YEBEH-05475; No of Pages 7 Epilepsy & Behavior xxx (2017) xxx–xxx
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Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy Susanne Knake a,1, Christine Roth a,1, Marcus Belke a,2, Jens Sonntag a, Tobias Kniess b, Soeren Krach c,3, Andreas Jansen d,e, Jens Sommer e, Frieder M. Paulus c,3, Barbara Carl f, Felix Rosenow a,4, Anke M. Hermsen a,4, Katja Menzler a,⁎ a
Epilepsy Center Hessen, Department of Neurology, Philipps-University Marburg, Marburg, Germany Clinic of Neurology, Bad Neustadt, Germany c Department of Child and Adolescent Psychiatry, Philipps-University Marburg, Marburg, Germany d Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany e Core-Unit Brainimaging, Faculty of Medicine, Philipps-University Marburg, Marburg, Germany f Epilepsy Center Hessen, Department of Neurosurgery, Philipps-University Marburg, Marburg, Germany b
a r t i c l e
i n f o
Article history: Received 19 June 2017 Revised 21 August 2017 Accepted 23 August 2017 Available online xxxx Keywords: JME Frontal thalamocortical networks Diffusion tensor imaging Voxel-based morphometry
a b s t r a c t Objective: Juvenile myoclonic epilepsy (JME) is the most common idiopathic generalized epilepsy syndrome. Neuropsychological, electrophysiological, and neuroimaging studies have led to the hypothesis that JME is related to dysfunction of frontal brain regions and mainly frontal thalamocortical networks. Methods: We investigated possible microstructural white matter abnormalities of 20 patients with JME as compared with 20 healthy control subjects using diffusion tensor imaging (DTI). We analyzed whole-head DTI scans without an a-priori hypothesis using Tract-Based Spatial Statistics (TBSS). To analyze associated gray matter changes, we applied voxel-based morphometry (VBM) to a 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequence. Neuropsychological testing and personality trait tests were performed to bridge the gap between structure and function. Results: In patients, DTI revealed microstructural white matter changes in anterior parts of the Corpus callosum, anterior parts of the cingulate gyrus, and widespread frontal white matter bilaterally as well as in anterior parts of the right thalamus, which were not accompanied by gray matter changes in VBM. Microstructural changes in the cingulum correlated with personality traits. Neuropsychological test results showed impaired attention and executive functions and reduced short-term memory in the patient group. Also, there was a tendency toward alexithymia and significantly higher scores on depression. Significance: The present study results showed neuropsychological deficits including frontal lobe cognitive performance and a tendency toward alexithymia as well as accompanying microstructural neuroimaging changes in patients with JME, which all point to alterations in frontal brain regions and frontal thalamocortical networks in these patients. © 2017 Elsevier Inc. All rights reserved.
1. Introduction
⁎ Corresponding author at: Center of Brain Imaging, Epilepsy Center Hessen, PhilippsUniversity Marburg, Department of Neurology, Baldingerstrasse, 35043 Marburg, Germany. E-mail address: [email protected] (K. Menzler). 1 Equal contribution. 2 Department of Neurology, Centre of Neurology and Neuropsychiatry, Landschaftsverband Rheinland Klinikum Düsseldorf, Heinrich-Heine-University Düsseldorf. 3 Department of Psychiatry and Psychotherapy, Social Neuroscience Lab, University of Lübeck, Lübeck, Germany. 4 Epilepsy Center Franfurt Rhine-Main, Center of Neurology and Neurosurgery, Goethe University, Frankfurt am Main, Germany.
Juvenile myoclonic epilepsy (JME) is the most common idiopathic generalized epilepsy syndrome, accounting for 5–10% of all epilepsy cases and 26% of idiopathic generalized epilepsies [1]. The patients typically present with myoclonic jerks, generalized tonic–clonic seizures, and less frequently absence seizures, which mainly occur in the morning hours after waking [1–3]. Patients with JME commonly show certain personality traits such as impulsivity and emotional instability and deficits in frontal lobe cognitive performance. These include deficits in attention, executive functions, and mental flexibility and decreases in processing speed. The results resemble those observed in patients with frontal lobe epilepsy [2,4].
http://dx.doi.org/10.1016/j.yebeh.2017.08.031 1525-5050/© 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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S. Knake et al. / Epilepsy & Behavior xxx (2017) xxx–xxx
Electroencephalography (EEG) shows generalized 3- to 6-Hz (poly-)spike–wave activity with a frontocentral predominance [1]. More recent studies suggest that these epileptic discharges originate in orbitofrontal, mesiofrontal, and temporal regions [5]. Furthermore, thalamocortical networks were shown to contribute to the generation of these discharges [6]. Routine magnetic resonance imaging (MRI) studies do not show any abnormalities related to JME on visual inspection [1]. However, advanced neuroimaging methods revealed subtle structural abnormalities in widespread frontal areas and frontal thalamocortical networks and suggested a dysfunction of frontal thalamocortical networks [1,6–17]. The present study investigated changes in white matter microstructure and gray matter volume in patients with JME. Additionally, we performed extensive neuropsychological and personality testing in order to investigate the neural correlates of potential neuropsychological and behavioral changes. 2. Patients and methods The study was approved by the local institutional review board. All subjects provided written informed consent to participate in this study. The study is in accordance with the Declaration of Helsinki. 2.1. Subjects Twenty patients with JME based on clinical and EEG criteria participated in the study. Patients were included if they had myoclonic jerks predominantly in the morning hours with or without additional generalized tonic–clonic seizures or absence seizures and generalized 3- to 6-Hz (poly-)spike–wave activity in the EEG. Exclusion criteria were any neurological or psychiatric disorders beside JME, and any known structural brain abnormalities and contraindications for MRI. Twenty healthy controls without a history of neurological disorders, matched for sex, age, and years of education served as control group. 2.2. Neuropsychological tests Standardized neuropsychological tests were used. We tested verbal learning and memory using the standardized German version of the American verbal learning and memory task (VLMT) and visual/figural learning and memory using a German test called Diagnostikum für Cerebralschädigung (diagnostic test for cerebral damage, DCS). Patients and control subjects performed 5 test runs in the VLMT and 6 test runs in the DCS trying to remember verbal or figural items. Also, in the VLMT, they performed an additional test run after a fixed time interval for delayed recall. We evaluated executive functions and attention with the D2 attention test and the Trail Making Test (TMT) A (digits only) and B (alteration of letters and digits). Short-term and working memory was evaluated using digit and block span forward and backward from the Wechsler Memory Scale (WMS), which is also influenced by attention. We also included the Word Fluency Test (WFT) to evaluate semantic and lexical verbal fluency and finding similarities from the Hamburg-Wechsler intelligence test for adults (HAWIE) to evaluate the capability of verbal abstraction. Alexithymia was evaluated using the Toronto Alexithymia Scale (TAS). The Beck's Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), and NEO five factor inventory (NEO FFI) were used to explore depression, anxiety, and personality traits. Statistical analysis was computed with IBM SPSS Statistics 22® (SPSS, IBM Company, Chicago, Illinois). For statistical analysis of the cognitive tests, a general linear model (GLM) was computed at the group-level (mANOVA) to compare the neuropsychological performance in the JME and the healthy control group. The tests were the dependent variables, group was the two-staged between-subject factor (“JME” vs. “controls”). If group variances were
