion of water, axial diffusivity (AD) whichs a measure of diffusion along the largest Eigen vector of dif-usion, and mean diffusivity (MD), a measure of the averageiffusion, property within a voxel (Pierpaoli et al., 1996).

Recent DTI studies indicate abnormalities in the micro-tructural integrity of cerebral white matter in both adultsDuncan, 2008; Yogarajah and Duncan, 2008) and childrenith epilepsy (Nilsson et al., 2008). Extensive researchas characterized the patterns of abnormalities in adultsith chronic established epilepsy, especially temporal lobepilepsy (Otte et al., 2012), as well as adults with otherpilepsy syndromes (Salmenpera et al., 2006). These whiteatter (WM) abnormalities are evident in regions both proxi-al and distal to the primary epileptic zone (Arfanakis et al.,

002; Concha et al., 2009; Diehl et al., 2008; Knake et al.,009; Rodrigo et al., 2007; Thivard et al., 2005). These find-ngs are believed to reflect structural disorganization andxpansion of extra cellular space, which is thought to reflecthe underlying physiological changes in white matter asso-iated with epilepsy, which may extend beyond the seizureocus.

In pediatric studies, temporal lobe epilepsy has beenhown to be associated with reduced FA in the anterior andosterior limbs of the of the internal capsule, splenium ofhe corpus callosum, as well as higher MD, RD and AD in theame structures (Meng et al., 2010). Additional DTI resultsrom pediatric temporal lobe epilepsy studies include sig-ificantly reduced FA in the hippocampus contralateral asell as ipsilateral to side of seizure onset (Kimiwada et al.,006), along with decreased anisotropy in white matterracts (uncinate, arcuate, and inferior longitudinal fascicu-us as well as corticospinal tract) both contralateral andpsilateral to the side of seizure onset (Govindan et al.,008).

Abnormalities in white matter integrity are not con-ned to children with established and chronic epilepsy,s reduced FA and increased RD in the posterior corpusallosum and cingulum have been reported in childrenith new onset idiopathic epilepsies (Hutchinson et al.,010). Recently, newly diagnosed and untreated childrenith childhood absence epilepsy (CAE) were found to have

ignificantly lower FA values in prefrontal white matter,

nterior cingulate, and increased MD in parietal lobe whiteatter, prefrontal white matter, and posterior cerebellar

emispheres, in addition to subcortical structures includingilateral putamen and posterior limbs of the internal

ENrF

erved.

apsule (Yang et al., 2012). A recent report of new-onsetixed syndrome groups revealed significantly reduced FA

n left postcentral, elevated RD in left posterior cingulumnd right external capsule, and elevated AD in left middleemporal white matter and left thalamus, in children withpilepsy relative to controls (Widjaja et al., 2012). Together,he findings from adult and childhood epilepsy suggest thatTI is a sensitive tool in detecting WM abnormalities thatay be due to etiological factors or acquired damage.To date, the causative mechanisms of the reported dif-

usion abnormalities and their course over time remainargely unknown (Hutchinson et al., 2010). Furthermore, theelationship between seizure remission and white matterntegrity has not been examined in pediatric epilepsy. Thistudy investigates, for the first time, whether these abnor-alities persist 5—6 years after epilepsy diagnosis and tohat degree epilepsy remission status (active versus remit-

ed) is associated with variations in white matter integrity. Inhis study we used the TBSS pipeline within FSL (the FMRIBoftware Library), which addresses the main shortfalls ofonventional voxel-wise approaches.

ethods and materials

ubject groups

articipants were 20 children and adolescents with epilepsyge 8—18 at the onset of epilepsy (9 female, 11 male)nd 29 normally developing participants (current aver-ge age ± SD = 18.34 ± 2.91 years; 13 female, 16 male).he epilepsy participants, and controls, were seen 5ears after their baseline evaluations. The epilepsy par-icipants were selected based on a diagnosis of idiopathicpilepsy with no other developmental disabilities or neu-ological disorders and normal brain MRI scans. Thepilepsy group contained 9 with active epilepsy (cur-ent average age ± SD = 18.61 ± 3.57 years; 5 female, 4ales), and 11 with remitted epilepsy (current average

