Subjective visual sensitivity in neurotypical adults: insights from a magnetic resonance spectroscopy study.

Introduction: Altered subjective visual sensitivity manifests as feelings of discomfort or overload elicited by intense and irritative visual stimuli. This can result in a host of visual aberrations including visual distortions, elementary visual hallucinations and visceral responses like dizziness and nausea, collectively referred to as "pattern glare." Current knowledge of the underlying neural mechanisms has focused on overall excitability of the visual cortex, but the individual contribution of excitatory and inhibitory systems has not yet been quantified. Methods: In this study, we focus on the role of glutamate and γ-aminobutyric acid (GABA) as potential mediators of individual differences in subjective visual sensitivity, measured by a computerized Pattern Glare Test-a series of monochromatic square-wave gratings with three different spatial frequencies, while controlling for psychological variables related to sensory sensitivity with multiple questionnaires. Resting neurotransmitter concentrations in primary visual cortex (V1) and right anterior insula were studied in 160 healthy participants using magnetic resonance spectroscopy. Results: Data showed significant differences in the perception of visual distortions (VD) and comfort scores between men and women, with women generally reporting more VD, and therefore the modulatory effect of sex was considered in a further examination. A general linear model analysis showed a negative effect of occipital glutamate on a number of reported visual distortions, but also a significant role of several background psychological traits. When assessing comfort scores in women, an important intervening variable was the menstrual cycle. Discussion: Our findings do not support that baseline neurotransmitter levels have a significant role in overreactivity to aversive stimuli in neurotypical population. However, we demonstrated that biological sex can have a significant impact on subjective responses. Based on this additional finding, we suggest that future studies investigate aversive visual stimuli while examining the role of biological sex.

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction.

OBJECTIVE: Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. METHODS: In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. RESULTS: The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. SIGNIFICANCE: Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

Prognostic value of the 5-SENSE Score to predict focality of the seizure-onset zone as assessed by stereoelectroencephalography: a prospective international multicentre validation study.

Introduction: Epilepsy surgery is the only curative treatment for patients with drug-resistant focal epilepsy. Stereoelectroencephalography (SEEG) is the gold standard to delineate the seizure-onset zone (SOZ). However, up to 40% of patients are subsequently not operated as no focal non-eloquent SOZ can be identified. The 5-SENSE Score is a 5-point score to predict whether a focal SOZ is likely to be identified by SEEG. This study aims to validate the 5-SENSE Score, improve score performance by incorporating auxiliary diagnostic methods and evaluate its concordance with expert decisions. Methods and analysis: Non-interventional, observational, multicentre, prospective study including 200 patients with drug-resistant epilepsy aged ≥15 years undergoing SEEG for identification of a focal SOZ and 200 controls at 22 epilepsy surgery centres worldwide. The primary objective is to assess the diagnostic accuracy and generalisability of the 5-SENSE in predicting focality in SEEG in a prospective cohort. Secondary objectives are to optimise score performance by incorporating auxiliary diagnostic methods and to analyse concordance of the 5-SENSE Score with the expert decisions made in the multidisciplinary team discussion. Ethics and dissemination: Prospective multicentre validation of the 5-SENSE score may lead to its implementation into clinical practice to assist clinicians in the difficult decision of whether to proceed with implantation. This study will be conducted in accordance with the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (2014). We plan to publish the study results in a peer-reviewed full-length original article and present its findings at scientific conferences. Trial registration number: NCT06138808.

Prenatal exposure to air pollution and maternal depression: Combined effects on brain aging and mental health in young adulthood.