nonhomogenous (according to Levene's test of variance homogeneity), degrees of freedom were adjusted. Differences in the questionnaires for alexithymia, depression, anxiety, and personality traits were evaluated in an exploratory analysis using t-tests for unpaired groups. We also correlated neuropsychological test results with disease duration, duration of seizure freedom, and neuroimaging results using Pearson's correlation coefficient. 2.3. Magnetic resonance imaging 2.3.1. MRI acquisition Diffusion tensor imaging (DTI) was used to quantify microstructural white matter changes. DTI is based on the measurement of molecular diffusion and its directionality which is influenced by the surrounding brain tissue, cell membranes, or myelin sheaths [18]. The DTI scans were collected on a 3 T MRI (Tim Trio Siemens Medical Solutions, Erlangen, Germany), using a circular polarized head array coil. We performed a single shot echo planar sequence with a twice-refocused spin echo pulse, optimized to minimize eddy current-induced image distortions, with the following parameters: TR/TE = 10,700/104 ms, flip angle = 90°, b = 1000 s/mm2, diffusion directions = 30, 256 × 256 mm FOV, and voxel size 2.0 × 2.0 × 2.4 mm. One T2 b0 image and 30 diffusion weighted b1000 images were collected during one scan. We acquired a 3D T1 magnetization prepared rapid gradient echo sequence (MPRAGE sequence, image parameters: TR/TE = 1900/ 2.52 ms, 256 × 256 mm FOV, flip angle = 9°, voxel size 1.0 × 1.0 × 1.0 mm) during the same session. Subtle gray matter changes were analyzed in this sequence using voxel-based morphometry (VBM). These imaging and analysis methods have been applied successfully to several neurodegenerative disorders, providing insight in the underlying pathophysiology of these conditions [19–21]. We investigated all images to be free of motion or ghosting, high frequency, and/or wrap-around artifacts at the time of image acquisition. 2.3.2. MRI analysis For DTI and VBM calculations, we used programs published by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) [22–25]. 2.3.2.1. DTI 2.3.2.1.1. DTI preprocessing and analysis. Image preprocessing was performed as described previously [26]. Diffusion volumes were motion-corrected and averaged using the FMRIB's Linear Image Registration Tool (FLIRT) with mutual information cost function to register each direction to the minimally eddy current distorted T2-weighted b0 DTI volume that had no diffusion weighting. We computed eigenvalues (λ1, λ2, λ3) and eigenvectors of the diffusion tensor matrix for each voxel from the DTI volumes for each patient and control subject on a voxel-by-voxel basis using conventional reconstruction methods. These tools are included in the FreeSurfer package (FreeSurfer version 4.2.0; http://surfer.nmr.mgh.harvard.edu/). 2.3.2.1.2. Fractional anisotropy and diffusivity map calculation. Brain tissue integrity was assessed using DTI measures of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) as described previously [26]. The primary measure acquired from the DTI data was the FA, a scalar metric unit describing the directionality of water diffusion. Fractional anisotropy is dependent on the orientational coherence of the diffusion compartments within a voxel and reflects the degree of tissue organization or alignment [18]. Fractional anisotropy was calculated using the standard formula defined previously [27]. To further characterize tissue organization, measures of AD (λ1) and RD ([λ2 + λ3] / 2) were examined. Axial diffusivity measures the diffusivity along the primary diffusion direction, RD represents the diffusivities along directions that are orthogonal to the primary diffusion direction. While all these metric parameters might be influenced by several factors including myelination,
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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density, and orientation of axons and fiber crossing, Song et al. could show that RD might be related to changes in myelination or glial cell morphology [28,29] while AD seems to contribute information regarding the integrity of axons [30] or changes in extra-axonal/extracellular space [31]. We obtained T2 b0 images using the exact parameters as the diffusion sensitive images except without any diffusion weighting to determine whether changes other than those in tissue microstructure contributed to the observed effects, such as technical artifacts or individual large-scale signal changes such as white matter signal abnormalities.
The modulated gray matter images were smoothed with an isotropic Gaussian kernel with a sigma of 3 mm. Finally, a voxelwise generalized linear model was applied using permutation-based nonparametric testing, correcting for multiple comparisons across space using the FSL tool randomize. Five hundred simulations were performed under the null hypothesis. The other parameters were chosen as described in the DTI section.
2.3.2.1.3. Nonlinear registration and Tract-Based Spatial Statistics (TBSS). A voxelwise statistical analysis of the FA data using TBSS was performed, which is part of the FSL data analysis suite (http://www. fmrib.ox.ac.uk/fsl/). First, brains from T2 b0 images were extracted using the brain extraction tool (BET). The extracted brains were then used to mask the brain on the FA images and were fed into the FSL TBSS processing stream (http://www.fmrib.ox.ac.uk/fsl/tbss/index. html). All subjects' masked FA data were registered to the FMRIB 58 brain. First, all brains underwent a linear coregistration to the standard space using the tool FLIRT and were then coregistered using the nonlinear registration tool FNIRT, which uses a b-spline representation of the registration warp field. Next, a mean FA image was created and thinned to create a mean FA skeleton, which represented the centers of all tracts the group had in common. We then projected each subject's aligned FA data onto this skeleton. Data were smoothed along the skeleton using an anatomical constraint to limit the smoothing to neighboring data within adjacent voxels along the skeleton. For smoothing, we used the neighboring voxels within a cube of 3 mm edge length to calculate the mean. The smoothing step was performed using matlab (Matlab 7.6.0.324 (R2008a), MathWorks, Aachen, Germany). All analyses were masked to only display regions with FA values of N 0.2 and b0.8 as an additional procedure to avoid examination of regions that are likely comprised of multiple tissue types or fiber orientations. The exact transformations derived for the anisotropy maps were applied to the AD and RD volumes for matched processing of all image volumes.
3.1. Patient characteristics
2.3.2.1.4. Group analysis. The resulting skeletonized images were fed into voxelwise cross-subject statistics. Cross-subject statistics were applied to analyze differences in FA, AD, and RD between patients with JME and healthy controls. The tools mri_glmfit and mri_glmfit-sim of the FreeSurfer package were used for group analysis. The data were fit into a generalized linear model, and an unpaired t-test was performed. Resulting data were corrected for multiple comparisons by a permutation-based approach. We performed 12.000 simulations under the null hypothesis. This approach was based on the AFNI null-z simulator (AlphaSim; http://afni.nimh.nih.gov/afni/doc/manual/ AlphaSim). Last, data were clustered with a minimum value of p = 0.01 for the cluster calculation. To display the results, all figures were made with exactly the same parameters, showing clusters with a significance of p b 0.01. The significance was given as the negative decadic logarithm of the p-value (p = 10−x). 2.3.2.2. VBM. Cortical and gray matter volumes were calculated with the FSL-VBM processing stream [22,23] using the T1-weighted dataset (MPRAGE sequence). This processing stream used an optimized VBM protocol described previously [24]. First, we extracted the brains of the structural images using the FSL tool BET and performed a gray matter segmentation before registering them to the MNI 152 standard space using FNIRT for nonlinear registration [25]. The resulting images were averaged and flipped along the x-axis to create a left–right symmetric, study-specific gray matter template. Second, all native gray matter images were nonlinearly registered to this study-specific template and modulated to correct for local expansion (or contraction) due to the nonlinear component of the spatial transformation.