ge ± SD = 18.52 ± 2.76 years; 4 female, 7 males). Eachhild’s epilepsy syndrome was defined in a research consen-us meeting by the pediatric epileptologist who reviewedll available clinical data (e.g., seizure description andhenomenology, EEG, clinical imaging, neurodevelopmentalistory) while blinded to all research cognitive, behavioral,nd neuroimaging data. Two levels of syndromic classifica-ion were undertaken including a broad band classificationi.e., idiopathic generalized epilepsies [IGE] and localization

pilepsy, Childhood Absence, Juvenile Absence, and IGEOS) and LRE groups (Benign Epilepsy with Centrotempo-al Spikes, Temporal Lobe Epilepsy, Frontal Lobe Epilepsy,ocal NOS).

Cerebral white matter integrity in children with active versus remitted epilepsy 265

Table 1 Demographic characteristics of the epilepsy and control groups.

Epilepsy (n = 20) Control (n = 29)

Remit (n = 11) Active (n = 9)Mean (SD) Mean (SD) Mean (SD)

Age (years) 18.52 (2.76) 18.61 (3.57) 18.34 (2.96)Gender 7 M/4 F 4 M/5 F 16 M/13 FIQ full scale score* 120.27 (9.50)* 107.00 (8.12)*ˆ 117.14 (10.63)ˆ

Age of onset (years) 11.53 (3.20) 11.95 (3.52) —Syndrome 6 ILRE/5 IGE 7 ILRE/2 IGE —Antiepileptic drugs (polytherapy, monotherapy, none) 0/0/11* 4/4/1* —

Superscript symbol pairs (‘‘*’’, ‘‘ˆ’’) denote significant differences; p < 0.05. ILRE = idiopathic localization-related epilepsy;

atssD

I

TtGMgTa(is

IGE = idiopathic generalized epilepsy.

Epilepsy remission was defined as remaining seizurefree for 12 months and no longer taking anti-epilepticdrugs (AEDs). Of the 11 participants with epilepsy remis-sion, 8 were on medications at the time of their baselineassessment and the average duration of AED treatment forthis group was 30 months. The remitted participants bydefinition were all withdrawn from AEDs at the time ofassessment. Children with localization-related (n = 6) andgeneralized epilepsy (n = 5) were equally represented inthe remitted group. The 29 control participants were first-degree cousins of the children with epilepsy who werecomparable in age, gender and handedness to the epilepsygroup (Table 1). The control group had no history of seizuresand no other developmental or neurological disease. Thefull details on the selection criteria are available elsewhere(Hermann et al., 2006). The results presented here involveDTI scans taken at the third visit—–5—6 years after their base-line evaluation. A total of 84 DTI scans were collected, butdue to a scanner malfunction, 32 scans contained image

artifacts and were not included in this analysis, leaving afinal sample size of 23 epilepsy participants and 29 controls.Three epilepsy subjects could not be confidently classifiedas active or remitted and thus were not included in the

btmn

Figure 1 The Johns Hopkins University Atlas (left) showing its 48 ua sample patient to demonstrate alignment post registration.

nalysis. This study was reviewed and approved by the Insti-utional Review Boards of both institutions. On the day oftudy participation families and children gave informed con-ent and assent and all procedures were consistent with theeclaration of Helsinki (1991).

mage acquisition

1, T2, and DWI MRI scans were acquired for each par-icipant. All MRI scans were collected on a clinical 1.5 TE Signa LX MRI scanner (General Electric Corporation,ilwaukee, WI). T1-weighted, three-dimensional, spoiledradient recalled echo (SPGR) scans were collected withR/TE = 24/5 ms, flip angle = 40◦, coronal acquisition with

reconstructed matrix size of 256 × 256, field of viewFOV) = 200 mm, slice thickness 1.5 mm and contiguous spac-ng. DTI scan parameters were as follows: one referencecan with b = 0 s/mm2 and 25 diffusion weighted scans with

= 1000 s/mm2 each with a unique set of gradient direc-ions optimized for DTI axial acquisition with reconstructedatrix size of 256 × 256, FOV = 240 mm, and slice thick-

ess = 3 mm.

nique white matter ROIs. The atlas is superimposed (right) on

266 I. Amarreh et al.

Table 2 Regions that were significantly abnormal in children with epilepsy (n = 20) compared to controls (n = 29) after correctionfor multiple comparisons.

