Hemispheric functional organization, as revealed by naturalistic neuroimaging, in pediatric epilepsy patients with cortical resections.

Functional changes in the pediatric brain following neural injuries attest to remarkable feats of plasticity. Investigations of the neurobiological mechanisms that underlie this plasticity have largely focused on activation in the penumbra of the lesion or in contralesional, homotopic regions. Here, we adopt a whole-brain approach to evaluate the plasticity of the cortex in patients with large unilateral cortical resections due to drug-resistant childhood epilepsy. We compared the functional connectivity (FC) in patients' preserved hemisphere with the corresponding hemisphere of matched controls as they viewed and listened to a movie excerpt in a functional magnetic resonance imaging (fMRI) scanner. The preserved hemisphere was segmented into 180 and 200 parcels using two different anatomical atlases. We calculated all pairwise multivariate statistical dependencies between parcels, or parcel edges, and between 22 and 7 larger-scale functional networks, or network edges, aggregated from the smaller parcel edges. Both the left and right hemisphere-preserved patient groups had widespread reductions in FC relative to matched controls, particularly for within-network edges. A case series analysis further uncovered subclusters of patients with distinctive edgewise changes relative to controls, illustrating individual postoperative connectivity profiles. The large-scale differences in networks of the preserved hemisphere potentially reflect plasticity in the service of maintained and/or retained cognitive function.

Asymmetrical cortical surface area decrease in epilepsy patients with postictal generalized electroencephalography suppression.

Postictal generalized electroencephalographic suppression is a possible electroencephalographic marker for sudden unexpected death in epilepsy. We aimed to investigate the cortical surface area abnormalities in epilepsy patients with postictal generalized electroencephalographic suppression. We retrospectively included 30 epilepsy patients with postictal generalized electroencephalographic suppression (PGES+), 21 epilepsy patients without postictal generalized electroencephalographic suppression (PGES-), and 30 healthy controls. Surface-based analysis on high-resolution T1-weighted images was conducted and cortical surface areas were compared among the three groups, alongside correlation analyses with seizure-related clinical variables. Compared with PGES- group, we identified reduced surface area in the bilateral insula with more extensive distribution in the right hemisphere in PGES+ group. The reduced right insular surface area was associated with younger seizure-onset age. When compared with healthy controls, PGES- group presented reduced surface area in the left caudal middle frontal gyrus; PGES+ group presented more widespread surface area reductions in the right posterior cingulate gyrus, left postcentral gyrus, middle frontal gyrus, and middle temporal gyrus. Our results suggested cortical microstructural impairment in patients with postictal generalized electroencephalographic suppression. The significant surface area reductions in the insular cortex supported the autonomic network involvement in the pathology of postictal generalized electroencephalographic suppression, and its right-sided predominance suggested the potential shared abnormal brain network for postictal generalized electroencephalographic suppression and sudden unexpected death in epilepsy.

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework.

This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain's functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient's brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the underlying Electrophysiological Source Imaging (ESI) problem. To guarantee a unique solution, most existing ESI methods pay more attention to solve this inverse problem by imposing physiological constraints. This paper proposes an efficient ESI approach based on simulation-driven deep learning. Epileptic High-resolution 256-channels scalp EEG (Hr-EEG) signals are simulated in a realistic manner to train the proposed patient-specific model. More particularly, a computational neural mass model developed in our team is used to generate the temporal dynamics of the activity of each dipole while the forward problem is solved using a patient-specific three-shell realistic head model and the boundary element method. A Temporal Convolutional Network (TCN) is considered in the proposed model to capture local spatial patterns. To enable the model to observe the EEG signals from different scale levels, the multi-scale strategy is leveraged to capture the overall features and fine-grain features by adjusting the convolutional kernel size. Then, the Long Short-Term Memory (LSTM) is used to extract temporal dependencies among the computed spatial features. The performance of the proposed method is evaluated through three different scenarios of realistic synthetic interictal Hr-EEG data as well as on real interictal Hr-EEG data acquired in three patients with drug-resistant partial epilepsy, during their presurgical evaluation. A performance comparison study is also conducted with two other deep learning-based methods and four classical ESI techniques. The proposed model achieved a Dipole Localization Error (DLE) of 1.39 and Normalized Hamming Distance (NHD) of 0.28 in the case of one patch with SNR of 10 dB. In the case of two uncorrelated patches with an SNR of 10 dB, obtained DLE and NHD were respectively 1.50 and 0.28. Even in the more challenging scenario of two correlated patches with an SNR of 10 dB, the proposed approach still achieved a DLE of 3.74 and an NHD of 0.43. The results obtained on simulated data demonstrate that the proposed method outperforms the existing methods for different signal-to-noise and source configurations. The good behavior of the proposed method is also confirmed on real interictal EEG data. The robustness with respect to noise makes it a promising and alternative tool to localize epileptic brain areas and to reconstruct their electrical activities from EEG signals.

