Epilepsy Research (2010) 88, 208—214

journa l homepage: www.e lsev ier .com/ locate /ep i lepsyres

Children with new-onset epilepsy exhibit diffusionabnormalities in cerebral white matter in theabsence of volumetric differences

Elizabeth Hutchinsona,d,∗, Dalin Pulsipherb, Kevin Dabbsc,Adan Myers y Gutierrezc, Raj Shethc, Jana Jonesc,Michael Seidenbergb, Elizabeth Meyeranda,d, Bruce Hermannc

a Neuroscience Training Program, University of Wisconsin, 7225 Medical Sciences Center, 1300 University Avenue,Madison, WI 53706, United Statesb Department of Psychology, Rosalind Franklin University of Medicine and Science, 3333 Green Bay Rd,North Chicago, IL 60064, United Statesc Department of Neurology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue,Madison, WI 53792, United Statesd Department of Medical Physics, University of Wisconsin, Wisconsin Institutes for Medical Research, 1111 Highland Avenue,Room 1005, Madison, WI 53705, United States

Received 9 April 2009; received in revised form 19 November 2009; accepted 22 November 2009Available online 30 December 2009

KEYWORDSDTI;Volumetrics;MRI;Epilepsy

Summary The purpose of this investigation was to examine the diffusion properties ofcerebral white matter in children with recent onset epilepsy (n = 19) compared to healthy con-trols (n = 11). Subjects underwent DTI with quantification of mean diffusion (MD), fractionalanisotropy (FA), axial diffusivity (Dax) and radial diffusivity (Drad) for regions of interest includ-ing anterior and posterior corpus callosum, fornix, cingulum, and internal and external capsules.Quantitative volumetrics were also performed for the corpus callosum and its subregions (ante-rior, midbody and posterior) and total lobar white and gray matter for the frontal, parietal,temporal and occipital lobes. The results demonstrated no group differences in total lobar grayor white matter volumes or volume of the corpus callosum and its subregions, but did showreduced FA and increased Drad in the posterior corpus callosum and cingulum. These resultsprovide the earliest indication of microstructural abnormality in cerebral white matter among

children with idiopathic epilepsies. This abnormality occurs in the context of normal volumetricsand suggests disruption in myelination processes.© 2009 Elsevier B.V. All rights reserved.

∗ Corresponding author at: Neuroscience Training Program, University of Wisconsin, 7225 Medical Sciences Center,1300 University Avenue, Madison, WI 53706, United States. Tel.: +1 608 216 4609; fax: +1 608 265 9840.

E-mail address: ehutchinson@wisc.edu (E. Hutchinson).

0920-1211/$ — see front matter © 2009 Elsevier B.V. All rights reserved.doi:10.1016/j.eplepsyres.2009.11.011

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Table 1 Syndrome distribution within the epilepsy patientgroup. Specific syndrome classifications within localizationrelated and idiopathic generalized epilepsy groups are listedand the numbers of patients within each group are given.

Syndrome Number

Localization related 11Benign rolandic 4Temporal lobe 2Frontal lobe 1Other focal 4

Idiopathic generalized 8

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Introduction

Diffusion tensor imaging (DTI) studies have characterizedabnormalities in the microstructural integrity of cerebralwhite matter in patients with chronic epilepsy includingtemporal lobe epilepsy (Duncan, 2008; Yogarajah et al.,2008; Yogarajah and Duncan, 2008) and other epilepsy syn-dromes such as juvenile myoclonic epilepsy (Deppe et al.,2008). Among patients with temporal lobe epilepsy, DTIabnormalities have been reported in regions both near to aswell as distant from the primary epileptic zone (Arfanakiset al., 2002; Concha et al., 2005, 2009; Gong et al., 2008;Gross et al., 2006; Kim et al., 2008; Lui et al., 2005; Thivardet al., 2005).