DTI parameters MNI coordinates Corresponding white matter cortical label(JHU-ICBM-DTI-81 white-matter labelsatlas)

Significantcontrast

p cor.

x y z

Reduced FA x = 1 y = −10 z = 16 Fornix (column and body of fornix) C > E <0.05x = −27 y = −25 z = 6 Retrolenticular part of internal capsule L C > E <0.05x = 24 y = −25 z = −19 Cingulum R C > E <0.05

Increased MD x = 1 y = −10 z = 16 Fornix (column and body of fornix) C < E <0.05x = 27 y = −17 z = 24 Superior corona radiata R C < E <0.05x = −28 y = −17 z = 24 Superior corona radiata L C < E <0.05x = 20 y = −5 z = 21 Superior fronto-occipital fasciculus R C < E <0.05

Increased RD x = 1 y = −10 z = 16 Fornix (column and body of fornix) C < E <0.05x = −27 y = −25 z = 6 Retrolenticular part of internal capsule L C < E <0.05

Increased AD x = 27 y = −17 z = 24 Superior corona radiata R C < E <0.05x = −28 y = −17 z = 24 Superior corona radiata L C < E <0.05x = 20 y = −5 z = 21 Superior fronto-occipital fasciculus R C < E <0.05

rior

D

Ipp(Few(tdMaoit

T

TtNgm

tAcacstpct

srTowl

owtttvt

vrlirin(ReURTtwMcp

x = −20 y = −5 z = 21 Supe

TI preprocessing

mages were transferred to an offline workstation forrocessing. After initial conversion to the NIFTI format,rocessing was performed with the FMRIB Software LibraryFSL) unless otherwise indicated (Woolrich et al., 2009). TheSL eddy current correction toolbox was used to correct forddy-current artifacts. Calculation of the diffusion tensoras performed in Hispeed, an in-house software package

Koay et al., 2006). Hispeed utilizes a nonlinear least squaresechnique to compute the tensor. The diffusion tensor wasecomposed into its eigenvalues and maps created for FA,D, RD and AD. These maps were then used in the voxel-wisenalyses to disambiguate differences in specific structuresf brain white matter. To address the structural differencesn white matter we conducted a voxel-wise analysis usingract-based spatial statistics (TBSS) on FA, MD, RD and AD.

BSS processing

he TBSS pipeline within FSL addresses the main limita-ions associated with a conventional voxel-wise approach.amely, TBSS is less vulnerable to registration mismatches inroup comparisons and does not require an arbitrarily deter-ined amount of spatial smoothing (Smith et al., 2006).All the FA images undergo voxel-wise nonlinear regis-

ration to a target image provided by FSL (FMRIB 1mm).n intermediate degree of freedom registration (DoF) washosen for TBSS to avoid poor alignment (insufficient DoF)nd ‘‘broken topology’’ (excess DoF). This process involvedarrying out only one registration per participant and gaveufficient alignment results. All images were transformed

o the MNI152 coordinate system where all subsequentrocessing were done. Next, the mean of all FA images wasreated, and then fed into the FA skeletonization algorithmo generate a ‘‘reference skeleton’’. This white matter

Ttcp

fronto-occipital fasciculus L C < E <0.05

keleton is a pseudo-anatomical representation intended toepresent the average of the centers of white matter tracts.he reference skeleton was then thresholded at an FA valuef 0.2, which eliminates skeletal remnants at the outerhite matter tissue where higher inter-subject variability

ikely causes poor alignment.The thresholded reference skeleton was then projected

nto the individual, registered FA images. A TBSS algorithmas used to search orthogonally outward from the skele-

on until the corresponding white matter tract center forhat participant was determined. The skeletons of all par-icipants were merged into a single 4D file. Cross-subjectoxel-wise statistical analysis was performed on the skele-ons to determine voxels of significant difference.