Automated segmentation of epilepsy surgical resection cavities: Comparison of four methods to manual segmentation.

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n = 44, 88 %) and Epic-CHOP (n = 42, 84 %) were able to detect more resection cavities than Resseg (n = 22, 44 %, P < 0.001) and Deep Resection (n = 23, 46 %, P < 0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts. In the temporal cohort, the SPM-based pipelines had higher detection rates, however there was no difference in the accuracy between methods. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study.

BACKGROUND: Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy. METHODS: Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN. RESULTS: Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables. CONCLUSIONS: Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.

Clustered mutations in the GRIK2 kainate receptor subunit gene underlie diverse neurodevelopmental disorders.

Kainate receptors (KARs) are glutamate-gated cation channels with diverse roles in the central nervous system. Bi-allelic loss of function of the KAR-encoding gene GRIK2 causes a nonsyndromic neurodevelopmental disorder (NDD) with intellectual disability and developmental delay as core features. The extent to which mono-allelic variants in GRIK2 also underlie NDDs is less understood because only a single individual has been reported previously. Here, we describe an additional eleven individuals with heterozygous de novo variants in GRIK2 causative for neurodevelopmental deficits that include intellectual disability. Five children harbored recurrent de novo variants (three encoding p.Thr660Lys and two p.Thr660Arg), and four children and one adult were homozygous for a previously reported variant (c.1969G>A [p.Ala657Thr]). Individuals with shared variants had some overlapping behavioral and neurological dysfunction, suggesting that the GRIK2 variants are likely pathogenic. Analogous mutations introduced into recombinant GluK2 KAR subunits at sites within the M3 transmembrane domain (encoding p.Ala657Thr, p.Thr660Lys, and p.Thr660Arg) and the M3-S2 linker domain (encoding p.Ile668Thr) had complex effects on functional properties and membrane localization of homomeric and heteromeric KARs. Both p.Thr660Lys and p.Thr660Arg mutant KARs exhibited markedly slowed gating kinetics, similar to p.Ala657Thr-containing receptors. Moreover, we observed emerging genotype-phenotype correlations, including the presence of severe epilepsy in individuals with the p.Thr660Lys variant and hypomyelination in individuals with either the p.Thr660Lys or p.Thr660Arg variant. Collectively, these results demonstrate that human GRIK2 variants predicted to alter channel function are causative for early childhood development disorders and further emphasize the importance of clarifying the role of KARs in early nervous system development.

Evidence based noninvasive presurgical evaluation for patients with drug resistant epilepsies.

PURPOSE OF REVIEW: To review the current practices and evidence for the diagnostic accuracy and the benefits of presurgical evaluation. RECENT FINDINGS: Preoperative evaluation of patients with drug-resistant focal epilepsies and subsequent epilepsy surgery leads to a significant proportion of seizure-free patients. Even those who are not completely seizure free postoperatively often experience improved quality of life with better social integration. Systematic reviews and meta-analysis on the diagnostic accuracy are available for Video-electroencephalographic (EEG) monitoring, magnetic resonance imaging (MRI), electric and magnetic source imaging, and functional MRI for lateralization of language and memory. There are currently no evidence-based international guidelines for presurgical evaluation and epilepsy surgery. SUMMARY: Presurgical evaluation is a complex multidisciplinary and multiprofessional clinical pathway. We rely on limited consensus-based recommendations regarding the required staffing or methodological expertise in epilepsy centers.

EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy.

Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.

Severity of GNAO1-Related Disorder Correlates with Changes in G-Protein Function.

OBJECTIVE: GNAO1-related disorders (OMIM #615473 and #617493), caused by variants in the GNAO1 gene, are characterized by developmental delay or intellectual disability, hypotonia, movement disorders, and epilepsy. Neither a genotype-phenotype correlation nor a clear severity score have been established for this disorder. The objective of this prospective and retrospective observational study was to develop a severity score for GNAO1-related disorders, and to delineate the correlation between the underlying molecular mechanisms and clinical severity. METHODS: A total of 16 individuals with GNAO1-related disorders harboring 12 distinct missense variants, including four novel variants (p.K46R, p.T48I, p.R209P, and p.L235P), were examined with repeated clinical assessments, video-electroencephalogram monitoring, and brain magnetic resonance imaging. The molecular pathology of each variant was delineated using a molecular deconvoluting platform. RESULTS: The patients displayed a wide variability in the severity of their symptoms. This heterogeneity was well represented in the GNAO1-related disorders severity score, with a broad range of results. Patients with the same variant had comparable severity scores, indicating that differences in disease profiles are not due to interpatient variability, but rather, to unique disease mechanisms. Moreover, we found a significant correlation between clinical severity scores and molecular mechanisms. INTERPRETATION: The clinical score proposed here provides further insight into the correlation between pathophysiology and phenotypic severity in GNAO1-related disorders. We found that each variant has a unique profile of clinical phenotypes and pathological molecular mechanisms. These findings will contribute to better understanding GNAO1-related disorders. Additionally, the severity score will facilitate standardization of patients categorization and assessment of response to therapies in development. ANN NEUROL 2023;94:987-1004.

EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy.

Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.

WONOEP 2022: Neurotechnology for the diagnosis of epilepsy.

Epilepsy's myriad causes and clinical presentations ensure that accurate diagnoses and targeted treatments remain a challenge. Advanced neurotechnologies are needed to better characterize individual patients across multiple modalities and analytical techniques. At the XVIth Workshop on Neurobiology of Epilepsy: Early Onset Epilepsies: Neurobiology and Novel Therapeutic Strategies (WONOEP 2022), the session on "advanced tools" highlighted a range of approaches, from molecular phenotyping of genetic epilepsy models and resected tissue samples to imaging-guided localization of epileptogenic tissue for surgical resection of focal malformations. These tools integrate cutting edge research, clinical data acquisition, and advanced computational methods to leverage the rich information contained within increasingly large datasets. A number of common challenges and opportunities emerged, including the need for multidisciplinary collaboration, multimodal integration, potential ethical challenges, and the multistage path to clinical translation. Despite these challenges, advanced epilepsy neurotechnologies offer the potential to improve our understanding of the underlying causes of epilepsy and our capacity to provide patient-specific treatment.

iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices.

OBJECTIVE: Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms. METHODS: We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts. RESULTS: We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations. SIGNIFICANCE: iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

Grading system for assessing the confidence in the epileptogenic zone reported in published studies: A Delphi consensus study.

OBJECTIVE: This study was undertaken to develop a standardized grading system based on expert consensus for evaluating the level of confidence in the localization of the epileptogenic zone (EZ) as reported in published studies, to harmonize and facilitate systematic reviews in the field of epilepsy surgery. METHODS: We conducted a Delphi study involving 22 experts from 18 countries, who were asked to rate their level of confidence in the localization of the EZ for various theoretical clinical scenarios, using different scales. Information provided in these scenarios included one or several of the following data: magnetic resonance imaging (MRI) findings, invasive electroencephalography summary, and postoperative seizure outcome. RESULTS: The first explorative phase showed an overall interrater agreement of .347, pointing to large heterogeneity among experts' assessments, with only 17% of the 42 proposed scenarios associated with a substantial level of agreement. A majority showed preferences for the simpler scale and single-item scenarios. The successive Delphi voting phases resulted in a majority consensus across experts, with more than two thirds of respondents agreeing on the rating of each of the tested single-item scenarios. High or very high levels of confidence were ascribed to patients with either an Engel class I or class IA postoperative seizure outcome, a well-delineated EZ according to all available invasive EEG (iEEG) data, or a well-delineated focal epileptogenic lesion on MRI. MRI signs of hippocampal sclerosis or atrophy were associated with a moderate level of confidence, whereas a low level was ascribed to other MRI findings, a poorly delineated EZ according to iEEG data, or an Engel class II-IV postoperative seizure outcome. SIGNIFICANCE: The proposed grading system, based on an expert consensus, provides a simple framework to rate the level of confidence in the EZ reported in published studies in a structured and harmonized way, offering an opportunity to facilitate and increase the quality of systematic reviews and guidelines in the field of epilepsy surgery.