The early onset and long duration of commonly investi-gated epilepsy syndromes such as temporal lobe epilepsyraises the possibility that both neurodevelopmental andprogressive processes may underlie the presence and dis-tributed nature of these abnormalities. To address theneurodevelopmental contribution, investigation of childrenwith a limited duration of epilepsy might prove helpful,but a remarkably limited number of studies have uti-lized DTI to understand the presence and course of whitematter changes over time. Pediatric studies have focusedprimarily on children with localization related and predomi-nantly temporal lobe epilepsy. Findings include significantlyreduced fractional anisotropy (FA) in the hippocampus con-tralateral as well as ipsilateral to side of seizure onset(Kimiwada et al., 2006); decreased anisotropy in whitematter tracts (uncinate, arcuate, and inferior longitudinalfasciculi as well as corticospinal tract) both contralateral aswell as ispilateral to the side of seizure onset (Govindan etal., 2008), and increased diffusivity in temporal lobe andcingulate gyrus white matter but without significant differ-ences in FA (Nilsson et al., 2008). Thus, even early in thecourse of epilepsy the microstructural integrity of cerebralwhite matter is affected.

The purpose here is to build upon these observationsby examining diffusion properties in specific cerebral whitematter tracts among children with recent onset and therebyvery limited duration of epilepsy, thus representing the ear-liest investigation of these tissue properties in the naturalcourse of childhood epilepsy. Our DTI studies were supple-mented by examination of corpus callosum and lobar grayand white matter tissue volumes. White matter volumeswere examined in order to determine whether abnormalitiesin diffusion properties occurred independently of cerebralwhite matter volume differences. Gray matter volumes wereexamined to address the possibility that diffusion abnor-malities were a secondary consequence of gray matterdegradation. While several white matter tracts were exam-ined in this study, a specific focus was the corpus callosumwhere relatively direct comparisons could be made betweenDTI and volume measurements.

Methods

Subject groups

The participants were children 8—18 years of age includ-ing 19 patients with recent onset idiopathic epilepsy (average

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Juvenile absence 2Juvenile myoclonic 5Other generalized 1

ge ± s.d. = 12.5 ± 3.4 years; 8 female, 11 male) and 11 healthyontrols (average age ± s.d. = 13.8 ± 3.4 years; 6 female, 5 male).n addition to age requirements, inclusion criteria for the patientroup included a diagnosis of epilepsy within the past 12 monthsnd no other developmental disabilities or neurological disorder.he patients met ILAE criteria for idiopathic epilepsy, meaninghat there were no identifiable lesions or other abnormalities onlinical MRI nor neurological deficits on examination. The groupas composed of 11 children with idiopathic generalized (averagege ± s.d. = 10.5 ± 1.8 years; 4 female, 7 male) and 8 with local-zation related epilepsies (average age ± s.d. = 15.2 ± 3.2 years; 4emale, 4 male). Age was accounted for by all analysis methods andhe distribution of syndromes within the patient group is given inable 1.

The following process was used to define the electroclinicalyndromes. All children with epilepsy underwent a standard clin-cal EEG which was digitized and independently reviewed andoded by the project pediatric epileptologist for the following: (1)ackground rhythm, (2) presence/rate of diffuse slow waves, (3)resence, laterality and location of focal slow waves, (4) presence,aterality and location of epileptiform discharges, (5) presence ofbnormalities activated by sleep, and (6) results of seizure onsetf ictal recordings were obtained. This review was conducted whilelinded to all MRI, cognitive, and psychiatric data. This informationas used with clinical characterization of seizures and history toefine electroclinical syndromes in consensus conference.

Controls were first-degree cousins who were age and genderatched with no history of seizures, early initial precipitating

njuries (e.g., febrile convulsions), no other developmental oreurological disease, or loss of consciousness greater than 5 min.articipants in both groups attended regular school. Further detailsegarding subject selection criteria are available in Hermann et al.2006). There were no significant differences between the epilepsynd control groups in age (12.5 vs. 13.8, p = 0.31), gender (53% males. 45% male, p = 0.70), or Full Scale IQ (96.6 vs. 97.1, p = 0.89).