The statistical analysis included investigation of eachoxel along the representative skeletons using masks ofegions of interest. In voxel-wise analyses of brain imagingike TBSS, the results are corrected for multiple compar-sons. While this is necessary to achieve statistically soundesults, these corrections may also remove any underly-ng group differences that exist. To address this issue, wearrowed our analysis to specific regions of interest (ROI)Fig. 1). These ROIs were selected based on a whole brainOI analysis of the DTI indices. Analysis was performed onach of the 48 WM ROIs designated in the Johns Hopkinsniversity White Matter Atlas (Mori and Crain, 2005). TheseOIs were used as masks in the statistical analysis step of theBSS pipeline. A two-sample t-test adjusted for age usingwo contrasts (Patients > Controls) and (Controls > Patients)as performed upon the diffusion map skeletons of FA,D, AD, RD. Similar analyses were conducted for all groupomparisons. Using FSL’s ‘‘randomize’’ function, the non-arametric test was executed with 5000 permutations. The

hreshold-Free Cluster Enhancement (TFCE) method withhe - T2 option was implemented to avoid an arbitraryluster threshold (Smith and Nichols, 2009). The optimizedarameters used were H = 2 and E = 0.5. All results were

Cerebral white matter integrity in children with active versus remitted epilepsy 267

Figure 2 Regions that were significantly abnormal in children with epilepsy (n = 20) compared to controls (n = 29) after multiplecomparison corrections. Gray is the standard FA template; overlaid is the white matter skeleton (yellow). (A) The right cingulumwith lower FA in children with epilepsy compared to controls (blue). (B—D) Left superior corona radiata, fornix and the left superior

tivelto th

Fffweo

Ce

WrFgTM

fronto-occipital fasciculus with higher MD, RD and AD, respecreferences to color in this figure legend, the reader is referred

then corrected for multiple comparisons using a thresholdof p < 0.05.

TBSS was also performed on the diffusion maps of othermeasures. Processing time was saved by using the pre-determined centers of the WM tracts of the FA analysis. Thecorresponding values were then used to create skeletonsof MD, RD and AD. Statistics were carried out in a similarmanner to the FA skeleton detailed above.

Results

Controls versus all children with epilepsy

TBSS revealed significant group differences in FA, MD, RDand AD between children with epilepsy (n = 20) and controls

(n = 29) in distributed regions across the brain (p < 0.05 cor-rected). The epilepsy group showed reduced FA in majorwhite matter tracts that included the left hemisphere inter-nal capsule, right cingulum and the fornix (Table 2 and

sscA

y, in children with epilepsy (red). (For interpretation of thee web version of the article.)

ig. 2). Additionally, there was an increase in MD in theornix, bilateral superior coronal radiata, and right superiorronto-occipital fasciculus. While AD in the epilepsy groupas increased in bilateral superior corona radiata, and bilat-ral superior fronto-occipital fasciculus, RD was increasednly in the left internal capsule and fornix.

ontrols versus children with active and remittedpilepsy

hen the epilepsy group was divided into active versusemitted groups, the active epilepsy group showed reducedA in bilateral superior fronto-occipital fasciculus, right cin-ulum and fornix compared to controls (p < 0.05 corrected).his active epilepsy group also exhibited an increase inD in bilateral superior fronto-occipital fasciculus and left

uperior coronal radiata. RD was increased in bilateraluperior fronto-occipital fasciculus, and the left superiororona radiata was the only region in which an increase inD was detected in this comparison (Table 3 and Fig. 3).

268 I. Amarreh et al.

Table 3 Regions that were significantly abnormal in children with active epilepsy (n = 9) compared to controls (n = 29) aftercorrection for multiple comparisons.

DTI parameters MNI coordinates Corresponding white matter cortical label(JHU-ICBM-DTI-81 white-matter labelsatlas)

Significantcontrast

p cor.

x y z

Reduced FA x = 1 y = −10 z = 16 Fornix (column and body of fornix) C > A <0.05x = 24 y = −25 z = −19 Cingulum R C > A <0.05x = 20 y = −5 z = 21 Superior fronto-occipital fasciculus R C > A <0.05x = −20 y = −5 z = 21 Superior fronto-occipital fasciculus L C > A <0.05

Increased MD x = −28 y = −17 z = 24 Superior corona radiata L C < A <0.05x = 20 y = −5 z = 21 Superior fronto-occipital fasciculus R C < A <0.05x = −20 y = −5 z = 21 Superior fronto-occipital fasciculus L C < A <0.05

Increased RD x = 20 y = −5 z = 21 Superior fronto-occipital fasciculus R C < A <0.05x = −20 y = −5 z = 21 Superior fronto-occipital fasciculus L C < A <0.05

