Microstructural white matter abnormality and frontal cognitive dysfunctions in juvenile myoclonic epilepsy

Authors


  • J.H.K and S.I.S contributed equally to this work.

Address correspondence to Ji Hyun Kim, Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 152-703, Guro-dong gil 97, Guro-dong, Guro-gu, Seoul, Korea. E-mail: jhkim.merrf@gmail.com

Summary

Purpose:  Previous neuroimaging studies provide growing evidence that patients with juvenile myoclonic epilepsy (JME) have both structural and functional abnormalities of the thalamus and frontal lobe gray matter. However, limited data are available regarding the issue of white matter (WM) involvement, making the microstructural WM changes in JME largely unknown. In the present study we investigated changes of WM integrity in patients with JME, and their relationships with cognitive functions and epilepsy-specific clinical factors.

Methods:  We performed diffusion tensor imaging (DTI) and neuropsychological assessment in 25 patients with JME and 30 control subjects matched for age, gender, and education level. Between-group comparisons of fractional anisotropy (FA) and mean diffusivity (MD) were carried out in a whole-brain voxel-wise manner by using tract-based spatial statistics (TBSS). In addition, both FA and MD were correlated with cognitive performance and epilepsy-specific clinical variables to investigate the influence of these clinical and cognitive factors on WM integrity changes.

Key Findings:  Neuropsychological evaluation revealed that patients with JME had poorer performance than control subjects on most of the frontal function tests. TBSS demonstrated that, compared to controls, patients with JME had significantly reduced FA and increased MD in bilateral anterior and superior corona radiata, genu and body of corpus callosum, and multiple frontal WM tracts. Disease severity, as assessed by the number of generalized tonic–clonic seizures in given years, was negatively correlated with FA and positively correlated with MD extracted from regions of significant differences between patients and controls in TBSS.

Significance:  Our findings of widespread disturbance of microstructural WM integrity in the frontal lobe and corpus callosum that interconnects frontal cortices could further support the pathophysiologic hypothesis of thalamofrontal network abnormality in JME. These WM abnormalities may implicate frontal cognitive dysfunctions and disease progression in JME.

Juvenile myoclonic epilepsy (JME) represents a common subsyndrome of idiopathic generalized epilepsy (IGE), clinically characterized by myoclonic seizures on awakening, generalized tonic–clonic seizures (GTCS), and less frequently by absence seizures (Janz, 1985). Typical interictal electroencephalography (EEG) features of JME consist of 4–6 Hz, generalized spike-wave or polyspike-wave discharges (GSWDs), dominantly with frontocentral accentuation (Janz, 1985).

The fundamental pathogenesis of JME or IGE remains elusive; however, cumulative evidence over the decades has suggested that abnormal thalamocortical circuit plays a key role in the generation of GSWDs (Blumenfeld, 2005). Recent neuroimaging studies using positron emission tomography (PET), automated voxel-based morphometry (VBM), and magnetic resonance spectroscopy (MRS), have contributed greatly to the understanding of structural and functional changes in JME (Anderson & Hamandi, 2011). Among the functional imaging modalities, diffusion tensor imaging (DTI) is an advanced and noninvasive magnetic resonance imaging (MRI) technique that is sensitive to cerebral white matter (WM) architecture of the human brain, providing valuable information about integrity and fiber orientation of the WM tracts in vivo. The most widely used parameters derived from DTI are fractional anisotropy (FA) and mean diffusivity (MD), both of which can provide complementary information on subtle abnormalities of the WM microstructure in diverse neurologic and psychiatric disorders (Le Bihan et al., 2001). Tract-based spatial statistics (TBSS) is a novel analytic tool of DTI datasets and provides an observer-independent, automated whole-brain voxel-wise analysis of FA and MD without the need for restriction to a priori brain regions (Smith et al., 2006, 2007). It can circumvent the problems of cross-subject image registration and random selection of spatial smoothing factors in other voxel-based DTI analysis by making use of the intrinsic anisotropic property of white matter and projecting the FA values of the tracts onto a virtual skeleton that runs through the median part of the tract. TBSS, therefore, reliably improves sensitivity, objectivity, and interpretability of voxel-wise comparisons of the microstructural WM integrity between groups of subjects (Smith et al., 2006, 2007). Actually, a recent DTI study has shown that TBSS method is more sensitive than statistical parametric mapping (SPM) method in detecting WM abnormalities in patients with mesial temporal lobe epilepsy (Focke et al., 2008).

