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Intrinsic hippocampal and thalamic networks in temporal lobe epilepsy with hippocampal sclerosis according to drug response
Seizure: European Journal of Epilepsy 76 (2020) 32–38
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Intrinsic hippocampal and thalamic networks in temporal lobe epilepsy with hippocampal sclerosis according to drug response
T
Ho-Joon Leea, Kang Min Parkb,* a b
Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
A R T I C LE I N FO
A B S T R A C T
Keywords: Epilepsy Magnetic resonance imaging Connectivity
Purpose: The aim of this study was to investigate whether intrinsic hippocampal or thalamic networks in patients with temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS) were different according to antiepileptic drug (AED) response. Methods: We enrolled 80 patients with TLE with HS and 40 healthy controls. Of the patients with TLE with HS, 43 were classified as a drug-resistant epilepsy (DRE) group, whereas 37 patients were enrolled as a drug-controlled epilepsy (DCE) group. We investigated the structural connectivity of the global brain, intrinsic hippocampal, and intrinsic thalamic networks based on structural volumes in the patients with DRE and DCE, and analyzed the differences between them. Results: There were significant alterations of the intrinsic hippocampal network compared with healthy controls. The average degree and the global efficiency were decreased, whereas the characteristic path length was increased in the patients with DRE compared with those in healthy controls. In the patients with DCE, only the small-worldness index was decreased compared with healthy controls. Compared to the patients with DCE, the mean clustering coefficient was increased in the patients with DRE. Conclusion: We found that the intrinsic hippocampal network in patients with TLE with HS was different according to AED response. The patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE compared with healthy controls. These findings suggested that the hippocampal network might be related to AED response and could be a new biomarker of medical outcome in patients with TLE with HS.
1. Introduction Temporal lobe epilepsy with hippocampal sclerosis (TLE with HS) is the most common etiology in drug-resistant epilepsy (DRE) of adults [1,2]. Pathological and neuroimaging studies have showed that patients with TLE with HS have hippocampal atrophy [1,2]. In addition, several quantitative magnetic resonance imaging (MRI) studies have demonstrated the volume reductions in the extra-temporal lobe as well as the extra-hippocampus in patients with TLE with HS [3]. With emerging evidences that epilepsy is a network disease, there have been numerous research studies with connectivity analysis in TLE with HS. Bernhardt et al. investigated the structural networks using graph theoretical analysis of MRI-based cortical thickness correlations, and patients with TLE with HS showed increased path length and clustering coefficient, altered distributions of hubs, and vulnerability to
targeted attacks [4]. Another study with diffusion tensor imaging (DTI) demonstrated a marked reduction of pair-wise connections, which was strongly lateralized to the ipsilateral temporal lobe in patients with TLE with HS compared with healthy controls [5]. A previous study investigated the anterior and posterior hippocampal networks using resting-state functional MRI (rs-fMRI), and it found distinct reorganized patterns of intra-hemispheric functional connectivity between anterior and posterior hippocampal networks, which were associated with deficits in memory [6]. All of these studies suggest alterations of structural or functional connectivity in patients with TLE with HS. However, most of these previous studies have analyzed the global brain network or local network using the hippocampus and thalamus as a whole structure despite their heterogeneity. There have been no studies regarding intrinsic hippocampal and thalamic networks in patients with TLE with HS by divisions of hippocampal subfields or thalamic nuclei.
⁎ Corresponding author at: Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea. E-mail address: [email protected] (K.M. Park).
https://doi.org/10.1016/j.seizure.2020.01.010 Received 7 October 2019; Received in revised form 31 December 2019; Accepted 15 January 2020 1059-1311/ © 2020 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.
Seizure: European Journal of Epilepsy 76 (2020) 32–38
H.-J. Lee and K.M. Park
subjects. All heathy control subjects had normal neurological findings and no history of medical, neurological, or psychiatric disease. All of them had a normal MRI on visual inspection.
It is known that approximately 60 % of patients with epilepsy remain constantly seizure-free with antiepileptic drugs (AEDs), whereas 25 % of those never achieve seizure control with medication [7]. In the past several years, several researchers have investigated the potential biomarkers of brain MRI for response to AED. Cendes and his colleague first investigated the relationship between gray matter volumes and patterns of AED response [8]. They found that patients with drug-resistant epilepsy (DRE) had more widespread gray matter atrophy compared with patients with drug-controlled epilepsy (DCE). A recent study using rs-fMRI demonstrated that healthy controls and patients with DCE showed similar functional connectivity patterns, whereas patients with DRE exhibited a significant bilateral decrease in thalamohippocampal functional connectivity [9]. Another study using rs-fMRI also showed significance for longer paths and reduced local efficiency within the default mode network of patients with DCE compared with those with DRE [10]. All of these studies suggest the potential biomarkers of brain MRI as an AED response. However, no research studies have investigated the differences of intrinsic hippocampal and thalamic networks in patients with TLE with HS according to AED response. The aim of this study was to investigate whether intrinsic hippocampal or thalamic networks using graph theoretical analysis based on hippocampal subfields and thalamic nuclei volumes in patients with TLE with HS were different according to AED response. We hypothesized that intrinsic hippocampal or thalamic networks were different according to AED response.
2.2. Acquisition of brain MRI All MRI scans were performed using a 3.0 T MRI scanner (AchievaTx, Phillips Healthcare, Best, The Netherlands) equipped with a 32-channel head coil. All patients with TLE with HS and healthy controls were subjected to conventional brain MRI protocols, including FLAIR and T2-weighted imaging, to exclude subjects with abnormal MRI findings on visual inspection. Moreover, all patients underwent contiguous 3D volumetric T1-weighted imaging with a high sagittal resolution appropriate for the analysis of structural volume. The 3D T1weighted images were obtained using a turbo-field echo sequence with the following parameters: TI =1300 ms, TR/TE = 8.6/3.96 ms, flip angle = 8°, and 1 mm3 isotropic voxel size. 2.3. Graph theoretical analysis The volumetric analysis was performed using the ‘recon-all’ function in the FreeSurfer program (http://surfer.nmr.mgh.harvard.edu/). Briefly, the processing stream of FreeSurfer consisted of several stages as follows: volume registration with the Talairach atlas, bias field correction, initial volumetric labeling, nonlinear alignment to the Talairach space, and final labeling of the volume. Then, the cortical surface of each hemisphere was inflated to an average spherical surface to locate both the pial surface and the white/gray matter boundary. Brain structures were segmented automatically on the basis of T1weighted images. The hippocampal subfields and thalamic nuclei were also segmented. We obtained the absolute structural volumes from these automated methods. Next, the volumetric measures were calculated using the following equation: the structural volumes (%) = (absolute structural volumes/total intracranial volumes) × 100. We performed structural connectivity analysis using Brain Analysis Using Graph Theory (BRAPH; http://braph.org). They were built for each group as a collection of nodes representing brain regions connected by edges corresponding to the connections between them. In the global brain network, the nodes were defined using the cortical volumes from 60 cortical and 13 subcortical regions provided by FreeSurfer (Table 2). In the hippocampal network analysis, we used the volumes of the right and left CA1, CA2-3, CA4, fimbria, hippocampus-amygdalatransition-area, hippocampal fissure, presubiculum, parasubiculum, subiculum, molecular layer, granule cell and molecular layer of dentate gyrus, and hippocampal tail. In the thalamic network analysis, we used the volumes of 50 individual thalamic nuclei, including right and left anteroventral nuclei in the anterior group; right and left laterodorsal and lateral posterior nuclei in the lateral group; right and left ventral anterior, ventral anterior magnocellular, ventral lateral anterior, ventral lateral posterior, ventromedial, and ventral posterolateral nuclei in the ventral group; right and left central medial, central lateral, paracentral, centromedian, and parafascicular nuclei in the intralaminar group; right and left paratenial, medial ventral, mediodorsal medial magnocellular, and mediodorsal lateral parvocellular nuclei in the medial group; and right and left lateral geniculate, medial geniculate,