INTRODUCTION: Both maternal depression problems during pregnancy and prenatal exposure to air pollution have been associated with changes in the brain as well as worse mood and anxiety in the offspring in adulthood. However, it is not clear whether these effects are independent or whether and how they might interact and impact the brain age and mental health of the young adult offspring. METHODS: A total of 202 mother-child dyads from a prenatal birth cohort were assessed for maternal depression during pregnancy through self-report questionnaires administered in the early 90s, exposure to air pollutants (Sulfur dioxide [SO2], nitrogen oxides [NOx], and suspended particle matter [SPM]) during each trimester based on maternal address and air quality data, mental health of the young adult offspring (28-30 years of age; 52% men, all of European ancestry) using self-report questionnaires for depression (Beck Depression Inventory), mood dysregulation (Profile of Mood States), anxiety (State-Trait Anxiety Inventory), and psychotic symptoms (Schizotypal Personality Questionnaire), and brain age, estimated from structural magnetic resonance imaging (MRI) and previously published neuroanatomical age prediction model using cortical thickness maps. The brain age gap estimate (BrainAGE) was computed by subtracting structural brain age from chronological age. Trajectories of exposure to air pollution during pregnancy were assessed using Growth Mixture Modeling. The interactions of prenatal depression and prenatal exposure to air pollutants on adult mental health and BrainAGE were assessed using hierarchical linear regression. RESULTS: We revealed two distinct trajectories of exposure to air pollution during pregnancy: "early exposure," characterized by high exposure during the first trimester, followed by a steady decrease, and "late exposure," characterized by low exposure during the first trimester, followed by a steady increase in the exposure during the subsequent trimesters. Maternal depression during the first half of pregnancy interacted with NOX exposure trajectory, predicting mood dysregulation and schizotypal symptoms in young adults. In addition, maternal depression during the second half of pregnancy interacted with both NOx and SO2 exposure trajectories, respectively, and predicted BrainAGE in young adults. In those with early exposure to NOx, maternal depression during pregnancy was associated with worse mental health and accelerated brain aging in young adulthood. In contrast, in those with early exposure to SO2, maternal depression during pregnancy was associated with slower brain aging in young adulthood. CONCLUSIONS: Our findings provide the first evidence of the combined effects of prenatal exposure to air pollution and maternal depression on mental health outcomes and brain age in young adult offspring. Moreover, they point out the importance of the timing and trajectory of the exposure during prenatal development.

MicroRNA-mediated regulation of neurotransmitter receptors in epilepsy: A systematic review.

BACKGROUND: Pathogenesis of epilepsy involves dysregulation of the neurotransmitter system contributing to hyper-excitability of neuronal cells. MicroRNA (miRNAs) are small non-coding RNAs known to play a crucial role in post-transcriptional regulation of gene expression. METHODS: The present review was prepared following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, employing a comprehensive search strategy to identify and extract data from published research articles. Keywords suchas epilepsy, micro RNA (micro RNAs, miRNA, miRNAs, miR), neurotransmitters (specific names), and neurotransmitter receptors (specific names) were used to construct the query. RESULTS: A total of 724 articles were identified using the keywords epilepsy, microRNA along with select neurotransmitter and neurotransmitter receptor names. After exclusions, the final selection consisted of 17 studies, most of which centered on glutamate and gamma-aminobutyric acid (GABA) receptors. Singular studies also investigated miRNAs affecting cholinergic, purinergic, and glycine receptors. CONCLUSION: This review offers a concise overview of the current knowledge on miRNA-mediated regulation of neurotransmitter receptors in epilepsy and highlights their potential for future clinical application.

High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?

Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.

Prenatal exposure to alcohol and its impact on reward processing and substance use in adulthood.

Heavy maternal alcohol drinking during pregnancy has been associated with altered neurodevelopment in the child but the effects of low-dose alcohol drinking are less clear and any potential safe level of alcohol use during pregnancy is not known. We evaluated the effects of prenatal alcohol on reward-related behavior and substance use in young adulthood and the potential sex differences therein. Participants were members of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort who participated in its neuroimaging follow-up in young adulthood. A total of 191 participants (28-30 years; 51% men) had complete data on prenatal exposure to alcohol, current substance use, and fMRI data from young adulthood. Maternal alcohol drinking was assessed during mid-pregnancy and pre-conception. Brain response to reward anticipation and reward feedback was measured using the Monetary Incentive Delay task and substance use in young adulthood was assessed using a self-report questionnaire. We showed that even a moderate exposure to alcohol in mid-pregnancy but not pre-conception was associated with robust effects on brain response to reward feedback (six frontal, one parietal, one temporal, and one occipital cluster) and with greater cannabis use in both men and women 30 years later. Moreover, mid-pregnancy but not pre-conception exposure to alcohol was associated with greater cannabis use in young adulthood and these effects were independent of maternal education and maternal depression during pregnancy. Further, the extent of cannabis use in the late 20 s was predicted by the brain response to reward feedback in three out of the nine prenatal alcohol-related clusters and these effects were independent of current alcohol use. Sex differences in the brain response to reward outcome emerged only during the no loss vs. loss contrast. Young adult men exposed to alcohol prenatally had significantly larger brain response to no loss vs. loss in the putamen and occipital region than women exposed to prenatal alcohol. Therefore, we conclude that even moderate exposure to alcohol prenatally has long-lasting effects on brain function during reward processing and risk of cannabis use in young adulthood.