Whereas most of the previous neuroimaging studies focused on the gray matter (GM) changes in JME, limited DTI data are available to date, making the microstructural WM changes underlying JME largely unknown (Deppe et al., 2008; Vulliemoz et al., 2010). Only a few postmortem studies showed microscopic structural abnormalities (so-called “microdysgenesis”) in the frontal white matter in a small number of patients with IGE including JME (Meencke & Janz, 1984). However, these subtle abnormalities in IGE were not replicated in a controlled, blinded histologic study (Opeskin et al., 2000). In the present study, we utilize TBSS analysis of FA and MD in order to evaluate the location and extent of WM abnormalities in patients with JME as compared to control subjects. In addition, we correlated these WM changes with epilepsy-specific clinical variables including age of onset, disease duration, and seizure frequency, and cognitive measures to investigate the influence of the clinical and cognitive factors on WM integrity.

Methods

Subjects

We prospectively recruited 27 right-handed patients with JME who were followed at least 1 year in the outpatient epilepsy clinic of Korea University Guro Hospital. The diagnosis of JME was based on electroclinical criteria according to the International League Against Epilepsy (ILAE) classification, and the inclusion criteria we used were as follows: (1) unequivocal seizure semiology of JME—myoclonic seizure involving the bilateral upper extremities exclusively or preferentially occurring early in the morning, with or without GTCS or absence seizure; (2) any types of habitual seizure beginning from the age of puberty or early twenties; (3) no evidence of developmental and neurologic abnormalities, and global cognitive impairment on Mini-Mental State Examination (MMSE score ≥28/30) (Crum et al., 1993); (4) at least one EEG examination demonstrating typical GSWDs on a normal background; (5) neither abnormal nor unusual findings on conventional MRI. Patients with comorbid neurologic, psychiatric, or chronic systemic disorders were excluded. All patients were not taking any medications except antiepileptic drugs (AEDs) at the time of study inclusion. Demographic data and clinical information such as seizure semiology, age at seizure onset, duration of epilepsy, seizure frequency (number of GTCS per year), and current AEDs were obtained through interviews with the patients and their parents and reviews of medical records.

For group comparison, 31 right-handed healthy volunteers matched for age, gender, and education years were recruited to serve as control subjects. All control subjects underwent neurologic examination as well as a detailed interview to ensure that they had (1) no neurologic abnormality and global cognitive impairment (MMSE score ≥28/30) (Crum et al., 1993); (2) no history of neurological, psychiatric, or systemic disorders; (3) no family history of epilepsy; and (4) no history of alcohol or drug abuse before the study inclusion. Controls subjects with abnormal or unusual MRI findings were also excluded. The local ethics committee approved the study protocol, and all participants gave written informed consent prior to study inclusion.

Neuropsychological evaluation

Neuropsychological assessments were carried out by an experienced neuropsychologist (S.Y.P) who was blinded to the clinical diagnosis, on the same day of MRI scanning in all participants. Because patients with JME are known to have frontal lobe dysfunction (Devinsky et al., 1997; Piazzini et al., 2008; Pulsipher et al., 2009), the neuropsychological battery was more weighted on the frontal executive functions. Assessed domains and the tests were as follows: (1) global cognitive function—Korean version of Mini-Mental State Examination (MMSE); (2) attention and working memory—Trail-Making Test part A, Digit Span Test (forward and backward); and (3) executive function—Trail-Making Test part B, Stroop Color-Word Test (word, color, word-color), Letter Fluency Test (words beginning with the three Korean letters). Group comparisons of demographic data and neuropsychological measures were made using the student t-test and chi-square test. Results were considered to be significant at p < 0.05. Statistical analyses were performed using the SPSS software package version 12.0 (SPSS Inc, Chicago, IL, U.S.A.).

