White matter development in children with benign childhood epilepsy with centro-temporal spikes

BRAINA JOURNAL OF NEUROLOGY

White matter development in children with benignchildhood epilepsy with centro-temporal spikesCarolina Ciumas,1,* Mani Saignavongs,1,* Faustine Ilski,1,2 Vania Herbillon,2 Agathe Laurent,1,2

Amelie Lothe,1 Rolf A. Heckemann,3,4 Julitta de Bellescize,2 Eleni Panagiotakaki,2

Salem Hannoun,5 Dominique Sappey Marinier,5,6 Alexandra Montavont,1,2,7

Karine Ostrowsky-Coste,2 Nathalie Bedoin8,9 and Philippe Ryvlin1,2,7

1 INSERM U1028, CNRS UMR5292, Centre de Recherche en Neuroscience de Lyon, Translational and Integrative Group in Epilepsy Research

(TIGER), Universite Lyon-1, Lyon, France

2 Department of Epilepsy, Sleep and Pediatric Neurophysiology, Hospices Civils de Lyon, Lyon, France

3 The Neurodis Foundation, Lyon, France

4 Institute for Neuroscience and Physiology, University of Gothenburg, Sweden

5 CREATIS-CNRS UMR 5220 & INSERM U1044, Lyon, France

6 CERMEP – Imagerie du Vivant, Lyon, France

7 Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon, France

8 Laboratoire Dynamique du Langage, CNRS UMR 5596, Lyon, France

9 Universite Lyon 2, Lyon, France

*These authors equally contributed to this work.

Correspondence to: Carolina Ciumas, MD, PhD,

TIGER, Translational and Integrative Group in Epilepsy Research,

INSERM U1028, CNRS UMR5292, Centre de Recherche en Neuroscience de Lyon,

Centre Hospitalier Le Vinatier, Batiment 452, 95 Boulevard Pinel,

69500 Bron, France

E-mail: [email protected]

Benign childhood epilepsy with centro-temporal spikes (BCECTS) is a unique form of non-lesional age-dependent epilepsy with

rare seizures, focal electroencepalographic abnormalities affecting the same well delineated cortical region in most patients, and

frequent mild to moderate cognitive dysfunctions. In this condition, it is hypothesized that interictal electroencepalographic

discharges might interfere with local brain maturation, resulting in altered cognition. Diffusion tensor imaging allows testing of

this hypothesis by investigating the white matter microstructure, and has previously proved sensitive to epilepsy-related alter-

ations of fractional anisotropy and diffusivity. However, no diffusion tensor imaging study has yet been performed with a focus

on BCECTS. We investigated 25 children suffering from BCECTS and 25 age-matched control subjects using diffusion tensor

imaging, 3D-T1 magnetic resonance imaging, and a battery of neuropsychological tests including Conner’s scale and Wechsler

Intelligence Scale for Children (fourth revision). Electroencephalography was also performed in all patients within 2 months of

the magnetic resonance imaging assessment. Parametric maps of fractional anisotropy, mean-, radial-, and axial diffusivity were

extracted from diffusion tensor imaging data. Patients were compared with control subjects using voxel-based statistics and

family-wise error correction for multiple comparisons. Each patient was also compared to control subjects. Fractional anisotropy

and diffusivity images were correlated to neuropsychological and clinical variables. Group analysis showed significantly reduced

fractional anisotropy and increased diffusivity in patients compared with control subjects, predominantly over the left pre- and

postcentral gyri and ipsilateral to the electroencephalographic focus. At the individual level, regions of significant differences

were observed in 10 patients (40%) for anisotropy (eight reduced fractional anisotropy, one increased fractional anisotropy,

doi:10.1093/brain/awu039 Brain 2014: of 12 | 1

Received August 23, 2013. Revised December 26, 2013. Accepted January 14, 2014.

� The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.

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one both), and 17 (56%) for diffusivity (13 increased, one reduced, three both). There were significant negative correlations

between fractional anisotropy maps and duration of epilepsy in the precentral gyri, bilaterally, and in the left postcentral gyrus.

Accordingly, 9 of 12 patients (75%) with duration of epilepsy 412 months showed significantly reduced fractional anisotropy

versus none of the 13 patients with duration of epilepsy 412 months. Diffusivity maps positively correlated with duration of

epilepsy in the cuneus. Children with BCECTS demonstrate alterations in the microstructure of the white matter, undetectable

with conventional magnetic resonance imaging, predominating over the regions displaying chronic interictal epileptiform dis-

charges. The association observed between diffusion tensor imaging changes, duration of epilepsy and cognitive performance

appears compatible with the hypothesis that interictal epileptic activity alters brain maturation, which could in turn lead to

cognitive dysfunction. However, such cross-sectional association does not demonstrate causality, and other hitherto unidentified

factors could represent the common cause to part or all of the observed findings.

Keywords: BCECTS; rolandic epilepsy; DTI, diffusion

Abbreviations: BCECTS = benign childhood epilepsy with centro-temporal spikes; DTI = diffusion tensor imaging; SPM = statisticalparametric mapping; WISC = Wechsler Intelligence Scale for Children

IntroductionThere is increasing evidence that childhood epilepsies are asso-

ciated with abnormal developmental trajectory of the brain

(Saito et al., 2008; Kanemura and Aihara, 2009; Kanemura

et al., 2011; Tosun et al., 2011), particularly of its white matter

(Hutchinson et al., 2010; Tosun et al., 2011; Widjaja et al., 2012).

Diffusion tensor imaging (DTI) enables investigations into this

issue, and was successfully applied to the study of brain develop-

ment in healthy children (Neil et al., 2002), as well as in autism

(Alexander et al., 2007; Ben Bashat et al., 2007; Langen et al.,

2012) and attention deficit hyperactivity disorder (Davenport

et al., 2010). In temporal lobe epilepsy, DTI studies have shown

large white matter abnormalities in regions supposedly participat-

ing in the epileptogenic network, either unilaterally (Arfanakis

et al., 2002; Thivard et al., 2005) or bilaterally (Concha et al.,

2005). Two recent DTI studies also reported white matter changes

in a mixed population of children with new onset generalized and

partial epilepsies (Hutchinson et al., 2010; Widjaja et al., 2012).

