A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN.

Objectives: Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. Methods: We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. Results: In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. Conclusions: This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

OBJECTIVE: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group. METHODS: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. RESULTS: Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset ( η ρ max 2 = .05) and longer epilepsy duration ( η ρ max 2 = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. SIGNIFICANCE: We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy.

Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19.

Although some studies have shown neuroimaging and neuropsychological alterations in post-COVID-19 patients, fewer combined neuroimaging and neuropsychology evaluations of individuals who presented a mild acute infection. Here we investigated cognitive dysfunction and brain changes in a group of mildly infected individuals. We conducted a cross-sectional study of 97 consecutive subjects (median age of 41 years) without current or history of psychiatric symptoms (including anxiety and depression) after a mild infection, with a median of 79 days (and mean of 97 days) after diagnosis of COVID-19. We performed semi-structured interviews, neurological examinations, 3T-MRI scans, and neuropsychological assessments. For MRI analyses, we included a group of non-infected 77 controls. The MRI study included white matter (WM) investigation with diffusion tensor images (DTI) and functional connectivity with resting-state functional MRI (RS-fMRI). The patients reported memory loss (36%), fatigue (31%) and headache (29%). The quantitative analyses confirmed symptoms of fatigue (83% of participants), excessive somnolence (35%), impaired phonemic verbal fluency (21%), impaired verbal categorical fluency (13%) and impaired logical memory immediate recall (16%). The WM analyses with DTI revealed higher axial diffusivity values in post-infected patients compared to controls. Compared to controls, there were no significant differences in the functional connectivity of the posterior cingulum cortex. There were no significant correlations between neuropsychological scores and neuroimaging features (including DTI and RS-fMRI). Our results suggest persistent cognitive impairment and subtle white matter abnormalities in individuals mildly infected without anxiety or depression symptoms. The longitudinal analyses will clarify whether these alterations are temporary or permanent.

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group. Methods: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in i) all epilepsies; ii) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS); iii) non-lesional temporal lobe epilepsy (TLE-NL); iv) genetic generalised epilepsy; and (v) extra-temporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Results: Across all epilepsies, reduced total cerebellar volume was observed (d=0.42). Maximum volume loss was observed in the corpus medullare (dmax=0.49) and posterior lobe grey matter regions, including bilateral lobules VIIB (dmax= 0.47), Crus I/II (dmax= 0.39), VIIIA (dmax=0.45) and VIIIB (dmax=0.40). Earlier age at seizure onset (ηρ2max=0.05) and longer epilepsy duration (ηρ2max=0.06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. Significance: We provide robust evidence of deep cerebellar and posterior lobe subregional grey matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in non-motor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellum subregions into neurobiological models of epilepsy.

Fully automatic segmentation of brain lacunas resulting from resective surgery using a 3D deep learning model.

The rapid and constant development of deep learning (DL) strategies is pushing forward the quality of object segmentation in images from diverse fields of interest. In particular, these algorithms can be very helpful in delineating brain abnormalities (lesions, tumors, lacunas, etc), enabling the extraction of information such as volume and location, that can inform doctors or feed predictive models. In this study, we describe ResectVol DL, a fully automatic tool developed to segment resective lacunas in brain images of patients with epilepsy. ResectVol DL relies on the nnU-Net framework that leverages the 3D U-Net deep learning architecture. T1-weighted MRI datasets from 120 patients (57 women; 31.5 ± 15.9 years old at surgery) were used to train (n=78) and test (n=48) our tool. Manual segmentations were carried out by five different raters and were considered as ground truth for performance assessment. We compared ResectVol DL with two other fully automatic methods: ResectVol 1.1.2 and DeepResection, using the Dice similarity coefficient (DSC), Pearson's correlation coefficient, and relative difference to manual segmentation. ResectVol DL presented the highest median DSC (0.92 vs. 0.78 and 0.90), the highest correlation coefficient (0.99 vs. 0.63 and 0.94) and the lowest median relative difference (9 vs. 44 and 12 %). Overall, we demonstrate that ResectVol DL accurately segments brain lacunas, which has the potential to assist in the development of predictive models for postoperative cognitive and seizure outcomes.