maging parameters

mages were obtained on a 1.5 T GE Signa MR scanner. Sequencescquired for each participant included: (i) T1-weighted, three-imensional SPGR acquired with the following parameters:E = 5 ms, TR = 24 ms, flip angle = 40◦, NEX = 1, slice thick-ess = 1.5 mm, slices = 124, plane = coronal, FOV = 20 cm × 20 cm,

atrix = 256 × 256; (ii) proton density (PD) and (iii) T2-eighted images acquired with the following parameters:E = 36 ms (for PD) or 96 ms (for T2), TR = 3000 ms, NEX = 1,lice thickness = 3.0 mm, slices = 64, slice plane = coronal,OV = 20 cm × 20 cm, matrix = 256 × 256; (iv) EPI DTI data series

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f one reference image (b = 0 s/mm2) and 13 diffusion weightedmages (b = 1000 s/mm2, non-colinear gradient orientations) withhe following parameters: TE = 76.6 ms, TR = 4000 ms, NEX = 4,lice thickness = 4 mm and gap thickness = 4 mm, slices = 15,lice plane = axial, FOV = 24 cm × 24 cm, matrix = 256 × 256reconstructed).

olumetric postacquisition processing

RIs were processed using a semi-automated software package,.e., Brain Research: Analysis of Images, Networks, and SystemsBRAINS2) (Andreasen et al., 1996; Harris et al., 1999). Neu-oimaging analyses were conducted blinded to group status andoth clinical and demographic characteristics of the subjects.he T1-weighted images were spatially normalized so that thenterior—posterior axis of the brain was realigned parallel to thenterior commissure—posterior commissure (AC—PC) line, and thenterhemispheric fissure was aligned on the other two axes. A six-oint linear transformation was used to warp the standard Talairachtlas space onto the resampled image. Images from the three pulseequences were then coregistered using a local adaptation of auto-ated image registration software. Following alignment of the

mage sets, all images were resampled into 1 mm cubic voxels, fol-owing which an automated algorithm classified each voxel as grayatter, white matter, CSF, blood, or ‘‘other.’’ Manual inspection and

orrection of the output of the neural network tracing were con-ucted. The brain images were then volume rendered using localtilities, producing tissue volumes for regions of interest within therain. The six outer boundaries of the brain form a bounding boxhat along with the AC—PC line produces a volumetric grid basedn the Talairach divisions. Each lobar volume measured is definedn this proportional coordinate system. Thus, all measurements arebtained in the image space of the subject without morphing themage to fit a standard brain atlas. Dependent measures were whitend gray matter volumes of the left and right frontal, parietal, tem-oral and occipital lobes. All volumes were adjusted for age andotal intracranial volume (ICV).

orpus callosum guidelines

anual trace of the corpus callosum began in the midsagittal plane.sing a previously established tracing protocol (Hermann et al.,003b), three coregistered image sets were used simultaneously toelineate the corpus callosum. Boundary identification was basedn a pre-manipulated trimodal image (a composite of the T1, T2,nd PD images) as the primary image set, with reference to theontinuously segmented image, and a discretely segmented imagen areas of poorly defined borders (such as the confluence of theornix and the ventral callosum). Additionally, voxel signal intensi-ies were used to separate white matter voxels from neighboringoxels of cerebral spinal fluid, blood vessel wall, and gray matter.n addition to the midsagittal slice, two additional slices extendingaterally from the midsagittal view were traced. The anterior andosterior extreme of the corpus callosum trace were kept consistenthen extending the trace to parasagittal slices. Although the lat-ral extent of the corpus callosum extends beyond the central fiveagittal slices, these regions were excluded from the trace due toifficulty in boundary determination. In addition, a BRAINS2 scriptas applied to automatically partition the CC into seven regionsefined by Witelson (1989). This technique used the inner convex-ty of the genu as a landmark in conjunction with the rostral—caudalxis length to subdivide the callosum. Witelson regions were then

ummed to create anterior (1—3), midbody (4—5), and posterior6—7) regions (Hermann et al., 2003a). An interrater agreementkappa) of 0.99 was achieved. Dependent measures were the vol-mes of the total corpus callosum as well as the anterior, midbodynd posterior regions of the corpus callosum. In order to account for

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ead size volumetric differences, all callosal volumes were adjustedor age and total intracranial volume (ICV).