One of these series reported decreased fractional anisotropy and

increased radial diffusivity in the posterior callosum and cingulum

(Hutchinson et al., 2010), whereas the other reported elevated

radial diffusivity in the bilateral posterior cingulum, increased

axial diffusivity in the left middle frontal, reduced axial diffusivity

in the left temporal, right parietal and right supramarginalis white

matter in partial epilepsy (Widjaja et al., 2012). To date no DTI

study has concentrated on patients with localization-related idio-

pathic epilepsy, and more specifically benign childhood epilepsy

with centro-temporal spikes (BCECTS).

BCECTS is often viewed as a developmental disease, character-

ized by an age-dependent onset, typically between 3 and 13

years, a male to female predominance, a genetic predisposition

and recovery during adolescence (ILAE, 1989; Panayiotopoulos,

2005). EEG typically shows focal medium to high-voltage

spikes or spike and waves over the centro-temporal region, usu-

ally unilaterally that may shift from side to side over time or

bilaterally.

Although considered benign, BCECTS is often associated with

cognitive disturbances thought to reflect the interference between

the epileptic focus and brain development. Both the age depend-

ence and cognitive abnormalities observed in BCECTS strongly

suggest the possibility of altered maturation of brain white

matter. To test this hypothesis, we undertook a DTI study in a

homogenous group of children with BCECTS and age-matched

control subjects. We also correlated the presence of DTI abnorm-

alities with several clinical and cognitive variables.

Materials and methods

ParticipantsWe included 25 patients with BCECTS aged 6–13 [mean age � stand-

ard deviation (SD) 9.6 � 1.9 years] and 25 healthy volunteers aged

6–16 (mean age � SD, 10.0 � 3.0 years), all attending regular schools.

There were no significant differences between patients and control

subjects in age P = 0.60 or gender (BCECTS = 72% males; control sub-

jects = 56% males; P = 0.34). Our selection criteria included: (i) diag-

nosed with BCECTS according to the current diagnostic criteria (ILAE,

1989); (ii) no anti-epileptic drug for 424 months; (iii) no other neuro-

logical disease; and (iv) normal MRI if available before inclusion.

Patients with no previously performed MRI who fulfilled all other cri-

teria were included in the protocol, but not in data analysis if

T1-weighted and DTI magnetic resonance images demonstrated sig-

nificant abnormalities at visual inspection.

Patients were recruited through our paediatric epilepsy outpatient

clinic and diagnosed on the basis of all available clinical and EEG data

by a paediatric epileptologist (J.D.B., E.P., K.O-C). Twenty patients

were right-handed, three left-handed, two ambidextrous; 19 control

subjects were right-handed, five left-handed and one ambidextrous.

The mean duration of epilepsy from onset to time of scanning was

20.7 months (SD = 20, min = 4, max = 84).

At the time of inclusion in the study, EEG spike foci were left-sided

in 14 patients, right-sided in two and bilateral in nine, including five

patients with left-side predominance. Fifteen patients had no anti-

epileptic drug, eight had one anti-epileptic drug, one had two anti-

epileptic drugs, and one had three anti-epileptic drugs. Three patients

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were treated with methylphenidate for co-morbid attention deficit

hyperactivity disorder, including one with no anti-epileptic drug treat-

ment (Table 1). Written consent to participate in the study was ob-

tained from the parents and from the children who could write. The

study was reviewed and approved by the local ethical committee.

Neuropsychological assessmentAll subjects were individually evaluated on the same day as the mag-

netic resonance examination. A comprehensive standardized neuropsy-

chological test battery was used, including a developmental screening

test providing measure of verbal and non-verbal intelligence, verbal-

auditory memory, visual processing speed, visual-spatial attention,

cognitive flexibility and inhibition. Some of the tests were performed

using a computer presentation, while others used printed material.

Adjusted norms were commercially available for all tests.

The Conners’ Parent Rating Scale (Conners, 1998, 1999; Conners

et al., 1998) was used to assess symptoms of attention deficit hyper-

activity disorder and other psychopathologies and behavioural prob-

lems. We included the six following subscales: hyperactivity/

impulsivity, psychosomatic, learning problems, indices of attention def-

icit hyperactivity disorder, anxiety, and conduct disorder. Individual

raw scores were converted into T-scores. The Wechsler Intelligence

Scale for Children (WISC-IV) and its four subscales (Wechsler, 1976;

Cooper, 1995; Weiss, 2006) were used to assess: (i) verbal

comprehension index; (ii) perceptual reasoning index; (iii) working

memory index; and (iv) processing speed index.

Magnetic resonance imaging

Data acquisition

MRI scans were performed without any sedation, and always in the

morning. Magnetic resonance images were acquired using a Siemens

Sonata whole-body 1.5 T MRI scanner with a Siemens Sonata body

coil. Three-dimensional T1 sequence imaging parameters were: sagittal

orientation, 160 slices, echo time = 3.55 ms, repetition time = 2400 ms,

inversion time = 1000 ms, field of view (in-plane) = 230 mm, flip

angle = 8�, slice thickness = 1.2 mm, voxel size = 1.2 � 1.2 � 1.2 mm3,

total scan time was 7.42 min. DTI images were acquired using the

following parameters: echo time = 86 ms, repetition time = 6900 ms,

field of view (in-plane) = 240 mm, voxel size = 2.5 � 2.5 � 2.5 mm3,

52 gradient images with four B0 volumes (1 B0) with no diffusion

sensitization (i.e. T2-weighted images) and 48 diffusion-weighted

images (b-value: 10 000 s/mm2) in 48 directions. The reconstruction

matrix was 96 � 96, voxel size 2.5 � 2.5 � 2.5 mm2, and total scan

time was 6.13 min.