Clinical and molecular characterization of patients with YWHAG-related epilepsy.

OBJECTIVE: YWHAG variant alleles have been associated with a rare disease trait whose clinical synopsis includes an early onset epileptic encephalopathy with predominantly myoclonic seizures, developmental delay/intellectual disability, and facial dysmorphisms. Through description of a large cohort, which doubles the number of reported patients, we further delineate the spectrum of YWHAG-related epilepsy. METHODS: We included in this study 24 patients, 21 new and three previously described, with pathogenic/likely pathogenic variants in YWHAG. We extended the analysis of clinical, electroencephalographic, brain magnetic resonance imaging, and molecular genetic information to 24 previously published patients. RESULTS: The phenotypic spectrum of YWHAG-related disorders ranges from mild developmental delay to developmental and epileptic encephalopathy (DEE). Epilepsy onset is in the first 2 years of life. Seizure freedom can be achieved in half of the patients (13/24, 54%). Intellectual disability (23/24, 96%), behavioral disorders (18/24, 75%), neurological signs (13/24, 54%), and dysmorphisms (6/24, 25%) are common. A genotype-phenotype correlation emerged, as DEE is more represented in patients with missense variants located in the ligand-binding domain than in those with truncating or missense variants in other domains (90% vs. 19%, p < .001). SIGNIFICANCE: This study suggests that pathogenic YWHAG variants cause a wide range of clinical presentations with variable severity, ranging from mild developmental delay to DEE. In this allelic series, a genotype-phenotype correlation begins to emerge, potentially providing prognostic information for clinical management and genetic counseling.

Magnetic resonance imaging findings in Kenyans and South Africans with active convulsive epilepsy: An observational study.

OBJECTIVE: Focal epilepsy is common in low- and middle-income countries. The frequency and nature of possible underlying structural brain abnormalities have, however, not been fully assessed. METHODS: We evaluated the possible structural causes of epilepsy in 331 people with epilepsy (240 from Kenya and 91 from South Africa) identified from community surveys of active convulsive epilepsy. Magnetic resonance imaging (MRI) scans were acquired on 1.5-Tesla scanners to determine the frequency and nature of any underlying lesions. We estimated the prevalence of these abnormalities using Bayesian priors (from an earlier pilot study) and observed data (from this study). We used a mixed-effect modified Poisson regression approach with the site as a random effect to determine the clinical features associated with neuropathology. RESULTS: MRI abnormalities were found in 140 of 240 (modeled prevalence = 59%, 95% confidence interval [CI]: 53%-64%) of people with epilepsy in Kenya, and in 62 of 91 (modeled prevalence = 65%, 95% CI: 57%-73%) in South Africa, with a pooled modeled prevalence of 61% (95% CI: 56%-66%). Abnormalities were common in those with a history of adverse perinatal events (15/23 [65%, 95% CI: 43%-84%]), exposure to parasitic infections (83/120 [69%, 95% CI: 60%-77%]) and focal electroencephalographic features (97/142 [68%, 95% CI: 60%-76%]), but less frequent in individuals with generalized electroencephalographic features (44/99 [44%, 95% CI: 34%-55%]). Most abnormalities were potentially epileptogenic (167/202, 82%), of which mesial temporal sclerosis (43%) and gliosis (34%) were the most frequent. Abnormalities were associated with co-occurrence of generalized non-convulsive seizures (relative risk [RR] = 1.12, 95% CI: 1.04-1.25), lack of family history of seizures (RR = 0.91, 0.86-0.96), convulsive status epilepticus (RR = 1.14, 1.08-1.21), frequent seizures (RR = 1.12, 1.04-1.20), and reported use of anti-seizure medication (RR = 1.22, 1.18-1.26). SIGNIFICANCE: MRI identified pathologies are common in people with epilepsy in Kenya and South Africa. Mesial temporal sclerosis, the most common abnormality, may be amenable to surgical correction. MRI may have a diagnostic value in rural Africa, but future longitudinal studies should examine the prognostic role.