2. Methods and materials 2.1. Subjects This study was approved by our hospital’s institutional review board. We retrospectively enrolled 80 patients with TLE with HS who visited the neurology department of our hospital from March 2010 to February 2019. All of the patients met the following criteria: 1) typical unilateral hippocampal sclerosis without any other lesions on brain MRI with visual inspections [increased T2-weighted/fluid-attenuated inversion recovery (FLAIR) imaging and reduced hippocampal volumes], 2) seizure semiology and electroencephalographic (EEG) findings compatible with mesial TLE, and 3) three-dimensional (3D) T1weighted images that were suitable for volumetric analysis. We investigated the clinical characteristics, including age, sex, age of seizure onset, duration of epilepsy, and AED load. The AED load was defined as the ratio “prescribed daily doses/defined daily doses, defined as the assumed average daily dose for maintenance therapy of adults with primary indications”, according to WHO definition (https://www. whocc.no/atc_ddd_index). We defined the patients with DRE by failure in adequate trials of two tolerated and appropriately selected AEDs to achieve sustained seizure freedom, based on the recommended criteria for DRE by the International League Against Epilepsy (ILAE) [11]. The others were defined as the patients with DCE. Of the 80 patients with TLE with HS, 43 patients were classified as a DRE group, whereas 37 patients were enrolled as a DCE group (Table 1). They included 37 patients (46 %) involving right HS and 43 patients (54 %) with left HS. The control group included 40 age- and sex-matched healthy
Table 1 Differences of clinical characteristics of patients with temporal lobe epilepsy with hippocampal sclerosis according to antiepileptic drug response. Characteristics
Patients with drug-resistant epilepsy (N = 43)
Patients with drug-controlled epilepsy (N = 37)
p-value
Age, years Male, N (%) Age of seizure onset, years Duration of epilepsy, months Antiepileptic drug load
40.1 ± 11.2 19 (44.1) 21.5 ± 12.1 224.1 ± 144.4 2.63 ± 1.04
39.1 ± 16.8 15 (40.5) 23.9 ± 16.0 178.4 ± 159.7 1.10 ± 0.91
0.751 0.743 0.499 0.237 * < 0.001
* p < 0.05. 33
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Table 2 Regions of interest as a node for network analysis in patients with temporal lobe epilepsy with hippocampal sclerosis and healthy subjects. Network
Side
Regions of interest as a node
Global brain network
Right
thalamus, caudate, putamen, pallidum, *†hippocampus, amygdala, bankssts, caudal anterior cingulate, caudal middle frontal, cuneus, entorhinal, fusiform, inferior parietal, inferior temporal, isthmus cingulate, lateral occipital, lateral orbitofrontal, lingual, medial orbitofrontal, middle temporal, parahippocampal, paracentral, pars opercularis, pars orbitalis, pars triangularis, pericalcarine, postcentral, posterior cingulate, precentral, precuneus, rostral anterior cingulate, rostral middle frontal, superior frontal, superior parietal, superior temporal, supramarginal, frontal pole, temporal pole, transverse temporal, insula cortex thalamus, caudate, putamen, pallidum, *†hippocampus, amygdala, bankssts, caudal anterior cingulate, caudal middle frontal, cuneus, entorhinal, fusiform, inferior parietal, inferior temporal, isthmus cingulate, lateral occipital, lateral orbitofrontal, lingual, medial orbitofrontal, middle temporal, parahippocampal, paracentral, pars opercularis, pars orbitalis, pars triangularis, pericalcarine, postcentral, posterior cingulate, precentral, precuneus, rostral anterior cingulate, rostral middle frontal, superior frontal, superior parietal, superior temporal, supramarginal, frontal pole, temporal pole, transverse temporal, insula cortex brainstem *†CA1, *†CA2-3, *†CA4, *†fimbria, *hippocampus-amygdala-transition-area, hippocampal fissure, *presubiculum, *parasubiculum, *†subiculum, *†molecular layer, *†granule cell and molecular layer of dentate gyrus, *hippocampal tail *†CA1, *†CA2-3, *†CA4, *†fimbria, hippocampus-amygdala-transition-area, hippocampal fissure, *†presubiculum, parasubiculum, *†subiculum, *†molecular layer, *†granule cell and molecular layer of dentate gyrus, †hippocampal tail anteroventral, laterodorsal, lateral posterior, ventral anterior, ventral anterior magnocellular, ventral lateral anterior, ventral lateral posterior, ventromedial, ventral posterolateral, central medial, central lateral, paracentral, centromedian, *†parafascicular, paratenial, medial ventral, mediodorsal medial magnocellular, mediodorsal lateral parvocellular, lateral geniculate, medial geniculate, suprageniculate, pulvinar anterior, pulvinar inferior, pulvinar lateral, pulvinar medial nuclei anteroventral, laterodorsal, lateral posterior, ventral anterior, ventral anterior magnocellular, ventral lateral anterior, ventral lateral posterior, ventromedial, ventral posterolateral, central medial, central lateral, paracentral, centromedian, *parafascicular, paratenial, medial ventral, mediodorsal medial magnocellular, mediodorsal lateral parvocellular, lateral geniculate, medial geniculate, †suprageniculate, pulvinar anterior, pulvinar inferior, pulvinar lateral, pulvinar medial nuclei
Left
Hippocampal network
Right and Left Right Left
Thalamic network
Right
Left
*Regions of significantly altered volumes in patients with drug-resistant epilepsy compared with healthy controls (p < 0.001). †Regions of significantly altered volumes in patients with drug-controlled epilepsy compared with healthy controls (p < 0.001).
continuous variables with normal distributions were presented as the mean value ± standard deviation, and those without normal distribution were described as the median value with the range. A two-sided pvalue less than 0.05 was considered to indicate statistical significance for all analyses. The p-value was corrected for false discovery rate in the case of nodal measures. In the analysis of volume differences between two groups, we set the p-value at 0.001 with Bonferroni correction (0.05/50). All statistical tests were performed using MedCalc® (MedCalc Software version 19, Ostend, Belgium; https://www.medcalc. org; 2019).
suprageniculate, pulvinar anterior, pulvinar inferior, pulvinar lateral, and pulvinar medial nuclei in the posterior group. The edges were calculated as the partial correlation coefficients between every pair of brain regions while controlling for the effects of age and gender. For each group, a structural undirected and weighted connectivity matrix was built with setting a threshold 0.5. Negative correlations were set to zero, and only positive values were used in the calculation. To detect differences between groups in the global brain, hippocampal, and thalamic networks topology, we calculated the average degree, the characteristic path length, the mean clustering coefficient, the global efficiency, the local efficiency, and the small-worldness index. To assess differences between groups in a local network topology, we calculated betweenness centrality. The average degree, total number of edges connected to a node, is average of the degrees of all nodes. The characteristic path length is average of the path lengths, average distance from a node to all other nodes, of all nodes. The mean clustering coefficient is average of the clustering coefficients, fraction of triangles present around a node, of all nodes. The global efficiency is average of the global efficiencies, average of the inverse shortest path length from a node to all other nodes, of all nodes. The local efficiency is average of the local efficiencies, global efficiency of a node calculated on the subgraph created by the node’s neighbors, of all nodes. The smallworldness graph has a similar characteristic path length as a random graph with the same degree distribution but is significantly more clustered. The betweenness centrality identifies nodes located on the most traveled paths, by measuring the number of shortest pathways in the network that pass through a given node [12–14]. We investigated the differences of network measures between the patients with DRE and DCE.
3. Results 3.1. Clinical characteristics of subjects Table 1 shows the clinical characteristics of patients with TLE with HS according to AED response. The clinical characteristics, such as age, male ratio, age of seizure onset, and duration of epilepsy in the patients with DRE were not different from those with DCE. However, the AED load was significantly higher in the patients with DRE compared with those with DCE (2.63 vs. 1.10, p < 0.001). The AED in the patients with DRE was as follow: lamotrigine in 15 patients, valproate in 12 patients, levetiracetam in 11 patients, carbamazepine in 11 patients, topiramate in 6 patients, oxcarbazepine in 5 patients, zonisamide in 4 patients, phenytoin in 3 patients, and clonazepam in 3 patients. The AED in the patients with DCE was as follow: lamotrigine in 13 patients, valproate in 12 patients, levetiracetam in 9 patients, carbamazepine in 7 patients, topiramate in 4 patients, zonisamide in 4 patients, clonazepam in 4 patients, oxcarbazepine in 3 patients, phenytoin in 3 patients, lacosamide in 1 patient, and perampanel in 1 patient. Of the 40 normal controls, 17 (42.5 %) were male and 23 (57.5 %) were female. The mean age was 38.1 ± 5.5 years. The age and male ratio in healthy controls were not significantly different from those in patients with DRE and DCE (p = 0.3085 and p = 0.8777 in patients with DRE; p = 0.7214 and p = 0.8625 in patients with DCE).