3. Results
Eight patients (40%) were male, and 12 (60%) were female. The mean age of the patients was 34.80 ± 13.31 years. The control group was sex- and age-matched. Their mean age was 33.50 ± 13.57 years. There was no significant difference in sex distribution or age between patients and control subjects. For patients, the mean disease duration was 20.42 ± 14.90 years. For patients characteristics see Table 1. 3.2. Neuropsychological tests Two patients did not complete neuropsychological testing. In one patient, results of the TAS are missing, and in the other patient, results of the WFT are also missing. We found a main effect for group F(15,22) = 2.45; p = 0.027 when comparing the neuropsychological test performance of patients with JME and matched healthy controls. Univariate analysis revealed decreased verbal and figural learning and memory functions in the delayed recall of the VLMT (p = 0.018) and in the DCS (sum score of bouts 1 to 6, p = 0.017) in patients with JME. Patients also showed slower performance in the D2 attention test (p = 0.010) and the TMT A (p = 0.035) and B (p = 0.036), which evaluate executive functions and attention. Impaired short-term memory was demonstrated by significantly decreased scores in the digit (p = 0.020) and block span forward (p = 0.023) of the WMS in the patient group. Regarding executive functions, we found semantic verbal fluency (p = 0.029) and verbal abstraction (p = 0.004) to be decreased in the patient group. The exploratory analysis of alexithymia, depression, anxiety, and personality traits revealed a tendency toward a decreased total TAS score (p = 0.063) and score for identification of emotions (p = 0.051) in the patient group, but results were not significant. Also, patients showed significantly higher scores on the BDI (p = 0.032). After Bonferroni correction, there was no significant correlation between alexithymia, depression, anxiety, and personality traits with cognitive test results. There was no significant correlation between disease duration or duration of seizure freedom and any of the neuropsychological test results or instruments used. Neuropsychological test results and questionnaires are summarized in Tables 2 and 3. 3.3. DTI We excluded the MRI of one patient from the analysis due to motion artifacts. The MRI of the corresponding age- and sex-matched control subject was also excluded from the analysis of DTI data. Analysis of the FA revealed areas of decreased FA in patients in anterior parts of the Corpus callosum, anterior parts of the cingulate gyrus and widespread frontal white matter bilaterally, right thalamus, and small areas in the right superior temporal gyrus and left internal capsule (Fig. 1). Radial diffusivity was significantly increased in the same areas (Fig. 2), whereas there was no difference in axial diffusion. To evaluate a possible association between these changes and changes in neuropsychological test results, we used the areas of altered FA in the Corpus callosum, the cingulate gyrus, and the biggest region of altered frontal white matter changes as regions of interest (ROI) to calculate the correlation between FA values and neuropsychological
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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Table 1 Patient characteristics. Sex
Age
Age at onset
Seizure types
Number of MJ per montha
Number of GTCS per montha
Number of AS per montha
Duration of seizure-free period since last seizurea (last seizure type)
Medication in mg/d
M M M F M F F F M F F F M M M F F F F F
58 32 23 29 43 17 52 51 23 23 51 41 54 34 45 24 23 31 22 20
3 17 11 11 14 16 10 14 19 13 12 14 Unknown 18 13 18 15 13 16 7
MJ, GTCS MJ, GTCS, AS MJ, GTCS MJ, GTCS, AS MJ, GTCS, AS MJ, GTCS, AS MJ MJ, GTCS MJ MJ, GTCS MJ, GTCS MJ, GTCS, AS MJ, GTCS, AS MJ, GTCS, AS MJ, GTCS MJ MJ, GTCS MJ, GTCS, AS MJ MJ, GTCS, AS
0 0 0 0 0 30 0 4 0 0 0 Rare 0 0 4 0 0 0 0 Very rare
0 0 0 0 0 4 0 0,5 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 months (GTCS) 132 months (GTCS) 32 months (GTCS) 30 months (GTCS) 30 months (GTCS) 1 day (AS, MJ), 1 month (GTCS) N120 months (MJ) 1 week (MJ), 2 months (GTCS) Unknown 48 months 18 months (MJ), 252 months (GTCS) Unknown (MJ), N120 months (GTCS) Unknown 168 months 1 week (MJ), 96 months (GTCS) 72 months (MJ) 8 months (GTCS) 60 months (AS), 180 months (GTCS) 72 months (MJ) Unknown (MJ), 48 months (GTCS)
LTG 125, LEV 3000, VPA 1500 LTG 50, LEV 2000, VPA 1250 VPA 1800 LEV 3000 LEV 4000, VPA 3000, CBZ 800 LTG 200, VPA 600–1200 No medication LEV 3750, VPA 1500 VPA 600 LTG 200 VPA 450 LTG 150 Primidon 250, PHT 600 LEV 2000 LTG 500 LTG 250 LTG 400 VPA 1500 LEV 1000 VPA 900
MJ: myoclonic jerks, GTCS: generalized tonic–clonic seizures, AS: absence seizures, LTG: lamotrigine, LEV: levetiracetam, VPA: valproate, CBZ: carbamazepine, PHT: phenytoin. a At the time of investigation.
4. Discussion
test results in patients with JME. After Bonferroni correction, none of these correlations was significant. The same ROIs were used to correlate FA values and results of the BDI, NEO FFI, STAI, and TAS. The correlation analysis revealed several associations between FA values in the cingulum and frontal white matter and personality traits in the NEO FFI (NEO FFI agreeableness and FA values in the right cingulum: R = − 0.649, p = 0.012; NEO FFI agreeableness and FA values in the left cingulum: R = −0.774, p = 0.001; NEO FFI conscientiousness and right frontal white matter: R = 0.581, p = 0.037; NEO FFI conscientiousness and right cingulum: R = 0.635, p = 0.020). After Bonferroni correction for multiple testing, the correlation between the left cingulum and the NEO FFI agreeableness still reached the significance level of p b 0.0012. There was no significant correlation between disease duration or duration of seizure freedom and FA values.
In the present study, we could show alterations in white matter microstructure in anterior parts of the Corpus callosum and in the cingulate gyrus of patients with JME as well as in widespread areas of frontal white matter bilaterally and the right thalamus. Changes in the cingulum were negatively correlated with the personality trait agreeableness. We observed no accompanying gray matter changes. Altered white matter integrity involved fibers of frontal thalamocortical networks and fibers connecting frontal areas of both hemispheres and might influence frontal lobe function in patients with JME. Accordingly, patients in this study showed impaired attention, executive functions and short-term memory, higher scores for depression, and a tendency toward decreased alexithymia scores.
3.4. VBM
4.1. Microstructural changes
VBM did not reveal any significant gray matter differences between patients with JME and healthy control subjects.
Earlier studies using different advanced neuroimaging methods reveal subtle structural and functional abnormalities in widespread frontal areas and frontal thalamocortical networks [1,6–17]. Thalamocortical dysfunction is even considered the key finding of JME by some authors [10]. Only few studies used DTI to investigate white matter microstructure in patients with JME and revealed changes in thalamocortical networks
Table 2 Neuropsychological test results. Test
Patients (mean ± SD)
Control subjects (mean ± SD)
pa
VLMT, bouts 1 to 5 VLMT, number of forgotten items in delayed recall VLMT, recognition DCS, bouts 1 to 6 D2 attention test TMT A TMT B WMS, digit span forward WMS, digit span backward WMS, block span forward WMS, block span backward WFT, semantic WFT, lexical WFT, lexical with alternating letters Finding similarities
56.16 ± 7.50 2.58 ± 1.38
59.90 ± 8.49 1.16 ± 2.09
0.159 0.018
13.47 ± 1.26 39.74 ± 10.76 155.47 ± 32.42 27.09 ± 8.45 74.19 ± 38.99 7.68 ± 2.08 7.32 ± 1.95 8.42 ± 1.89 8.42 ± 1.54 24.42 ± 5.67 14.05 ± 4.08 12.95 ± 3.98 23.05 ± 4.17
13.89 ± 2.21 47.59 ± 8.44 190.37 ± 45.95 21.92 ± 5.90 52.06 ± 20.84 9.26 ± 1.91 8.26 ± 1.79 9.74 ± 1.49 8.74 ± 1.70 30.47 ± 10.15 14.89 ± 3.91 14.00 ± 3.90 26.53 ± 2.61
0.475 0.017 0.010 0.035 0.036 0.020 0.127 0.023 0.551 0.029 0.520 0.416 0.004
VLMT: verbal learning and memory test, DCS: Diagnostikum für Cerebralschädigung, TMT: Trail Making Test A (digits only) and B (alternating letters and digits), WMS: Wechsler Memory Scale, WFT: Word Fluency Test. a ANOVA, italics: p b 0.05.
Table 3 Questionnaires. Test
Patients (mean ± SD)
Control subjects (mean ± SD)
p
TAS, description of emotion TAS, identification of emotions TAS, externally-oriented thinking TAS, total BDI STAI NEO FFI, neuroticism NEO FFI, extraversion NEO FFI, openness NEO FFI, compatibility NEO FFI, conscientiousness
2.45 ± 0.84 2.17 ± 0.57 2.43 ± 0.63 2.34 ± 0.58 10.15 ± 7.49 40.80 ± 4.68 28.53 ± 8.44 32.00 ± 6.35 35.00 ± 6.81 38.21 ± 6.03 37.92 ± 6.68
2.20 ± 0.82 1.81 ± 0.56 2.13 ± 0.57 2.03 ± 0.40 4.39 ± 6.70 43.72 ± 5.64 24.22 ± 11.78 32.94 ± 6.36 38.56 ± 5.25 41.06 ± 5.74 36.50 ± 5.73
0.347 0.051 0.133 0.063 0.032 0.120 0.245 0.680 0.100 0.148 0.529
BDI: Beck's Depression Inventory, NEO FFI: NEO five factor inventory, STAI: State-Trait Anxiety Inventory. TAS: Toronto Alexithymia Scale, italics: p b 0.05.
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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Fig. 1. Areas of altered FA in patients with JME compared with healthy controls. A blue–light blue color indicated clusters, where the measured FA was significantly decreased in the group with JME compared with controls, whereas the red to yellow color indicated a significant increase of the FA. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
including several frontal areas, the Corpus callosum, and fibers connecting medial and anterior thalamic nuclei with the frontal lobe [11,13,14]. In the present study, we included 20 patients with JME and used the hypothesis-free analysis approach TBSS. Tract-Based Spatial Statistics uses a white matter skeleton, which includes only voxels that are part of the white matter in all subjects, thereby avoiding confounding by interindividual anatomical brain differences. The results are very reliable at the cost of excluding brain regions with a high interindividual variability. Using this approach, we demonstrated widespread microstructural changes in patients in anterior parts of the Corpus callosum and cingulate gyrus bilaterally as well as in widespread areas of frontal white matter, thereby supporting earlier DTI results [11,13,14]. Earlier
studies could relate changes in myelination to altered RD values, but unchanged AD [28,29]. Therefore, the changes observed in this study might reflect changes in myelination and glial cell morphology. However, other factors like the degree of fiber density and orientation as well as fiber coherence and the number of crossing fibers might influence the observed changes. The white matter changes were not related to alterations in gray matter volume. This seems to contradict earlier study results [1,15,32], but results of earlier VBM studies are conflicting, showing either increased [1,32] or reduced [1,15] gray matter in frontal or temporal brain regions or normal results [33,34]. Gray matter changes therefore seem to be less reliable to characterize microstructural brain changes in patients with JME.
