Interictal high-frequency oscillations, spikes, and connectivity profiles: A fingerprint of epileptogenic brain pathologies.

OBJECTIVE: Focal cortical dysplasia (FCD), hippocampal sclerosis (HS), nonspecific gliosis (NG), and normal tissue (NT) comprise the majority of histopathological results of surgically treated drug-resistant epilepsy patients. Epileptic spikes, high-frequency oscillations (HFOs), and connectivity measures are valuable biomarkers of epileptogenicity. The question remains whether they could also be utilized for preresective differentiation of the underlying brain pathology. This study explored spikes and HFOs together with functional connectivity in various epileptogenic pathologies. METHODS: Interictal awake stereoelectroencephalographic recordings of 33 patients with focal drug-resistant epilepsy with seizure-free postoperative outcomes were analyzed (15 FCD, 8 HS, 6 NT, and 4 NG). Interictal spikes and HFOs were automatically identified in the channels contained in the overlap of seizure onset zone and resected tissue. Functional connectivity measures (relative entropy, linear correlation, cross-correlation, and phase consistency) were computed for neighboring electrode pairs. RESULTS: Statistically significant differences were found between the individual pathologies in HFO rates, spikes, and their characteristics, together with functional connectivity measures, with the highest values in the case of HS and NG/NT. A model to predict brain pathology based on all interictal measures achieved up to 84.0% prediction accuracy. SIGNIFICANCE: The electrophysiological profile of the various epileptogenic lesions in epilepsy surgery patients was analyzed. Based on this profile, a predictive model was developed. This model offers excellent potential to identify the nature of the underlying lesion prior to resection. If validated, this model may be particularly valuable for counseling patients, as depending on the lesion type, different outcomes are achieved after epilepsy surgery.

A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials.

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics. To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.

The role of central autonomic nervous system dysfunction in Takotsubo syndrome: a systematic review.

INTRODUCTION: Takotsubo syndrome (TTS), also known as stress cardiomyopathy or "broken heart" syndrome, is a mysterious condition that often mimics an acute myocardial infarction. Both are characterized by left ventricular systolic dysfunction. However, this dysfunction is reversible in the majority of TTS patients. PURPOSE: Recent studies surprisingly demonstrated that TTS, initially perceived as a benign condition, has a long-term prognosis akin to myocardial infarction. Therefore, the health consequences and societal impact of TTS are not trivial. The pathophysiological mechanisms of TTS are not yet completely understood. In the last decade, attention has been increasingly focused on the putative role of the central nervous system in the pathogenesis of TTS. CONCLUSION: In this review, we aim to summarize the state of the art in the field of the brain-heart axis, regional structural and functional brain abnormalities, and connectivity aberrancies in TTS.

Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging.

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.

Cerebrocerebellar structural covariance in temporal lobe epilepsy with hippocampal sclerosis.

PURPOSE: The purpose of the study was to evaluate cerebral morphological changes in temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) and their relationship to the cerebellum. METHODS: The study cohort included 21 patients with intractable TLE-HS (14 left-sided, 7 right-sided) and 38 healthy controls (HC). All patients later underwent anteromedial temporal lobe resection. All subjects were examined using a 1.5-T magnetic resonance imaging (MRI). Volumes of distinct cerebral and cerebellar structures were measured using voxel-based morphometry. The structural covariance of temporal lobe structures, insula, and thalamus with cerebellar substructures was examined using partial least squares regression. RESULTS: Morphological changes were more significant in the group with left TLE-HS when comparing left-sided with right-sided structures as well as when comparing patients with controls. The gray matter volume (GMV) of the temporal lobe structures was smaller ipsilaterally to the seizure onset side in most cases. There was a significant amygdala enlargement contralateral to the side of hippocampal sclerosis in both patients with right and left TLE-HS as compared with controls. Selected vermian structures in patients with left but not right TLE-HS had significantly larger GMV than the identical substructures in controls. The structural covariance differed significantly between patients with left and right TLE-HS as compared with HC. The analysis revealed significant negative covariance between anterior vermis and mesial temporal structures in the group with left TLE-HS. No significance was observed for the group with right TLE-HS. CONCLUSION: There is significant asymmetry in the GMV of cerebral and cerebellar structures in patients with TLE-HS. Morphological changes are distinctly more pronounced in patients with left TLE-HS. The observed structural covariance between the cerebellum and supratentorial structures in TLE-HS suggests associations beyond the mesial temporal lobe structures and thalamus.

