Epilepsia, 53(8):1371–1378, 2012 doi: 10.1111/j.1528-1167.2012.03544.x
FULL-LENGTH ORIGINAL RESEARCH
Microstructural white matter abnormality and frontal cognitive dysfunctions in juvenile myoclonic epilepsy *Ji Hyun Kim, ySang-il Suh, *So-Yeon Park, *Woo-Keun Seo, zInSong Koh, *Seong-Beom Koh, and yHae Young Seol Departments of *Neurology and yRadiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea; and zDepartment of Physiology, Hanyang University College of Medicine, Seoul, Korea
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.
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).
Accepted April 23, 2012; Early View publication June 18, 2012. 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:
[email protected] J.H.K and S.I.S contributed equally to this work. Wiley Periodicals, Inc. ª 2012 International League Against Epilepsy
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. KEY WORDS: Juvenile myoclonic epilepsy, Diffusion tensor imaging, White matter integrity, Frontal executive dysfunction.
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
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1372 J. H. Kim et al. 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 voxelwise 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 Epilepsia, 53(8):1371–1378, 2012 doi: 10.1111/j.1528-1167.2012.03544.x
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 MiniMental State Examination (MMSE); (2) attention and working memory—Trail-Making Test part A, Digit Span Test (forward and backward); and (3) executive function—TrailMaking 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
1373 White Matter Abnormality in JME (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 magnetizationprepared 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 ‘‘tbssnon-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). Twosample 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, Trailmaking 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; Epilepsia, 53(8):1371–1378, 2012 doi: 10.1111/j.1528-1167.2012.03544.x
1374 J. H. Kim et al. 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). Neuropsychological assessment Mean scores for neuropsychological tests and betweengroup 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
Table 1. Clinical characteristics and neuropsychological tests in patients with JME and control subjects
Demographic and clinical data Age (years) Gender (F:M) Education years Seizure semiology
Age at seizure onset (years) Duration of epilepsy (years) Seizure frequency (No. of GTCS/3 years) Neuropsychological data Mini-Mental State Examination Trail-Making Part A (s) Digit Span Forward scaled score Digit Span Backward scaled score Trail-Making Part B (s) Letter fluency test Stroop I (word, s) Stroop II (color, s) Stroop III (color-word, s)
JME patients (n = 25)
Control subjects (n = 30)
p-Value
25.3 ± 7.6 15:10 14.4 ± 2.3 MS (100%), GTCS (96%), AS (44%) 14.7 ± 3.1 (range, 10–25) 10.6 ± 7.7 (range, 2–29) 4.5 ± 4.7 (range, 0–18)
25.5 ± 6.2 17:13 14.5 ± 1.7
0.910 0.800 0.790
29.3 ± 0.8
29.6 ± 0.6
0.110
31.6 ± 15.1 8.6 ± 2.8
20.6 ± 7.0 11.5 ± 1.6
0.002 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.
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