Case report: Insulinoma masquerades as epilepsy - quantitative EEG analysis.

Insulinomas are rare gastrointestinal tumors with an incidence of 1-3 per million inhabitants annually. These tumors result in excessive insulin production, culminating in hypoglycemia. Such hypoglycemia triggers various central nervous system (CNS) manifestations, including headache, confusion, abnormal behavior, and epileptic seizures, which can lead to misdiagnosis as epilepsy. This case report documents a 46-year-old male who presented seizure-like episodes. Episodes occurred mainly during the night, lasting several minutes to hours. Initial seizures were characterized by bizarre behavior and altered responsiveness. Over time, seizure frequency, complexity, and severity escalated. We managed to record two episodes during long-term EEG and report, as the first ones, the detailed quantitative EEG analysis of these hypoglycemia-related events. EEG changes preceded the development of clear-cut pathological motor activity in tens of minutes and were present in all investigated frequency bands. The development of profound motor activity was associated with other increases in EEG power spectra in all frequencies except for delta. The most pronounced changes were found over the left temporal region, which can be the most susceptible to hypoglycemia. In our patient, the seizure-like episodes completely disappeared after the insulinoma removal, which demonstrates their relationship to hypoglycemia.

Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase shifts.

Recently, in the past decade, high-frequency oscillations (HFOs), very high-frequency oscillations (VHFOs), and ultra-fast oscillations (UFOs) were reported in epileptic patients with drug-resistant epilepsy. However, to this day, the physiological origin of these events has yet to be understood. Our study establishes a mathematical framework based on bifurcation theory for investigating the occurrence of VHFOs and UFOs in depth EEG signals of patients with focal epilepsy, focusing on the potential role of reduced connection strength between neurons in an epileptic focus. We demonstrate that synchronization of a weakly coupled network can generate very and ultra high-frequency signals detectable by nearby microelectrodes. In particular, we show that a bistability region enables the persistence of phase-shift synchronized clusters of neurons. This phenomenon is observed for different hippocampal neuron models, including Morris-Lecar, Destexhe-Paré, and an interneuron model. The mechanism seems to be robust for small coupling, and it also persists with random noise affecting the external current. Our findings suggest that weakened neuronal connections could contribute to the production of oscillations with frequencies above 1000 Hz, which could advance our understanding of epilepsy pathology and potentially improve treatment strategies. However, further exploration of various coupling types and complex network models is needed.

We have built a mathematical framework to examine how a reduced neuronal coupling within an epileptic focus could lead to very high-frequency (VHFOs) and ultra-fast oscillations (UFOs) in depth EEG signals. By analyzing weakly coupled neurons, we found a bistability synchronization region where in-phase and anti-phase synchrony persist. These dynamics can be detected as very high-frequency EEG signals. The principle of weak coupling aligns with the disturbances in neuronal connections often observed in epilepsy; moreover, VHFOs are important markers of epileptogenicity. Our findings point to the potential significance of weakened neuronal connections in producing VHFOs and UFOs related to focal epilepsy. This could enhance our understanding of brain disorders. We emphasize the need for further investigations of weakly coupled neurons.

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.

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.

Low prevalence of neural autoantibodies in perioperative cerebrospinal fluid samples of epilepsy surgery patients: A multicenter prospective study.