2.4. Statistical analysis Comparisons of the factors were analyzed using the chi-square test for categorical variables. Continuous variables were analyzed using the Student t-test or the Mann-Whitney U test according to whether data were normally distributed. In the comparison of the network measures, we tested the statistical significance of the differences using nonparametric permutation tests with 1000 permutations. The categorical variables were presented as the frequency and percentage. The
3.2. Structural volumes according to AED response Table 2 shows the regions of altered volumes in patients with TLE 34
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H.-J. Lee and K.M. Park
3.4. Structural local networks according to AED response
with HS compared with healthy controls. In the patients with DRE, there were alterations of volume in several regions compared with healthy controls, including the right hippocampus (0.2253 % vs. 0.2839 %), CA1 (0.0353 % vs. 0.0454 %), CA3 (0.0115 % vs. 0.0148 %), CA4 (0.0139 % vs. 0.0187 %), fimbria (0.0040 % vs. 0.0057 %), hippocampus-amygdala-transition-area (0.0034 % vs. 0.0042 %), presubiculum (0.0165 % vs. 0.0209 %), parasubiculum (0.0030 % vs. 0.0036 %), subiculum (0.0250 % vs. 0.0315 %), molecular layer (0.0316 % vs. 0.0411 %), granule cell and molecular layer of dentate gyrus (0.0162 % vs. 0.0218 %), hippocampal tail (0.0313 % vs. 0.0371 %), and parafasicular nucleus (0.0035 % vs. 0.0042 %), and left hippocampus (0.22.5 % vs. 0.2701 %), CA1 (0.0347 % vs. 0.0434 %), CA2-3 (0.0108 % vs. 0.0133 %), CA4 (0.0137 % vs. 0.0175 %), fimbria (0.0044 % vs. 0.0057 %), presubiculum (0.0186 % vs. 0.0218 %), molecular layer (0.0315 % vs. 0.0394 %), granule cell and molecular layer of dentate gyrus (0.0161 % vs. 0.0207 %), and parafasicular nucleus (0.0036 % vs. 0.0041 %). In the patients with DCE, there were also alterations of volume in several regions compared with healthy controls, including the right hippocampus (0.2410 % vs. 0.2839 %), CA1 (0.0379 % vs. 0.0454 %), CA3 (0.0123 % vs. 0.0148 %), CA4 (0.0150 % vs. 0.0187 %), fimbria (0.0044 % vs. 0.0057 %), subiculum (0.0261 % vs. 0.0315 %), molecular layer (0.0336 % vs. 0.0411 %), granule cell and molecular layer of dentate gyrus (0.0175 % vs. 0.0218 %), and parafasicular nucleus (0.0036 % vs. 0.0042 %), and left hippocampus (0.2233 % vs. 0.2701 %), CA1 (0.0344 % vs. 0.0434 %), CA2-3 (0.0112 % vs. 0.0133 %), CA4 (0.0138 % vs. 0.0175 %), fimbria (0.0044 % vs. 0.0057 %), presubiculum (0.0180 % vs. 0.0218 %), subiculum (0.0252 % vs. 0.0311 %), molecular layer (0.0312 % vs. 0.0394 %), granule cell and molecular layer of dentate gyrus (0.0162 % vs. 0.0207 %), hippocampal tail (0.0297 % vs. 0.0362 %), and suprageniculate nucleus (0.0010 % vs. 0.0008 %). In the analysis of the alterations of structural volumes between the patients with DRE and DCE, there were no regions that were different between them.
In the patients with DRE, there were alterations of the intrinsic hippocampal local network compared with healthy controls. The betweenness centralities of the left hippocampal fissure, left fimbria, and right fimbria were significantly increased, whereas those of the right molecular layer, and right granule cell and molecular layer of dentate gyrus were decreased compared to healthy controls (0.138 vs. 0.000, p = 0.001; 0.146 vs. 0.000, p = 0.001; 0.332 vs. 0.000, p = 0.010; 0.000 vs. 0.015, p = 0.019; 0.000 vs. 0.004, p = 0.035; respectively). In the patients with DCE, there were also alterations of the intrinsic hippocampal local network. The betweenness centralities of the left and right fimbria were increased, whereas those of the left CA1, left granule cell and molecular layer of dentate gyrus, and right molecular layer were decreased compared to healthy controls (0.023 vs. 0.000, p = 0.045; 0.260 vs. 0.000, p = 0.003; 0.000 vs. 0.063, p = 0.003; 0.000 vs. 0.027, p = 0.006; 0.000 vs. 0.015, p = 0.006; respectively). The betweenness centralities of the left CA1, and left hippocampal fissure were increased, whereas that of the left parasubiculum was decreased in the patients with DRE compared to those with DCE controls (0.027 vs. 0.000, p = 0.030; 0.138 vs. 0.000, p = 0.027; 0.000 vs. 0.039, p = 0.028; respectively). However, in the patients with DRE and DCE, the betweenness centralities of all of the nodes in the global brain and intrinsic thalamic networks were not different from those of healthy controls. 4. Discussion We found that the intrinsic hippocampal network in patients with TLE with HS was different according to AED response. Although both the patients with DRE and DCE commonly had hippocampal atrophy compared with healthy controls, the patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE. In addition, we found that the mean clustering coefficient was increased in the patients with DRE compared to those with DCE. However, the global brain and intrinsic thalamic networks in the patients with DRE and DCE were not different from those in healthy controls. We also demonstrated that there were significant differences of alterations in the intrinsic hippocampal local network according to AED response. These findings suggested that the intrinsic hippocampal network might be related to AED response, and it could be a new biomarker of medical outcome in patients with TLE with HS. The introduction of high-field MRI scanners coupled with development of imaging analysis software enables automated segmentation of the hippocampal subfields and thalamic nuclei. Previous studies found subfield-specific volumetric differences in patients with epilepsy compared with healthy controls, demonstrating that certain subfields may be more susceptible to changes caused by the disease than others subfields [15,16]. The ILAE proposed the classification of TLE with HS by visual histopathologic examination of en bloc resected surgical specimens [17]. ILAE type 1 refers always to severe neuronal cell loss and gliosis predominantly in CA1 and CA4 regions, compared with CA1 predominant neuronal cell loss and gliosis (HS ILAE type 2), or CA4 predominant neuronal cell loss and gliosis (HS ILAE type 3). Interestingly, the imaging analysis based on MRI showed high compatibility with histopathological findings [16]. In this study, we found that both the patients with DRE and DCE commonly had hippocampal atrophy compared with healthy controls, especially in CA1, CA2-2, CA4, fimbria, subiculum, and the granule cell and molecular layer of the dentate gyrus. In addition to gross volumetric analysis, the understanding of connectivity patterns of different hippocampal subfields may demonstrate important details regarding epileptogenic abnormality of the subfields [15,16]. This is the first attempt to quantify intrinsic hippocampal connectivity using hippocampal subfields volumes in patients with TLE with HS according to AED response. In this study, we found that the patients with DRE had decreased average degree and global
3.3. Structural global networks according to AED response Table 3 shows the alterations of structural connectivity, including the global brain, intrinsic hippocampal, and intrinsic thalamic networks, in patients with TLE with HS according to AED response compared with healthy controls. In the patients with DRE, there were alterations of the intrinsic hippocampal network compared with healthy controls (Fig. 1). The average degree and the global efficiency were significantly decreased, whereas the characteristic path length was increased in the patients with DRE compared with those in healthy controls (7.714 vs. 12.813, p = 0.001; 0.411 vs. 0.565, p = 0.025; 3.637 vs. 2.067, p = 0.001; respectively). However, all of the network measures in the global brain and intrinsic thalamic networks were not different from healthy controls. In the patients with DCE, there were alterations of the intrinsic hippocampal network compared with healthy controls. The smallworldness index was significantly decreased in the patients with DCE compared with that in healthy controls. However, all of the other network measures in the intrinsic thalamic network were not different from those of healthy controls. Furthermore, the global brain and intrinsic thalamic networks were not different from those of healthy controls (0.719 vs. 0.950, p = 0.013). In the analysis of the alterations of structural network between the patients with DRE and DCE, there was significant difference of the intrinsic hippocampal network. Compared to the patients with DCE, the mean clustering coefficient was significantly increased in the patients with DRE (0.343 vs. 0.483, p = 0.015). However, all of the network measures in the global brain and intrinsic thalamic networks were not different between them. 35
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Table 3 Alterations of structural connectivity, including the global brain, hippocampal, and thalamic networks, in patients with temporal lobe epilepsy with hippocampal sclerosis according to antiepileptic drug response. Global brain network
average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness
Patients with DRE 77.457 2.777 0.401 1.458 0.350 0.951 Patients with DCE 78.469 2.198 0.510 2.385 0.477 0.947 Patients with DRE 77.457 2.777 0.401 1.458 0.351 0.952
Healthy controls 77.432 2.598 0.438 1.759 0.398 0.944 Healthy controls 77.432 2.598 0.438 1.759 0.399 0.943 Patients with DCE 78.469 2.198 0.510 2.385 0.477 0.948
Difference −0.025 −0.179 0.037 0.302 0.048 −0.006 Difference −1.037 0.400 −0.071 −0.625 −0.078 −0.004 Difference 1.012 −0.579 0.109 0.927 0.126 −0.003
CI lower −6.349 −1.081 −0.138 −1.004 −0.189 −0.043 CI lower −3.034 −0.897 −0.145 −1.245 −0.173 −0.050 CI lower −3.977 −0.691 −0.118 −0.997 −0.154 −0.046
CI upper 4.394 1.027 0.141 0.973 0.168 0.034 CI upper 4.036 0.883 0.151 1.336 0.181 0.056 CI upper 2.514 0.704 0.118 1.013 0.148 0.042
p-value 0.888 0.799 0.729 0.666 0.644 0.831 p-value 0.480 0.446 0.430 0.424 0.468 0.839 p-value 0.407 0.152 0.137 0.141 0.163 0.990
Patients with DRE 14.250 3.638 0.412 1.168 0.483 0.868 Patients with DCE 22.4167 2.732 0.491 1.262 0.343 0.719 Patients with DRE 14.250 3.638 0.412 1.168 0.483 0.873
Healthy controls 22.917 2.068 0.566 1.579 0.537 0.949 Healthy controls 22.9167 2.068 0.566 1.579 0.537 0.950 Patients with DCE 22.417 2.732 0.491 1.262 0.343 0.719
Difference 8.667 −1.570 0.154 0.411 0.053 0.080 Difference 0.5 −0.665 0.075 0.318 0.193 0.231 Difference 8.167 −0.906 0.079 0.093 −0.140 −0.155
CI lower −2.461 −0.981 −0.117 −0.543 −0.197 −0.183 CI lower −1.6696 −0.906 −0.128 −0.610 −0.168 −0.171 CI lower −8.258 −1.175 −0.078 −0.192 −0.104 −0.136
CI upper 2.003 0.901 0.119 0.572 0.185 0.181 CI upper 1.7908 0.897 0.123 0.580 0.166 0.186 CI upper 8.080 1.149 0.088 0.232 0.126 0.160