Identifying cellular markers of focal cortical dysplasia type II with cell-type deconvolution and single-cell signatures.

Focal cortical dysplasia (FCD) is a brain malformation that causes medically refractory epilepsy. FCD is classified into three categories based on structural and cellular abnormalities, with FCD type II being the most common and characterized by disrupted organization of the cortex and abnormal neuronal development. In this study, we employed cell-type deconvolution and single-cell signatures to analyze bulk RNA-seq from multiple transcriptomic studies, aiming to characterize the cellular composition of brain lesions in patients with FCD IIa and IIb subtypes. Our deconvolution analyses revealed specific cellular changes in FCD IIb, including neuronal loss and an increase in reactive astrocytes (astrogliosis) when compared to FCD IIa. Astrogliosis in FCD IIb was further supported by a gene signature analysis and histologically confirmed by glial fibrillary acidic protein (GFAP) immunostaining. Overall, our findings demonstrate that FCD II subtypes exhibit differential neuronal and glial compositions, with astrogliosis emerging as a hallmark of FCD IIb. These observations, validated in independent patient cohorts and confirmed using immunohistochemistry, offer novel insights into the involvement of glial cells in FCD type II pathophysiology and may contribute to the development of targeted therapies for this condition.

Transcriptome analyses of the cortex and white matter of focal cortical dysplasia type II: Insights into pathophysiology and tissue characterization.

Introduction: Focal cortical dysplasia (FCD) is a common cause of pharmacoresistant epilepsy. According to the 2022 International League Against Epilepsy classification, FCD type II is characterized by dysmorphic neurons (IIa and IIb) and may be associated with balloon cells (IIb). We present a multicentric study to evaluate the transcriptomes of the gray and white matters of surgical FCD type II specimens. We aimed to contribute to pathophysiology and tissue characterization. Methods: We investigated FCD II (a and b) and control samples by performing RNA-sequencing followed by immunohistochemical validation employing digital analyses. Results: We found 342 and 399 transcripts differentially expressed in the gray matter of IIa and IIb lesions compared to controls, respectively. Cholesterol biosynthesis was among the main enriched cellular pathways in both IIa and IIb gray matter. Particularly, the genes HMGCS1, HMGCR, and SQLE were upregulated in both type II groups. We also found 12 differentially expressed genes when comparing transcriptomes of IIa and IIb lesions. Only 1 transcript (MTRNR2L12) was significantly upregulated in FCD IIa. The white matter in IIa and IIb lesions showed 2 and 24 transcripts differentially expressed, respectively, compared to controls. No enriched cellular pathways were detected. GPNMB, not previously described in FCD samples, was upregulated in IIb compared to IIa and control groups. Upregulations of cholesterol biosynthesis enzymes and GPNMB genes in FCD groups were immunohistochemically validated. Such enzymes were mainly detected in both dysmorphic and normal neurons, whereas GPNMB was observed only in balloon cells. Discussion: Overall, our study contributed to identifying cortical enrichment of cholesterol biosynthesis in FCD type II, which may correspond to a neuroprotective response to seizures. Moreover, specific analyses in either the gray or the white matter revealed upregulations of MTRNR2L12 and GPNMB, which might be potential neuropathological biomarkers of a cortex chronically exposed to seizures and of balloon cells, respectively.

Interictal and postictal 18F-FDG PET/CT in epileptogenic zone localization.