Probabilistic functional tractography of the human cortex revisited.

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.

Very high-frequency oscillations: Novel biomarkers of the epileptogenic zone.

OBJECTIVE: In the present study, we aimed to investigate depth electroencephalographic (EEG) recordings in a large cohort of patients with drug-resistant epilepsy and to focus on interictal very high-frequency oscillations (VHFOs) between 500Hz and 2kHz. We hypothesized that interictal VHFOs are more specific biomarkers for epileptogenic zone compared to traditional HFOs. METHODS: Forty patients with focal epilepsy who underwent presurgical stereo-EEG (SEEG) were included in the study. SEEG data were recorded with a sampling rate of 25kHz, and a 30-minute resting period was analyzed for each patient. Ten patients met selected criteria for analyses of correlations with surgical outcome: detection of interictal ripples (Rs), fast ripples (FRs), and VHFOs; resective surgery; and at least 1 year of postoperative follow-up. Using power envelope computation and visual inspection of power distribution matrixes, electrode contacts with HFOs and VHFOs were detected and analyzed. RESULTS: Interictal very fast ripples (VFRs; 500-1,000Hz) were detected in 23 of 40 patients and ultrafast ripples (UFRs; 1,000-2,000Hz) in almost half of investigated subjects (n = 19). VFRs and UFRs were observed only in patients with temporal lobe epilepsy and were recorded exclusively from mesiotemporal structures. The UFRs were more spatially restricted in the brain than lower-frequency HFOs. When compared to R oscillations, significantly better outcomes were observed in patients with a higher percentage of removed contacts containing FRs, VFRs, and UFRs. INTERPRETATION: Interictal VHFOs are relatively frequent abnormal phenomena in patients with epilepsy, and appear to be more specific biomarkers for epileptogenic zone when compared to traditional HFOs. Ann Neurol 2017;82:299-310.

MicroRNA and mesial temporal lobe epilepsy with hippocampal sclerosis: Whole miRNome profiling of human hippocampus.

OBJECTIVE: Mesial temporal lobe epilepsy (mTLE) is a severe neurological disorder characterized by recurrent seizures. mTLE is frequently accompanied by neurodegeneration in the hippocampus resulting in hippocampal sclerosis (HS), the most common morphological correlate of drug resistance in mTLE patients. Incomplete knowledge of pathological changes in mTLE+HS complicates its therapy. The pathological mechanism underlying mTLE+HS may involve abnormal gene expression regulation, including posttranscriptional networks involving microRNAs (miRNAs). miRNA expression deregulation has been reported in various disorders, including epilepsy. However, the miRNA profile of mTLE+HS is not completely known and needs to be addressed. METHODS: Here, we have focused on hippocampal miRNA profiling in 33 mTLE+HS patients and nine postmortem controls to reveal abnormally expressed miRNAs. In this study, we significantly reduced technology-related bias (the most common source of false positivity in miRNA profiling data) by combining two different miRNA profiling methods, namely next generation sequencing and miRNA-specific quantitative real-time polymerase chain reaction. RESULTS: These methods combined have identified and validated 20 miRNAs with altered expression in the human epileptic hippocampus; 19 miRNAs were up-regulated and one down-regulated in mTLE+HS patients. Nine of these miRNAs have not been previously associated with epilepsy, and 19 aberrantly expressed miRNAs potentially regulate the targets and pathways linked with epilepsy (such as potassium channels, γ-aminobutyric acid, neurotrophin signaling, and axon guidance). SIGNIFICANCE: This study extends current knowledge of miRNA-mediated gene expression regulation in mTLE+HS by identifying miRNAs with altered expression in mTLE+HS, including nine novel abnormally expressed miRNAs and their putative targets. These observations further encourage the potential of microRNA-based biomarkers or therapies.