p-value *0.001 *0.004 *0.025 0.288 0.628 0.549 p-value 0.747 0.201 0.325 0.395 0.053 *0.028 p-value 0.087 0.221 0.162 0.583 *0.015 0.066
CI upper 3.555 0.877 0.177 1.359 0.204 0.061 CI upper 3.500 0.736 0.149 1.081 0.190 0.105 CI upper 3.478 0.822 0.151 1.089 0.163 0.091
p-value 0.982 0.790 0.746 0.743 0.753 0.884 p-value 0.593 0.595 0.680 0.715 0.651 0.350 p-value 0.610 0.802 0.912 0.914 0.865 0.555
Hippocampal network average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness Thalamic network average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness
average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness
Patients with DRE 48.280 2.073 0.551 2.189 0.515 0.935 Patients with DCE 46.640 2.218 0.538 2.133 0.493 0.897 Patients with DRE 48.280 2.073 0.551 2.189 0.515 0.933
Healthy controls 48.200 1.968 0.580 2.420 0.552 0.942 Healthy controls 48.200 1.968 0.580 2.420 0.552 0.943 Patients with DCE 46.640 2.218 0.538 2.133 0.493 0.899
Difference −0.080 −0.105 0.029 0.230 0.037 0.007 Difference 1.560 −0.250 0.042 0.287 0.059 0.046 Difference −1.640 0.145 −0.013 −0.057 −0.022 −0.035
CI lower −3.675 −0.823 −0.183 −1.311 −0.196 −0.067 CI lower −3.196 −0.735 −0.153 −1.133 −0.177 −0.086 CI lower −3.997 −0.806 −0.146 −1.010 −0.168 −0.095
DRE: drug-resistant epilepsy, DCE: drug-controlled epilepsy. * p < 0.05.
DRE could induce decreased intrinsic hippocampal connectivity [18]. Conversely, there is evidence suggesting that patients with DRE experience synaptic reorganization within certain cortical structures, such as the hippocampus [19], and this change can produce resistance to AEDs. Thus, causality between disruptions of the intrinsic hippocampal network and poor AED response were not clearly determined. In
efficiency with increased characteristic path length compared with healthy controls, and the patients with DCE only had a decreased smallworldness index, which suggests that the patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE. The mechanisms for this finding are not fully understood. Seizureinduced excito-toxic damage by more frequent seizures in patients with 36
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compared with the patients with DCE and healthy controls [30]. However, all of these studies did not investigate the intrinsic thalamic network, but the connections between the thalamus as a whole and other structures. In this study, we found that there were no alterations of the intrinsic thalamic network in both the patients with DRE and DCE compared with healthy controls. The reason is unknown, but unlike the hippocampus (the primary origin of TLE with HS), the relationship among the thalamic nuclei seems to be relatively maintained despite involvements of TLE with HS. There were several limitations to this study. First, this was a retrospective study. Thus, we were unable to discern whether the findings observed in this study were effects of epilepsy or whether they were underlying causes that made the participants susceptible to seizure activity. In addition, AED therapy could effect on brain connectivity. One connectivity study using EEG showed that AED significantly enhanced both global power spectral density and connectivity for all frequency bands, similar for all connectivity measures [31]. Another study based on functional MRI demonstrated the effect of AED on the functional MRI activation pattern, resulting alterations of brain connectivity [32]. Although our results of connectivity were produced by co-plasticity based on brain volumes, they could be affected by AED therapy. To discover this causality, we have to include newly diagnosed patients with TLE with HS, and longitudinal follow-up studies are needed. Second, although we divided the patients with TLE with HS into DRE and DCE by criteria proposed by the ILAE [11], the AED response could be a dynamic. Brodie et al. investigated the temporal patterns of outcome in patients with newly diagnosed epilepsy. They demonstrated that 22 % of the patients had delayed but sustained seizure freedom, and 16 % of the patients had fluctuations between periods of seizure freedom and relapse [7]. Thus, whether the patients belong to the DRE or the DCE group can change over time. Third, we analyzed hippocampal subfield and thalamic nuclei volumes using the latest version of FreeSurfer program. We could not confirm the hippocampal subfield and thalamic nuclei volumes at the histological level. However, among the most sophisticated programs of MRI analysis currently available, the FreeSurfer represents a set of automated tools for the reconstruction of the brain's structures most widely utilized. The FreeSurfer usually offers a higher and more robust reproducibility compared with other neuroimaging analysis techniques [33]. In addition, a previous study using MRI-based thalamic nuclei volume analysis like our study demonstrated a good agreement with previous histological studies and showed an excellent-test-retest reliability [34].
Fig. 1. Alterations of intrinsic hippocampal network in the patients with temporal lobe epilepsy with hippocampal sclerosis according to antiepileptic drug response. It shows that the patients with DRE have more severe disruptions of the intrinsic hippocampal network than those with DCE compared to healthy controls. DRE: drug-resistant epilepsy, DCE: drug-controlled epilepsy. *p < 0.05, Comparison with healthy controls.
addition, we found that the mean clustering coefficient was increased in the patients with DRE compared to the patients with DCE. The global efficiency and characteristic path length are related with integration, whereas the mean clustering coefficient is associated with segregation [12–14]. Thus, our results suggested the decreased integration with increased segregation in the patients with DRE. We can specultate decreased integration represents the damaging consequences of recurrent seizures or an adaptive mechanism to prevent seizure spread out of the epileptogenic zones, and increased segregation may contribute more synchronized brain network, which more prone to recurrent seizures even with AEDs treatment [12–14]. There are emerging research studies to introduce the alterations of brain network in epilepsy into clinical practice, such as seizure localization [20,21], surgical outcome prediction [22], neuropsychological implications [6,23], and association with medical outcomes [9,10,24]. There are several reports in line with our present study. Wu et al. investigated the amplitude of low-frequency fluctuation based on rs-fMRI in patients with frontal lobe epilepsy, and the patients with DRE showed significantly decreased ALFF in the left ventromedial prefrontal cortex, right superior fontal gyrus, and supramarginal gyrus, relative to those with DCE [24]. In addition, in TLE patients, the thalamo-hippocampal functional connectivity [6] or activation of the default-mode network [6] was significantly different between the patients with DRE and DCE. All of these studies demonstrated that the brain connectivity could be a potential marker for AED response. There have been several studies showing involvement of the thalamus in patients with TLE with HS, with potential roles in seizure initiation and propagation [25–27]. The hippocampus has important reciprocal connections to the thalamus [28]. In this study, we also found that there were alterations of several thalamic nuclei volumes, including the parafasicular and suprageniculate nuclei. Furthermore, there have been studies regarding the thalamic network in patients with TLE according to AED response. A previous study based on DTI showed that TLE patients with DRE exhibited a decrease in connectivity involving the ipsilateral thalamocortical regions [29]. Another study evaluated the presurgical brain functional architecture presented in patients with TLE using graph theoretical measures of rs-fMRI data, and found that patients with DRE displayed a specific increase in nodal hubness involving both the ipsilateral and contralateral thalami
5. Conclusions We found that the intrinsic hippocampal network was different according to AED response. The patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE compared with healthy controls. These findings suggested that the intrinsic hippocampal network might be related to AED response and it could be a new biomarker of medical outcome in patients with TLE with HS. Declaration of Competing Interest None. Acknowledgement This work was supported by a 2018 Inje University research grant. References [1] Blumcke I. Neuropathology of focal epilepsies: a critical review. Epilepsy Behav 2009;15:34–9. https://doi.org/10.1016/j.yebeh.2009.02.033. [2] Sidhu MK, Duncan JS, Sander JW. Neuroimaging in epilepsy. Curr Opin Neurol
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Contents lists available at ScienceDirect
Seizure: European Journal of Epilepsy journal homepage: www.elsevier.com/locate/seizure
Intrinsic hippocampal and thalamic networks in temporal lobe epilepsy with hippocampal sclerosis according to drug response
T
Ho-Joon Leea, Kang Min Parkb,* a b
Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
A R T I C LE I N FO
A B S T R A C T
Keywords: Epilepsy Magnetic resonance imaging Connectivity
Purpose: The aim of this study was to investigate whether intrinsic hippocampal or thalamic networks in patients with temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS) were different according to antiepileptic drug (AED) response. Methods: We enrolled 80 patients with TLE with HS and 40 healthy controls. Of the patients with TLE with HS, 43 were classified as a drug-resistant epilepsy (DRE) group, whereas 37 patients were enrolled as a drug-controlled epilepsy (DCE) group. We investigated the structural connectivity of the global brain, intrinsic hippocampal, and intrinsic thalamic networks based on structural volumes in the patients with DRE and DCE, and analyzed the differences between them. Results: There were significant alterations of the intrinsic hippocampal network compared with healthy controls. The average degree and the global efficiency were decreased, whereas the characteristic path length was increased in the patients with DRE compared with those in healthy controls. In the patients with DCE, only the small-worldness index was decreased compared with healthy controls. Compared to the patients with DCE, the mean clustering coefficient was increased in the patients with DRE. Conclusion: We found that the intrinsic hippocampal network in patients with TLE with HS was different according to AED response. The patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE compared with healthy controls. These findings suggested that the hippocampal network might be related to AED response and could be a new biomarker of medical outcome in patients with TLE with HS.