Magnetic resonance imaging acquisition

MRI examination was performed on a Siemens Trio 3T scanner (Erlangen, Germany) with a 12-channel phased array head coil. A single-shot spin-echo echoplanar imaging sequence was used for acquisition of DTI data. The scanning parameters were 30 noncollinear diffusion directions (b-value = 1,000 s/mm2) with two nondiffusion gradient (b-value = 0 s/mm2), repetition time (TR) 6,500 msec, echo time (TE) 89 msec, field of view (FOV) 230 × 230, matrix 128 × 128, and 50 axial slices (voxel size = 1.8 × 1.8 × 3 mm3). The acquisitions were repeated two times to improve the signal-to-noise ratio and to reproduce more diffusion directionalities. Particular attention was taken to center the subject in the head coil and to restrain head movements with cushions and adhesive medical tape.

In addition to DTI data, the following conventional MR images were acquired to examine structural abnormalities: (1) high-resolution three-dimensional magnetization-prepared rapid acquisition gradient echo (MPRAGE) images—TR 1,780 msec, TE 2.34 msec, isotropic voxel of 1 mm3; (2) axial T2-weighted and fluid-attenuated inversion recovery (FLAIR) images (4-mm thickness); (3) oblique coronal T2-weighted and FLAIR images perpendicular to the long axis of hippocampus (3-mm thickness). The MR images of all participants were reviewed by two experienced neuroradiologists (S.I.S. and H.Y.S.) for any structural abnormalities and reported as normal.

Diffusion tensor imaging analysis

The raw DICOM files of each DTI were converted to a single multivolume NIfTi file using MRIcron software (http://www.cabiatl.com/mricro/mricron/dcm2nii.html). DTI data were then preprocessed on a Linux workstation by using the FMRIB’s Diffusion Toolbox (FDT), a part of FSL 4.1 software package (http://www.fmrib.ox.ac.uk/fsl). First, DTI data were visually inspected for image quality, and then corrected for eddy current and head motion by registering each subject’s 30 diffusion weighted images to their own non–diffusion-weighted image using FMRIB Linear Image Registration Tool (FLIRT). Brain extraction tool (BET) implemented in FSL (Oxford University, Oxford, U.K.) was used to remove nonbrain structures and background noise by applying a fractional intensity threshold of 0.35. Next, a diffusion tensor model was fitted at each voxel using DTIFIT to generate FA and MD maps.

The resulting FA and MD maps were fed into TBSS to carry out whole-brain voxel-wise statistical analysis of FA and MD between patients and control subjects. The initial step of TBSS consisted of direct registration of individual FA images to the 1 × 1 × 1 mm3 Montreal Neurological Institute (MNI152) standard space by normalization to the FMRIB58 FA template using the FMRIB’s Nonlinear Registration Tool (FNIRT). The transformed FA images of all participants were averaged to create a mean FA image, and this mean FA image was then thinned to create the white matter “skeleton” (a representation of WM tracts common to all subjects). A nonmaximum suppression algorithm was applied afterward to search the image voxels with highest FA value along the direction perpendicular to the local tract surface to create a mean FA skeleton. An FA threshold of 0.2 was further applied to exclude the skeleton voxels, which may contain gray matter. Following thresholding of the mean FA skeleton, each participant’s transformed FA map was projected onto the mean FA skeleton to create a skeletonized FA map. In a separate process using the FA image–derived skeleton, the maximum values along the direction perpendicular to the tract of MD image were also projected to a separate skeleton image by using “tbss-non-FA” script.

Each participant’s skeletonized FA and MD images were used for voxel-wise analysis of group differences between patients with JME and control subjects. A nonparametric test with 5,000 random permutations was performed by using “Randomise” program (http://www.fmrib.ox.ac.uk/fsl/randomise/index.html) (Nichols & Holmes, 2002). Two-sample t-test was employed for between-group comparisons with age, sex, and education years treated as covariates of no interest. Statistical significance was thresholded at p < 0.01, corrected for multiple comparisons using threshold-free cluster enhancement (TFCE) (Smith & Nichols, 2009). Anatomic location of the white matter tracts of significant difference revealed by TBSS results was determined by Johns Hopkins University DTI-based white matter atlases (Wakana et al., 2004) that are distributed with FSL (http://www.fmrib.ox.ac.uk/fsl/data/atlas-descriptions.html).