OBJECTIVE: Refractory epilepsy may have an underlying autoimmune etiology. Our aim was to assess the prevalence of neural autoantibodies in a multicenter national prospective cohort of patients with drug-resistant epilepsy undergoing epilepsy surgery utilizing comprehensive clinical, serologic, and histopathological analyses. METHODS: We prospectively recruited patients undergoing epilepsy surgery for refractory focal epilepsy not caused by a brain tumor from epilepsy surgery centers in the Czech Republic. Perioperatively, we collected cerebrospinal fluid (CSF) and/or serum samples and performed comprehensive commercial and in-house assays for neural autoantibodies. Clinical data were obtained from the patients' medical records, and histopathological analysis of resected brain tissue was performed. RESULTS: Seventy-six patients were included, mostly magnetic resonance imaging (MRI)-lesional cases (74%). Mean time from diagnosis to surgery was 21 ± 13 years. Only one patient (1.3%) had antibodies in the CSF and serum (antibodies against glutamic acid decarboxylase 65) in relevant titers; histology revealed focal cortical dysplasia (FCD) III (FCD associated with hippocampal sclerosis [HS]). Five patients' samples displayed CSF-restricted oligoclonal bands (OCBs; 6.6%): three cases with FCD (one with FCD II and two with FCD I), one with HS, and one with negative histology. Importantly, eight patients (one of them with CSF-restricted OCBs) had findings on antibody testing in individual serum and/or CSF tests that could not be confirmed by complementary tests and were thus classified as nonspecific, yet could have been considered specific without confirmatory testing. Of these, two had FCD, two gliosis, and four HS. No inflammatory changes or lymphocyte cuffing was observed histopathologically in any of the 76 patients. SIGNIFICANCE: Neural autoantibodies are a rare finding in perioperatively collected serum and CSF of our cohort of mostly MRI-lesional epilepsy surgery patients. Confirmatory testing is essential to avoid overinterpretation of autoantibody-positive findings.

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.

Metabolic connectivity as a predictor of surgical outcome in mesial temporal lobe epilepsy.

OBJECTIVE: The study investigated metabolic connectivity (MC) differences between patients with unilateral drug-resistant mesial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (HS) and healthy controls (HCs), based on [18 F]-fluorodeoxyglucose (FDG)-PET data. We focused on the MC changes dependent on the lateralization of the epileptogenic lobe and on correlations with postoperative outcomes. METHODS: FDG-PET scans of 47 patients with unilateral MTLE with histopathologically proven HS and 25 HC were included in the study. All the patients underwent a standard anterior temporal lobectomy and were more than 2 years after the surgery. MC changes were compared between the two HS groups (left HS, right HS) and HC. Differences between the metabolic network of seizure-free and non-seizure-free patients after surgery were depicted afterward. Network changes were correlated with clinical characteristics. RESULTS: The study showed widespread metabolic network changes in the HS patients as compared to HC. The changes were more extensive in the right HS than in the left HS. Unfavorable surgical outcomes were found in patients with decreased MC within the network including both the lesional and contralesional hippocampus, ipsilesional frontal operculum, and contralesional insula. Favorable outcomes correlated with decreased MC within the network involving both orbitofrontal cortices and the ipsilesional temporal lobe. SIGNIFICANCE: There are major differences in the metabolic networks of left and right HS, with more extensive changes in right HS. The changes within the metabolic network could help predict surgical outcomes in patients with HS. MC may identify patients with potentially unfavorable outcomes and direct them to a more detailed presurgical evaluation. PLAIN LANGUAGE SUMMARY: Metabolic connectivity is a promising method for metabolic network mapping. Metabolic networks in mesial temporal lobe epilepsy are dependent on lateralization of the epileptogenic lobe and could predict surgical outcomes.

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.

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.

Longitudinal study of epigenetic aging and its relationship with brain aging and cognitive skills in young adulthood.

Introduction: The proportion of older adults within society is sharply increasing and a better understanding of how we age starts to be critical. However, given the paucity of longitudinal studies with both neuroimaging and epigenetic data, it remains largely unknown whether the speed of the epigenetic clock changes over the life course and whether any such changes are proportional to changes in brain aging and cognitive skills. To fill these knowledge gaps, we conducted a longitudinal study of a prenatal birth cohort, studied epigenetic aging across adolescence and young adulthood, and evaluated its relationship with brain aging and cognitive outcomes. Methods: DNA methylation was assessed using the Illumina EPIC Platform in adolescence, early and late 20 s, DNA methylation age was estimated using Horvath's epigenetic clock, and epigenetic age gap (EpiAGE) was calculated as DNA methylation age residualized for batch, chronological age and the proportion of epithelial cells. Structural magnetic resonance imaging (MRI) was acquired in both the early 20 s and late 20 s using the same 3T Prisma MRI scanner and brain age was calculated using the Neuroanatomical Age Prediction using R (NAPR) platform. Cognitive skills were assessed using the Wechsler Adult Intelligence Scale (WAIS) in the late 20 s. Results: The EpiAGE in adolescence, the early 20 s, and the late 20 s were positively correlated (r = 0.34-0.47), suggesting that EpiAGE is a relatively stable characteristic of an individual. Further, a faster pace of aging between the measurements was positively correlated with EpiAGE at the end of the period (r = 0.48-0.77) but negatively correlated with EpiAGE at the earlier time point (r = -0.42 to -0.55), suggesting a compensatory mechanism where late matures might be catching up with the early matures. Finally, higher positive EpiAGE showed small (Adj R2 = 0.03) but significant relationships with a higher positive brain age gap in all participants and lower full-scale IQ in young adult women in the late 20 s. Discussion: We conclude that the EpiAGE is a relatively stable characteristic of an individual across adolescence and early adulthood, but that it shows only a small relationship with accelerated brain aging and a women-specific relationship with worse performance IQ.