TI postacquisition processing

ffline processing of the DTI datasets was completed using the dif-usion II toolbox for SPM5 in Matlab. The reference and diffusioneighted images were first corrected for motion and field inhomo-eneity artifacts by coregistration of the image volumes. Next, theiffusion tensor was calculated at each voxel and maps for the meaniffusion (MD), fractional anisotropy (FA), axial diffusivity (Dax) andadial diffusivity (Drad) were calculated for each subject.

In order to quantify diffusion indices of six specific white mat-er structures, the anterior and posterior corpus callosum, fornix,ingulum, internal capsule and external capsule, binary masks ofegions of interest (ROIs) were created using FSL software (Smitht al., 2004) and according to anatomical landmarks on axial slicesf the FA map. All ROI masks were bilateral. For the anterior andosterior corpus callosum a single axial slice was selected for whichhe structure was the most apparent and ROIs were constrained tohe medial 13.5 mm of each structure for consistency with volumet-ic data. For the other white matter structures, all slices containinggiven structure were included in creating the ROI mask. Initially,asks were coarsely drawn to include structure edges and surround-

ng gray matter, but to exclude all other white matter. Next, an FAhreshold was applied to refine the rough masks to include onlyhite matter voxels. This FA threshold was set at 0.5 for the corpusallosum regions and 0.45 for the other regions. For the anterior andosterior corpus callosum masks, the anterior and posterior edgesere further eroded by two voxels in order to reduce partial vol-me effects near the structure edges. ROI masks were applied toTI index maps to calculate mean values for each structure for eachubject. The FA map for a representative subject is shown in Fig. 1ith the ROI masks overlaid. Analysis of covariance with age as aovariate was used to test for group differences after accountingor age effects.

esults

obar gray and white matter volumes

here were no significant differences between the epilepsynd control groups in age and ICV adjusted gray andhite matter volumes. Specifically, there were no signifi-ant univariate effects for lobar frontal (p = 0.94), parietalp = 0.46), temporal (p = 0.56), or occipital (p = 0.98) whiteatter volumes. There were also no significant univariate

ffects for lobar frontal (p = 0.73), parietal (p = 0.38), tem-oral (p = 0.46) or occipital (p = 0.44) gray matter volumessee Fig. 2).

orpus callosum volumes

here were no significant differences between the epilepsynd control groups in age and ICV corrected volumes ofhe total corpus callosum (p = 0.69) as well as the ante-ior (p = 0.60), midbody (p = 0.98), and posterior (p = 0.15)egions (see Fig. 3).

iffusion indices

TI indices of FA, MD, Dax and Drad were analyzed forhe anterior and posterior corpus callosum, fornix, cingu-

Children with new-onset epilepsy 211

Figure 1 Three axial slices from a representative FA map show image quality and ROI locations. Color coded ROIs are labeledcorpus callosum, red—–fornix, magenta—–cingulum, green—–internalrences to color in this figure legend, the reader is referred to the

as follows: cyan—–anterior corpus callosum, yellow—–posteriorcapsule, blue—–external capsule. (For interpretation of the refeweb version of this article.)

lum, internal capsule and external capsule and comparedbetween epilepsy patients and controls. Analysis of covari-ance with age as a covariate showed a group difference ofreduced FA for the posterior corpus callosum (p = 0.04) andcingulum (p = 0.04) in the epilepsy group. Additionally, therewas an increase in MD for the posterior corpus callosum(p = 0.03). There were no significant group differences forDax in any region. However, Drad was significantly increasedin the cingulum (p = 0.03) and posterior corpus callosum(p = 0.03) (see Fig. 4).

When the epilepsy group was divided into localizationrelated (LRE) and idiopathic generalized (IGE) groups, non-significant trends were observed to follow the findings ofthe pooled group for both the posterior corpus callosum andcingulum. Additionally, there was significantly increased MDand Drad (p = 0.039 and 0.043 respectively) in the fornix ofthe LRE group, but not the IGE group compared to controls

(see Supplemental Table 1).

Figure 3 Mean volumes for anterior, midbody, posterior andtotal corpus callosum in children with epilepsy compared tohealthy controls. There were no group differences across anyof these regions of interest.

Figure 2 Mean total white (left panel) and gray (right panel) matter volumes for frontal, parietal, temporal and occipital lobesin children with epilepsy compared to healthy controls. There were no group differences across any of these regions of interest.