Correlation analysis

To delineate the possible correlations between WM integrity changes and both clinical and neuropsychological variables, we extracted each patient’s FA and MD values from regions of significant differences between patients and controls in TBSS (TFCE-corrected p < 0.01) by using the “fslmaths” and “fslmeants” scripts. The extracted FA and MD values were then correlated with clinical variables (age of seizure onset, duration of epilepsy, frequency of GTCS) and neuropsychological measures (MMSE score, Trail-making part A and B time, Stroop test time, Letter fluency test score, Digit span forward and backward scaled scores), by using simple linear regression analysis (p < 0.05). Multiple linear regression analysis was then performed to assess the influence of eight variables of clinical importance (duration of epilepsy, frequency of GTCS, Stroop color-word time, Letter fluency test score, Digit span forward and backward scaled scores, Trail making part B minus part A time, and MMSE score) on both FA and MD changes. Bonferroni correction was further applied to correct for multiple comparisons, with p < 0.00625 (0.05/8) indicating a significant correlation.

Results

Clinical characteristics

Two patients and one control subject were excluded because of MR image distortion from movement artifacts or dental devices. Twenty-five patients (15 women; mean age 25.3 ± 7.6 years; range of age 16–39 years) and 30 control subjects (17 women; mean age 25.5 ± 6.2 years; range of age 18–40 years) entered into the analysis. Two groups did not differ in age, gender, and education years (all p > 0.05, Table 1). Mean age of seizure onset was 14.7 ± 3.1 years (range = 10–25 years), and mean duration of epilepsy was 10.6 ± 7.7 years (range = 2–29 years). Semiologic features included myoclonic seizure in 25 patients (100%), GTCS in 24 (96%), and absence seizure in 11 (44%). All patients had at least one EEG with typical GSWDs on a normal background in their serial examinations. AEDs at the time of study consisted of valproate (VPA) monotherapy in 17 (68%), lamotrigine (LTG) monotherapy in three (12%), levetiracetam (LEV) monotherapy in one (4%), topiramate monotherapy in one (4%), VPA + LTG polytherapy in two (8%), and VPA + LEV polytherapy in one (4%). Number of GTCS for the last 3 years ranged from 0–18 (mean 4.5 ± 4.7).

Table 1.   Clinical characteristics and neuropsychological tests in patients with JME and control subjects
 JME patients (n = 25)Control subjects (n = 30)p-Value
  1. F, female; M, male; MS, myoclonic seizure; GTCS, generalized tonic–clonic seizure; AS, absence seizure. Data are presented as mean ± standard deviation.

Demographic and clinical data   
 Age (years)25.3 ± 7.625.5 ± 6.20.910
 Gender (F:M)15:1017:130.800
 Education years14.4 ± 2.314.5 ± 1.70.790
 Seizure semiologyMS (100%), GTCS (96%), AS (44%)  
 Age at seizure onset (years)14.7 ± 3.1 (range, 10–25)  
 Duration of epilepsy (years)10.6 ± 7.7 (range, 2–29)  
 Seizure frequency (No. of GTCS/3 years)4.5 ± 4.7 (range, 0–18)  
Neuropsychological data   
 Mini-Mental State Examination29.3 ± 0.829.6 ± 0.60.110
 Trail-Making Part A (s)31.6 ± 15.120.6 ± 7.00.002
 Digit Span Forward scaled score8.6 ± 2.811.5 ± 1.6<0.001
 Digit Span Backward scaled score6.4 ± 2.410.6 ± 2.4<0.001
 Trail-Making Part B (s)72.2 ± 51.539.9 ± 13.10.005
 Letter fluency test35.1 ± 9.049.1 ± 10.9<0.001
 Stroop I (word, s)14.0 ± 2.912.7 ± 3.20.141
 Stroop II (color, s)14.9 ± 2.713.3 ± 3.50.071
 Stroop III (color-word, s)18.7 ± 3.316.3 ± 3.60.013

Neuropsychological assessment

Mean scores for neuropsychological tests and between-group differences are summarized in Table 1. There was no difference in MMSE score between the groups (p = 0.110). Results of attention and working memory tests showed that patients with JME had poorer performance than controls in Trail-Making Test part A (p = 0.002), Digit Span forward score (p < 0.001), and Digit Span backward score (p < 0.001). Patients with JME also performed worse than controls in executive function tests, including Trail-Making Test part B (p = 0.005), Letter Fluency test (p < 0.001), and Stroop color-word test (p = 0.013).