CCL2/MCP-1, interleukin-8, and fractalkine/CXC3CL1: Potential biomarkers of epileptogenesis and pharmacoresistance in childhood epilepsy.

OBJECTIVE: The pathophysiological processes leading to epileptogenesis and pharmacoresistance in epilepsy have been the subject of extensive preclinical and clinical research. The main impact on clinical practice is the development of new targeted therapies for epilepsy. We studied the importance of neuroinflammation in the development of epileptogenesis and pharmacoresistance in childhood epilepsy patients. METHODS: A cross-sectional study conducted at two epilepsy centers in the Czech Republic compared 22 pharmacoresistant patients and 4 pharmacodependent patients to 9 controls. We analyzed the ProcartaPlex™ 9-Plex immunoassay panel consisting of interleukin (IL)-6, IL-8, IL-10, IL-18, CXCL10/IP-10, monocyte chemoattractant protein 1 (CCL2/MCP-1), B lymphocyte chemoattractant (BLC), tumor necrosis factor-alpha (TNF-α), and chemokine (C-X3-X motif) ligand 1 (fractalkine/CXC3CL1) to determine their alterations in cerebrospinal fluid (CSF) and blood plasma, concurrently. RESULTS: The analysis of 21 paired CSF and plasma samples in pharmacoresistant patients compared to controls revealed a significant elevation of CCL2/MCP-1 in CSF (p < 0.000512) and plasma (p < 0.00.017). Higher levels of fractalkine/CXC3CL1 were revealed in the plasma of pharmacoresistant patients than in controls (p < 0.0704), and we determined an upward trend in CSF IL-8 levels (p < 0.08). No significant differences in CSF and plasma levels were detected between pharmacodependent patients and controls. CONCLUSION: Elevated CCL2/MCP-1 in CSF and plasma, elevated levels of fractalkine/CXC3CL1 in CSF, and a trend toward elevated IL-8 in the CSF of patients with pharmacoresistant epilepsy indicate these cytokines as potential biomarkers of epileptogenesis and pharmacoresistance. CCL2/MCP-1was detected in blood plasma; this assessment may be easily achieved in clinical practice without the invasiveness of a spinal tap. However, due to the complexity of neuroinflammation in epilepsy, further studies are warranted to confirm our findings.

Bariatric surgery and its impact on depressive symptoms, cognition, brain and inflammation.

Background: Obesity has been associated with depressive symptoms and impaired cognition, but the mechanisms underlying these relationships are not well understood. It is also not clear whether reducing adiposity reverses these behavioral outcomes. The current study tested the impact of bariatric surgery on depressive symptoms, cognition, and the brain; using a mediation model, we also examined whether the relationship between changes in adiposity after the surgery and those in regional thickness of the cerebral cortex are mediated by changes in low-grade inflammation (as indexed by C-reactive protein; CRP). Methods: A total of 18 bariatric patients completed 3 visits, including one baseline before the surgery and two post-surgery measurements acquired at 6- and 12-months post-surgery. Each visit consisted of a collection of fasting blood sample, magnetic resonance imaging of the brain and abdomen, and assessment of depressive symptoms and cognition. Results: After surgery, we observed reductions of both visceral fat (p< 0.001) and subcutaneous fat (p< 0.001), less depressive symptoms (p< 0.001), improved verbal reasoning (p< 0.001), and reduced CRP (p< 0.001). Mediation analyses revealed that the relationships between the surgery-related changes in visceral fat and cortical thickness in depression-related regions are mediated by changes in CRP (ab=-.027, SE=.012, 95% CI [-.054, -,006]). Conclusion: These findings suggest that some of the beneficial effects of bariatric surgery on brain function and structure are due to a reduction of adiposity-related low-grade systemic inflammation.

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.