212 E. Hutchinson et al.

Figure 4 Mean DTI values (FA, MD, Dax and Drad) of six white matter structures (anterior and posterior corpus callosum, fornix,c andc

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ingulum, internal capsule and external capsule) for epilepsyovariance and error bars indicate standard error.

iscussion

he primary finding of this study is the presence of DTIbnormalities in the white matter of children with veryecent onset idiopathic epilepsies compared to control chil-ren. These diffusion abnormalities occur in the context ofomparable volumes of lobar gray and white matter in therontal, parietal, temporal and occipital lobes. Volumetricnalysis of the corpus callosum also shows no difference forhe total corpus callosum as well as for the anterior, pos-erior or middle subregions in particular. Taken together,hese findings imply an early vulnerability of the structuralntegrity of cerebral white matter in new-onset epilepsy tohich DTI, but not volumetric analysis, is sensitive.

Our results extend previous DTI investigations of chil-ren with epilepsy (Govindan et al., 2008; Kimiwada et al.,006; Nilsson et al., 2008) by demonstrating that not onlyre these abnormalities evident in children with a relativelyhort duration of disorder, but also they can be seen near tohe time of diagnosis of epilepsy, and occur in other epilepsyyndromes.

The corpus callosum has been previously implicated indults with chronic temporal lobe epilepsy and to incur bothTI (Arfanakis et al., 2002; Kim et al., 2008; Thivard etl., 2005) and volumetric (Hermann et al., 2003b; Webert al., 2007) abnormalities. In the present study the pos-erior corpus callosum exhibits reduced FA, increased Drad

nd increased MD in epilepsy patients compared to controls.

nterestingly, the finding of abnormal DTI took place in thebsence of volumetric change in the same callosal region.his finding is in direct support of the idea that DTI is ableo detect abnormalities not evident using conventional MRIechniques (Duncan, 2002). Other MRI studies have shown

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TI abnormalities in the presence of volumetric differencesGong et al., 2008), however to our knowledge this is therst study of epilepsy patients to exhibit a structure specificbnormality in diffusion but not volume.

Structural studies of the normal development of whiteatter in children suggest that the anterior corpus callo-

um matures before the mid or posterior regions (Giedd etl., 1999; Hasan et al., 2009). Our finding of DTI abnormal-ty in the posterior but not anterior corpus callosum mayndicate a preferential damage to later myelinating callosalegions. Furthermore, radial but not axial diffusivity wasltered in the posterior corpus callosum. Although there iso definitive microstructural change certain to underlie thisattern, there are a growing number of DTI studies relat-ng altered Drad to myelination abnormalities (Budde et al.,007; Song et al., 2005) and showing a reduction of Drad dur-ng normal white matter development in humans thought toorrespond to compacting fibers and myelination (Alexandert al., 2007; Hasan et al., 2009; Snook et al., 2005). It is pos-ible that the myelination of axons in the patients is slowedy the epileptogenic process or that there is seizure-relatedamage to the posterior corpus callosum myelin sheath sur-ounding the onset of epilepsy. In support of the view ofyelin as a primary target, there is no observed group dif-

erence in gray matter volume in any lobar region to suggestxonal degeneration secondary to neuronal loss, althoughhe limitations of volumetric measurements are recognizeds specific layers of the neuropil may be affected but notetected by these volumetric measurements.

The other region that displays abnormal DTI values is theingulum, in which FA is reduced and Drad is increased. Thisnding is in agreement with several studies of chronic tem-oral lobe epilepsy populations (Concha et al., 2005, 2009;

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McDonald et al., 2008) and suggests a vulnerability of thecingulum that may accompany the onset of epilepsy. Theinternal capsule, external capsule and fornix were not sig-nificantly different from control values. It is possible thatthere is no change in these structures; however, they shouldnot be ruled out of future studies without first addressingthe limitations of this study.

Future studies will be needed to more fully character-ize the structural state of the newly epileptic brain. Thisstudy provides an important initial characterization of DTIindices in this group, however there are several limitationsthat must be addressed. These include the small sample sizeand mixed epilepsy syndromes of the patient group and theimaging limitations of resolution and acquisition gap thick-ness, which was large and precluded the use of tractographyor analysis of small white matter structures as well as mea-surement of complete white matter structures.