Evidence for interictal blood-brain barrier dysfunction in people with epilepsy.

OBJECTIVE: Interictal blood-brain barrier dysfunction in chronic epilepsy has been demonstrated in animal models and pathological specimens. Ictal blood-brain barrier dysfunction has been shown in humans in vivo using an experimental quantitative magnetic resonance imaging (MRI) protocol. Here, we hypothesized that interictal blood-brain barrier dysfunction is also present in people with drug-resistant epilepsy. METHODS: Thirty-nine people (21 females, mean age at MRI ± SD = 30 ± 8 years) with drug-resistant epilepsy were prospectively recruited and underwent interictal T1-relaxometry before and after administration of a paramagnetic contrast agent. Likewise, quantitative T1 was acquired in 29 people without epilepsy (12 females, age at MRI = 48 ± 18 years). Quantitative T1 difference maps were calculated and served as a surrogate imaging marker for blood-brain barrier dysfunction. Values of quantitative T1 difference maps inside hemispheres ipsilateral to the presumed seizure onset zone were then compared, on a voxelwise level and within presumed seizure onset zones, to the contralateral side of people with epilepsy and to people without epilepsy. RESULTS: Compared to the contralateral side, ipsilateral T1 difference values were significantly higher in white matter (corrected p < .05), gray matter (uncorrected p < .05), and presumed seizure onset zones (p = .04) in people with epilepsy. Compared to people without epilepsy, significantly higher T1 difference values were found in the anatomical vicinity of presumed seizure onset zones (p = .004). A subgroup of people with hippocampal sclerosis demonstrated significantly higher T1 difference values in the ipsilateral hippocampus and in regions strongly interconnected with the hippocampus compared to people without epilepsy (corrected p < .01). Finally, z-scores reflecting the deviation of T1 difference values within the presumed seizure onset zone were associated with verbal memory performance (p = .02) in people with temporal lobe epilepsy. SIGNIFICANCE: Our results indicate a blood-brain barrier dysfunction in drug-resistant epilepsy that is detectable interictally in vivo, anatomically related to the presumed seizure onset zone, and associated with cognitive deficits.

Stereo-EEG-based ictal functional connectivity in patients with periventricular nodular heterotopia-related epilepsy.

Nodular heterotopia (NH)-related drug-resistant epilepsy is challenging due to the deep location of the NH and the complexity of the underlying epileptogenic network. Using ictal stereo-electroencephalography (SEEG) and functional connectivity (FC) analyses in 14 patients with NH-related drug-resistant epilepsy, we aimed to determine the leading structure during seizures. For this purpose, we compared node IN and OUT strength between bipolar channels inside the heterotopia and inside gray matter, at the group level and at the individual level. At seizure onset, the channels within NH belonging to the epileptogenic and/or propagation network showed higher node OUT-strength than the channels within the gray matter (p = .03), with higher node OUT-strength than node IN-strength (p = .03). These results are in favor of a "leading" role of NH during seizure onset when involved in the epileptogenic- or propagation-zone network (50% of patients). However, when looking at the individual level, no significant difference between NH and gray matter was found, except for one patient (in two of three seizures). This result confirms the heterogeneity and the complexity of the epileptogenic network organization in NH and the need for SEEG exploration to characterize more precisely patient-specific epileptogenic network organization.

Morphometric magnetic resonance imaging (MRI) postprocessing in MRI-negative patients with first unprovoked seizure.