1. Introduction Temporal lobe epilepsy with hippocampal sclerosis (TLE with HS) is the most common etiology in drug-resistant epilepsy (DRE) of adults [1,2]. Pathological and neuroimaging studies have showed that patients with TLE with HS have hippocampal atrophy [1,2]. In addition, several quantitative magnetic resonance imaging (MRI) studies have demonstrated the volume reductions in the extra-temporal lobe as well as the extra-hippocampus in patients with TLE with HS [3]. With emerging evidences that epilepsy is a network disease, there have been numerous research studies with connectivity analysis in TLE with HS. Bernhardt et al. investigated the structural networks using graph theoretical analysis of MRI-based cortical thickness correlations, and patients with TLE with HS showed increased path length and clustering coefficient, altered distributions of hubs, and vulnerability to
targeted attacks [4]. Another study with diffusion tensor imaging (DTI) demonstrated a marked reduction of pair-wise connections, which was strongly lateralized to the ipsilateral temporal lobe in patients with TLE with HS compared with healthy controls [5]. A previous study investigated the anterior and posterior hippocampal networks using resting-state functional MRI (rs-fMRI), and it found distinct reorganized patterns of intra-hemispheric functional connectivity between anterior and posterior hippocampal networks, which were associated with deficits in memory [6]. All of these studies suggest alterations of structural or functional connectivity in patients with TLE with HS. However, most of these previous studies have analyzed the global brain network or local network using the hippocampus and thalamus as a whole structure despite their heterogeneity. There have been no studies regarding intrinsic hippocampal and thalamic networks in patients with TLE with HS by divisions of hippocampal subfields or thalamic nuclei.
⁎ Corresponding author at: Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea. E-mail address: [email protected] (K.M. Park).
https://doi.org/10.1016/j.seizure.2020.01.010 Received 7 October 2019; Received in revised form 31 December 2019; Accepted 15 January 2020 1059-1311/ © 2020 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.
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subjects. All heathy control subjects had normal neurological findings and no history of medical, neurological, or psychiatric disease. All of them had a normal MRI on visual inspection.
It is known that approximately 60 % of patients with epilepsy remain constantly seizure-free with antiepileptic drugs (AEDs), whereas 25 % of those never achieve seizure control with medication [7]. In the past several years, several researchers have investigated the potential biomarkers of brain MRI for response to AED. Cendes and his colleague first investigated the relationship between gray matter volumes and patterns of AED response [8]. They found that patients with drug-resistant epilepsy (DRE) had more widespread gray matter atrophy compared with patients with drug-controlled epilepsy (DCE). A recent study using rs-fMRI demonstrated that healthy controls and patients with DCE showed similar functional connectivity patterns, whereas patients with DRE exhibited a significant bilateral decrease in thalamohippocampal functional connectivity [9]. Another study using rs-fMRI also showed significance for longer paths and reduced local efficiency within the default mode network of patients with DCE compared with those with DRE [10]. All of these studies suggest the potential biomarkers of brain MRI as an AED response. However, no research studies have investigated the differences of intrinsic hippocampal and thalamic networks in patients with TLE with HS according to AED response. The aim of this study was to investigate whether intrinsic hippocampal or thalamic networks using graph theoretical analysis based on hippocampal subfields and thalamic nuclei volumes in patients with TLE with HS were different according to AED response. We hypothesized that intrinsic hippocampal or thalamic networks were different according to AED response.
2.2. Acquisition of brain MRI All MRI scans were performed using a 3.0 T MRI scanner (AchievaTx, Phillips Healthcare, Best, The Netherlands) equipped with a 32-channel head coil. All patients with TLE with HS and healthy controls were subjected to conventional brain MRI protocols, including FLAIR and T2-weighted imaging, to exclude subjects with abnormal MRI findings on visual inspection. Moreover, all patients underwent contiguous 3D volumetric T1-weighted imaging with a high sagittal resolution appropriate for the analysis of structural volume. The 3D T1weighted images were obtained using a turbo-field echo sequence with the following parameters: TI =1300 ms, TR/TE = 8.6/3.96 ms, flip angle = 8°, and 1 mm3 isotropic voxel size. 2.3. Graph theoretical analysis The volumetric analysis was performed using the ‘recon-all’ function in the FreeSurfer program (http://surfer.nmr.mgh.harvard.edu/). Briefly, the processing stream of FreeSurfer consisted of several stages as follows: volume registration with the Talairach atlas, bias field correction, initial volumetric labeling, nonlinear alignment to the Talairach space, and final labeling of the volume. Then, the cortical surface of each hemisphere was inflated to an average spherical surface to locate both the pial surface and the white/gray matter boundary. Brain structures were segmented automatically on the basis of T1weighted images. The hippocampal subfields and thalamic nuclei were also segmented. We obtained the absolute structural volumes from these automated methods. Next, the volumetric measures were calculated using the following equation: the structural volumes (%) = (absolute structural volumes/total intracranial volumes) × 100. We performed structural connectivity analysis using Brain Analysis Using Graph Theory (BRAPH; http://braph.org). They were built for each group as a collection of nodes representing brain regions connected by edges corresponding to the connections between them. In the global brain network, the nodes were defined using the cortical volumes from 60 cortical and 13 subcortical regions provided by FreeSurfer (Table 2). In the hippocampal network analysis, we used the volumes of the right and left CA1, CA2-3, CA4, fimbria, hippocampus-amygdalatransition-area, hippocampal fissure, presubiculum, parasubiculum, subiculum, molecular layer, granule cell and molecular layer of dentate gyrus, and hippocampal tail. In the thalamic network analysis, we used the volumes of 50 individual thalamic nuclei, including right and left anteroventral nuclei in the anterior group; right and left laterodorsal and lateral posterior nuclei in the lateral group; right and left ventral anterior, ventral anterior magnocellular, ventral lateral anterior, ventral lateral posterior, ventromedial, and ventral posterolateral nuclei in the ventral group; right and left central medial, central lateral, paracentral, centromedian, and parafascicular nuclei in the intralaminar group; right and left paratenial, medial ventral, mediodorsal medial magnocellular, and mediodorsal lateral parvocellular nuclei in the medial group; and right and left lateral geniculate, medial geniculate,
2. Methods and materials 2.1. Subjects This study was approved by our hospital’s institutional review board. We retrospectively enrolled 80 patients with TLE with HS who visited the neurology department of our hospital from March 2010 to February 2019. All of the patients met the following criteria: 1) typical unilateral hippocampal sclerosis without any other lesions on brain MRI with visual inspections [increased T2-weighted/fluid-attenuated inversion recovery (FLAIR) imaging and reduced hippocampal volumes], 2) seizure semiology and electroencephalographic (EEG) findings compatible with mesial TLE, and 3) three-dimensional (3D) T1weighted images that were suitable for volumetric analysis. We investigated the clinical characteristics, including age, sex, age of seizure onset, duration of epilepsy, and AED load. The AED load was defined as the ratio “prescribed daily doses/defined daily doses, defined as the assumed average daily dose for maintenance therapy of adults with primary indications”, according to WHO definition (https://www. whocc.no/atc_ddd_index). We defined the patients with DRE by failure in adequate trials of two tolerated and appropriately selected AEDs to achieve sustained seizure freedom, based on the recommended criteria for DRE by the International League Against Epilepsy (ILAE) [11]. The others were defined as the patients with DCE. Of the 80 patients with TLE with HS, 43 patients were classified as a DRE group, whereas 37 patients were enrolled as a DCE group (Table 1). They included 37 patients (46 %) involving right HS and 43 patients (54 %) with left HS. The control group included 40 age- and sex-matched healthy