Objective: To evaluate the performance of 18F-fluorodeoxyglucose positron-emission tomography/computed tomography ( 18F-FDG PET/CT) in localizing epileptogenic zones, comparing 18F-FDG injection performed in the traditional interictal period with that performed near the time of a seizure. Materials and Methods: We evaluated patients with refractory epilepsy who underwent 18F-FDG PET/CT. The reference standards for localization of the epileptogenic zone were histopathology and follow-up examinations (in patients who underwent surgery) or serial electroencephalography (EEG) recordings, long-term video EEG, and magnetic resonance imaging (in patients who did not). The 18F-FDG injection was performed whether the patient had an epileptic seizure during the EEG monitoring period or not. The 18F-FDG PET/CT results were categorized as concordant or discordant with the reference standards. Results: Of the 110 patients evaluated, 10 were in a postictal group (FDG injection after a seizure) and 100 were in the interictal group. The 18F-FDG PET/CT was concordant with the reference standards in nine (90%) of the postictal group patients and in 60 (60%) of the interictal group patients. Among the nine postictal group patients in whom the results were concordant, the 18F-FDG PET/CT showed hypermetabolism and hypometabolism in the epileptogenic zone in four (44.4%) and five (55.6%), respectively. Conclusion: Our data indicate that 18F-FDG PET/CT is a helpful tool for localization of the epileptogenic zone and that EEG monitoring is an important means of correlating the findings. In addition, postictal 18F-FDG PET/CT is able to identify the epileptogenic zone by showing either hypometabolism or hypermetabolism.

Objetivo: Avaliar a capacidade da PET/CT FDG detectar a zona epileptogênica, com injeção da FDG realizada tanto no período interictal como perto de uma crise epiléptica. Materiais e Métodos: Foram avaliados pacientes com epilepsia de difícil controle que realizaram PET/CT FDG. A zona epileptogênica foi definida pelo follow up/anatomopatológico ou eletroencefalogramas (EEGs) seriados, telemetria e ressonância magnética. PET/CT FDG foi realizada independentemente se o paciente tinha crise epiléptica durante a monitoração com EEG ou no período interictal. Os resultados foram definidos como concordantes ou discordantes, comparando com a zona epileptogênica. Resultados: Foram incluídos no estudo 110 pacientes: 10 no grupo pós-ictal (injeção de FDG depois da crise) e 100 no grupo interictal. A PET/CT FDG foi concordante com a zona epileptogênica em nove pacientes do grupo pós-ictal (90%) e 60 pacientes do grupo interictal (60%). Entre os nove pacientes concordantes do grupo pós-ictal, quatro mostraram hipermetabolismo (44,4%) e cinco mostraram hipometabolismo na zona epileptogênica (55,6%). Conclusão: Nossos resultados confirmaram que a PET/CT FDG é uma ferramenta útil na localização da zona epileptogênica e a monitoração com EEG é muito importante para correlacionar os achados. Além disso, PET/CT FDG realizada no período pós-ictal é capaz de identificar a zona epileptogênica, mostrando tanto hipometabolismo como hipermetabolismo.

Interictal and postictal 18F-FDG PET/CT in epileptogenic zone localization / PET/CT FDG- 18F interictal e pós-ictal na localização da zona epileptogênica

Abstract Objective: To evaluate the performance of 18F-fluorodeoxyglucose positron-emission tomography/computed tomography ( 18F-FDG PET/CT) in localizing epileptogenic zones, comparing 18F-FDG injection performed in the traditional interictal period with that performed near the time of a seizure. Materials and Methods: We evaluated patients with refractory epilepsy who underwent 18F-FDG PET/CT. The reference standards for localization of the epileptogenic zone were histopathology and follow-up examinations (in patients who underwent surgery) or serial electroencephalography (EEG) recordings, long-term video EEG, and magnetic resonance imaging (in patients who did not). The 18F-FDG injection was performed whether the patient had an epileptic seizure during the EEG monitoring period or not. The 18F-FDG PET/CT results were categorized as concordant or discordant with the reference standards. Results: Of the 110 patients evaluated, 10 were in a postictal group (FDG injection after a seizure) and 100 were in the interictal group. The 18F-FDG PET/CT was concordant with the reference standards in nine (90%) of the postictal group patients and in 60 (60%) of the interictal group patients. Among the nine postictal group patients in whom the results were concordant, the 18F-FDG PET/CT showed hypermetabolism and hypometabolism in the epileptogenic zone in four (44.4%) and five (55.6%), respectively. Conclusion: Our data indicate that 18F-FDG PET/CT is a helpful tool for localization of the epileptogenic zone and that EEG monitoring is an important means of correlating the findings. In addition, postictal 18F-FDG PET/CT is able to identify the epileptogenic zone by showing either hypometabolism or hypermetabolism.