OBJECTIVE: The aim of the study was to evaluate the benefits of morphometric magnetic resonance imaging (MRI) postprocessing in patients presenting with a first seizure and negative MRI results and to investigate these findings in the context of the clinical and electroencephalographic data, seizure recurrence rates, and epilepsy diagnosis in these patients. METHODS: We retrospectively reviewed 97 MRI scans of patients with first unprovoked epileptic seizure and no evidence of epileptogenic lesion on clinical routine MRI. Morphometric Analysis Program (MAP; v2018), automated postprocessing software, was used to identify subtle, potentially epileptogenic lesions in the three-dimensional T1-weighted MRI data. The resulting probability maps were examined together with the conventional MRI images by a reviewer who remained blinded to the patients' clinical and electroencephalographical data. Clinical data were prospectively collected between February 2018 and May 2023. RESULTS: Among the apparently MRI-negative patients, a total of 18 of 97 (18.6%) showed cortical changes suggestive of focal cortical dysplasia. Within the population with positive MAP findings (MAP+), seizure recurrence rates were 61.1% and 66.7% at 1 and 2 years after the first unprovoked seizure, respectively. Conversely, patients with negative MAP findings (MAP-) had lower seizure recurrence rates of 27.8% and 34.2% at 1 and 2 years after the first unprovoked seizure, respectively. Patients with MAP+ findings were significantly more likely to be diagnosed with epilepsy than those patients with MAP- findings (χ2 [1, n = 97] = 14.820, p < .001, odds ratio = 21.371, 95% CI = 2.710-168.531) during a mean follow-up time of 22.51 months (SD = 16.7 months, range = 1-61 months). SIGNIFICANCE: MRI postprocessing can be a valuable tool for detecting subtle epileptogenic lesions in patients with a first seizure and negative MRI results. Patients with first seizure and MAP+ findings had high seizure recurrence rates, meeting the criteria for beginning epilepsy.

Neurological morbidity of surgery for suprasylvian operculoinsular epilepsy.

OBJECTIVE: The objective of this study was to identify risk factors associated with surgery-related neurological morbidity in patients with drug-resistant epilepsy undergoing suprasylvian operculoinsular resections. As secondary outcomes, we also analyzed the risk factors for ischemic lesion (IL) of corona radiata and seizure recurrence. METHODS: A retrospective analysis was conducted on a cohort of patients who underwent suprasylvian operculoinsular resections for drug-resistant epilepsy. The association of several presurgical, surgical, and postsurgical factors with both primary (persistent neurological deficits) and secondary (structural abnormalities on postoperative magnetic resonance imaging [MRI] and seizure recurrence) postoperative outcomes was investigated with univariate and multivariate statistical analysis. RESULTS: The study included a total of 65 patients; 46.2% of patients exhibited postoperative neurological deficits, but only 12.3% experienced persistent deficits. On postoperative MRI, IL in the corona radiata and corticospinal tract Wallerian degeneration (CSTWd) were seen in 68% and 29% of cases, respectively. Only CSTWd was significantly associated with persistent neurological deficits (relative risk [RR] = 2.6). Combined operculoinsular resection (RR = 3.62) and surgery performed on the left hemisphere (RR = .37) were independently associated with IL in the corona radiata. Variables independently associated with CSTWd were the presence of malacic components in the IL (RR = 1.96), right central operculum resection (RR = 1.79), and increasing age at surgery (RR = 1.03). Sixty-two patients had a postoperative follow-up > 12 months (median = 56, interquartile range = 30.75-73.5), and 62.9% were in Engel class I at last outpatient control. The risk of seizure recurrence was reduced by selective opercular resection (RR = .25) and increased by the histological diagnosis of aspecific gliosis (RR = 1.39). SIGNIFICANCE: This study provides insights into the risk factors associated with surgery-related neurological morbidity, as well as further evidence on the postoperative occurrence of subcortical injury and seizure recurrence in epileptic patients undergoing suprasylvian operculoinsular resections. The findings highlighted in this study may be useful to better understand the processes supporting the increased surgical risk in the operculoinsular region.