Tract-based spatial statistics

After controlling for age, sex, and education years as confounding covariates, TBSS of FA demonstrated a large cluster of significant FA reductions in patients with JME as compared to controls (cluster size = 6,866 mm3, MNI coordinates of local maxima = −21/−17/38, TFCE-corrected p = 0.005). WM tracts of significant FA reductions were highly symmetric and included bilateral superior and anterior corona radiata, genu and body of corpus callosum, and multiple middle and superior frontal WM tracts (Fig. 1A). No WM tracts of significantly increased FA were found in patients with JME as compared to controls. TBSS of MD uncovered two large clusters of significant MD increases in patients as compared to controls (cluster size = 5,044 mm3, MNI coordinates of local maxima = −18/31/18, TFCE-corrected p = 0.005; cluster size = 1,927 mm3, MNI coordinates of local maxima = −38/−45/25, TFCE-corrected p = 0.006). WM tracts of significant MD increases coincided in general with those of FA decreases and included bilateral superior and anterior corona radiata, genu and body of corpus callosum, bilateral middle and superior frontal WM tracts, and left superior parietal lobule WM. No WM tracts of significantly reduced MD were found in patients with JME as compared to controls at the same threshold.

Figure 1.


Tract-based spatial statistics (TBSS) analysis of fractional anisotropy (FA) and mean diffusivity (MD). Regions of significant FA reductions (A, highlighted in red-yellow) and MD increases (B, highlighted in blue) in patients with juvenile myoclonic epilepsy compared with control subjects, are superimposed on the mean FA image and white matter skeleton (green) across all subjects (p < 0.01, corrected for multiple comparisons). The left side of the image corresponds to the right hemisphere of the brain.

Correlation analysis

As is depicted in Fig. 2A, there was a significant negative correlation between FA values extracted from the significant cluster from TBSS and number of GTCS per 3 years (p = 0.010). FA values were not correlated with the other clinical variables and scores of all neuropsychological tests (all p > 0.05). There was also a significant positive correlation between MD values extracted from the significant clusters from TBSS and frequency of GTCS per 3 years (p = 0.004, Fig. 2B). No correlations were found between MD values and the other clinical variables and scores of all neuropsychological tests (all p > 0.05). In multiple linear regression analysis including eight variables of clinical importance, only frequency of GTCS negatively correlated with FA values (p = 0.004) and positively correlated with MD values (p = 0.006), both of which are significant after Bonferroni correction (p < 0.00625).

Figure 2.


Simple linear regression analysis of seizure frequency with fractional anisotropy (FA) and mean diffusivity (MD) values extracted from regions of significant differences between patients and controls in TBSS. Number of generalized tonic–clonic seizures during the 3 years negatively correlate with FA values (A) and positively correlated with MD values (B) (p = 0.010 and p = 0.004, respectively). Note that MD values are arbitrary numbers (original MD value × 10,000).

Discussion

The aims of the present study were to investigate microstructural WM integrity in patients with JME and to correlate WM changes with clinical and cognitive variables. Using whole-brain voxel-wise analysis of DTI, we identified widespread WM microstructural abnormalities, as manifested by reduced FA and increased MD, in JME patients in comparison to healthy controls. Two DTI-derived parameters, FA and MD, are sensitive to subtle WM abnormalities in a number of neurologic and psychiatric disorders. Decreased FA reflects a reduced microstructural integrity within the WM tracts, and factors influencing FA include membrane and myelin integrity and fiber density (Beaulieu, 2002). MD increases with microscopic barrier disruption and extracellular fluid accumulation, and therefore, increased MD is encountered in various pathologic conditions that accompany tissue degeneration and edema (Assaf, 2008). There is robust pathologic evidence that FA and MD are directly affected by myelin content of WM and, to a lesser degree, by axonal count in postmortem brains of multiple sclerosis (Schmierer et al., 2007). Taken together, our findings of reduced FA and increased MD could be interpreted as degradations of microstructural organization of WM tracts in JME.