Increased AD x = −28 y = −17 z = 24 Superior corona radiata L C < A <0.05

Figure 3 Regions that were significantly abnormal in children with active epilepsy (n = 9) compared to controls (n = 29) aftermultiple corrections. Gray is the standard FA template; overlaid is the white matter skeleton (yellow). (A) The right cingulum withlower FA in children with epilepsy compared to controls (blue). (B—D) Left superior corona radiata, right superior fronto-occipitalfasciculus and the left superior coronal radiata with higher MD, RD and AD, respectively, in children with epilepsy (red). (Forinterpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

Cerebral white matter integrity in children with active versus remitted epilepsy 269

Table 4 Regions that were significantly abnormal in children with active epilepsy (n = 9) compared to children with remittedepilepsy (n = 11) after correction for multiple comparisons.

DTI parameters MNI coordinates Corresponding white matter cortical label(JHU-ICBM-DTI-81 white-matter labelsatlas)

Significantcontrast

p cor.

x y z

Reduced FA x = 5 y = −37 z = 16 Splenium of corpus callosum A < R <0.05x = −27 y = −25 z = 6 Retrolenticular part of internal capsule L A < R <0.05x = 29 y = −25 z = 6 Retrolenticular part of internal capsule R A < R <0.05x = 66 y = 101 z = 48 Cingulum R A < R <0.05x = 27 y = −17 z = 24 Superior corona radiata R A < R <0.05

Increased MD x = 27 y = −17 z = 24 Superior corona radiata R A > R <0.05x = −28 y = −17 z = 24 Superior corona radiata L A > R <0.05x = −20 y = −5 z = 21 Superior fronto-occipital fasciculus L A > R <0.05

Increased RD x = 5 y = −37 z = 16 Splenium of corpus callosum A > R <0.05x = 24 y = −25 z = −19 Cingulum R A > Rx = 29 y = −25 z = 6 Retrolenticular part of internal capsule R A > R <0.05x = −28 y = −17 z = 24 Superior corona radiata L A > Rx = 27 y = −17 z = 24 Superior corona radiata R A > R <0.05

olent

dtad

sahpctbgarpoi

fenaddtce

tia

Increased AD x = −27 y = −25 z = 6 Retr

In contrast, there were no differences in any of the DTIindices between the remitted epilepsy group and controls.

Children with active versus children with remittedepilepsy

Comparing the active (n = 9) versus remitted (n = 11) epilepsygroups, TBSS analysis revealed group differences in FA, MD,RD and AD distributed across several regions of the brain(p < 0.05 corrected) (Table 1). The active epilepsy groupshowed reduced FA in the bilateral internal capsule, rightcingulum, right superior corona radiata, and the spleniumof the corpus callosum compared to children with remit-ted epilepsy (p < 0.05 corrected) (Table 4 and Fig. 4). Theactive epilepsy group also exhibited increased MD bilaterallyin superior coronal radiata, and left superior fronto-occipitalfasciculus. The active epilepsy group also showed increasedRD in the right internal capsule, right cingulum, bilateralsuperior corona radiata and the splenium of the corpuscallosum compared to children with remitted epilepsy. Theleft internal capsule in the active group was the only regionwith an increase in AD compared to the remitted group(p < 0.05 corrected).

Discussion

The primary finding of this study is the differential integrityof cerebral white matter microstructure in children withactive versus remitted epilepsy 5 years after diagnosis.Children with persisting active epilepsy exhibited reducedFA and increased MD, RD and AD compared to controls.

Additionally, the active epilepsy group presented similarresults of reduced FA and increased MD, RD and AD whencompared to the remitted epilepsy group. In contrast,children with remitted epilepsy showed no significant

ace�

icular part of internal capsule L A > R <0.05

ifferences compared to the healthy control group. This ishe first study to examine cerebral white matter structures a function of remission status at a fixed time point postiagnosis (5—6 years after diagnosis).