Table 1 Differences of clinical characteristics of patients with temporal lobe epilepsy with hippocampal sclerosis according to antiepileptic drug response. Characteristics
Patients with drug-resistant epilepsy (N = 43)
Patients with drug-controlled epilepsy (N = 37)
p-value
Age, years Male, N (%) Age of seizure onset, years Duration of epilepsy, months Antiepileptic drug load
40.1 ± 11.2 19 (44.1) 21.5 ± 12.1 224.1 ± 144.4 2.63 ± 1.04
39.1 ± 16.8 15 (40.5) 23.9 ± 16.0 178.4 ± 159.7 1.10 ± 0.91
0.751 0.743 0.499 0.237 * < 0.001
* p < 0.05. 33
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Table 2 Regions of interest as a node for network analysis in patients with temporal lobe epilepsy with hippocampal sclerosis and healthy subjects. Network
Side
Regions of interest as a node
Global brain network
Right
thalamus, caudate, putamen, pallidum, *†hippocampus, amygdala, bankssts, caudal anterior cingulate, caudal middle frontal, cuneus, entorhinal, fusiform, inferior parietal, inferior temporal, isthmus cingulate, lateral occipital, lateral orbitofrontal, lingual, medial orbitofrontal, middle temporal, parahippocampal, paracentral, pars opercularis, pars orbitalis, pars triangularis, pericalcarine, postcentral, posterior cingulate, precentral, precuneus, rostral anterior cingulate, rostral middle frontal, superior frontal, superior parietal, superior temporal, supramarginal, frontal pole, temporal pole, transverse temporal, insula cortex thalamus, caudate, putamen, pallidum, *†hippocampus, amygdala, bankssts, caudal anterior cingulate, caudal middle frontal, cuneus, entorhinal, fusiform, inferior parietal, inferior temporal, isthmus cingulate, lateral occipital, lateral orbitofrontal, lingual, medial orbitofrontal, middle temporal, parahippocampal, paracentral, pars opercularis, pars orbitalis, pars triangularis, pericalcarine, postcentral, posterior cingulate, precentral, precuneus, rostral anterior cingulate, rostral middle frontal, superior frontal, superior parietal, superior temporal, supramarginal, frontal pole, temporal pole, transverse temporal, insula cortex brainstem *†CA1, *†CA2-3, *†CA4, *†fimbria, *hippocampus-amygdala-transition-area, hippocampal fissure, *presubiculum, *parasubiculum, *†subiculum, *†molecular layer, *†granule cell and molecular layer of dentate gyrus, *hippocampal tail *†CA1, *†CA2-3, *†CA4, *†fimbria, hippocampus-amygdala-transition-area, hippocampal fissure, *†presubiculum, parasubiculum, *†subiculum, *†molecular layer, *†granule cell and molecular layer of dentate gyrus, †hippocampal tail anteroventral, laterodorsal, lateral posterior, ventral anterior, ventral anterior magnocellular, ventral lateral anterior, ventral lateral posterior, ventromedial, ventral posterolateral, central medial, central lateral, paracentral, centromedian, *†parafascicular, paratenial, medial ventral, mediodorsal medial magnocellular, mediodorsal lateral parvocellular, lateral geniculate, medial geniculate, suprageniculate, pulvinar anterior, pulvinar inferior, pulvinar lateral, pulvinar medial nuclei anteroventral, laterodorsal, lateral posterior, ventral anterior, ventral anterior magnocellular, ventral lateral anterior, ventral lateral posterior, ventromedial, ventral posterolateral, central medial, central lateral, paracentral, centromedian, *parafascicular, paratenial, medial ventral, mediodorsal medial magnocellular, mediodorsal lateral parvocellular, lateral geniculate, medial geniculate, †suprageniculate, pulvinar anterior, pulvinar inferior, pulvinar lateral, pulvinar medial nuclei
Left
Hippocampal network
Right and Left Right Left
Thalamic network
Right
Left
*Regions of significantly altered volumes in patients with drug-resistant epilepsy compared with healthy controls (p < 0.001). †Regions of significantly altered volumes in patients with drug-controlled epilepsy compared with healthy controls (p < 0.001).
continuous variables with normal distributions were presented as the mean value ± standard deviation, and those without normal distribution were described as the median value with the range. A two-sided pvalue less than 0.05 was considered to indicate statistical significance for all analyses. The p-value was corrected for false discovery rate in the case of nodal measures. In the analysis of volume differences between two groups, we set the p-value at 0.001 with Bonferroni correction (0.05/50). All statistical tests were performed using MedCalc® (MedCalc Software version 19, Ostend, Belgium; https://www.medcalc. org; 2019).
suprageniculate, pulvinar anterior, pulvinar inferior, pulvinar lateral, and pulvinar medial nuclei in the posterior group. The edges were calculated as the partial correlation coefficients between every pair of brain regions while controlling for the effects of age and gender. For each group, a structural undirected and weighted connectivity matrix was built with setting a threshold 0.5. Negative correlations were set to zero, and only positive values were used in the calculation. To detect differences between groups in the global brain, hippocampal, and thalamic networks topology, we calculated the average degree, the characteristic path length, the mean clustering coefficient, the global efficiency, the local efficiency, and the small-worldness index. To assess differences between groups in a local network topology, we calculated betweenness centrality. The average degree, total number of edges connected to a node, is average of the degrees of all nodes. The characteristic path length is average of the path lengths, average distance from a node to all other nodes, of all nodes. The mean clustering coefficient is average of the clustering coefficients, fraction of triangles present around a node, of all nodes. The global efficiency is average of the global efficiencies, average of the inverse shortest path length from a node to all other nodes, of all nodes. The local efficiency is average of the local efficiencies, global efficiency of a node calculated on the subgraph created by the node’s neighbors, of all nodes. The smallworldness graph has a similar characteristic path length as a random graph with the same degree distribution but is significantly more clustered. The betweenness centrality identifies nodes located on the most traveled paths, by measuring the number of shortest pathways in the network that pass through a given node [12–14]. We investigated the differences of network measures between the patients with DRE and DCE.
3. Results 3.1. Clinical characteristics of subjects Table 1 shows the clinical characteristics of patients with TLE with HS according to AED response. The clinical characteristics, such as age, male ratio, age of seizure onset, and duration of epilepsy in the patients with DRE were not different from those with DCE. However, the AED load was significantly higher in the patients with DRE compared with those with DCE (2.63 vs. 1.10, p < 0.001). The AED in the patients with DRE was as follow: lamotrigine in 15 patients, valproate in 12 patients, levetiracetam in 11 patients, carbamazepine in 11 patients, topiramate in 6 patients, oxcarbazepine in 5 patients, zonisamide in 4 patients, phenytoin in 3 patients, and clonazepam in 3 patients. The AED in the patients with DCE was as follow: lamotrigine in 13 patients, valproate in 12 patients, levetiracetam in 9 patients, carbamazepine in 7 patients, topiramate in 4 patients, zonisamide in 4 patients, clonazepam in 4 patients, oxcarbazepine in 3 patients, phenytoin in 3 patients, lacosamide in 1 patient, and perampanel in 1 patient. Of the 40 normal controls, 17 (42.5 %) were male and 23 (57.5 %) were female. The mean age was 38.1 ± 5.5 years. The age and male ratio in healthy controls were not significantly different from those in patients with DRE and DCE (p = 0.3085 and p = 0.8777 in patients with DRE; p = 0.7214 and p = 0.8625 in patients with DCE).