The finding of white matter vulnerability in this studyis in conceptual agreement with previous MRI studies ofpatients with epilepsy. Diffuse abnormalities in cerebralwhite matter in focal epilepsy have been reported usingboth traditional volumetrics and voxel based morphometry(Hermann et al., 2003b; McMillan et al., 2004; Mueller et al.,2006; Seidenberg et al., 2005; Yu et al., 2008). Vulnerabil-ity of frontal—temporal connections including the uncinateand arcuate fasciculi have been described in temporal lobeepilepsy (Lin et al., 2008; Rodrigo et al., 2007) with verifi-cation of this abnormal connectivity and demonstration oftheir important linkages to well known cognitive abnormal-ities (Diehl et al., 2008; Flugel et al., 2006; Lui et al., 2005;McDonald et al., 2008; Powell et al., 2007; Yogarajah et al.,2008). Understanding the cause, course, and consequencesof microstructural abnormalities of cerebral white mattershould help to understand the etiology of neurobehavioralcomorbidities of epilepsy.

Acknowledgements

The authors would like to thank the UW Institute for Clini-cal and Translational Research (ICTR) and support from thefollowing sources: NIH RO1-44351, T90-DK070079 and R90-DK071515.

Appendix A. Supplementary data

Supplementary data associated with this article canbe found, in the online version, at doi:10.1016/j.eplepsyres.2009.11.011.

References

Alexander, A.L., Lee, J.E., Lazar, M., Boudos, R., DuBray, M.B.,Oakes, T.R., Miller, J.N., Lu, J., Jeong, E.K., McMahon, W.M.,Bigler, E.D., Lainhart, J.E., 2007. Diffusion tensor imaging ofthe corpus callosum in autism. Neuroimage 34, 61—73.

Andreasen, N.C., Rajarethinam, R., Cizadlo, T., Arndt, S., Swayze2nd, V.W., Flashman, L.A., O’Leary, D.S., Ehrhardt, J.C., Yuh,W.T., 1996. Automatic atlas-based volume estimation of humanbrain regions from MR images. J. Comput. Assist. Tomogr. 20,98—106.

H

213

rfanakis, K., Hermann, B.P., Rogers, B.P., Carew, J.D., Seidenberg,M., Meyerand, M.E., 2002. Diffusion tensor MRI in temporal lobeepilepsy. Magn. Reson. Imaging 20, 511—519.

udde, M.D., Kim, J.H., Liang, H.F., Schmidt, R.E., Russell, J.H.,Cross, A.H., Song, S.K., 2007. Toward accurate diagnosis of whitematter pathology using diffusion tensor imaging. Magn. Reson.Med. 57, 688—695.

oncha, L., Beaulieu, C., Gross, D.W., 2005. Bilateral limbic dif-fusion abnormalities in unilateral temporal lobe epilepsy. Ann.Neurol. 57, 188—196.

oncha, L., Beaulieu, C., Collins, D.L., Gross, D.W., 2009.White-matter diffusion abnormalities in temporal-lobe epilepsywith and without mesial temporal sclerosis. J. Neurol. Neuro-surg. Psychiatry 80, 312—319.

eppe, M., Kellinghaus, C., Duning, T., Moddel, G., Mohammadi, S.,Deppe, K., Schiffbauer, H., Kugel, H., Keller, S.S., Ringelstein,E.B., Knecht, S., 2008. Nerve fiber impairment of anterior tha-lamocortical circuitry in juvenile myoclonic epilepsy. Neurology71, 1981—1985.