Both DTI and T1-weighted data were visually inspected (C.C, Ph.R)

to detect artefacts arising from subject motion or scanner malfunction,

and confirm the lack of visually detectable abnormality.

Table 1 Patients’ data

Subjects Age Sex Side EEGepilepticfocus

Diseaseduration,m

Age atonset, y

AEDtreatment

Hyperactivitytreatment

Heredity Number ofseizures perlifetime

Lastseizure

Subgroup 1, `12 months of disease

1 10 y, 6 m M Bilateral T 84 3 LEV, VAL, LAM � � 20 3 m

2 9 y, 4 m M Bilateral C-T 60 5 VAL, CLO � + 20 1 m

3 12 y, 0 m M Left C-T 60 7 � � � 6 10 m

4 11 y, 2 m M Left C-T 34 8 CLO Methylphenidate � 5 3 m

5 11 y, 0 m F Left C-T 23 9 VAL � � 6 2 m

6 8 y, 7 m M Bilateral C-P and P-O 23 6 VAL � + 20 2 d

7 10 y, 2 m F Left C-T 20 8 � � + 2 11 m

8 9 y, 3 m F Right C-T 20 6 � � � 4 4 m

9 7 y, 9 m F Bilateral C-T-P 19 6 � � � � �

10 8 y, 5 m M Left C-T 18 7 � � + 2 5 m

11 7 y, 2 m M Left C 18 6 � � + 1 18 m

12 13 y, 9 m M Bilateral F-C 13 12 VAL � � 3 16 m

Subgroup 2, 412 months of disease

13 11 y, 11 m M Bilateral C, O 12 11 VAL Methylphenidate � 2 1 m

14 8 y, 0 m M Left C-T 12 7 � Methylphenidate � � �

15 9 y, 4 m F Left F 12 8 � � � 3 11 m

16 10 y, 2 m M Bilateral C-T 11 9 CLO � + 22 1 m

17 10 y, 10 m M Left F-C-T 10 10 � � � 1 4 m

18 8 y, 7 m M Bilateral C-T 10 7 � � + 3 9 m

19 6 y, 1 m F Left F-C-T 10 5 VAL � + 4 8 m

20 12 y, 7 m F Left F-C-T 8 12 � � + 1 8 m

21 6 y, 10 m M Right F-C-T 6 6 � � � 5 2 m

22 8 y, 4 m M Left C-T 5 7 � � + 1 1 m

23 10 y, 4 m M Left C-T 5 9 CBZ � � 2 4 m

24 7 y, 11 m M Bilateral C-T 4 7 � � � 1 4 m

25 11 y, 1 m M Left C-P 2 11 � � � 1 1 m

m = months; y = years; C-T = centro-temporal; T = temporal; C-P = centro-parietal; C-T-P = centro-temporo-parietal; F-C-T = fronto-centro-temporal; M = male;F = female; AED = antiepileptic drug; LEV = levetiracetam; VAL = sodium valproate; CLO = clobazam; CBZ = carbamazepine; LAM = lamotrigine.

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Image analysis

DTI is used to investigate the microstructural features of white matter,

and relies on the principle that water molecules diffuse mostly along

tissue boundaries rather than across them (Beaulieu, 2002; Chua et al.,

2008). This anisotropy of diffusion is increased in regions of high

axonal integrity and strong myelination, and decreased in regions

where white matter is less well organized (Basser, 1995). Fractional

anisotropy and mean diffusivity are two quantitative indices of diffu-

sion reflecting the integrity of the brain tissue (Basser and Pierpaoli,

1996; Pierpaoli et al., 1996). Two other DTI-derived indices were con-

sidered: axial diffusivity (largest eigenvalue corresponding to the dif-

fusivity of water in the direction parallel to the fibre bundles) and

radial diffusivity [average of the two smallest eigenvalues (L2 and

L3), which measures water diffusion perpendicular to the axonal

wall]. Decreased fractional anisotropy and increased mean-, axial-

and radial diffusivity values suggest alterations of white matter.

DTI and 3D T1 data were preprocessed using statistical parametric

mapping (SPM)8 (http://www.fil.ion.ucl.ac.uk/spm/software/spm8/),

Matlab 2009 (The MathWorks) and FSL diffusion toolbox 4.1.9

(http://www.fmrib.ox.ac.uk/fsl/) (Smith et al., 2004).

Distortions in the DTI raw images, induced by the eddy currents and

head motion, were corrected by affine co-registration to B0 image

using FSL’s ‘eddy current correction’ (Jenkinson and Smith, 2001).

Non-brain tissues were then removed using the Brain Extraction Tool

(http://fsl.fmrib.ox.ac.uk/fsl/bet2/) and a brain mask was generated

to further define the space in which diffusion parameters were calcu-

lated (Smith, 2002). A diffusion tensor model was computed at each

voxel providing maps of fractional anisotropy, mean- and axial diffu-

sivity using DTIFIT (http://www.fmrib.ox.ac.uk/fsl/fdt/fdt_dtifit.html).

Radial diffusivity maps were calculated by averaging the second and

third eigenvalues (L2 and L3) maps using fslmaths. To create a custo-

mized template, B0 images from the control group were normalized to

the T2 template in SPM8, and estimated parameters were applied to

corresponding diffusion maps (fractional anisotropy, mean-, axial- and

radial diffusivity). The resulting maps, converted into MNI space, were

averaged to form an initial template, and smoothed to an

8 � 8 � 8 mm full-width at half-maximum Gaussian kernel (SPM rou-

tine template creation procedure). These study-specific templates for

fractional anisotropy and diffusion maps (mean-, axial- and radial dif-

fusivity) were used for the spatial normalization of all original maps,

following the standard procedure of SPM8. This involved minimizing a

least-square criterion of differences between the volume to be normal-

ized and the template volume, using a 12 parameters affine registra-

tion, before non-linear spatial normalization using 3D cosine-based

functions. Maps were then smoothed with a 10 � 10 � 10 mm full-

width at half maximum Gaussian kernel.