Fig. 2. Areas of altered RD in patients with JME compared with healthy controls. A blue–light blue color indicated clusters, where the measured RD was significantly decreased in the group with JME compared with controls, whereas the red to yellow color indicated a significant increase of the RD. For visualization purposes, clusters were dilated, using the mean dilation method of fslmath (a tool included in the FSL stream). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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S. Knake et al. / Epilepsy & Behavior xxx (2017) xxx–xxx
4.2. Neuropsychological and clinical correlation
4.3. Limitations
Patients with JME have a higher risk for psychiatric disorders including personality (Cluster B) and anxiety disorders and substance abuse. Also, these patients show deficits in frontal lobe cognitive performance, which correlated with psychiatric comorbidities in earlier studies [35]. We could confirm deficits in frontal lobe cognitive performance, attention, executive functions, and short-term memory as well as depression and a tendency toward alexithymia, but did not find a significant correlation between cognitive deficits and test results for depression, anxiety, alexithymia, and personality traits. An earlier study related mesiofrontal and frontobasal cortical abnormalities in surface morphometry to personality traits like emotional instability, immaturity, lack of discipline, and rapid mood changes in patients with JME [36]. In line with these results, we could show a negative correlation between microstructural changes in the cingulum and the personality trait agreeableness. Patients with JME also showed a reduced N-acetyl aspartate concentration in frontal brain regions in MRS and frontal hypometabolism in positron emission tomography (PET) studies related to frontal lobe cognitive dysfunction and cognitive flexibility [7,16,37]. The present results expanded our understanding of the pathophysiology underlying cognitive deficits by relating microstructural white matter changes to altered frontal lobe cognitive performance, attention, executive functions, and short-term memory on a group level, even though no significant correlation between these neuropsychological test results and FA values in these brain regions could be observed on a single patient level. The anterior part of the cingulate gyrus, which was affected in the present study, plays an important role in affection, cognition, monitoring behavior, detecting errors, and in adaptive decision making. Previous studies have related structural abnormalities in this brain area to impulsivity in bipolar disorder [38]. The changes in this brain area detected in the present study and its negative correlation with the personality trait agreeableness might therefore be a pathophysiological correlate for the impulsivity, emotional instability, and deficits in frontal lobe cognitive performance commonly seen in patients with JME [2,4]. This hypothesis is supported by an earlier study showing differences in gray matter volume in the anterior cingulum in subjects with high alexithymia scores as compared with low alexithymia scores [39]. The Corpus callosum contains fibers connecting superior frontal regions, premotor regions, and the supplementary motor area (SMA) [13,34]. Most interestingly, earlier studies also describe changes in the SMA of patients with JME [14,17]. Vollmar et al. could show coactivation of cognitive networks and primary and supplementary motor areas during cognitive tasks in patients with JME, which might relate to the provocation of myoclonic jerks by cognitive tasks or complicated sequential movements in patients with JME [17]. The same group also found increased structural and functional connectivity between mesial frontal regions, the prefrontal cognitive cortex and motor, and SMAs which might underlie the present study results and may also be related to the facilitation of myoclonic jerks by cognitive or complicated motor tasks [40]. Besides neuropsychological deficits, the microstructural changes observed in the present study might also be related to the generation of spike–wave discharges. Functional MRI revealed activation in mesial frontal areas several seconds before generalized spike–wave complexes appeared on the EEG, followed by widespread deactivation in frontal, parietal, and cingulate areas during spike–wave complexes [41]. Also, microstructural changes in the Corpus callosum suggest alterations in commissural connectivity and, therefore, have been related to abnormal bihemispheric synchronization during generalized spike–wave discharges in earlier studies [13,14,42]. The changes in thalamocortical networks and Corpus callosum in the present study might therefore be associated with the generation of the spike–wave discharges seen in patients with idiopathic generalized epilepsy.
Limitations of this study include a possible confound by anticonvulsant medication. Other factors that might influence the results and explain the heterogeneity of neuroimaging data in this and other JME imaging studies include disease duration, seizure frequency, interictal EEG activity, and the genetic heterogeneity of JME. 4.4. Conclusions The neuropsychological and neuroimaging data presented here support a contribution of frontal brain areas and frontal thalamocortical networks in the pathophysiology of JME [1,7,11,13,14,16,32]. Our findings provide insight into microstructural white matter changes and a possible relation to personality traits, alexithymia, and decreased frontal lobe cognitive performance in these patients. Also, these findings might be related to altered interhemispheric connectivity and connectivity of the SMA, which is thought to be the correlate of abnormal synchronization during generalized spike and wave discharges and of praxis induction, i.e., the facilitation of myoclonic jerking by cognitive or complicated motor tasks. Funding The work was supported by the research grant of the Rhön-Klinikum AG (grant number FL-67). The funding source was not involved in the study design; data collection, analysis, and interpretation; manuscript preparation; or the decision to submit the article for publication. Conflicts of interest Tobias Kniess has received speaker honoraria from Eisai and UCB. FR has received personal fees from Bayer-Vital, Cerbomed, Eisai, Hexal, and Sandoz, personal fees and support for continuing medical education activities from Desitin, Novartis, Shire, and UCB Pharma, and grants from DFG (German Research Foundation) and the European Union. Katja Menzler has received honoraria as advisory board member from Eisai and UCB. The other authors report no conflict of interest. References [1] Anderson J, Hamandi K. Understanding juvenile myoclonic epilepsy: contributions from neuroimaging. Epilepsy Res 2011;94:127–37. [2] Janz D, Christian W. Impulsiv-Petit mal. Dtsch Z Nervenheilkd 1957;176:346–86. [3] Prasad A, Kuzniecky RI, Knowlton RC, Welty TE, Martin RC, Mendez M, et al. Evolving antiepileptic drug treatment in juvenile myoclonic epilepsy. Arch Neurol 2003;60: 1100–5. [4] Frascareli Pascalicchio T, de Araujo Filho GM, Helena da Silva Noffs M, Lin K, Otavio S, Vidal-Dourado M, et al. Neuropsychological profile of patients with juvenile myoclonic epilepsy: a controlled study of 50 patients. Epilepsy Behav 2007;10:263–7. [5] Holmes MD, Quiring J, Tucker DM. Evidence that juvenile myoclonic epilepsy is a disorder of frontotemporal corticothalamic networks. Neuroimage 2010;49:80–93. [6] Kim J Bin, Suh S il, Seo WK, Oh K, Koh SB, Kim JH. Altered thalamocortical functional connectivity in idiopathic generalized epilepsy. Epilepsia 2014;55:592–600. [7] Swartz BE, Simpkins F, Halgren E, Mandelkern M, Brown C, Krisdakumtorn T, et al. Visual working memory in primary generalized epilepsy: an (18)FDG-PET study. Neurology 1996;47:1203–12. [8] Hattingen E, Luckerath C, Pellikan S, Vronski D, Roth C, Knake S, et al. Frontal and thalamic changes of GABA concentration indicate dysfunction of thalamofrontal networks in juvenile myoclonic epilepsy. Epilepsia 2014;55:1030–7. [9] Paulus FM, Krach S, Blanke M, Roth C, Belke M, Sommer J, et al. Fronto-insula network activity explains emotional dysfunctions in juvenile myoclonic epilepsy: combined evidence from pupillometry and fMRI. Cortex 2015;65:219–31. [10] Koepp MJ, Woermann F, Savic I, Wandschneider B. Juvenile myoclonic epilepsy — neuroimaging findings. Epilepsy Behav 2013;28:S40–4. [11] Deppe M, Kellinghaus C, Duning T, Moeddel G, Mohammadi S, Deppe K, et al. Nerve fiber impairment of anterior thalamocortical circuitry in juvenile myoclonic epilepsy. Neurology 2008;71:1981–5. [12] O'Muircheartaigh J, Vollmar C, Barker GJ, Kumari V, Symms MR, Thompson P, et al. Abnormal thalamocortical structural and functional connectivity in juvenile myoclonic epilepsy. Brain 2012;135:3635–44.