2.4. Statistical analysis Comparisons of the factors were analyzed using the chi-square test for categorical variables. Continuous variables were analyzed using the Student t-test or the Mann-Whitney U test according to whether data were normally distributed. In the comparison of the network measures, we tested the statistical significance of the differences using nonparametric permutation tests with 1000 permutations. The categorical variables were presented as the frequency and percentage. The
3.2. Structural volumes according to AED response Table 2 shows the regions of altered volumes in patients with TLE 34
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3.4. Structural local networks according to AED response
with HS compared with healthy controls. In the patients with DRE, there were alterations of volume in several regions compared with healthy controls, including the right hippocampus (0.2253 % vs. 0.2839 %), CA1 (0.0353 % vs. 0.0454 %), CA3 (0.0115 % vs. 0.0148 %), CA4 (0.0139 % vs. 0.0187 %), fimbria (0.0040 % vs. 0.0057 %), hippocampus-amygdala-transition-area (0.0034 % vs. 0.0042 %), presubiculum (0.0165 % vs. 0.0209 %), parasubiculum (0.0030 % vs. 0.0036 %), subiculum (0.0250 % vs. 0.0315 %), molecular layer (0.0316 % vs. 0.0411 %), granule cell and molecular layer of dentate gyrus (0.0162 % vs. 0.0218 %), hippocampal tail (0.0313 % vs. 0.0371 %), and parafasicular nucleus (0.0035 % vs. 0.0042 %), and left hippocampus (0.22.5 % vs. 0.2701 %), CA1 (0.0347 % vs. 0.0434 %), CA2-3 (0.0108 % vs. 0.0133 %), CA4 (0.0137 % vs. 0.0175 %), fimbria (0.0044 % vs. 0.0057 %), presubiculum (0.0186 % vs. 0.0218 %), molecular layer (0.0315 % vs. 0.0394 %), granule cell and molecular layer of dentate gyrus (0.0161 % vs. 0.0207 %), and parafasicular nucleus (0.0036 % vs. 0.0041 %). In the patients with DCE, there were also alterations of volume in several regions compared with healthy controls, including the right hippocampus (0.2410 % vs. 0.2839 %), CA1 (0.0379 % vs. 0.0454 %), CA3 (0.0123 % vs. 0.0148 %), CA4 (0.0150 % vs. 0.0187 %), fimbria (0.0044 % vs. 0.0057 %), subiculum (0.0261 % vs. 0.0315 %), molecular layer (0.0336 % vs. 0.0411 %), granule cell and molecular layer of dentate gyrus (0.0175 % vs. 0.0218 %), and parafasicular nucleus (0.0036 % vs. 0.0042 %), and left hippocampus (0.2233 % vs. 0.2701 %), CA1 (0.0344 % vs. 0.0434 %), CA2-3 (0.0112 % vs. 0.0133 %), CA4 (0.0138 % vs. 0.0175 %), fimbria (0.0044 % vs. 0.0057 %), presubiculum (0.0180 % vs. 0.0218 %), subiculum (0.0252 % vs. 0.0311 %), molecular layer (0.0312 % vs. 0.0394 %), granule cell and molecular layer of dentate gyrus (0.0162 % vs. 0.0207 %), hippocampal tail (0.0297 % vs. 0.0362 %), and suprageniculate nucleus (0.0010 % vs. 0.0008 %). In the analysis of the alterations of structural volumes between the patients with DRE and DCE, there were no regions that were different between them.
In the patients with DRE, there were alterations of the intrinsic hippocampal local network compared with healthy controls. The betweenness centralities of the left hippocampal fissure, left fimbria, and right fimbria were significantly increased, whereas those of the right molecular layer, and right granule cell and molecular layer of dentate gyrus were decreased compared to healthy controls (0.138 vs. 0.000, p = 0.001; 0.146 vs. 0.000, p = 0.001; 0.332 vs. 0.000, p = 0.010; 0.000 vs. 0.015, p = 0.019; 0.000 vs. 0.004, p = 0.035; respectively). In the patients with DCE, there were also alterations of the intrinsic hippocampal local network. The betweenness centralities of the left and right fimbria were increased, whereas those of the left CA1, left granule cell and molecular layer of dentate gyrus, and right molecular layer were decreased compared to healthy controls (0.023 vs. 0.000, p = 0.045; 0.260 vs. 0.000, p = 0.003; 0.000 vs. 0.063, p = 0.003; 0.000 vs. 0.027, p = 0.006; 0.000 vs. 0.015, p = 0.006; respectively). The betweenness centralities of the left CA1, and left hippocampal fissure were increased, whereas that of the left parasubiculum was decreased in the patients with DRE compared to those with DCE controls (0.027 vs. 0.000, p = 0.030; 0.138 vs. 0.000, p = 0.027; 0.000 vs. 0.039, p = 0.028; respectively). However, in the patients with DRE and DCE, the betweenness centralities of all of the nodes in the global brain and intrinsic thalamic networks were not different from those of healthy controls. 4. Discussion We found that the intrinsic hippocampal network in patients with TLE with HS was different according to AED response. Although both the patients with DRE and DCE commonly had hippocampal atrophy compared with healthy controls, the patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE. In addition, we found that the mean clustering coefficient was increased in the patients with DRE compared to those with DCE. However, the global brain and intrinsic thalamic networks in the patients with DRE and DCE were not different from those in healthy controls. We also demonstrated that there were significant differences of alterations in the intrinsic hippocampal local network according to AED response. These findings suggested that the intrinsic hippocampal network might be related to AED response, and it could be a new biomarker of medical outcome in patients with TLE with HS. The introduction of high-field MRI scanners coupled with development of imaging analysis software enables automated segmentation of the hippocampal subfields and thalamic nuclei. Previous studies found subfield-specific volumetric differences in patients with epilepsy compared with healthy controls, demonstrating that certain subfields may be more susceptible to changes caused by the disease than others subfields [15,16]. The ILAE proposed the classification of TLE with HS by visual histopathologic examination of en bloc resected surgical specimens [17]. ILAE type 1 refers always to severe neuronal cell loss and gliosis predominantly in CA1 and CA4 regions, compared with CA1 predominant neuronal cell loss and gliosis (HS ILAE type 2), or CA4 predominant neuronal cell loss and gliosis (HS ILAE type 3). Interestingly, the imaging analysis based on MRI showed high compatibility with histopathological findings [16]. In this study, we found that both the patients with DRE and DCE commonly had hippocampal atrophy compared with healthy controls, especially in CA1, CA2-2, CA4, fimbria, subiculum, and the granule cell and molecular layer of the dentate gyrus. In addition to gross volumetric analysis, the understanding of connectivity patterns of different hippocampal subfields may demonstrate important details regarding epileptogenic abnormality of the subfields [15,16]. This is the first attempt to quantify intrinsic hippocampal connectivity using hippocampal subfields volumes in patients with TLE with HS according to AED response. In this study, we found that the patients with DRE had decreased average degree and global
3.3. Structural global networks according to AED response Table 3 shows the alterations of structural connectivity, including the global brain, intrinsic hippocampal, and intrinsic thalamic networks, in patients with TLE with HS according to AED response compared with healthy controls. In the patients with DRE, there were alterations of the intrinsic hippocampal network compared with healthy controls (Fig. 1). The average degree and the global efficiency were significantly decreased, whereas the characteristic path length was increased in the patients with DRE compared with those in healthy controls (7.714 vs. 12.813, p = 0.001; 0.411 vs. 0.565, p = 0.025; 3.637 vs. 2.067, p = 0.001; respectively). However, all of the network measures in the global brain and intrinsic thalamic networks were not different from healthy controls. In the patients with DCE, there were alterations of the intrinsic hippocampal network compared with healthy controls. The smallworldness index was significantly decreased in the patients with DCE compared with that in healthy controls. However, all of the other network measures in the intrinsic thalamic network were not different from those of healthy controls. Furthermore, the global brain and intrinsic thalamic networks were not different from those of healthy controls (0.719 vs. 0.950, p = 0.013). In the analysis of the alterations of structural network between the patients with DRE and DCE, there was significant difference of the intrinsic hippocampal network. Compared to the patients with DCE, the mean clustering coefficient was significantly increased in the patients with DRE (0.343 vs. 0.483, p = 0.015). However, all of the network measures in the global brain and intrinsic thalamic networks were not different between them. 35
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Table 3 Alterations of structural connectivity, including the global brain, hippocampal, and thalamic networks, in patients with temporal lobe epilepsy with hippocampal sclerosis according to antiepileptic drug response. Global brain network
average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness
Patients with DRE 77.457 2.777 0.401 1.458 0.350 0.951 Patients with DCE 78.469 2.198 0.510 2.385 0.477 0.947 Patients with DRE 77.457 2.777 0.401 1.458 0.351 0.952
Healthy controls 77.432 2.598 0.438 1.759 0.398 0.944 Healthy controls 77.432 2.598 0.438 1.759 0.399 0.943 Patients with DCE 78.469 2.198 0.510 2.385 0.477 0.948
Difference −0.025 −0.179 0.037 0.302 0.048 −0.006 Difference −1.037 0.400 −0.071 −0.625 −0.078 −0.004 Difference 1.012 −0.579 0.109 0.927 0.126 −0.003
CI lower −6.349 −1.081 −0.138 −1.004 −0.189 −0.043 CI lower −3.034 −0.897 −0.145 −1.245 −0.173 −0.050 CI lower −3.977 −0.691 −0.118 −0.997 −0.154 −0.046
CI upper 4.394 1.027 0.141 0.973 0.168 0.034 CI upper 4.036 0.883 0.151 1.336 0.181 0.056 CI upper 2.514 0.704 0.118 1.013 0.148 0.042