We also searched for differences in brain volume between groups

using an optimized procedure provided by VBM8 toolbox (Ch. Gaser,

University of Jena, Germany; http://dbm.neuro.uni-jena.de/vbm.html)

applied on the T1 images. Briefly, each image was bias corrected, op-

timally normalized using rigid-body transformation (with translation

and rotation only) and segmented using the ‘unified segmentation’

approach (Ashburner and Friston, 2000). The resulting modulated

grey and white matter images were smoothed with an 8-mm full-

width at half-maximum kernel. The total intracranial volume was auto-

matically calculated through VBM8 toolbox and corresponded to the

sum of the three tissue fractions: grey and white matter, and CSF. This

approach has been considered accurate for estimation of total intra-

cranial volume (Pengas et al., 2009).

Statistical analysis

Statistical parametric mapping analysis of diffusiontensor imaging data

Group analysis

Statistical analysis was performed using the general linear model as

implemented in SPM8. Between-group analyses were processed by

contrasting diffusion maps and adding both gender and age as cov-

ariates of no interest, as these measures might influence the structural

brain development (Good et al., 2001). The statistical maps were

thresholded at a level of P = 0.05 and corrected for multiple compari-

sons using the conservative SPM family-wise error (FWE), with cluster

size 55 voxels. We used an inclusive mask to restrict analyses to

voxels of white matter only. Stereotactic coordinates of significant

clusters are reported in MNI space.

Individual analysis

An individual analysis was conducted by comparing voxel-wise diffu-

sion values of each patient to those of the control group, using a two-

sample t-test (Group 1, one patient; Group 2, all control subjects). Age

and gender were used as covariates of no interest. We used a less

conservative statistical threshold than that used for the group analysis,

with a cut-off of P5 0.001 at the voxel level, and a correction

for multiple comparisons at the cluster level (P5 0.05). According

to the number of voxels tested within the explicit mask of interest

(white matter), significant clusters needed to include more than 150

voxels.

Statistical parametric mapping analysis of brain volume

Group analysis

We tested whether differences between groups’ global and regional

grey and white matter could account for diffusion findings. The global

values of the grey matter, white matter and CSF volumes were ob-

tained from non-normalized segmented images as well as regional

volumetric differences between groups and assessed on a voxel-by-

voxel basis. Voxel-based morphometry analysis was computed using

an analysis of covariance with age, gender and the total amount of

grey and white matter as confounders (ANCOVA).

Neuropsychological data analysis

Neuropsychological scores were compared between patients with

BCECTS and control subjects using an unpaired t-test. Statistical sig-

nificance was set to P5 0.005 to account for Bonferroni correction

and the number of test performed (n = 10).

Clinical correlations

Multiple regression analyses (SPM8) were conducted to examine the

relationship between diffusion maps and age (in patients and control

subjects), duration of disease, age of onset, total number of seizures

reported in seizure diary since onset of epilepsy, time since last seizure,

presence of anti-epileptic drug treatment at the time of the study, and

all cognitive scores. These analyses were conducted using masks cor-

responding to the significant clusters resulting from the SPM group

analysis of the DTI data.


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http://www.fmrib.ox.ac.uk/fsl/

http://fsl.fmrib.ox.ac.uk/fsl/bet2/

http://www.fmrib.ox.ac.uk/fsl/fdt/fdt_dtifit.html

http://dbm.neuro.uni-jena.de/vbm.html



ResultsVoxel-based analysis of diffusion tensorimagingPatients showed significant differences as compared to control sub-

jects on all four DTI parametric images, using the most conservative

family-wise error corrected P-value50.05 (Table 2 and Fig. 1).

Specifically, patients demonstrated: (i) decreased fractional aniso-

tropy in the left pre- and postcentral gyri; (ii) increased mean dif-

fusivity over the left pre- and postcentral gyri, left middle frontal

gyrus, left inferior parietal lobule, left and right cuneus, right middle

and medial frontal gyri; (iii) increased axial diffusivity over the left

precentral gyrus, left superior parietal and paracentral lobule and

right anterior cingulate gyrus; and (iv) increased radial diffusivity

in the left pre- and postcentral gyri, left medial frontal gyrus, left

inferior parietal lobule, left precuneus and right anterior and poster-

ior cingulate gyri. No increases in fractional anisotropy or decreases

in mean-, axial-, and radial diffusivity were detected in patients.

Single subject statistical parametricmapping analysisComparisons of each of the 25 patients with the control group

also showed significant abnormalities in patients at the individual

level, with decreased fractional anisotropy in nine (36%),

increased mean diffusivity in 13 (52%), increased axial diffusivity

in 11 (44%) and increased radial diffusivity in 13 (52%)

(Supplementary Table 1). Although the abnormal pattern of

decreased anisotropy and increased diffusivity varied among pa-

tients, clusters were mostly located in the frontal and parietal

lobes, always predominating ipsilateral to, and often localized in

the same brain region as, the main EEG focus. Apart from the

corpus callosum, which was affected in almost all patients showing

significant clusters, the main abnormal area was the operculoinsu-

lar region. Increased diffusivity was generally more extensive than

decreased fractional anisotropy, and accounted for all abnormal-

ities discordant with the side of the EEG focus (Supplementary

Table 1 and Supplementary Fig. 1A–D).

A few abnormalities were observed in individual patients in the

opposite direction as the pattern described above, i.e. increased

fractional anisotropy and decreased diffusivity. Increased fractional

anisotropy was detected in two patients, one of whom also

showed significantly decreased fractional anisotropy over the

region of his EEG focus. Decreased diffusivity was found in four

patients, mostly affecting the cuneus in two, left parietal lobule in

one and the frontal lobes in the other.