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Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
Contents lists available at ScienceDirect
Epilepsy & Behavior journal homepage: www.elsevier.com/locate/yebeh
Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy Susanne Knake a,1, Christine Roth a,1, Marcus Belke a,2, Jens Sonntag a, Tobias Kniess b, Soeren Krach c,3, Andreas Jansen d,e, Jens Sommer e, Frieder M. Paulus c,3, Barbara Carl f, Felix Rosenow a,4, Anke M. Hermsen a,4, Katja Menzler a,⁎ a
Epilepsy Center Hessen, Department of Neurology, Philipps-University Marburg, Marburg, Germany Clinic of Neurology, Bad Neustadt, Germany c Department of Child and Adolescent Psychiatry, Philipps-University Marburg, Marburg, Germany d Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany e Core-Unit Brainimaging, Faculty of Medicine, Philipps-University Marburg, Marburg, Germany f Epilepsy Center Hessen, Department of Neurosurgery, Philipps-University Marburg, Marburg, Germany b
a r t i c l e
i n f o
Article history: Received 19 June 2017 Revised 21 August 2017 Accepted 23 August 2017 Available online xxxx Keywords: JME Frontal thalamocortical networks Diffusion tensor imaging Voxel-based morphometry
a b s t r a c t Objective: Juvenile myoclonic epilepsy (JME) is the most common idiopathic generalized epilepsy syndrome. Neuropsychological, electrophysiological, and neuroimaging studies have led to the hypothesis that JME is related to dysfunction of frontal brain regions and mainly frontal thalamocortical networks. Methods: We investigated possible microstructural white matter abnormalities of 20 patients with JME as compared with 20 healthy control subjects using diffusion tensor imaging (DTI). We analyzed whole-head DTI scans without an a-priori hypothesis using Tract-Based Spatial Statistics (TBSS). To analyze associated gray matter changes, we applied voxel-based morphometry (VBM) to a 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequence. Neuropsychological testing and personality trait tests were performed to bridge the gap between structure and function. Results: In patients, DTI revealed microstructural white matter changes in anterior parts of the Corpus callosum, anterior parts of the cingulate gyrus, and widespread frontal white matter bilaterally as well as in anterior parts of the right thalamus, which were not accompanied by gray matter changes in VBM. Microstructural changes in the cingulum correlated with personality traits. Neuropsychological test results showed impaired attention and executive functions and reduced short-term memory in the patient group. Also, there was a tendency toward alexithymia and significantly higher scores on depression. Significance: The present study results showed neuropsychological deficits including frontal lobe cognitive performance and a tendency toward alexithymia as well as accompanying microstructural neuroimaging changes in patients with JME, which all point to alterations in frontal brain regions and frontal thalamocortical networks in these patients. © 2017 Elsevier Inc. All rights reserved.
1. Introduction
⁎ Corresponding author at: Center of Brain Imaging, Epilepsy Center Hessen, PhilippsUniversity Marburg, Department of Neurology, Baldingerstrasse, 35043 Marburg, Germany. E-mail address: [email protected] (K. Menzler). 1 Equal contribution. 2 Department of Neurology, Centre of Neurology and Neuropsychiatry, Landschaftsverband Rheinland Klinikum Düsseldorf, Heinrich-Heine-University Düsseldorf. 3 Department of Psychiatry and Psychotherapy, Social Neuroscience Lab, University of Lübeck, Lübeck, Germany. 4 Epilepsy Center Franfurt Rhine-Main, Center of Neurology and Neurosurgery, Goethe University, Frankfurt am Main, Germany.
Juvenile myoclonic epilepsy (JME) is the most common idiopathic generalized epilepsy syndrome, accounting for 5–10% of all epilepsy cases and 26% of idiopathic generalized epilepsies [1]. The patients typically present with myoclonic jerks, generalized tonic–clonic seizures, and less frequently absence seizures, which mainly occur in the morning hours after waking [1–3]. Patients with JME commonly show certain personality traits such as impulsivity and emotional instability and deficits in frontal lobe cognitive performance. These include deficits in attention, executive functions, and mental flexibility and decreases in processing speed. The results resemble those observed in patients with frontal lobe epilepsy [2,4].
http://dx.doi.org/10.1016/j.yebeh.2017.08.031 1525-5050/© 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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S. Knake et al. / Epilepsy & Behavior xxx (2017) xxx–xxx
Electroencephalography (EEG) shows generalized 3- to 6-Hz (poly-)spike–wave activity with a frontocentral predominance [1]. More recent studies suggest that these epileptic discharges originate in orbitofrontal, mesiofrontal, and temporal regions [5]. Furthermore, thalamocortical networks were shown to contribute to the generation of these discharges [6]. Routine magnetic resonance imaging (MRI) studies do not show any abnormalities related to JME on visual inspection [1]. However, advanced neuroimaging methods revealed subtle structural abnormalities in widespread frontal areas and frontal thalamocortical networks and suggested a dysfunction of frontal thalamocortical networks [1,6–17]. The present study investigated changes in white matter microstructure and gray matter volume in patients with JME. Additionally, we performed extensive neuropsychological and personality testing in order to investigate the neural correlates of potential neuropsychological and behavioral changes. 2. Patients and methods The study was approved by the local institutional review board. All subjects provided written informed consent to participate in this study. The study is in accordance with the Declaration of Helsinki. 2.1. Subjects Twenty patients with JME based on clinical and EEG criteria participated in the study. Patients were included if they had myoclonic jerks predominantly in the morning hours with or without additional generalized tonic–clonic seizures or absence seizures and generalized 3- to 6-Hz (poly-)spike–wave activity in the EEG. Exclusion criteria were any neurological or psychiatric disorders beside JME, and any known structural brain abnormalities and contraindications for MRI. Twenty healthy controls without a history of neurological disorders, matched for sex, age, and years of education served as control group. 2.2. Neuropsychological tests Standardized neuropsychological tests were used. We tested verbal learning and memory using the standardized German version of the American verbal learning and memory task (VLMT) and visual/figural learning and memory using a German test called Diagnostikum für Cerebralschädigung (diagnostic test for cerebral damage, DCS). Patients and control subjects performed 5 test runs in the VLMT and 6 test runs in the DCS trying to remember verbal or figural items. Also, in the VLMT, they performed an additional test run after a fixed time interval for delayed recall. We evaluated executive functions and attention with the D2 attention test and the Trail Making Test (TMT) A (digits only) and B (alteration of letters and digits). Short-term and working memory was evaluated using digit and block span forward and backward from the Wechsler Memory Scale (WMS), which is also influenced by attention. We also included the Word Fluency Test (WFT) to evaluate semantic and lexical verbal fluency and finding similarities from the Hamburg-Wechsler intelligence test for adults (HAWIE) to evaluate the capability of verbal abstraction. Alexithymia was evaluated using the Toronto Alexithymia Scale (TAS). The Beck's Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), and NEO five factor inventory (NEO FFI) were used to explore depression, anxiety, and personality traits. Statistical analysis was computed with IBM SPSS Statistics 22® (SPSS, IBM Company, Chicago, Illinois). For statistical analysis of the cognitive tests, a general linear model (GLM) was computed at the group-level (mANOVA) to compare the neuropsychological performance in the JME and the healthy control group. The tests were the dependent variables, group was the two-staged between-subject factor (“JME” vs. “controls”). If group variances were
nonhomogenous (according to Levene's test of variance homogeneity), degrees of freedom were adjusted. Differences in the questionnaires for alexithymia, depression, anxiety, and personality traits were evaluated in an exploratory analysis using t-tests for unpaired groups. We also correlated neuropsychological test results with disease duration, duration of seizure freedom, and neuroimaging results using Pearson's correlation coefficient. 2.3. Magnetic resonance imaging 2.3.1. MRI acquisition Diffusion tensor imaging (DTI) was used to quantify microstructural white matter changes. DTI is based on the measurement of molecular diffusion and its directionality which is influenced by the surrounding brain tissue, cell membranes, or myelin sheaths [18]. The DTI scans were collected on a 3 T MRI (Tim Trio Siemens Medical Solutions, Erlangen, Germany), using a circular polarized head array coil. We performed a single shot echo planar sequence with a twice-refocused spin echo pulse, optimized to minimize eddy current-induced image distortions, with the following parameters: TR/TE = 10,700/104 ms, flip angle = 90°, b = 1000 s/mm2, diffusion directions = 30, 256 × 256 mm FOV, and voxel size 2.0 × 2.0 × 2.4 mm. One T2 b0 image and 30 diffusion weighted b1000 images were collected during one scan. We acquired a 3D T1 magnetization prepared rapid gradient echo sequence (MPRAGE sequence, image parameters: TR/TE = 1900/ 2.52 ms, 256 × 256 mm FOV, flip angle = 9°, voxel size 1.0 × 1.0 × 1.0 mm) during the same session. Subtle gray matter changes were analyzed in this sequence using voxel-based morphometry (VBM). These imaging and analysis methods have been applied successfully to several neurodegenerative disorders, providing insight in the underlying pathophysiology of these conditions [19–21]. We investigated all images to be free of motion or ghosting, high frequency, and/or wrap-around artifacts at the time of image acquisition. 2.3.2. MRI analysis For DTI and VBM calculations, we used programs published by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) [22–25]. 2.3.2.1. DTI 2.3.2.1.1. DTI preprocessing and analysis. Image preprocessing was performed as described previously [26]. Diffusion volumes were motion-corrected and averaged using the FMRIB's Linear Image Registration Tool (FLIRT) with mutual information cost function to register each direction to the minimally eddy current distorted T2-weighted b0 DTI volume that had no diffusion weighting. We computed eigenvalues (λ1, λ2, λ3) and eigenvectors of the diffusion tensor matrix for each voxel from the DTI volumes for each patient and control subject on a voxel-by-voxel basis using conventional reconstruction methods. These tools are included in the FreeSurfer package (FreeSurfer version 4.2.0; http://surfer.nmr.mgh.harvard.edu/). 2.3.2.1.2. Fractional anisotropy and diffusivity map calculation. Brain tissue integrity was assessed using DTI measures of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) as described previously [26]. The primary measure acquired from the DTI data was the FA, a scalar metric unit describing the directionality of water diffusion. Fractional anisotropy is dependent on the orientational coherence of the diffusion compartments within a voxel and reflects the degree of tissue organization or alignment [18]. Fractional anisotropy was calculated using the standard formula defined previously [27]. To further characterize tissue organization, measures of AD (λ1) and RD ([λ2 + λ3] / 2) were examined. Axial diffusivity measures the diffusivity along the primary diffusion direction, RD represents the diffusivities along directions that are orthogonal to the primary diffusion direction. While all these metric parameters might be influenced by several factors including myelination,
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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density, and orientation of axons and fiber crossing, Song et al. could show that RD might be related to changes in myelination or glial cell morphology [28,29] while AD seems to contribute information regarding the integrity of axons [30] or changes in extra-axonal/extracellular space [31]. We obtained T2 b0 images using the exact parameters as the diffusion sensitive images except without any diffusion weighting to determine whether changes other than those in tissue microstructure contributed to the observed effects, such as technical artifacts or individual large-scale signal changes such as white matter signal abnormalities.