Resumo Objetivo: Avaliar a capacidade da PET/CT FDG detectar a zona epileptogênica, com injeção da FDG realizada tanto no período interictal como perto de uma crise epiléptica. Materiais e Métodos: Foram avaliados pacientes com epilepsia de difícil controle que realizaram PET/CT FDG. A zona epileptogênica foi definida pelo follow up/anatomopatológico ou eletroencefalogramas (EEGs) seriados, telemetria e ressonância magnética. PET/CT FDG foi realizada independentemente se o paciente tinha crise epiléptica durante a monitoração com EEG ou no período interictal. Os resultados foram definidos como concordantes ou discordantes, comparando com a zona epileptogênica. Resultados: Foram incluídos no estudo 110 pacientes: 10 no grupo pós-ictal (injeção de FDG depois da crise) e 100 no grupo interictal. A PET/CT FDG foi concordante com a zona epileptogênica em nove pacientes do grupo pós-ictal (90%) e 60 pacientes do grupo interictal (60%). Entre os nove pacientes concordantes do grupo pós-ictal, quatro mostraram hipermetabolismo (44,4%) e cinco mostraram hipometabolismo na zona epileptogênica (55,6%). Conclusão: Nossos resultados confirmaram que a PET/CT FDG é uma ferramenta útil na localização da zona epileptogênica e a monitoração com EEG é muito importante para correlacionar os achados. Além disso, PET/CT FDG realizada no período pós-ictal é capaz de identificar a zona epileptogênica, mostrando tanto hipometabolismo como hipermetabolismo.

Morphological, cellular, and molecular basis of brain infection in COVID-19 patients.

Although increasing evidence confirms neuropsychiatric manifestations associated mainly with severe COVID-19 infection, long-term neuropsychiatric dysfunction (recently characterized as part of "long COVID-19" syndrome) has been frequently observed after mild infection. We show the spectrum of cerebral impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, ranging from long-term alterations in mildly infected individuals (orbitofrontal cortical atrophy, neurocognitive impairment, excessive fatigue and anxiety symptoms) to severe acute damage confirmed in brain tissue samples extracted from the orbitofrontal region (via endonasal transethmoidal access) from individuals who died of COVID-19. In an independent cohort of 26 individuals who died of COVID-19, we used histopathological signs of brain damage as a guide for possible SARS-CoV-2 brain infection and found that among the 5 individuals who exhibited those signs, all of them had genetic material of the virus in the brain. Brain tissue samples from these five patients also exhibited foci of SARS-CoV-2 infection and replication, particularly in astrocytes. Supporting the hypothesis of astrocyte infection, neural stem cell-derived human astrocytes in vitro are susceptible to SARS-CoV-2 infection through a noncanonical mechanism that involves spike-NRP1 interaction. SARS-CoV-2-infected astrocytes manifested changes in energy metabolism and in key proteins and metabolites used to fuel neurons, as well as in the biogenesis of neurotransmitters. Moreover, human astrocyte infection elicits a secretory phenotype that reduces neuronal viability. Our data support the model in which SARS-CoV-2 reaches the brain, infects astrocytes, and consequently, leads to neuronal death or dysfunction. These deregulated processes could contribute to the structural and functional alterations seen in the brains of COVID-19 patients.

Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data.

OBJECTIVE: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. RESULTS: In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. SIGNIFICANCE: From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.

Circulating Metabolites as Biomarkers of Disease in Patients with Mesial Temporal Lobe Epilepsy.