DTI indices are highly sensitive in measuring micro-tructural differences across different tissues. Seizuressociated changes have been observed in both animal anduman studies, which generally show a pattern of earlyostictal depression, followed by normalization and thenhronic elevation of mean diffusivity (Righini et al., 1994). Inhis study, children with active epilepsy showed reduced FAilaterally in superior fronto-occipital fasciculus, right cin-ulum, and the fornix compared to controls. Comparing thective with the remitted group, reduced FA was observed inegions such as the internal capsule and splenium of the cor-us callosum. The reduction in FA, which represents the ratiof anisotropic components of diffusion, suggests a reductionn axonal density (Concha et al., 2010).

RD in the active group was elevated in bilateral superiorronto-occipital fasciculus compared to controls. The activepilepsy group also showed increased RD in the right inter-al capsule, right cingulum, bilateral superior corona radiatand the splenium of the corpus callosum compared to chil-ren with remitted epilepsy. This increase in perpendiculariffusivity measured by RD, which is the mean of the magni-ude of diffusion or the eigenvalues of �medium and �minor, isonsistent with myelin degradation (Beaulieu, 2002; Conchat al., 2006; Song et al., 2002, 2003).

AD was increased in the left superior corona radiata inhe active epilepsy group compared to controls. The leftnternal capsule in the active group was the only region withn increase in AD compared to the remitted group. While

n increase in perpendicular diffusivity measured by RD isonsistent with myelin degradation (Beaulieu, 2002; Conchat al., 2006; Song et al., 2002, 2003), decreased AD, which ismajor and represents the magnitude of principle diffusivity,

270 I. Amarreh et al.

Figure 4 Regions that were significantly abnormal in children with active epilepsy (n = 9) compared to children with remittedepilepsy (n = 11) after multiple corrections. Gray is the standard FA template; overlaid is the white matter skeleton (yellow). (A)The right cingulum with lower FA in children with active epilepsy compared to in children with remitted epilepsy (blue). (B—D) Leftsuperior corona radiata, right internal capsule and the left internal capsule with higher MD, RD and AD, respectively, in childrenw or int

i2svbRa

cdt(wtsc

ori

cWfifiigop

sptdtgb

ith epilepsy (red). (For interpretation of the references to colhe article.)

s considered to reflect axonal degeneration (Kim et al.,007; Song et al., 2003; Thomalla et al., 2004). Although,everal studies relate AD to axonal degeneration, othersiew the nature of the underlying changes related to AD toe inconclusive. In the current study, the findings of elevatedD, AD and reduced FA suggests that axonal and myelinbnormalities are present in children with active epilepsy.

Using ROIs placed in the anterior and posterior corpusallosum, fornix, cingulum, internal and external capsule,iffusion differences were evaluated in the same popula-ion of children with epilepsy examined in here at baselineHutchinson et al., 2010). Children with new onset epilepsyere found to have reduced FA and increased RD in the pos-

erior corpus callosum and cingulum. Here we found theame pattern in the posterior corpus callosum and rightingulum of children with active epilepsy.

The present findings are the first to reveal differentialutcomes in white matter integrity as a function of seizureemission status 5—6 years after diagnosis. Whether theres recovery of DTI abnormalities reported in children with

vsls

this figure legend, the reader is referred to the web version of

hronic and new onset epilepsy (Hutchinson et al., 2010;idjaja et al., 2012) remains to be determined. While ourndings suggest there may be recovery, our data are insuf-cient to document this point. Needed is a longitudinal

nvestigation of children with DTI both at baseline and lon-itudinal follow-up where changes can be related to seizureutcome status—–a research question we are pursuing atresent.

There are several limitations to our study. First, theample size was small, and investigation of a larger sam-le would be most helpful in determining the reliability ofhe present findings. The small sample size resulted in chil-ren with heterogeneous epilepsy syndromes being groupedogether for the analysis in both the active and remissionroups. There was no difference (tested by Chi Square)etween the proportion of LRE and IGE subjects in the active

ersus remitted groups. However, given our small sampleize, the differences in the number of LRE versus IGE is aimitation of this study that we plan to address in futuretudies. We are currently recruiting a new and larger cohort

s re

M

M

N

O

P

R

R

S

S

S

S

S

T

T

W

W

Y

Cerebral white matter integrity in children with active versu

that will have both baseline and follow-up DTI scans. Sec-ond, all nine participants in the active group remain on AEDsand the contributions of seizures/epilepsy syndromes versusmedications cannot be isolated here.