p-value 0.888 0.799 0.729 0.666 0.644 0.831 p-value 0.480 0.446 0.430 0.424 0.468 0.839 p-value 0.407 0.152 0.137 0.141 0.163 0.990
Patients with DRE 14.250 3.638 0.412 1.168 0.483 0.868 Patients with DCE 22.4167 2.732 0.491 1.262 0.343 0.719 Patients with DRE 14.250 3.638 0.412 1.168 0.483 0.873
Healthy controls 22.917 2.068 0.566 1.579 0.537 0.949 Healthy controls 22.9167 2.068 0.566 1.579 0.537 0.950 Patients with DCE 22.417 2.732 0.491 1.262 0.343 0.719
Difference 8.667 −1.570 0.154 0.411 0.053 0.080 Difference 0.5 −0.665 0.075 0.318 0.193 0.231 Difference 8.167 −0.906 0.079 0.093 −0.140 −0.155
CI lower −2.461 −0.981 −0.117 −0.543 −0.197 −0.183 CI lower −1.6696 −0.906 −0.128 −0.610 −0.168 −0.171 CI lower −8.258 −1.175 −0.078 −0.192 −0.104 −0.136
CI upper 2.003 0.901 0.119 0.572 0.185 0.181 CI upper 1.7908 0.897 0.123 0.580 0.166 0.186 CI upper 8.080 1.149 0.088 0.232 0.126 0.160
p-value *0.001 *0.004 *0.025 0.288 0.628 0.549 p-value 0.747 0.201 0.325 0.395 0.053 *0.028 p-value 0.087 0.221 0.162 0.583 *0.015 0.066
CI upper 3.555 0.877 0.177 1.359 0.204 0.061 CI upper 3.500 0.736 0.149 1.081 0.190 0.105 CI upper 3.478 0.822 0.151 1.089 0.163 0.091
p-value 0.982 0.790 0.746 0.743 0.753 0.884 p-value 0.593 0.595 0.680 0.715 0.651 0.350 p-value 0.610 0.802 0.912 0.914 0.865 0.555
Hippocampal network average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness Thalamic network average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness
average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness average degree characteristic path length global efficiency local efficiency mean clustering coefficient small-worldness
Patients with DRE 48.280 2.073 0.551 2.189 0.515 0.935 Patients with DCE 46.640 2.218 0.538 2.133 0.493 0.897 Patients with DRE 48.280 2.073 0.551 2.189 0.515 0.933
Healthy controls 48.200 1.968 0.580 2.420 0.552 0.942 Healthy controls 48.200 1.968 0.580 2.420 0.552 0.943 Patients with DCE 46.640 2.218 0.538 2.133 0.493 0.899
Difference −0.080 −0.105 0.029 0.230 0.037 0.007 Difference 1.560 −0.250 0.042 0.287 0.059 0.046 Difference −1.640 0.145 −0.013 −0.057 −0.022 −0.035
CI lower −3.675 −0.823 −0.183 −1.311 −0.196 −0.067 CI lower −3.196 −0.735 −0.153 −1.133 −0.177 −0.086 CI lower −3.997 −0.806 −0.146 −1.010 −0.168 −0.095
DRE: drug-resistant epilepsy, DCE: drug-controlled epilepsy. * p < 0.05.
DRE could induce decreased intrinsic hippocampal connectivity [18]. Conversely, there is evidence suggesting that patients with DRE experience synaptic reorganization within certain cortical structures, such as the hippocampus [19], and this change can produce resistance to AEDs. Thus, causality between disruptions of the intrinsic hippocampal network and poor AED response were not clearly determined. In
efficiency with increased characteristic path length compared with healthy controls, and the patients with DCE only had a decreased smallworldness index, which suggests that the patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE. The mechanisms for this finding are not fully understood. Seizureinduced excito-toxic damage by more frequent seizures in patients with 36
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compared with the patients with DCE and healthy controls [30]. However, all of these studies did not investigate the intrinsic thalamic network, but the connections between the thalamus as a whole and other structures. In this study, we found that there were no alterations of the intrinsic thalamic network in both the patients with DRE and DCE compared with healthy controls. The reason is unknown, but unlike the hippocampus (the primary origin of TLE with HS), the relationship among the thalamic nuclei seems to be relatively maintained despite involvements of TLE with HS. There were several limitations to this study. First, this was a retrospective study. Thus, we were unable to discern whether the findings observed in this study were effects of epilepsy or whether they were underlying causes that made the participants susceptible to seizure activity. In addition, AED therapy could effect on brain connectivity. One connectivity study using EEG showed that AED significantly enhanced both global power spectral density and connectivity for all frequency bands, similar for all connectivity measures [31]. Another study based on functional MRI demonstrated the effect of AED on the functional MRI activation pattern, resulting alterations of brain connectivity [32]. Although our results of connectivity were produced by co-plasticity based on brain volumes, they could be affected by AED therapy. To discover this causality, we have to include newly diagnosed patients with TLE with HS, and longitudinal follow-up studies are needed. Second, although we divided the patients with TLE with HS into DRE and DCE by criteria proposed by the ILAE [11], the AED response could be a dynamic. Brodie et al. investigated the temporal patterns of outcome in patients with newly diagnosed epilepsy. They demonstrated that 22 % of the patients had delayed but sustained seizure freedom, and 16 % of the patients had fluctuations between periods of seizure freedom and relapse [7]. Thus, whether the patients belong to the DRE or the DCE group can change over time. Third, we analyzed hippocampal subfield and thalamic nuclei volumes using the latest version of FreeSurfer program. We could not confirm the hippocampal subfield and thalamic nuclei volumes at the histological level. However, among the most sophisticated programs of MRI analysis currently available, the FreeSurfer represents a set of automated tools for the reconstruction of the brain's structures most widely utilized. The FreeSurfer usually offers a higher and more robust reproducibility compared with other neuroimaging analysis techniques [33]. In addition, a previous study using MRI-based thalamic nuclei volume analysis like our study demonstrated a good agreement with previous histological studies and showed an excellent-test-retest reliability [34].
Fig. 1. Alterations of intrinsic hippocampal network in the patients with temporal lobe epilepsy with hippocampal sclerosis according to antiepileptic drug response. It shows that the patients with DRE have more severe disruptions of the intrinsic hippocampal network than those with DCE compared to healthy controls. DRE: drug-resistant epilepsy, DCE: drug-controlled epilepsy. *p < 0.05, Comparison with healthy controls.
addition, we found that the mean clustering coefficient was increased in the patients with DRE compared to the patients with DCE. The global efficiency and characteristic path length are related with integration, whereas the mean clustering coefficient is associated with segregation [12–14]. Thus, our results suggested the decreased integration with increased segregation in the patients with DRE. We can specultate decreased integration represents the damaging consequences of recurrent seizures or an adaptive mechanism to prevent seizure spread out of the epileptogenic zones, and increased segregation may contribute more synchronized brain network, which more prone to recurrent seizures even with AEDs treatment [12–14]. There are emerging research studies to introduce the alterations of brain network in epilepsy into clinical practice, such as seizure localization [20,21], surgical outcome prediction [22], neuropsychological implications [6,23], and association with medical outcomes [9,10,24]. There are several reports in line with our present study. Wu et al. investigated the amplitude of low-frequency fluctuation based on rs-fMRI in patients with frontal lobe epilepsy, and the patients with DRE showed significantly decreased ALFF in the left ventromedial prefrontal cortex, right superior fontal gyrus, and supramarginal gyrus, relative to those with DCE [24]. In addition, in TLE patients, the thalamo-hippocampal functional connectivity [6] or activation of the default-mode network [6] was significantly different between the patients with DRE and DCE. All of these studies demonstrated that the brain connectivity could be a potential marker for AED response. There have been several studies showing involvement of the thalamus in patients with TLE with HS, with potential roles in seizure initiation and propagation [25–27]. The hippocampus has important reciprocal connections to the thalamus [28]. In this study, we also found that there were alterations of several thalamic nuclei volumes, including the parafasicular and suprageniculate nuclei. Furthermore, there have been studies regarding the thalamic network in patients with TLE according to AED response. A previous study based on DTI showed that TLE patients with DRE exhibited a decrease in connectivity involving the ipsilateral thalamocortical regions [29]. Another study evaluated the presurgical brain functional architecture presented in patients with TLE using graph theoretical measures of rs-fMRI data, and found that patients with DRE displayed a specific increase in nodal hubness involving both the ipsilateral and contralateral thalami
5. Conclusions We found that the intrinsic hippocampal network was different according to AED response. The patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE compared with healthy controls. These findings suggested that the intrinsic hippocampal network might be related to AED response and it could be a new biomarker of medical outcome in patients with TLE with HS. Declaration of Competing Interest None. Acknowledgement This work was supported by a 2018 Inje University research grant. References [1] Blumcke I. Neuropathology of focal epilepsies: a critical review. Epilepsy Behav 2009;15:34–9. https://doi.org/10.1016/j.yebeh.2009.02.033. [2] Sidhu MK, Duncan JS, Sander JW. Neuroimaging in epilepsy. Curr Opin Neurol
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