Eslicarbazepine-induced hyponatremia: A retrospective single-center real clinical practice study.

Hyponatremia is a typical side effect of antiseizure drugs from the dibenzazepine family. The study investigated the prevalence of hyponatremia in patients with epilepsy who were treated with eslicarbazepine. We aimed to determine the prevalence of hyponatremia, reveal the factors leading to the discontinuation of treatment, and identify possible risk factors for the development of hyponatremia including the dose dependency. The medical records of 164 patients with epilepsy taking eslicarbazepine in our center were analyzed. The overall prevalence of hyponatremia was 30.5%. The prevalence of mild hyponatremia, seen in 14%-20% of patients, was not dose dependent. The prevalence of moderate and severe hyponatremia was significantly dose dependent. The severity of hyponatremia was significantly dose dependent. Severe hyponatremia was found in 6.1% of patients. Hyponatremia was asymptomatic in the majority of cases, and in 48% did not require any management. Hyponatremia was the reason for discontinuation in 6.2% of patients. The major risk factor for developing hyponatremia was older age. The study shows that eslicarbazepine-induced hyponatremia is usually mild and asymptomatic. It usually does not require any management and seldom leads to treatment discontinuation. Hyponatremia is dose dependent. Another major risk for developing hyponatremia (besides dose) is older age.

Timing matters for accurate identification of the epileptogenic zone.

OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). METHODS: We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). RESULTS: On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. CONCLUSIONS: The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. SIGNIFICANCE: Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.

Shadows of very high-frequency oscillations can be detected in lower frequency bands of routine stereoelectroencephalography.

Very high-frequency oscillations (VHFOs, > 500 Hz) are more specific in localizing the epileptogenic zone (EZ) than high-frequency oscillations (HFOs, < 500 Hz). Unfortunately, VHFOs are not visible in standard clinical stereo-EEG (SEEG) recordings with sampling rates of 1 kHz or lower. Here we show that "shadows" of VHFOs can be found in frequencies below 500 Hz and can help us to identify SEEG channels with a higher probability of increased VHFO rates. Subsequent analysis of Logistic regression models on 141 SEEG channels from thirteen patients shows that VHFO "shadows" provide additional information to gold standard HFO analysis and can potentially help in precise EZ delineation in standard clinical recordings.

Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification.

Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.

Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis.

Objective.The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process, we propose an automatic method, a novel approach to enhance the optimization of neural network architectures for processing intracranial electroencephalogram (iEEG) data.Approach.We present a genetic algorithm, which optimizes neural network architecture and signal pre-processing parameters for iEEG classification.Main results.Our method improved the macroF1 score of the state-of-the-art model in two independent datasets, from St. Anne's University Hospital (Brno, Czech Republic) and Mayo Clinic (Rochester, MN, USA), from 0.9076 to 0.9673 and from 0.9222 to 0.9400 respectively.Significance.By incorporating principles of evolutionary optimization, our approach reduces the reliance on human intuition and empirical guesswork in architecture design, thus promoting more efficient and effective neural network models. The proposed method achieved significantly improved results when compared to the state-of-the-art benchmark model (McNemar's test,p≪ 0.01). The results indicate that neural network architectures designed through machine-based optimization outperform those crafted using the subjective heuristic approach of a human expert. Furthermore, we show that well-designed data preprocessing significantly affects the models' performance.

Interictal invasive very high-frequency oscillations in resting awake state and sleep.