Comparisons between each of the healthy children and the rest

of the control group showed significant clusters at the individual

level, with decreased fractional anisotropy in three control subjects

Table 2 Group comparisons

Left/Right Area of white matter P-value K Z-score x, y, z

Fractional anisotropy maps, control subjects versus BCECTS

Left Precentral gyrus 0.003 28 4.90 �48, �4, 24

Left Postcentral gyrus 0.012 11 4.66 �46, �30, 36

Mean diffusivity maps, BCECTS versus control subjects


Right Postcentral gyrus 0.006 63 5.09 24, �36, 58

Left Cuneus 0.004 91 5.04 �24, �80, 20

Right Medial frontal gyrus 0.008 54 4.96 12, �24, 64

Right Cuneus 0.007 56 4.82 22, �68, 18

Left Middle frontal gyrus 0.011 41 4.6 �34, 36, 16

Left Inferior parietal lobule 0.018 23 4.54 �54, �24, 28

Right Cuneus 0.033 6 4.39 20, �86, 20

Right Middle frontal gyrus 0.031 7 4.37 0, �36, 10

Axial diffusivity maps, BCECTS versus control subjects


Left Superior parietal lobule 0.023 9 4.67 �22, �48, 58

Right Cingulate gyrus 0.010 26 4.62 18, 6, 24

Left Paracentral lobule 0.010 26 4.57 �10, �32, 58

Radial diffusivity maps, BCECTS versus control subjects



Left Inferior parietal lobule 0.004 51 5.18 �52, �24, 28

Left Medial frontal gyrus 0.001 82 4.91 �10, �2, 52

Left Precuneus 0.025 7 4.65 �32, �66, 34


Right Posterior cingulate 0.013 18 4.55 28, �62, 22

Right Cingulate gyrus 0.015 16 4.52 16, 8, 26

FWE P = 0.05, k = 5; x, y, z = coordinates for the local maxima of the cluster.


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(12%), increased mean-, radial- and axial diffusivity also in three

control subjects (12%), and a combination of increased fractional

anisotropy and decreased diffusivity in one subject (4%). Clusters

were smaller than those detected in patients and were located in

the right posterior cingulate, left internal capsule, left cuneus and

left inferior parietal lobule.

Volumetric measurementsThere were no significant differences in the global brain vol-

ume (total grey matter, total white matter, and total intracranial

volume) between patients and control subjects (Supplementary

Table 2). Similarly, SPM analysis of covariance showed no signifi-

cant group difference for grey or white matter in any region.

Results of neuropsychologicalassessmentPatients with BCECTS showed significantly lower performance

than control subjects at the Conner’s scale for the following

scores: hyperactivity/impulsivity (BCECTS 60.1 � 12 versus control

subjects 43.1 � 8.1; P = 0.0036) and indices of attention deficit

hyperactivity disorder (BCECTS 59.7 � 10 versus control subjects

42.7 � 7.3; P = 0.00043). A non-significant trend towards abnor-

mal scores for conduct disorder (mean � SD, BCECTS 52.6 � 13.7

versus control subjects 45.1 � 6.9; P = 0.0092) and learning prob-

lems (BCECTS 56.7 � 10.7 versus control subjects 44.2 � 9.5;

P = 0.0054) was also observed.

Patients with BCECTS showed significantly lower performance

than control subjects on the following WISC-IV subscales

(Table 3): verbal comprehension index (mean � SD, BCECTS

95.3 � 18.9 versus control subjects 115.5 � 14.6, P = 0.0039)

and processing speed index (BCECTS 86.2 � 18.2 versus control

subjects 113.8 � 14.2, P = 0.0004), with a non-significant trend

for working memory index (BCECTS 89.9 � 15.9 versus control

subjects 103.2 � 14.3, P = 0.028). No differences between

groups were observed for the perceptual reasoning index subscale

and for the individual memory tests.

Some individual patients also showed scores below two standard

deviations from the normal range: one patient on verbal

Figure 1 Group comparison. Regions of significant changes in the anisotropy and diffusivity maps. Clusters are superimposed on the

study-specific unsmoothed templates for fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD).

Coordinates (x, y, z) show the slice on the horizontal, bottom left (x), sagittal top left (y) and coronal, top right (z) view of each template

set. The colour bar shows the T-score and the magnitude of statistical difference.


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comprehension index (4%), two on working memory index (8%)

and four on processing speed index (16%) subscales. In addition,

four patients with BCECTS (16%) had a delay in oral or written

language, six (24%) had an attention disorder and one (4%) suf-

fered from dyspraxia.

Correlation analysis

Neuropsychological tests

Fractional anisotropy in the postcentral gyrus negatively correlated

with anxiety and learning scores of the Conner’s scale (Table 4),

whereas it positively correlated with the processing speed index

(WISC-IV) in the precentral gyrus. Both correlations indicated

greater fractional anisotropy abnormalities in patients with lower

cognitive performance. Diffusivity maps showed no correlation

with the neuropsychological tests.

Clinical variables

Fractional anisotropy correlated positively with age of onset and

negatively with duration of epilepsy, indicating more abnormalities

in patients with long duration of epilepsy Fig. 2. The correlation

with age of onset was observed in the left and right precentral

gyri, left parietal superior lobule and right medial frontal and cin-

gulate gyri, whereas the correlation with duration of epilepsy was

noted in the left pre- and postcentral gyri, right precentral gyrus

and parietal lobe white matter. Mean diffusivity also positively

correlated with duration of epilepsy in the right cuneus (Fig. 2,

Supplementary Table 3), again indicating more abnormalities in

patients with longer disease duration. All diffusivity parameters,

but not fractional anisotropy, positively correlated with the

number of anti-epileptic drugs, primarily over the cuneus and fu-

siform gyri (Supplementary Table 3). No correlations were

observed in the opposite directions.