It is noteworthy that most distinctive regions of both FA and MD changes are highly symmetrical and mainly confined to bilateral superior and anterior corona radiata, bilateral superior and middle frontal WM, and genu and body of corpus callosum. Anterior and superior corona radiata are projection fibers that, together with the anterior and posterior limbs of internal capsule, comprise the anterior and superior thalamic radiations, respectively. These thalamic radiations contain reciprocal (thalamocortical and corticothalamic) connections between the thalamus and multiple parts of the cerebral cortex, especially the frontal cortex. Genu and body of the corpus callosum are predominantly made up of interhemispheric commissural fibers that interconnect the prefrontal, premotor, and supplementary motor areas (SMAs) (Park et al., 2008). Therefore, our results reflect microstructural WM degradations of the thalamofrontal connections as well as parts of the corpus callosum connecting both frontal cortices, further supporting the hypothesis of thalamofrontal circuit dysfunction as the major pathophysiologic mechanism underling JME.

To our knowledge, there are two published studies evaluating WM integrity changes in JME using a whole-brain voxel-wise manner. Deppe et al. (2008) first reported significant FA reductions in WM regions associated with anterior thalamus and prefrontal cortex in 10 patients with JME, suggesting that JME is associated with WM abnormalities of the thalamofrontal network. In a more recent study recruiting 28 patients, O’Muircheartaigh et al. (2011) demonstrated focal structural abnormalities in the SMA (GM volume reduction) and the corpus callosum (reduced FA) as well as frontal executive dysfunctions, highlighting a pivotal role of frontal lobe abnormality in the pathogenesis of JME. In addition, two recent studies using region-of-interest analysis of DTI have also shown reduced FA and increased MD in the WM connecting SMA (Vulliemoz et al., 2010), and reduced FA in the thalamocortical and frontal WM in JME (Keller et al., 2011). Although the regions of FA reductions and MD increases are more widespread in our study, our results show some consistency with the above-mentioned DTI studies in that microstructural integrity of the frontal WM and corpus callosum that interconnects frontal cortices are disrupted in JME. Moreover, our findings of significant correlations of FA and MD with GTCS frequency suggest that these WM alterations could be the consequences of disease progression and that both FA and MD may have a potential role as biomarkers for the pathologic process in JME.

The neuroanatomic basis that underlies JME is not fully elucidated to date; however, converging evidence from functional neuroimaging studies has provided a robust basis to the hypothesis of thalamofrontal dysfunction in the fundamental pathogenesis of JME. Specifically, PET studies using fluorodeoxyglucose showed increased metabolism in bilateral thalami (Kim et al., 2005) and decreased metabolism in the prefrontal and premotor cortices in patients with JME (Swartz et al., 1996). Studies using voxel-based morphometry have shown evidence for subtle GM abnormalities of the thalamus and frontal cortex in patients with JME, although there is some variance across the studies in which frontal structures are affected and in whether the frontal abnormality is associated with an increase or reduction in volume (Woermann et al., 1999; Betting et al., 2006; Helms et al., 2006; Kim et al., 2007; Tae et al., 2008). MRS studies have repeatedly demonstrated metabolic dysfunctions in the thalamus and frontal cortex, particularly in prefrontal regions (Bernasconi et al., 2003; Simister et al., 2003; Savic et al., 2004; Lin et al., 2009). Contribution of the thalamocortical network to the generation of GSWDs has been highlighted in simultaneous EEG-fMRI studies that showed both thalamic activation and widespread cortical deactivation in JME and other IGE syndromes (Gotman et al., 2005; Hamandi et al., 2006).