Interictal very high-frequency oscillations (VHFOs, 500-2000 Hz) in a resting awake state seem to be, according to a precedent study of our team, a more specific predictor of a good outcome of the epilepsy surgery compared to traditional interictal high-frequency oscillations (HFOs, 80-500 Hz). In this study, we retested this hypothesis on a larger cohort of patients. In addition, we also collected patients' sleep data and hypothesized that the occurrence of VHFOs in sleep will be greater than in resting state. We recorded interictal invasive electroencephalographic (iEEG) oscillations in 104 patients with drug-resistant epilepsy in a resting state and in 35 patients during sleep. 21 patients in the rest study and 11 patients in the sleep study met the inclusion criteria (interictal HFOs and VHFOs present in iEEG recordings, a surgical intervention and a postoperative follow-up of at least 1 year) for further evaluation of iEEG data. In the rest study, patients with good postoperative outcomes had significantly higher ratio of resected contacts with VHFOs compared to HFOs. In sleep, VHFOs were more abundant than in rest and the percentage of resected contacts in patients with good and poor outcomes did not considerably differ in any type of oscillations. In conclusion, (1) our results confirm, in a larger patient cohort, our previous work about VHFOs being a specific predictor of the area which needs to be resected; and (2) that more frequent sleep VHFOs do not further improve the results.

Twenty-five years of epilepsy surgery at a Central European comprehensive epilepsy center-Trends in intervention delay and outcomes.

OBJECTIVE: We analyzed trends in patients' characteristics, outcomes, and waiting times over the last 25 years at our epilepsy surgery center situated in Central Europe to highlight possible areas of improvement in our care for patients with drug-resistant epilepsy. METHODS: A total of 704 patients who underwent surgery at the Brno Epilepsy Center were included in the study, 71 of those were children. Patients were separated into three time periods, 1996-2000 (n = 95), 2001-2010 (n = 295) and 2011-2022 (n = 314) based on first evaluation at the center. RESULTS: The average duration of epilepsy before surgery in adults remained high over the last 25 years (20.1 years from 1996 to 2000, 21.3 from 2001 to 2010, and 21.3 from 2011 to 2020, P = 0.718). There has been a decrease in rate of surgeries for temporal lobe epilepsy in the most recent time period (67%-70%-52%, P < 0.001). Correspondingly, extratemporal resections have become more frequent with a significant increase in surgeries for focal cortical dysplasia (2%-8%-19%, P < 0.001). For resections, better outcomes (ILAE scores 1a-2) have been achieved in extratemporal lesional (0%-21%-61%, P = 0.01, at least 2-year follow-up) patients. In temporal lesional patients, outcomes remained unchanged (at least 77% success rate). A longer duration of epilepsy predicted a less favorable outcome for resective procedures (P = 0.024) in patients with disease duration of less than 25 years. SIGNIFICANCE: The spectrum of epilepsy surgery is shifting toward nonlesional and extratemporal cases. While success rates of extratemporal resections at our center are getting better, the average duration of epilepsy before surgical intervention is still very long and is not improving. This underscores the need for stronger collaboration between epileptologists and outpatient neurologists to ensure prompt and effective treatment for patients with drug-resistant epilepsy.

The role of voxel-based morphometry in the detection of cortical dysplasia within the temporal pole in patients with intractable mesial temporal lobe epilepsy.

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.

Multimodal combination of neuroimaging methods for localizing the epileptogenic zone in MR-negative epilepsy.

The objective was to determine the optimal combination of multimodal imaging methods (IMs) for localizing the epileptogenic zone (EZ) in patients with MR-negative drug-resistant epilepsy. Data from 25 patients with MR-negative focal epilepsy (age 30 ± 10 years, 16M/9F) who underwent surgical resection of the EZ and from 110 healthy controls (age 31 ± 9 years; 56M/54F) were used to evaluate IMs based on 3T MRI, FDG-PET, HD-EEG, and SPECT. Patients with successful outcomes and/or positive histological findings were evaluated. From 38 IMs calculated per patient, 13 methods were selected by evaluating the mutual similarity of the methods and the accuracy of the EZ localization. The best results in postsurgical patients for EZ localization were found for ictal/ interictal SPECT (SISCOM), FDG-PET, arterial spin labeling (ASL), functional regional homogeneity (ReHo), gray matter volume (GMV), cortical thickness, HD electrical source imaging (ESI-HD), amplitude of low-frequency fluctuation (ALFF), diffusion tensor imaging, and kurtosis imaging. Combining IMs provides the method with the most accurate EZ identification in MR-negative epilepsy. The PET, SISCOM, and selected MRI-post-processing techniques are useful for EZ localization for surgical tailoring.