Post hoc analyses were performed to further investigate the

impact of duration of epilepsy on anisotropy, diffusivity, and cog-

nitive performance. We separated patients with BCECTS into those

whose duration of epilepsy was 412 months (Subgroup 1, n = 12,

mean age � SD 9.9 � 1.9 years, four females) and those whose

duration of epilepsy was 412 months (Subgroup 2, n = 13, mean

age � SD 9.2 � 2.0 years, three females) (Table 1). There was no

significant difference between these two subgroups for age

(P = 0.33) or gender (P = 0.59). Subgroup 1 showed significantly

reduced fractional anisotropy and increased diffusivity values as

compared to control subjects over the left pre- and postcentral

gyri, as well as in many other brain regions, primarily left-sided

(Supplementary Table 4). Conversely, Subgroup 2 did not show

any significant difference compared to control subjects. Significant

differences were also observed when directly comparing the two

subgroups. Subgroup 1 demonstrated lower fractional anisotropy

than Subgroup 2 in the cingulate gyrus, anterior corpus callosum,

and precuneus, all bilaterally, as well as over the left inferior fron-

tal and left transverse gyri, the right superior and medial frontal

Table 3 Neuropsychological testing results

Controls, mean � SD BCECTS, mean � SD P-value

The Conners’ Parent Rating Scale

Hyperactivity/impulsivity 43.1 � 8.1 60.1 � 12 0.0036*

Psychosomatic 50.1 � 12.8 66.3 � 14.6 0.1

Learning Problems 44.2 � 9.5 56.7 � 10.7 0.0054

Indices of ADHD 42.7 � 7.3 59.7 � 10 0.0004*

Anxiety 51.6 � 9.5 55.1 � 10.4 0.3

Conduct Disorder 45.1 � 6.9 52.6 � 13.7 0.0092

The Wechsler Intelligence Scale for Children

Verbal comprehension index 115.5 � 14.6 95.3 � 18.9 0.0039*

Perceptual reasoning index 106.3 � 11.4 99.4 � 13.8 0.16

Working memory index 103.2 � 14.3 89.9 � 15.9 0.028

Processing speed index 113.8 � 14.2 86.2 � 18.2 0.0004*

ADHD = attention deficit hyperactivity disorder; *P50.005 after Bonferroni correction.

Table 4 Correlation with neuropsychological variables in BCECTS

Left/right Area of white matter P-value k Z-score x, y, z

Negative correlation with anxiety score, Conner’s scale and fractional anisotropy maps


Negative correlation with learning score, Conner’s scale and fractional anisotropy maps


Positive correlation with processing speed index, fractional anisotropy maps, duration of epilepsy fractional anisotropy

Right Precentral gyrus 0.044 6 3.22 28, �18, 52

FWE P5 0.05, k = 5; x, y, z = coordinates for the local maxima of the cluster.


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gyri, and the right parahippocampal and fusiform gyri. Subgroup 1

also showed greater diffusivity than Subgroup 2 in the frontal

lobes. These abnormalities were more diffuse for mean diffusivity

than for axial- and radial diffusivity. Accordingly, all individual

patients showing significant fractional anisotropy abnormalities,

and the majority of those with significant mean-, axial-, or radial

diffusivity abnormalities, belonged to Subgroup 1 (Supplementary

Table 4).

Neuropsychological tests also proved to distinguish the two sub-

groups, with only Subgroup 1 showing significantly worse per-

formance than control subjects on the processing speed index

subscale of the WISC-IV (mean � SD, BCECTS 79.3 � 16.2

versus control subjects 110.9 � 14.9; P = 0.0026) whereas for

conduct disorder score of the Conner’s scale (BCECTS

61.3 � 17.2 versus control subjects 43.2 � 5, P = 0.048), and

the two following subscales of the WISC-IV: verbal comprehen-

sion index (BCECTS 91.1 � 17.9 versus control subjects

117.9 � 12.2, P = 0.008) and perceptual reasoning index

(BCECTS 91.7 � 7.5 versus control subjects 106.6 � 10.1;

P = 0.01) there was an apparent trend towards significance.

DiscussionThe present study found alterations in the microstructure of the

white matter in brain regions affected by the epileptic focus in

children with BCECTS, both at the group and individual levels.

Abnormalities in fractional anisotropy and diffusivity predominated

over the left pre- and postcentral gyri, with changes in diffusivity

extending to adjacent brain regions. These alterations in the

microstructure of the white matter were more marked in patients

with longer duration of epilepsy and in those with worse cognitive

performance, and were neither confounded by age nor by grey

and white matter volumes. Although such cross-sectional findings

cannot demonstrate causality, co-localization of EEG foci and DTI

changes in BCECTS is consistent with the hypothesis that the two

disorders might be directly related to each other, though one

cannot exclude a third common causal factor. The greater DTI

abnormalities observed with longer duration of epilepsy also sup-

ports the view that chronic interictal epileptiform discharges might

lead to microstructural alterations of the white matter, rather than

the opposite, an issue that will be later addressed by the ongoing

longitudinal follow-up of our study population.

Normal brain developmentIt has been shown that fractional anisotropy and mean diffusivity

follow exponential age trajectories (Schmithorst et al., 2002; Taki

et al., 2012). Mature white matter tracts demonstrate high aniso-

tropy values that have been linked to myelinization and brain

maturation (Schneider et al., 2004). Fractional anisotropy increases

from childhood to adulthood, reaches a peak between 20–42

years and then decreases during late adulthood at a rate slower

than the initial increase. Mean diffusivity follows an opposite dy-

namic, initially decreasing, reaching a minimum between 18–43

years, and then increasing at a slower rate (Lebel et al., 2012).

Furthermore, there is a difference in maturation between males

and females with, for example, the left superior longitudinal fas-

ciculus showing a slower rate of maturation in girls than in boys

(Taki et al., 2012). This should be noted as we had more male

subjects in the patient group (72%) than in the control group

(56%). Though not statistically significant, this difference was

partly taken into account by including gender as a covariate in

all group- and individual analyses. Furthermore, the differences

observed in healthy subjects point to higher fractional anisotropy

and lower mean diffusivity values in males than females (Lebel

et al., 2012). Given a greater proportion of males included in

our patient group compared with the control group, the expected

Figure 2 Correlation analysis between fractional anisotropy (FA) and mean diffusivity (MD) maps and the duration of epilepsy. Clusters

are superimposed on the study-specific unsmoothed templates.