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

uncan, J.S., 2002. Neuroimaging methods to evaluate the etiologyand consequences of epilepsy. Epilepsy Res. 50, 131—140.

uncan, J.S., 2008. Imaging the brain’s highways-diffusion tensorimaging in epilepsy. Epilepsy Curr. 8, 85—89.

lugel, D., Cercignani, M., Symms, M.R., O’Toole, A., Thompson,P.J., Koepp, M.J., Foong, J., 2006. Diffusion tensor imaging find-ings and their correlation with neuropsychological deficits inpatients with temporal lobe epilepsy and interictal psychosis.Epilepsia 47, 941—944.

iedd, J.N., Blumenthal, J., Jeffries, N.O., Rajapakse, J.C.,Vaituzis, A.C., Liu, H., Berry, Y.C., Tobin, M., Nelson, J., Castel-lanos, F.X., 1999. Development of the human corpus callosumduring childhood and adolescence: a longitudinal MRI study.Prog. Neuropsychopharmacol. Biol. Psychiatry 23, 571—588.

ong, G., Concha, L., Beaulieu, C., Gross, D.W., 2008. Thalamic dif-fusion and volumetry in temporal lobe epilepsy with and withoutmesial temporal sclerosis. Epilepsy Res. 80, 184—193.

ovindan, R.M., Makki, M.I., Sundaram, S.K., Juhasz, C.,Chugani, H.T., 2008. Diffusion tensor analysis of temporal andextra-temporal lobe tracts in temporal lobe epilepsy. EpilepsyRes. 80, 30—41.

ross, D.W., Concha, L., Beaulieu, C., 2006. Extratemporal whitematter abnormalities in mesial temporal lobe epilepsy demon-strated with diffusion tensor imaging. Epilepsia 47, 1360—1363.

arris, G., Andreasen, N.C., Cizadlo, T., Bailey, J.M., Bockholt, H.J.,Magnotta, V.A., Arndt, S., 1999. Improving tissue classificationin MRI: a three-dimensional multispectral discriminant analy-sis method with automated training class selection. J. Comput.Assist. Tomogr. 23, 144—154.

asan, K.M., Kamali, A., Iftikhar, A., Kramer, L.A., Papanicolaou,A.C., Fletcher, J.M., Ewing-Cobbs, L., 2009. Diffusion tensortractography quantification of the human corpus callosum fiberpathways across the lifespan. Brain Res. 1249, 91—100.

ermann, B., Hansen, R., Seidenberg, M., Magnotta, V., O’Leary, D.,2003a. Neurodevelopmental vulnerability of the corpus callosumto childhood onset localization-related epilepsy. Neuroimage 18,284—292.

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

ermann, B.P., Seidenberg, M., Bell, B., Rutecki, P., Sheth,R., Sutula, T., Wendt, G., O’Leary, D., Magnotta, V., 2003b.Extratemporal quantitative MRI volumetrics and neuropsycho-logical function in temporal lobe epilepsy. J. Int. Neuropsychol.Soc. 9, 353—362.

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K

K

L

L

M

M

M

N

P

R

S

S

S

S

T

W

W

Y

Y

14

im, H., Piao, Z., Liu, P., Bingaman, W., Diehl, B., 2008. Secondarywhite matter degeneration of the corpus callosum in patientswith intractable temporal lobe epilepsy: a diffusion tensor imag-ing study. Epilepsy Res. 81, 136—142.

imiwada, T., Juhasz, C., Makki, M., Muzik, O., Chugani, D.C.,Asano, E., Chugani, H.T., 2006. Hippocampal and thalamic dif-fusion abnormalities in children with temporal lobe epilepsy.Epilepsia 47, 167—175.

in, J.J., Riley, J.D., Juranek, J., Cramer, S.C., 2008. Vulnerabilityof the frontal—temporal connections in temporal lobe epilepsy.Epilepsy Res. 82, 162—170.

ui, Y.W., Nusbaum, A.O., Barr, W.B., Johnson, G., Babb, J.S.,Orbach, D., Kim, A., Laliotis, G., Devinsky, O., 2005. Correla-tion of apparent diffusion coefficient with neuropsychologicaltesting in temporal lobe epilepsy. AJNR Am. J. Neuroradiol. 26,1832—1839.

cDonald, C.R., Ahmadi, M.E., Hagler, D.J., Tecoma, E.S., Iragui,V.J., Gharapetian, L., Dale, A.M., Halgren, E., 2008. Diffusiontensor imaging correlates of memory and language impairmentsin temporal lobe epilepsy. Neurology 71, 1869—1876.