In summary, this study provides the first examinationof the relationship of cerebral white matter integrity andseizure remission in childhood epilepsy 5—6 years after thediagnosis of epilepsy. Abnormal cerebral white matter wasdetected in the active epilepsy group, but not in childrenwith remitted seizures or controls. These findings may haveimplications for the prediction of seizure outcomes and long-term treatment of children with new onset epilepsy.

References

Arfanakis, K., Hermann, B.P., Rogers, B.P., Carew, J.D., Seidenberg,M., Meyerand, M.E., 2002. Diffusion tensor MRI in temporal lobeepilepsy. Magnetic Resonance Imaging 20 (7), 511—519.

Beaulieu, C., 2002. The basis of anisotropic water diffusion in thenervous system? A technical review. NMR in Biomedicine 15(7—8), 435—455.

Concha, L., Beaulieu, C., Collins, D.L., Gross, D.W., 2009. White-matter diffusion abnormalities in temporal-lobe epilepsy withand without mesial temporal sclerosis. Journal of Neurology,Neurosurgery & Psychiatry 80 (3), 312—319.

Concha, L., Gross, D.W., Wheatley, B.M., Beaulieu, C., 2006. Dif-fusion tensor imaging of time-dependent axonal and myelindegradation after corpus callosotomy in epilepsy patients. Neu-roImage 32 (3), 1090—1099.

Concha, L., Livy, D.J., Beaulieu, C., Wheatley, B.M., Gross, D.W.,2010. In vivo diffusion tensor imaging and histopathology of thefimbria—fornix in temporal lobe epilepsy. The Journal of Neuro-science 30 (3), 996—1002.

Diehl, B., Busch, R.M., Duncan, J.S., Piao, Z., Tkach, J., Lüders,H.O., 2008. Abnormalities in diffusion tensor imaging of theuncinate fasciculus relate to reduced memory in temporal lobeepilepsy. Epilepsia 49 (8), 1409—1418.

Duncan, J.S., 2008. Imaging the brain’s highways—–diffusion tensorimaging in epilepsy. Epilepsy Currents/American Epilepsy Society8 (4), 85—89.

Govindan, R.M., Makki, M.I., Sundaram, S.K., Juhász, C., Chugani,H.T., 2008. Diffusion tensor analysis of temporal and extra-temporal lobe tracts in temporal lobe epilepsy. EpilepsyResearch 80 (1), 30—41.

Hermann, B., Jones, J., Sheth, R., Dow, C., Koehn, M., Seidenberg,M., 2006. Children with new-onset epilepsy: neuropsychologicalstatus and brain structure. Brain 129 (Pt 10), 2609—2619.

Hutchinson, E., Pulsipher, D., Dabbs, K., Gutierrez, A.M., Sheth, R.,Jones, J., et al., 2010. Children with new-onset epilepsy exhibitdiffusion abnormalities in cerebral white matter in the absenceof volumetric differences. Epilepsy Research 88 (2—3), 208—214.

Kim, J.H., Loy, D.N., Liang, H.F., Trinkaus, K., Schmidt, R.E., Song,S.K., 2007. Noninvasive diffusion tensor imaging of evolvingwhite matter pathology in a mouse model of acute spinal cordinjury. Magnetic Resonance in Medicine 58 (2), 253—260.

Kimiwada, T., Juhász, C., Makki, M., Muzik, O., Chugani, D.C.,Asano, E., et al., 2006. Hippocampal and thalamic diffusionabnormalities in children with temporal lobe epilepsy. Epilepsia47 (1), 167—175.

Knake, S., Salat, D.H., Halgren, E., Halko, M.A., Greve, D.N., Grant,P.E., 2009. Changes in white matter microstructure in patients

with TLE and hippocampal sclerosis. Epileptic Disorders: Inter-national Epilepsy Journal with Videotape 11 (3), 244—250.