The modulated gray matter images were smoothed with an isotropic Gaussian kernel with a sigma of 3 mm. Finally, a voxelwise generalized linear model was applied using permutation-based nonparametric testing, correcting for multiple comparisons across space using the FSL tool randomize. Five hundred simulations were performed under the null hypothesis. The other parameters were chosen as described in the DTI section.
2.3.2.1.3. Nonlinear registration and Tract-Based Spatial Statistics (TBSS). A voxelwise statistical analysis of the FA data using TBSS was performed, which is part of the FSL data analysis suite (http://www. fmrib.ox.ac.uk/fsl/). First, brains from T2 b0 images were extracted using the brain extraction tool (BET). The extracted brains were then used to mask the brain on the FA images and were fed into the FSL TBSS processing stream (http://www.fmrib.ox.ac.uk/fsl/tbss/index. html). All subjects' masked FA data were registered to the FMRIB 58 brain. First, all brains underwent a linear coregistration to the standard space using the tool FLIRT and were then coregistered using the nonlinear registration tool FNIRT, which uses a b-spline representation of the registration warp field. Next, a mean FA image was created and thinned to create a mean FA skeleton, which represented the centers of all tracts the group had in common. We then projected each subject's aligned FA data onto this skeleton. Data were smoothed along the skeleton using an anatomical constraint to limit the smoothing to neighboring data within adjacent voxels along the skeleton. For smoothing, we used the neighboring voxels within a cube of 3 mm edge length to calculate the mean. The smoothing step was performed using matlab (Matlab 7.6.0.324 (R2008a), MathWorks, Aachen, Germany). All analyses were masked to only display regions with FA values of N 0.2 and b0.8 as an additional procedure to avoid examination of regions that are likely comprised of multiple tissue types or fiber orientations. The exact transformations derived for the anisotropy maps were applied to the AD and RD volumes for matched processing of all image volumes.
3.1. Patient characteristics
2.3.2.1.4. Group analysis. The resulting skeletonized images were fed into voxelwise cross-subject statistics. Cross-subject statistics were applied to analyze differences in FA, AD, and RD between patients with JME and healthy controls. The tools mri_glmfit and mri_glmfit-sim of the FreeSurfer package were used for group analysis. The data were fit into a generalized linear model, and an unpaired t-test was performed. Resulting data were corrected for multiple comparisons by a permutation-based approach. We performed 12.000 simulations under the null hypothesis. This approach was based on the AFNI null-z simulator (AlphaSim; http://afni.nimh.nih.gov/afni/doc/manual/ AlphaSim). Last, data were clustered with a minimum value of p = 0.01 for the cluster calculation. To display the results, all figures were made with exactly the same parameters, showing clusters with a significance of p b 0.01. The significance was given as the negative decadic logarithm of the p-value (p = 10−x). 2.3.2.2. VBM. Cortical and gray matter volumes were calculated with the FSL-VBM processing stream [22,23] using the T1-weighted dataset (MPRAGE sequence). This processing stream used an optimized VBM protocol described previously [24]. First, we extracted the brains of the structural images using the FSL tool BET and performed a gray matter segmentation before registering them to the MNI 152 standard space using FNIRT for nonlinear registration [25]. The resulting images were averaged and flipped along the x-axis to create a left–right symmetric, study-specific gray matter template. Second, all native gray matter images were nonlinearly registered to this study-specific template and modulated to correct for local expansion (or contraction) due to the nonlinear component of the spatial transformation.
3. Results
Eight patients (40%) were male, and 12 (60%) were female. The mean age of the patients was 34.80 ± 13.31 years. The control group was sex- and age-matched. Their mean age was 33.50 ± 13.57 years. There was no significant difference in sex distribution or age between patients and control subjects. For patients, the mean disease duration was 20.42 ± 14.90 years. For patients characteristics see Table 1. 3.2. Neuropsychological tests Two patients did not complete neuropsychological testing. In one patient, results of the TAS are missing, and in the other patient, results of the WFT are also missing. We found a main effect for group F(15,22) = 2.45; p = 0.027 when comparing the neuropsychological test performance of patients with JME and matched healthy controls. Univariate analysis revealed decreased verbal and figural learning and memory functions in the delayed recall of the VLMT (p = 0.018) and in the DCS (sum score of bouts 1 to 6, p = 0.017) in patients with JME. Patients also showed slower performance in the D2 attention test (p = 0.010) and the TMT A (p = 0.035) and B (p = 0.036), which evaluate executive functions and attention. Impaired short-term memory was demonstrated by significantly decreased scores in the digit (p = 0.020) and block span forward (p = 0.023) of the WMS in the patient group. Regarding executive functions, we found semantic verbal fluency (p = 0.029) and verbal abstraction (p = 0.004) to be decreased in the patient group. The exploratory analysis of alexithymia, depression, anxiety, and personality traits revealed a tendency toward a decreased total TAS score (p = 0.063) and score for identification of emotions (p = 0.051) in the patient group, but results were not significant. Also, patients showed significantly higher scores on the BDI (p = 0.032). After Bonferroni correction, there was no significant correlation between alexithymia, depression, anxiety, and personality traits with cognitive test results. There was no significant correlation between disease duration or duration of seizure freedom and any of the neuropsychological test results or instruments used. Neuropsychological test results and questionnaires are summarized in Tables 2 and 3. 3.3. DTI We excluded the MRI of one patient from the analysis due to motion artifacts. The MRI of the corresponding age- and sex-matched control subject was also excluded from the analysis of DTI data. Analysis of the FA revealed areas of decreased FA in patients in anterior parts of the Corpus callosum, anterior parts of the cingulate gyrus and widespread frontal white matter bilaterally, right thalamus, and small areas in the right superior temporal gyrus and left internal capsule (Fig. 1). Radial diffusivity was significantly increased in the same areas (Fig. 2), whereas there was no difference in axial diffusion. To evaluate a possible association between these changes and changes in neuropsychological test results, we used the areas of altered FA in the Corpus callosum, the cingulate gyrus, and the biggest region of altered frontal white matter changes as regions of interest (ROI) to calculate the correlation between FA values and neuropsychological
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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Table 1 Patient characteristics. Sex
Age
Age at onset
Seizure types
Number of MJ per montha
Number of GTCS per montha
Number of AS per montha
Duration of seizure-free period since last seizurea (last seizure type)
Medication in mg/d
M M M F M F F F M F F F M M M F F F F F
58 32 23 29 43 17 52 51 23 23 51 41 54 34 45 24 23 31 22 20
3 17 11 11 14 16 10 14 19 13 12 14 Unknown 18 13 18 15 13 16 7
MJ, GTCS MJ, GTCS, AS MJ, GTCS MJ, GTCS, AS MJ, GTCS, AS MJ, GTCS, AS MJ MJ, GTCS MJ MJ, GTCS MJ, GTCS MJ, GTCS, AS MJ, GTCS, AS MJ, GTCS, AS MJ, GTCS MJ MJ, GTCS MJ, GTCS, AS MJ MJ, GTCS, AS
0 0 0 0 0 30 0 4 0 0 0 Rare 0 0 4 0 0 0 0 Very rare
0 0 0 0 0 4 0 0,5 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 months (GTCS) 132 months (GTCS) 32 months (GTCS) 30 months (GTCS) 30 months (GTCS) 1 day (AS, MJ), 1 month (GTCS) N120 months (MJ) 1 week (MJ), 2 months (GTCS) Unknown 48 months 18 months (MJ), 252 months (GTCS) Unknown (MJ), N120 months (GTCS) Unknown 168 months 1 week (MJ), 96 months (GTCS) 72 months (MJ) 8 months (GTCS) 60 months (AS), 180 months (GTCS) 72 months (MJ) Unknown (MJ), 48 months (GTCS)
LTG 125, LEV 3000, VPA 1500 LTG 50, LEV 2000, VPA 1250 VPA 1800 LEV 3000 LEV 4000, VPA 3000, CBZ 800 LTG 200, VPA 600–1200 No medication LEV 3750, VPA 1500 VPA 600 LTG 200 VPA 450 LTG 150 Primidon 250, PHT 600 LEV 2000 LTG 500 LTG 250 LTG 400 VPA 1500 LEV 1000 VPA 900
MJ: myoclonic jerks, GTCS: generalized tonic–clonic seizures, AS: absence seizures, LTG: lamotrigine, LEV: levetiracetam, VPA: valproate, CBZ: carbamazepine, PHT: phenytoin. a At the time of investigation.