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physiological gender differences in fractional anisotropy and mean

diffusivity bias our main finding toward the null (i.e. decreased

fractional anisotropy and increased mean diffusivity in patients).

Finally, these physiological differences are primarily observed over

the cingulum and superior longitudinal fasciculus (Lebel et al.,

2012), and not within the pre- and postcentral regions where

we detected our main abnormalities. Overall, it is unlikely that

the non-significant gender difference between our patients’ and

control populations have impacted our findings.

Diffusion changes in epilepsyDiffusion parameters might change over short periods of time in

patients with epilepsy as a consequence of seizures that are asso-

ciated with an immediate decrease in diffusion, followed by an

increase after 72 h (Diehl et al., 2001; Salmenpera et al., 2006;

Yu and Tan, 2008). However, most DTI studies in epilepsy have

concentrated on interictal findings, when diffusion is typically

increased (Thivard et al., 2006). This is particularly relevant for

BCECTS where the occurrence of seizures is very rare, from one

to six over the entire duration of the disease in 70% of cases

(Rosser, 2007). Interictal DTI studies in children with epilepsy

have mostly been performed in temporal lobe epilepsy, showing

reduction of fractional anisotropy in the hippocampus (Kimiwada

et al., 2006) and in the white matter tracts ipsilateral to left tem-

poral foci (Govindan et al., 2008), or increased mean-, axial- and

radial diffusivity in the temporal and cingulate white matter (Lee

et al., 2004; Nilsson et al., 2008). Similarly, the coefficient of

diffusion (ACD), which is often used interchangeably with mean

diffusivity, increased interictally in the epileptogenic hippocampus

of adult patients with temporal lobe epilepsy (Kantarci et al.,

2002; Duzel et al., 2004). Abnormalities of fractional anisotropy

and mean diffusivity have also been reported in malformations of

cortical development, as well as within normal appearing epileptic

tissue (Eriksson et al., 2001; Rugg-Gunn et al., 2001). A few

studies have addressed the issue of white matter integrity in chil-

dren with new onset epilepsy in relation to cognitive development

(Hutchinson et al., 2010; Widjaja et al., 2012). These studies

included populations of mixed epilepsy syndromes, including

only a minority of BCECTS [four in Hutchinson et al. (2010) and

none mentioned in Widjaja et al. (2012)], and reported decreased

fractional anisotropy and increased radial diffusivity in the poster-

ior corpus callosum (Hutchinson et al., 2010), increased radial dif-

fusivity in the cingulum (Hutchinson et al., 2010; Widjaja et al.,

2012), and increased axial diffusivity in the left middle frontal

region (Widjaja et al., 2012). Although many of our patients

demonstrated abnormalities in the corpus callosum, it is interesting

to note that this region failed to show significant difference at the

group level, a finding that might reflect the variation in the portion

of the corpus callosum affected in each patient.

Our data in BCECTS showed localized regions of white matter

alteration in brain regions that have not been reported in previous

studies. Specifically, the white matter in the left pre- and postcen-

tral gyri demonstrated abnormalities in all four diffusion maps con-

sistent with the usual location of the epileptic foci delineated in

BCECTS (Baumgartner et al., 1996; Pataraia et al., 2008).

Accordingly, functional MRI of epileptic spikes in BCECTS patients

have shown increased BOLD responses in the face area around

the central sulcus (Archer et al., 2003). The left side predominance

of DTI abnormalities reported in our study appears likely to reflect

the fact that the majority of our patients had a left-sided EEG

focus. Indeed, diffusion changes ipsilateral to the epileptic focus

have been observed in other epileptic syndromes (Kimiwada et al.,

2006; Govindan et al., 2008; Meng et al., 2010). The group

findings observed in our BCECTS population were strengthened

by similar observations at the individual level in many patients.

The interindividual variability did not seem to reflect the potential

impact of seizures on diffusion, given that there was no correlation

between DTI changes and seizure frequency, and MRI was per-

formed 42 days after the last seizure in all patients, and 41

month in 88% of patients. All of our patients had a typical EEG

pattern for BCECTS and underwent an EEG investigation within

the 2 months preceding the MRI examination. However, EEG

abnormalities in BCECTS might shift from one side to the other

according to a dynamic that might not have been captured by

available EEGs, possibly accounting for the few discordances

observed between the lateralization of DTI and EEG abnormalities

at the individual level.

In principle, alterations of the white matter microstructure milieu

might result from small vessel alterations, loss of axonal structure,

or gliosis, all of which reduce directional diffusion (Niquet et al.,

1995; Oberheim et al., 2008). In non-lesional epilepsy, decreased

fractional anisotropy is usually considered a reflection of the dis-

ruption of axonal integrity, and more specifically, of altered dens-

ity or organization of fibres. However, it may also reflect myelin

abnormalities. Increased diffusivity (mean, axial and radial) is usu-

ally thought to result from decreased tissue density, reflecting cell

loss of both neurons and glia, or, as described in developmental

studies, a delayed maturation. This latter hypothesis seems most

likely in BCECTS, which age-dependent onset and remission has

long suggested an underlying abnormality of brain maturation

(Panayiotopoulos et al., 2008).

The possibility that brain regions generating centro-temporal

spikes also suffer delayed maturation in BCECTS raises speculative

hypotheses on the relation between these two abnormalities. One

hypothesis would be that both EEG and DTI findings derive from

another common factor, such as a genetic predisposition to cor-

tical hyperexcitability and delayed maturation. The genetic back-

ground of BCECTS is a complex issue however, with some twin

data suggesting that the typical form of this idiopathic syndrome is

not heavily dependent on traditional genetic factors (Vadlamudi

et al., 2004), whereas atypical forms of BCECTS with language

impairment are more likely to share a genetic origin. Indeed,

recent reports have disclosed various genes, such as SRPX2,

GRIN2A and ELP4, which mutation of could lead to such condi-

tions (Rudolf et al., 2009; Lesca et al., 2013). Another possibility

would be that delayed brain maturation, regardless of its mech-

anisms, would be responsible for the development of cortical

hyperexcitability and BCECTS electroclinical features. Conversely,

the centro-temporal spike focus could interfere with the biological

process underlying brain maturation, or produce irreversible alter-

ations of neuronal connectivity in the developing brain (Holmes

and Ben-Ari, 2001). In fact, all of the three above hypotheses

could be combined. The observation that duration of epilepsy


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was the main factor associated with interindividual DTI differences

in our BCECTS population does not necessarily help clarify this

issue, but is consistent with the view that delayed maturation

might partly result from prolonged interictal EEG abnormalities.