cMillan, A.B., Hermann, B.P., Johnson, S.C., Hansen, R.R., Sei-denberg, M., Meyerand, M.E., 2004. Voxel-based morphometryof unilateral temporal lobe epilepsy reveals abnormalities incerebral white matter. Neuroimage 23, 167—174.

ueller, S.G., Laxer, K.D., Cashdollar, N., Buckley, S., Paul, C.,Weiner, M.W., 2006. Voxel-based optimized morphometry (VBM)of gray and white matter in temporal lobe epilepsy (TLE) withand without mesial temporal sclerosis. Epilepsia 47, 900—907.

ilsson, D., Go, C., Rutka, J.T., Rydenhag, B., Mabbott, D.J., Snead3rd, O.C., Raybaud, C.R., Widjaja, E., 2008. Bilateral diffusiontensor abnormalities of temporal lobe and cingulate gyrus whitematter in children with temporal lobe epilepsy. Epilepsy Res. 81,128—135.

owell, H.W., Parker, G.J., Alexander, D.C., Symms, M.R., Boulby,P.A., Wheeler-Kingshott, C.A., Barker, G.J., Koepp, M.J., Dun-can, J.S., 2007. Abnormalities of language networks in temporal

lobe epilepsy. Neuroimage 36, 209—221.

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 temporal lobeepilepsy. Initial findings. Eur. Radiol. 17, 1663—1668.

Y

E. Hutchinson et al.

eidenberg, M., Kelly, K.G., Parrish, J., Geary, E., Dow, C., Rutecki,P., Hermann, B., 2005. Ipsilateral and contralateral MRI volumet-ric abnormalities in chronic unilateral temporal lobe epilepsyand their clinical correlates. Epilepsia 46, 420—430.

mith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F.,Behrens, T.E., Johansen-Berg, H., Bannister, P.R., De Luca, M.,Drobnjak, I., Flitney, D.E., Niazy, R.K., Saunders, J., Vickers,J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M.,2004. Advances in functional and structural MR image anal-ysis and implementation as FSL. Neuroimage 23 (Suppl. 1),S208—219.

nook, L., Paulson, L.A., Roy, D., Phillips, L., Beaulieu, C., 2005.Diffusion tensor imaging of neurodevelopment in children andyoung adults. Neuroimage 26, 1164—1173.

ong, S.K., Yoshino, J., Le, T.Q., Lin, S.J., Sun, S.W., Cross,A.H., Armstrong, R.C., 2005. Demyelination increases radialdiffusivity in corpus callosum of mouse brain. Neuroimage 26,132—140.

hivard, L., Lehericy, S., Krainik, A., Adam, C., Dormont, D., Chi-ras, J., Baulac, M., Dupont, S., 2005. Diffusion tensor imagingin medial temporal lobe epilepsy with hippocampal sclerosis.Neuroimage 28, 682—690.

eber, B., Luders, E., Faber, J., Richter, S., Quesada, C.M., Urbach,H., Thompson, P.M., Toga, A.W., Elger, C.E., Helmstaedter, C.,2007. Distinct regional atrophy in the corpus callosum of patientswith temporal lobe epilepsy. Brain 130, 3149—3154.

itelson, S.F., 1989. Hand and sex differences in the isthmus andgenu of the human corpus callosum. A postmortem morphologi-cal study. Brain 112 (Pt 3), 799—835.

ogarajah, M., Powell, H.W., Parker, G.J., Alexander, D.C., Thomp-son, P.J., Symms, M.R., Boulby, P., Wheeler-Kingshott, C.A.,Barker, G.J., Koepp, M.J., Duncan, J.S., 2008. Tractographyof the parahippocampal gyrus and material specific memoryimpairment in unilateral temporal lobe epilepsy. Neuroimage 40,1755—1764.

ogarajah, M., Duncan, J.S., 2008. Diffusion-based magnetic res-

onance imaging and tractography in epilepsy. Epilepsia 49,189—200.

u, A., Li, K., Li, L., Shan, B., Wang, Y., Xue, S., 2008. Whole-brainvoxel-based morphometry of white matter in medial temporallobe epilepsy. Eur. J. Radiol. 65, 86—90.