A major challenge in the clinical management of patients with mesial temporal lobe epilepsy (MTLE) is identifying those who do not respond to antiseizure medication (ASM), allowing for the timely pursuit of alternative treatments such as epilepsy surgery. Here, we investigated changes in plasma metabolites as biomarkers of disease in patients with MTLE. Furthermore, we used the metabolomics data to gain insights into the mechanisms underlying MTLE and response to ASM. We performed an untargeted metabolomic method using magnetic resonance spectroscopy and multi- and univariate statistical analyses to compare data obtained from plasma samples of 28 patients with MTLE compared to 28 controls. The patients were further divided according to response to ASM for a supplementary and preliminary comparison: 20 patients were refractory to treatment, and eight were responsive to ASM. We only included patients using carbamazepine in combination with clobazam. We analyzed the group of patients and controls and found that the profiles of glucose (p = 0.01), saturated lipids (p = 0.0002), isoleucine (p = 0.0001), ß-hydroxybutyrate (p = 0.0003), and proline (p = 0.02) were different in patients compared to controls (p < 0.05). In addition, we found some suggestive metabolites (without enough predictability) by multivariate analysis (VIP scores > 2), such as lipoproteins, lactate, glucose, unsaturated lipids, isoleucine, and proline, that might be relevant to the process of pharmacoresistance in the comparison between patients with refractory and responsive MTLE. The identified metabolites for the comparison between MTLE patients and controls were linked to different biological pathways related to cell-energy metabolism and pathways related to inflammatory processes and the modulation of neurotransmitter release and activity in MTLE. In conclusion, in addition to insights into the mechanisms underlying MTLE, our results suggest that plasma metabolites may be used as disease biomarkers. These findings warrant further studies exploring the clinical use of metabolites to assist in decision-making when treating patients with MTLE.

Benchmarking the proteomic profile of animal models of mesial temporal epilepsy.

OBJECTIVES: We compared the proteomic signatures of the hippocampal lesion induced in three different animal models of mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE+HS): the systemic pilocarpine model (PILO), the intracerebroventricular kainic acid model (KA), and the perforant pathway stimulation model (PPS). METHODS: We used shotgun proteomics to analyze the proteomes and find enriched biological pathways of the dorsal and ventral dentate gyrus (DG) isolated from the hippocampi of the three animal models. We also compared the proteomes obtained in the animal models to that from the DG of patients with pharmacoresistant MTLE+HS. RESULTS: We found that each animal model presents specific profiles of proteomic changes. The PILO model showed responses predominantly related to neuronal excitatory imbalance. The KA model revealed alterations mainly in synaptic activity. The PPS model displayed abnormalities in metabolism and oxidative stress. We also identified common biological pathways enriched in all three models, such as inflammation and immune response, which were also observed in tissue from patients. However, none of the models could recapitulate the profile of molecular changes observed in tissue from patients. SIGNIFICANCE: Our results indicate that each model has its own set of biological responses leading to epilepsy. Thus, it seems that only using a combination of the three models may one replicate more closely the mechanisms underlying MTLE+HS as seen in patients.

The Relationship Between Depression and Anxiety Symptoms of Adult PWE and Caregivers in a Tertiary Center.

Background: Although several studies have emphasized the association between epilepsy and psychiatric disorders, fewer have investigated the impact of epilepsy on caregivers' emotional status, mainly in adult people with epilepsy (PWE). Here we investigated depressive symptoms, suicidal ideation, and anxiety symptoms in a large group of adult PWE and their caregivers. Methods: We analyzed symptoms of depression [with the Beck Depression Inventory-II (BDI-II)], suicidal ideation (with BDI-II item 9), and anxiety symptoms (with the Beck Anxiety Inventory) in a large group of adult PWE [N = 548 (60% women; median age 41)] and caregivers [N = 191 (72% women; median age 47)] from a Brazilian tertiary center, considering sociodemographic and clinical aspects. We also applied the Liverpool Adverse Events Profile to assess anti-seizure drugs adverse events. Results: While the presence (p = 0.026) (and intensity, p = 0.007) of depressive symptoms and suicidal ideation (p = 0.02) were higher in PWE compared to caregivers, the proportion of clinical anxiety symptoms (p = 0.32) (and the intensity, p = 0.13) was similar in both groups. Although the rates of suicidal ideation were higher in focal epilepsy (20%), both generalized genetic epilepsy and caregivers also presented elevated frequencies (11%) of suicidal ideation. The analyses of 120 patient-caregiver dyads revealed that the intensity of depressive symptoms in PWE (but not anxiety) correlated with the intensity of depressive (r = 0.35; p < 0.001) and anxiety (r = 0.25; p = 0.01) symptoms in their caregivers. In the multivariate analyses of PWE, focal epilepsy (compared to GGE) was associated with clinical depressive symptoms (odds ratio, OR 2.1) and suicidal ideation (OR 3.2), while recurrent seizures (compared to the seizure-free group) were associated with suicidal ideation (OR 2.6) and anxiety symptoms (OR 2.1). Also, caregivers with anxiety symptoms were 8 times more likely to exhibit depressive symptoms, and those with depressive symptoms were 8 times more likely to present anxiety symptoms. Conclusion: Our study suggests that specific attention for the caregivers' mental health is as essential as PWE. There is an urgent need for more studies involving caregivers to identify their emotional distress and provide adequate treatment.

Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy.

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

Junctional instability in neuroepithelium and network hyperexcitability in a focal cortical dysplasia human model.

Focal cortical dysplasia is a highly epileptogenic cortical malformation with few treatment options. Here, we generated human cortical organoids from patients with focal cortical dysplasia type II. Using this human model, we mimicked some focal cortical dysplasia hallmarks, such as impaired cell proliferation, the presence of dysmorphic neurons and balloon cells, and neuronal network hyperexcitability. Furthermore, we observed alterations in the adherens junctions zonula occludens-1 and partitioning defective 3, reduced polarization of the actin cytoskeleton, and fewer synaptic puncta. Focal cortical dysplasia cortical organoids showed downregulation of the small GTPase RHOA, a finding that was confirmed in brain tissue resected from these patients. Functionally, both spontaneous and optogenetically-evoked electrical activity revealed hyperexcitability and enhanced network connectivity in focal cortical dysplasia organoids. Taken together, our findings suggest a ventricular zone instability in tissue cohesion of neuroepithelial cells, leading to a maturational arrest of progenitors or newborn neurons, which may predispose to cellular and functional immaturity and compromise the formation of neural networks in focal cortical dysplasia.

A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies.

AIMS: The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis. METHODS: Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy. RESULTS: We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia. CONCLUSIONS: These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings.

OBJECTIVE: This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. METHODS: We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction. RESULTS: Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. SIGNIFICANCE: This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

EEG Signal Connectivity for Characterizing Interictal Activity in Patients With Mesial Temporal Lobe Epilepsy.

Over the last decade, several methods for analysis of epileptiform signals in electroencephalography (EEG) have been proposed. These methods mainly use EEG signal features in either the time or the frequency domain to separate regular, interictal, and ictal brain activity. The aim of this work was to evaluate the feasibility of using functional connectivity (FC) based feature extraction methods for the analysis of epileptiform discharges in EEG signals. These signals were obtained from EEG-fMRI sessions of 10 patients with mesial temporal lobe epilepsy (MTLE) with unilateral hippocampal atrophy. The connectivity functions investigated were motif synchronization, imaginary coherence, and magnitude squared coherence in the alpha, beta, and gamma bands of the EEG. EEG signals were sectioned into 1-s epochs and classified according to (using neurologist markers): activity far from interictal epileptiform discharges (IED), activity immediately before an IED and, finally, mid-IED activity. Connectivity matrices for each epoch for each FC function were built, and graph theory was used to obtain the following metrics: strength, cluster coefficient, betweenness centrality, eigenvector centrality (both local and global), and global efficiency. The statistical distributions of these metrics were compared among the three classes, using ANOVA, for each FC function. We found significant differences in all global (p < 0.001) and local (p < 0.00002) graph metrics of the far class compared with before and mid for motif synchronization on the beta band; local betweenness centrality also pointed to a degree of lateralization on the frontotemporal structures. This analysis demonstrates the potential of FC measures, computed using motif synchronization, for the characterization of epileptiform activity of MTLE patients. This methodology may be helpful in the analysis of EEG-fMRI data applied to epileptic foci localization. Nonetheless, the methods must be tested with a larger sample and with other epileptic phenotypes.

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.