Indeed, all patients showing significantly decreased fractional an-

isotropy suffered from BCECTS for 41 year. Furthermore, the

longer the duration of the disease, the greater the reduction in

fractional anisotropy and increase in diffusivity around the left

central sulcus. Weaker associations were also found with age of

onset, most likely reflecting the role of epilepsy duration, given

that these two clinical variables strongly correlated with each

other. Alternatively, one might hypothesize that younger children

might be more sensitive to the deleterious impact of BCECTS on

brain maturation. Interestingly, adults, children and adolescents

with temporal lobe epilepsy also demonstrate fractional anisotropy

and axial diffusivity abnormalities that correlate with duration of

epilepsy, suggesting a causal role of the epileptic activity in pro-

moting DTI changes across epilepsy syndromes and age (Meng

et al., 2010; Keller et al., 2012). In these series, however, duration

of epilepsy might have been confounded by duration of treat-

ment, which is unlikely to be the case in our study. Indeed,

changes in fractional anisotropy did not correlate with the

number of anti-epileptic drug treatments, whereas diffusivity

maps only showed modest correlation with this parameter over

small sized clusters.

Despite its benign seizure outcome, BCECTS is often associated

with mild neuropsychological and/or learning disability such as low

academic achievement in reading, numeracy and/or spelling abil-

ity, drawing, visuo-spatial skills, attention, visuo-spatial memory

(Baglietto et al., 2001; Pinton et al., 2006; Goldberg-Stern

et al., 2010; Bedoin et al., 2012), cognitive flexibility, picture

naming, fluency, visuo-perceptual skills and visuo-motor coordin-

ation (Baglietto et al., 2001; Brancati et al., 2012). Patients also

exhibit difficulties in phonologic awareness that affects literacy,

with subsequent memory problems and poor academic perform-

ance (Northcott et al., 2005, 2007). Similar to these findings, our

patients had more learning difficulties at school and exhibited de-

ficient scores on neuropsychological scales testing attention hyper-

activity disorder, visuo-motor coordination, written and spoken

language, verbal reasoning, working memory and processing

speed. Some of these abnormalities were more pronounced in

patients with longer duration of epilepsy, including those detected

with the Conduct Disorders score of Conner’s Scale, and the

verbal comprehension index, perceptual reasoning index and pro-

cessing speed index subscales of the WISC-IV. Most importantly,

fractional anisotropy abnormalities correlated with the processing

speed index of WISC-IV and the anxiety and learning scores of the

Conner’s scale. This association between DTI abnormalities and

cognitive disturbances in BCECTS raises the same unsolved issue

as that discussed above regarding the respective role and impact

on cognition of an underlying genetic predisposition, interictal EEG

abnormalities and delayed maturation. Indeed, the cross-sectional

design of our study does not allow for a conclusion on causality

between these various abnormalities. The ongoing 5-year longitu-

dinal follow-up of our cohorts of patients with BCECTS and con-

trol children should help partly address this issue in the future.

Specifically, the dynamic of cognitive, EEG and DTI findings over

time will provide insights into their potential relationship. For ex-

ample, we might observe that children recruited in our study at

the onset of their epilepsy and who did not demonstrate delayed

maturation at that stage, will later develop such abnormalities,

supporting the view that interictal EEG discharges have an

impact on brain maturation. This view would also be supported

by the observation that brain maturation over the central region

may improve or normalize once the EEG focus has faded out.

Conversely, DTI abnormalities might prove stable over time, sug-

gesting a predisposing condition that would promote the develop-

ment of the more severe forms of BCECTS associated with

cognitive dysfunction. The possibility that such a predisposing con-

dition may be genetically driven might also be investigated in the

future since most of our patients underwent blood sampling for

this purpose.

The detection of DTI abnormalities at the individual level in

BCECTS patients raises the possibility of using such information

as a clinically useful biomarker. This would first require demon-

strating that this biomarker helps in assessing prognosis or decision

making regarding treatment, which is currently not the case.

Moreover, to detect DTI abnormalities at the individual level,

one needs to compare patients’ magnetic resonance images to a

normal database of age-matched children, ideally acquired on the

same scan. This represents a strong limitation to the development

of DTI for clinical purpose. Quantification of intraindividual DTI

changes over time, which is currently underway, might offer a

solution to this problem.

AcknowledgementsWe thank all subjects that took part in this research, as well as

their parents for the time spent during all evaluations. We are

indebted to many healthy volunteers families and teaching per-

sonnel from school "Sainte Marie Lyon les Maristes". In addition

we would like to thank Veronique Laplane for her invaluable help

with control subjects recruitment. We would also like to thank the

medical staff at CERMEP and nursing and secretary staff from the

Department of Epilepsy, Sleep and Pediatric Neurophysiology for

their collaboration and assistance and Danielle Ibarrola for tech-

nical support.

FundingThe study was supported by a grant from PICRI (Partenariats

Institution-Citoyens pour la Recherche et l’Innovation) Region

Ile-de-France, Epilepsie France, Fondation Le Roch-Les

Mousquetaires and Fondation IDEE. Dr Ciumas received financial

support from Fondation pour la Recherche Medicale. Dr Laurent

received financial support from PICRI. Dr Amelie Lothe and

Faustine Ilski received financial support from Fondation Le Roch-

Les Mousquetaires.

Supplementary materialSupplementary material is available at Brain online.


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White matter development in children with benign childhood epilepsy with centro-temporal spikes

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