4. Discussion
test results in patients with JME. After Bonferroni correction, none of these correlations was significant. The same ROIs were used to correlate FA values and results of the BDI, NEO FFI, STAI, and TAS. The correlation analysis revealed several associations between FA values in the cingulum and frontal white matter and personality traits in the NEO FFI (NEO FFI agreeableness and FA values in the right cingulum: R = − 0.649, p = 0.012; NEO FFI agreeableness and FA values in the left cingulum: R = −0.774, p = 0.001; NEO FFI conscientiousness and right frontal white matter: R = 0.581, p = 0.037; NEO FFI conscientiousness and right cingulum: R = 0.635, p = 0.020). After Bonferroni correction for multiple testing, the correlation between the left cingulum and the NEO FFI agreeableness still reached the significance level of p b 0.0012. There was no significant correlation between disease duration or duration of seizure freedom and FA values.
In the present study, we could show alterations in white matter microstructure in anterior parts of the Corpus callosum and in the cingulate gyrus of patients with JME as well as in widespread areas of frontal white matter bilaterally and the right thalamus. Changes in the cingulum were negatively correlated with the personality trait agreeableness. We observed no accompanying gray matter changes. Altered white matter integrity involved fibers of frontal thalamocortical networks and fibers connecting frontal areas of both hemispheres and might influence frontal lobe function in patients with JME. Accordingly, patients in this study showed impaired attention, executive functions and short-term memory, higher scores for depression, and a tendency toward decreased alexithymia scores.
3.4. VBM
4.1. Microstructural changes
VBM did not reveal any significant gray matter differences between patients with JME and healthy control subjects.
Earlier studies using different advanced neuroimaging methods reveal subtle structural and functional abnormalities in widespread frontal areas and frontal thalamocortical networks [1,6–17]. Thalamocortical dysfunction is even considered the key finding of JME by some authors [10]. Only few studies used DTI to investigate white matter microstructure in patients with JME and revealed changes in thalamocortical networks
Table 2 Neuropsychological test results. Test
Patients (mean ± SD)
Control subjects (mean ± SD)
pa
VLMT, bouts 1 to 5 VLMT, number of forgotten items in delayed recall VLMT, recognition DCS, bouts 1 to 6 D2 attention test TMT A TMT B WMS, digit span forward WMS, digit span backward WMS, block span forward WMS, block span backward WFT, semantic WFT, lexical WFT, lexical with alternating letters Finding similarities
56.16 ± 7.50 2.58 ± 1.38
59.90 ± 8.49 1.16 ± 2.09
0.159 0.018
13.47 ± 1.26 39.74 ± 10.76 155.47 ± 32.42 27.09 ± 8.45 74.19 ± 38.99 7.68 ± 2.08 7.32 ± 1.95 8.42 ± 1.89 8.42 ± 1.54 24.42 ± 5.67 14.05 ± 4.08 12.95 ± 3.98 23.05 ± 4.17
13.89 ± 2.21 47.59 ± 8.44 190.37 ± 45.95 21.92 ± 5.90 52.06 ± 20.84 9.26 ± 1.91 8.26 ± 1.79 9.74 ± 1.49 8.74 ± 1.70 30.47 ± 10.15 14.89 ± 3.91 14.00 ± 3.90 26.53 ± 2.61
0.475 0.017 0.010 0.035 0.036 0.020 0.127 0.023 0.551 0.029 0.520 0.416 0.004
VLMT: verbal learning and memory test, DCS: Diagnostikum für Cerebralschädigung, TMT: Trail Making Test A (digits only) and B (alternating letters and digits), WMS: Wechsler Memory Scale, WFT: Word Fluency Test. a ANOVA, italics: p b 0.05.
Table 3 Questionnaires. Test
Patients (mean ± SD)
Control subjects (mean ± SD)
p
TAS, description of emotion TAS, identification of emotions TAS, externally-oriented thinking TAS, total BDI STAI NEO FFI, neuroticism NEO FFI, extraversion NEO FFI, openness NEO FFI, compatibility NEO FFI, conscientiousness
2.45 ± 0.84 2.17 ± 0.57 2.43 ± 0.63 2.34 ± 0.58 10.15 ± 7.49 40.80 ± 4.68 28.53 ± 8.44 32.00 ± 6.35 35.00 ± 6.81 38.21 ± 6.03 37.92 ± 6.68
2.20 ± 0.82 1.81 ± 0.56 2.13 ± 0.57 2.03 ± 0.40 4.39 ± 6.70 43.72 ± 5.64 24.22 ± 11.78 32.94 ± 6.36 38.56 ± 5.25 41.06 ± 5.74 36.50 ± 5.73
0.347 0.051 0.133 0.063 0.032 0.120 0.245 0.680 0.100 0.148 0.529
BDI: Beck's Depression Inventory, NEO FFI: NEO five factor inventory, STAI: State-Trait Anxiety Inventory. TAS: Toronto Alexithymia Scale, italics: p b 0.05.
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
S. Knake et al. / Epilepsy & Behavior xxx (2017) xxx–xxx
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Fig. 1. Areas of altered FA in patients with JME compared with healthy controls. A blue–light blue color indicated clusters, where the measured FA was significantly decreased in the group with JME compared with controls, whereas the red to yellow color indicated a significant increase of the FA. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
including several frontal areas, the Corpus callosum, and fibers connecting medial and anterior thalamic nuclei with the frontal lobe [11,13,14]. In the present study, we included 20 patients with JME and used the hypothesis-free analysis approach TBSS. Tract-Based Spatial Statistics uses a white matter skeleton, which includes only voxels that are part of the white matter in all subjects, thereby avoiding confounding by interindividual anatomical brain differences. The results are very reliable at the cost of excluding brain regions with a high interindividual variability. Using this approach, we demonstrated widespread microstructural changes in patients in anterior parts of the Corpus callosum and cingulate gyrus bilaterally as well as in widespread areas of frontal white matter, thereby supporting earlier DTI results [11,13,14]. Earlier
studies could relate changes in myelination to altered RD values, but unchanged AD [28,29]. Therefore, the changes observed in this study might reflect changes in myelination and glial cell morphology. However, other factors like the degree of fiber density and orientation as well as fiber coherence and the number of crossing fibers might influence the observed changes. The white matter changes were not related to alterations in gray matter volume. This seems to contradict earlier study results [1,15,32], but results of earlier VBM studies are conflicting, showing either increased [1,32] or reduced [1,15] gray matter in frontal or temporal brain regions or normal results [33,34]. Gray matter changes therefore seem to be less reliable to characterize microstructural brain changes in patients with JME.