Koay, C.G., Chang, L.-C., Carew, J.D., Pierpaoli, C., Basser, P.J.,2006. A unifying theoretical and algorithmic framework for least

Y

mitted epilepsy 271

squares methods of estimation in diffusion tensor imaging. Jour-nal of Magnetic Resonance 182 (1), 115.

eng, L., Xiang, J., Kotecha, R., Rose, D., Zhao, H., Zhao, D., et al.,2010. White matter abnormalities in children and adolescentswith temporal lobe epilepsy. Magnetic Resonance Imaging 28 (9),1290—1298.

ori, S., Crain, B.J., 2005. MRI Atlas of Human White Matter. Else-vier, Amsterdam, Boston.

ilsson, D., Go, C., Rutka, J.T., Rydenhag, B., Mabbott, D.J., SneadIii, O.C., et al., 2008. Bilateral diffusion tensor abnormalities oftemporal lobe and cingulate gyrus white matter in children withtemporal lobe epilepsy. Epilepsy Research 81 (2—3), 128—135.

tte, W.M., van Eijsden, P., Sander, J.W., Duncan, J.S., Dijkhuizen,R.M., Braun, K.P.J., 2012. A meta-analysis of white matterchanges in temporal lobe epilepsy as studied with diffusion ten-sor imaging. Epilepsia 53 (4), 659—667.

ierpaoli, C., Jezzard, P., Basser, P.J., Barnett, A., Di Chiro, G.,1996. Diffusion tensor MR imaging of the human brain. Radiology201 (3), 637—648.

ighini, A., Pierpaoli, C., Alger, J.R., Di Chiro, G., 1994. Brainparenchyma apparent diffusion coefficient alterations associ-ated with experimental complex partial status epilepticus.Magnetic Resonance Imaging 12 (6), 865—871.

odrigo, S., Oppenheim, C., Chassoux, F., Golestani, N., Cointepas,Y., Poupon, C., Semah, F., Mangin, J.F., Le Bihan, D., Meder,J.F., 2007. Uncinate fasciculus fiber tracking in mesial tempo-ral lobe epilepsy. Initial findings. European Radiology 17 (7),1663—1668.

almenpera, T.M., Symms, M.R., Boulby, P.A., Barker, G.J., Dun-can, J.S., 2006. Postictal diffusion weighted imaging. EpilepsyResearch 70 (2—3), 133—143.

mith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D.,Nichols, T.E., Mackay, C.E., et al., 2006. Tract-based spatialstatistics: voxelwise analysis of multi-subject diffusion data.NeuroImage 31 (4), 1487—1505.

mith, S.M., Nichols, T.E., 2009. Threshold-free cluster enhance-ment: addressing problems of smoothing, threshold dependenceand localisation in cluster inference. NeuroImage 44 (1), 83—98.

ong, S., Sun, S., Ramsbottom, M., Chang, C., Russell, J., Cross, A.,2002. Dysmyelination revealed through MRI as increased radial(but unchanged axial) diffusion of water. NeuroImage 17 (3),1429—1436.

ong, S.-K., Sun, S.-W., Ju, W.-K., Lin, S.-J., Cross, A.H., Neufeld,A.H., 2003. Diffusion tensor imaging detects and differentiatesaxon and myelin degeneration in mouse optic nerve after retinalischemia. NeuroImage 20 (3), 1714—1722.

hivard, L., Lehéricy, S., Krainik, A., Adam, C., Dormont, D., Chiras,J., et al., 2005. Diffusion tensor imaging in medial temporallobe epilepsy with hippocampal sclerosis. NeuroImage 28 (3),682—690.

homalla, G., Glauche, V., Koch, M.A., Beaulieu, C., Weiller, C.,Röther, J., 2004. Diffusion tensor imaging detects early Walle-rian degeneration of the pyramidal tract after ischemic stroke.NeuroImage 22 (4), 1767.

idjaja, E., Kis, A., Go, C., Raybaud, C., Snead, O.C., Smith, M.L.,2013. Abnormal white matter on diffusion tensor imaging in chil-dren with new-onset seizures. Epilepsy Res. 104 (Mar (1-2)),105-111.

oolrich, M.W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S.,Behrens, T., et al., 2009. Bayesian analysis of neuroimaging datain FSL. NeuroImage 45 (1 Suppl.), S173—S186.

ang, T., Guo, Z., Luo, C., Li, Q., Yan, B., Liu, L., et al., 2012. Whitematter impairment in the basal ganglia-thalamocortical circuitof drug-naïve childhood absence epilepsy. Epilepsy Research 99

(3), 267—273.

ogarajah, M., Duncan, J.S., 2008. Diffusion-based magnetic res-onance imaging and tractography in epilepsy. Epilepsia 49 (2),189—200.