A Novel Statistical Model for Predicting the Efficacy of Vagal Nerve Stimulation in Patients With Epilepsy (Pre-X-Stim) Is Applicable to Different EEG Systems.

Background: Identifying patients with intractable epilepsy who would benefit from therapeutic chronic vagal nerve stimulation (VNS) preoperatively remains a major clinical challenge. We have developed a statistical model for predicting VNS efficacy using only routine preimplantation electroencephalogram (EEG) recorded with the TruScan EEG device (Brazdil et al., 2019). It remains to be seen, however, if this model can be applied in different clinical settings. Objective: To validate our model using EEG data acquired with a different recording system. Methods: We identified a validation cohort of eight patients implanted with VNS, whose preimplantation EEG was recorded on the BrainScope device and who underwent the EEG recording according to the protocol. The classifier developed in our earlier work, named Pre-X-Stim, was then employed to classify these patients as predicted responders or non-responders based on the dynamics in EEG power spectra. Predicted and real-world outcomes were compared to establish the applicability of this classifier. In total, two validation experiments were performed using two different validation approaches (single classifier or classifier voting). Results: The classifier achieved 75% accuracy, 67% sensitivity, and 100% specificity. Only two patients, both real-life responders, were classified incorrectly in both validation experiments. Conclusion: We have validated the Pre-X-Stim model on EEGs from a different recording system, which indicates its application under different technical conditions. Our approach, based on preoperative EEG, is easily applied and financially undemanding and presents great potential for real-world clinical use.

An optimized voxel-based morphometric study of gray matter changes in patients with left-sided and right-sided mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE/HS).

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.

Stereo-electroencephalography (SEEG) reference based on low-variance signals.

For a correct assessment of stereo-electroencephalographic (SEEG) recordings, a proper signal electrical reference is necessary. Such a reference might be physical or virtual. Physical reference can be noisy and a proper virtual reference calculation is often time-consuming. This paper uses the variance of the SEEG signals to calculate the reference from relatively low noise signals to reduce the contamination by distant sources, while maintaining negligible computing time.Ten patients with SEEG recordings were used in this study. 20-second long recordings from each patient, sampled at 5000 Hz, were used to calculate variances of SEEG signals and a low-variance (LV) subset of signals was selected for each patient. Consequently, 4 different reference signals were calculated using: 1) an average signal from WM contacts only (AVG_WM); 2) an average signal from LV contacts only (AVG_LV); 3) independent component analysis (ICA) method from WM contacts only (ICA_WM); and 4) ICA method from LV signals only (ICA_LV). Also, the original testing reference, an average signal from all SEEG contacts (AVG) was utilized. Finally, bipolar signals and average signals from anatomical structures were calculated and used to evaluate reference signals.91.7% of the WM SEEG contacts were found below the average variance. ICA_LV showed the best and AVG_WM the worst overall results. AVG_LV had the most positive impact on minimizing the mutual correlations between separate brain structures and correcting the outliers. The average processing time for ICA methods was 66.72 seconds and 0.7870 seconds for AVG methods (100 000 samples, 125.7±20.4 SEEG signals).Utilizing the LV data subset improves the reference signal. WM references are difficult to obtain and seem to be more susceptible to errors caused by low number of WM contacts in the dataset. ICA_LV can be considered as one of the best reference estimations, however the calculation is very demanding and time consuming. AVG_LV shows good and stable results, while it is based on a straightforward methodology and outstandingly fast calculation.