A number of neurocognitive studies have pointed to frontal executive dysfunctions in patients with JME (Devinsky et al., 1997; Piazzini et al., 2008; Pulsipher et al., 2009), which is in line with frontal cortical abnormalities demonstrated in functional imaging studies. In addition to the role of GM, that of cerebral WM in cognitive functions is now increasingly being recognized owing to the advent of MR techniques for exploring WM connections in vivo, such as DTI. Therefore, there is growing evidence that WM integrity is closely related to various domains of cognitive functions through a disconnection mechanism between distant cerebral regions, as demonstrated in both normal aging process (Madden et al., 2009) and a wide range of neurodegenerative disorders (Catani, 2006). Previous DTI studies performed in patients with traumatic brain injury (Kinnunen et al., 2011), multiple sclerosis (Hecke et al., 2010), mild cognitive impairment, and Alzheimer’s disease (Chen et al., 2009) showed significant correlations between frontal executive dysfunctions and DTI changes of the corpus callosum and frontal WM including corona radiata. Likewise, our finding of poor performance on frontal function tests in patients with JME could be attributed to widespread WM disturbances in the frontal WM, corpus callosum, and corona radiata. However, we could not find significant correlations between cognitive measures and DTI changes; therefore, a longitudinal study with greater levels of cognitive decline would provide some hints on causal relations between cognitive changes and DTI changes.

There are some potential limitations of our study that we should consider. Firstly, our study is cross-sectional and thus interpretation of our results regarding cause and consequence is limited. Based on our findings of significant correlations between disease severity as assessed by the number of GTCS in given years and both FA and MD changes, we speculate that repeated seizures could result in progressive disruption of WM integrity. Conversely, the observed WM changes may be the cause rather than the consequence of recurrent seizures. The corona radiata and frontal WM tracts comprise thalamofrontal connections that are known to be the major epileptic network underlying JME or IGE. Moreover, the corpus callosum is considered the principal pathway of epileptiform discharge synchronization and seizure spread from one hemisphere to another in a detailed electrophysiologic study (Matsuo et al., 2003). Clinical evidence may support this premise, since corpus callosotomy could decrease the severity and frequency of generalized seizures in patients with medically intractable IGE (Jenssen et al., 2006; Nei et al., 2006). Therefore, it seems plausible that WM tracts involved in thalamofrontal connections and corpus callosum could act as a modulating system of epileptogenic susceptibility of the thalamocortical network, and disturbed integrity of these WM could promote epileptic discharges and generalized seizures in JME. Future prospective studies with a longitudinal design, segregating patients with excellent seizure control from those without, would provide converging evidence to elucidate causal relations between DTI changes and disease progression. Secondly, the possible effects of AEDs on DTI results cannot be entirely discounted. Most (80%) of our patients were taking valproate at the time of MRI examination. There is one DTI study showing that patients with bipolar disorder who are taking mood stabilizers including valproate, had decreased FA in the optic radiation and anterior thalamic radiation as compared to patients not taking mood stabilizers (Versace et al., 2008). However, to our knowledge, there is no robust evidence of correlations between load of AEDs, especially valproate, and WM integrity changes. Moreover, when we correlated daily valproate dosage (N = 20) with FA and MD values in regions of TBSS differences, we found no significant correlations (both p > 0.05). Therefore, it seems unlikely that valproate and other AEDs have affected our DTI results. Lastly, we should consider the effects of AEDs on cognitive functions. Most (92%) of our patients remained on valproate or lamotrigine at the time of study, both of which are known to have little negative effects on cognitive functions (Brunbech & Sabers, 2002). Likewise, we found no significant correlations between daily valproate dosage (N = 20) and neurocognitive measures (all p > 0.05). Only one of our patients was taking topiramate, a drug that is well known to have detrimental effects on multiple cognitive domains including frontal functions (Meador et al., 2005), which makes it unlikely that the use of topiramate affected our cognitive results.

In conclusion, we have found that patients with JME show widespread disturbance of microstructural WM integrity in the frontal lobe and corpus callosum that interconnects frontal cortices, which correlates significantly with seizure severity. The observed WM changes further support the pathophysiologic hypothesis of thalamofrontal network abnormality underlying JME, and may implicate frontal cognitive dysfunctions and disease progression.

Acknowledgments

This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (Grant No. 20100004827, 20110005418) and a Korea University Grant. The authors are very grateful to the participants for taking part in the present study.

Disclosure

None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Ancillary