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Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand-alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR-negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel-wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion-weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR-negative epilepsy patients., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Introduction: Morphological correlates of nonpathological déjà vu (DV) have been identified recently within the human brain. Significantly reduced gray matter volume (GMV) within a set of cortical and subcortical regions reported in subjects experiencing DV seems to mirror the distribution of GMV reduction in mesial temporal lobe epilepsy (MTLE) patients but vary in terms of the hippocampus. Another condition associated with hippocampal GMV reduction and DV alike disturbance in memory processing is schizophrenia (SCH). Here, we tested the hypothesis that hippocampal involvement in nonpathological DV resembles more closely the pattern of GMV decrease observed in MTLE compared with that occurring in SCH., Methods: Using automated segmentation of the MRI data we compared the medians of GMV within 12 specific hippocampal subfields in healthy individuals that do (DV+; N = 87) and do not report déjà vu experience (DV-; N = 26), and patients with MTLE (N = 47) and SCH (N = 29). By Pearson correlation, we then evaluated the similarity of MTLE and SCH groups to DV+ group with respect to spatial distribution of GMV deviation from DV- group., Results: Significant GMV decrease was found in MTLE group in most of the subfields. There were just trends in the hippocampal GMV decrease found in DV+ or SCH groups. Concerning the spatial distribution of GMV decrease, we revealed statistically significant correlation for the left hippocampus for SCH vs DV+. Otherwise there was no statistically significant correlation., Conclusions: Our findings reveal structural features of hippocampal involvement in nonpathological DV, MTLE, and SCH. Despite our expectations, the pattern of GMV reduction in the DV+ relative to the DV- group does not resemble the pattern observed in MTLE any more than that observed in SCH. The highly similar patterns of the three clinical groups rather suggest an increased vulnerability of certain hippocampal subfields; namely, Cornu Ammonis (CA)4, CA3, dentate gyrus granular cell layer (GC-DG), hippocampal-amygdaloid transition area (HATA) and subiculum., (© 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.)

Purpose: To determine whether voxel-based morphometry (VBM) might contribute to the detection of cortical dysplasia within the temporal pole in patients with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE/HS)., Methods: Eighteen patients with intractable MTLE/HS and 30 sex- and age-matched healthy controls were included in the study. All of the patients fulfilled the diagnostic criteria for MTLE/HS and underwent anteromedial temporal resection. VBM without a modulation step was applied to the magnetic resonance (MR) images of the brain. Statistical parametric maps were used to compare structural characteristics such as gray matter concentration (GMC) within the temporal pole among patients and controls separately. The acquired data were then statistically analyzed to determine the congruency between visually inspected MR imaging (MRI) scans and VBM results in the detection of morphologic abnormalities in the temporal pole compared to postoperative histopathologic findings of cortical dysplasia., Key Findings: Histopathologic examination revealed cortical dysplasia within the temporal pole in 11 patients. In detail, according to Palmini's classification, mild malformations of cortical development (mMCDs) were disclosed in three patients, focal cortical dysplasia (FCD) type Ia in three patients, and FCD type Ib in five patients. Some type of structural temporal pole abnormality was suggested by VBM in 14 patients and by visually inspected MRI scans in 11 patients. The results of VBM were in agreement with the presence/absence of cortical dysplasia in 13 patients (72.2%); this correspondence was significant (p = 0.047). In one case, VBM was false negative and in four cases it was false positive. There was congruence between the results of visual analysis and histologic proof in 55.6% of examined patients, which was not significant., Significance: We found that VBM made a superior contribution to the detection of temporopolar structural malformations (cortical dysplasia) compared to visual inspection. The agreement with postoperative histopathologic proof was clearly significant for VBM results and nonsignificant for visual inspection., (Wiley Periodicals, Inc. © 2012 International League Against Epilepsy.)

Purpose: To determine whether changes in gray matter volume (GMV) differ according to the affected side in mesial temporal lobe epilepsy/hippocampal sclerosis (MTLE/HS) syndrome, and moreover to test the hypothesis of more pronounced structural changes in right-sided MTLE/HS. This hypothesis (especially that the contralateral thalamus is more affected in right-sided MTLE/HS) arose from the results of our recent study, wherein more expressed structural and functional changes were observed in a small sample of patients with right-sided MTLE/HS (Brázdil et al., 2009)., Methods: Twenty patients with left-sided and 20 with right-sided MTLE/HS and 40 sex- and age-matched healthy controls were included in the study. Voxel-based morphometry (VBM) with a modulation step was applied to magnetic resonance imaging (MRI) brain images. Statistical parametric maps were used to compare structural changes between patients and controls separately for the left- and right-sided MTLE/HS subgroups. We also compared the local GMV of the brain structures (insula and thalamus) between the subgroups of patients., Results: In the subgroup with right-sided MTLE/HS, a reduction of GMV was detected in the mesiotemporal structures and the ipsilateral thalamus (as in left-sided MTLE/HS), but also notably in the ipsilateral insula and contralateral thalamus. A statistical analysis revealed a significantly more extensive reduction of GMV in the ipsilateral/contralateral insula and the contralateral thalamus in the subgroup with right-sided compared to left-sided MTLE/HS., Conclusion: We found asymmetrical morphologic changes in patients with left- and right-sided MTLE/HS syndrome (more pronounced in right-sided MTLE/HS). These differences could be theoretically explained by different neuronal networks and pathophysiologic changes in temporolimbic structures.

Background: Chronic vagal nerve stimulation (VNS) is a well-established non-pharmacological treatment option for drug-resistant epilepsy. This study sought to develop a statistical model for prediction of VNS efficacy. We hypothesized that reactivity of the electroencephalogram (EEG) to external stimuli measured during routine preoperative evaluation differs between VNS responders and non-responders. Materials and Methods: Power spectral analyses were computed retrospectively on pre-operative EEG recordings from 60 epileptic patients with VNS. Thirty five responders and 25 non-responders were compared on the relative power values in four standard frequency bands and eight conditions of clinical assessment—eyes opening/closing, photic stimulation, and hyperventilation. Using logistic regression, groups of electrodes within anatomical areas identified as maximally discriminative by n leave-one-out iterations were used to classify patients. The reliability of the predictive model was verified with an independent data-set from 22 additional patients. Results: Power spectral analyses revealed significant differences in EEG reactivity between responders and non-responders; specifically, the dynamics of alpha and gamma activity strongly reflected VNS efficacy. Using individual EEG reactivity to develop and validate a predictive model, we discriminated between responders and non-responders with 86% accuracy, 83% sensitivity, and 90% specificity. Conclusion: We present a new statistical model with which EEG reactivity to external stimuli during routine presurgical evaluation can be seen as a promising avenue for the identification of patients with favorable VNS outcome. This novel method for the prediction of VNS efficacy might represent a breakthrough in the management of drug-resistant epilepsy, with wide-reaching medical and economic implications. [ABSTRACT FROM AUTHOR]

Abstract In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human. [ABSTRACT FROM AUTHOR]