Fig. 2. Areas of altered RD in patients with JME compared with healthy controls. A blue–light blue color indicated clusters, where the measured RD was significantly decreased in the group with JME compared with controls, whereas the red to yellow color indicated a significant increase of the RD. For visualization purposes, clusters were dilated, using the mean dilation method of fslmath (a tool included in the FSL stream). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Knake S, et al, Microstructural white matter changes and their relation to neuropsychological deficits in patients with juvenile myoclonic epilepsy, Epilepsy Behav (2017), http://dx.doi.org/10.1016/j.yebeh.2017.08.031
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4.2. Neuropsychological and clinical correlation
4.3. Limitations
Patients with JME have a higher risk for psychiatric disorders including personality (Cluster B) and anxiety disorders and substance abuse. Also, these patients show deficits in frontal lobe cognitive performance, which correlated with psychiatric comorbidities in earlier studies [35]. We could confirm deficits in frontal lobe cognitive performance, attention, executive functions, and short-term memory as well as depression and a tendency toward alexithymia, but did not find a significant correlation between cognitive deficits and test results for depression, anxiety, alexithymia, and personality traits. An earlier study related mesiofrontal and frontobasal cortical abnormalities in surface morphometry to personality traits like emotional instability, immaturity, lack of discipline, and rapid mood changes in patients with JME [36]. In line with these results, we could show a negative correlation between microstructural changes in the cingulum and the personality trait agreeableness. Patients with JME also showed a reduced N-acetyl aspartate concentration in frontal brain regions in MRS and frontal hypometabolism in positron emission tomography (PET) studies related to frontal lobe cognitive dysfunction and cognitive flexibility [7,16,37]. The present results expanded our understanding of the pathophysiology underlying cognitive deficits by relating microstructural white matter changes to altered frontal lobe cognitive performance, attention, executive functions, and short-term memory on a group level, even though no significant correlation between these neuropsychological test results and FA values in these brain regions could be observed on a single patient level. The anterior part of the cingulate gyrus, which was affected in the present study, plays an important role in affection, cognition, monitoring behavior, detecting errors, and in adaptive decision making. Previous studies have related structural abnormalities in this brain area to impulsivity in bipolar disorder [38]. The changes in this brain area detected in the present study and its negative correlation with the personality trait agreeableness might therefore be a pathophysiological correlate for the impulsivity, emotional instability, and deficits in frontal lobe cognitive performance commonly seen in patients with JME [2,4]. This hypothesis is supported by an earlier study showing differences in gray matter volume in the anterior cingulum in subjects with high alexithymia scores as compared with low alexithymia scores [39]. The Corpus callosum contains fibers connecting superior frontal regions, premotor regions, and the supplementary motor area (SMA) [13,34]. Most interestingly, earlier studies also describe changes in the SMA of patients with JME [14,17]. Vollmar et al. could show coactivation of cognitive networks and primary and supplementary motor areas during cognitive tasks in patients with JME, which might relate to the provocation of myoclonic jerks by cognitive tasks or complicated sequential movements in patients with JME [17]. The same group also found increased structural and functional connectivity between mesial frontal regions, the prefrontal cognitive cortex and motor, and SMAs which might underlie the present study results and may also be related to the facilitation of myoclonic jerks by cognitive or complicated motor tasks [40]. Besides neuropsychological deficits, the microstructural changes observed in the present study might also be related to the generation of spike–wave discharges. Functional MRI revealed activation in mesial frontal areas several seconds before generalized spike–wave complexes appeared on the EEG, followed by widespread deactivation in frontal, parietal, and cingulate areas during spike–wave complexes [41]. Also, microstructural changes in the Corpus callosum suggest alterations in commissural connectivity and, therefore, have been related to abnormal bihemispheric synchronization during generalized spike–wave discharges in earlier studies [13,14,42]. The changes in thalamocortical networks and Corpus callosum in the present study might therefore be associated with the generation of the spike–wave discharges seen in patients with idiopathic generalized epilepsy.
Limitations of this study include a possible confound by anticonvulsant medication. Other factors that might influence the results and explain the heterogeneity of neuroimaging data in this and other JME imaging studies include disease duration, seizure frequency, interictal EEG activity, and the genetic heterogeneity of JME. 4.4. Conclusions The neuropsychological and neuroimaging data presented here support a contribution of frontal brain areas and frontal thalamocortical networks in the pathophysiology of JME [1,7,11,13,14,16,32]. Our findings provide insight into microstructural white matter changes and a possible relation to personality traits, alexithymia, and decreased frontal lobe cognitive performance in these patients. Also, these findings might be related to altered interhemispheric connectivity and connectivity of the SMA, which is thought to be the correlate of abnormal synchronization during generalized spike and wave discharges and of praxis induction, i.e., the facilitation of myoclonic jerking by cognitive or complicated motor tasks. Funding The work was supported by the research grant of the Rhön-Klinikum AG (grant number FL-67). The funding source was not involved in the study design; data collection, analysis, and interpretation; manuscript preparation; or the decision to submit the article for publication. Conflicts of interest Tobias Kniess has received speaker honoraria from Eisai and UCB. FR has received personal fees from Bayer-Vital, Cerbomed, Eisai, Hexal, and Sandoz, personal fees and support for continuing medical education activities from Desitin, Novartis, Shire, and UCB Pharma, and grants from DFG (German Research Foundation) and the European Union. Katja Menzler has received honoraria as advisory board member from Eisai and UCB. The other authors report no conflict of interest. References [1] Anderson J, Hamandi K. Understanding juvenile myoclonic epilepsy: contributions from neuroimaging. Epilepsy Res 2011;94:127–37. [2] Janz D, Christian W. Impulsiv-Petit mal. Dtsch Z Nervenheilkd 1957;176:346–86. [3] Prasad A, Kuzniecky RI, Knowlton RC, Welty TE, Martin RC, Mendez M, et al. Evolving antiepileptic drug treatment in juvenile myoclonic epilepsy. Arch Neurol 2003;60: 1100–5. [4] Frascareli Pascalicchio T, de Araujo Filho GM, Helena da Silva Noffs M, Lin K, Otavio S, Vidal-Dourado M, et al. Neuropsychological profile of patients with juvenile myoclonic epilepsy: a controlled study of 50 patients. Epilepsy Behav 2007;10:263–7. [5] Holmes MD, Quiring J, Tucker DM. Evidence that juvenile myoclonic epilepsy is a disorder of frontotemporal corticothalamic networks. Neuroimage 2010;49:80–93. [6] Kim J Bin, Suh S il, Seo WK, Oh K, Koh SB, Kim JH. Altered thalamocortical functional connectivity in idiopathic generalized epilepsy. Epilepsia 2014;55:592–600. [7] Swartz BE, Simpkins F, Halgren E, Mandelkern M, Brown C, Krisdakumtorn T, et al. Visual working memory in primary generalized epilepsy: an (18)FDG-PET study. Neurology 1996;47:1203–12. [8] Hattingen E, Luckerath C, Pellikan S, Vronski D, Roth C, Knake S, et al. Frontal and thalamic changes of GABA concentration indicate dysfunction of thalamofrontal networks in juvenile myoclonic epilepsy. Epilepsia 2014;55:1030–7. [9] Paulus FM, Krach S, Blanke M, Roth C, Belke M, Sommer J, et al. Fronto-insula network activity explains emotional dysfunctions in juvenile myoclonic epilepsy: combined evidence from pupillometry and fMRI. Cortex 2015;65:219–31. [10] Koepp MJ, Woermann F, Savic I, Wandschneider B. Juvenile myoclonic epilepsy — neuroimaging findings. Epilepsy Behav 2013;28:S40–4. [11] Deppe M, Kellinghaus C, Duning T, Moeddel G, Mohammadi S, Deppe K, et al. Nerve fiber impairment of anterior thalamocortical circuitry in juvenile myoclonic epilepsy. Neurology 2008;71:1981–5. [12] O'Muircheartaigh J, Vollmar C, Barker GJ, Kumari V, Symms MR, Thompson P, et al. Abnormal thalamocortical structural and functional connectivity in juvenile myoclonic epilepsy. Brain 2012;135:3635–44.
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