Shared genetic aetiology of Alzheimer's disease and age-related macular degeneration by APOC1 and APOE genes.

Background: Alzheimer's disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights into the underlying pathogenesis and inform integrated prevention and treatment for both diseases. Methods: A stratified quantile-quantile (QQ) plot was constructed to detect the pleiotropy among AD and AMD based on genome-wide association studies data from 17 008 patients with AD and 30 178 patients with AMD. A Bayesian conditional false discovery rate-based (cFDR) method was used to identify pleiotropic genes. UK Biobank was used to verify the pleiotropy analysis. Biological network and enrichment analysis were conducted to explain the biological reason for pleiotropy phenomena. A diagnostic test based on gene expression data was used to predict biomarkers for AD and AMD based on pleiotropic genes and their regulators. Results: Significant pleiotropy was found between AD and AMD (significant leftward shift on QQ plots). APOC1 and APOE were identified as pleiotropic genes for AD-AMD (cFDR <0.01). Network analysis revealed that APOC1 and APOE occupied borderline positions on the gene co-expression networks. Both APOC1 and APOE genes were enriched on the herpes simplex virus 1 infection pathway. Further, machine learning-based diagnostic tests identified that APOC1, APOE (areas under the curve (AUCs) >0.65) and their upstream regulators, especially ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and AMD (AUCs >0.8). Conclusion: In this study, we confirmed the genetic pleiotropy between AD and AMD and identified APOC1 and APOE as pleiotropic genes. Further, the integration of multiomics data identified ZNF131, ADNP2 and HINFP as novel diagnostic biomarkers for AD and AMD.

Association of Localizing 18F-FDG-PET Hypometabolism and Outcome Following Epilepsy Surgery: Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVES: Although commonly used in the evaluation of patients for epilepsy surgery, the association between the detection of localizing 18fluorine fluorodeoxyglucose PET (18F-FDG-PET) hypometabolism and epilepsy surgery outcome is uncertain. We conducted a systematic review and meta-analysis to determine whether localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery. METHODS: A systematic literature search was undertaken. Eligible publications included evaluation with 18F-FDG-PET before epilepsy surgery, with ≥10 participants, and those that reported surgical outcome at ≥12 months. Random-effects meta-analysis was used to calculate the odds of achieving a favorable outcome, defined as Engel class I, International League Against Epilepsy class 1-2, or seizure-free, with localizing 18F-FDG-PET hypometabolism, defined as concordant with the epilepsy surgery resection zone. Meta-regression was used to characterize sources of heterogeneity. RESULTS: The database search identified 8,916 studies, of which 98 were included (total patients n = 4,104). Localizing 18F-FDG-PET hypometabolism was associated with favorable outcome after epilepsy surgery for all patients with odds ratio (OR) 2.68 (95% CI 2.08-3.45). Subgroup analysis yielded similar findings for those with (OR 2.64, 95% CI 1.54-4.52) and without epileptogenic lesion detected on MRI (OR 2.49, 95% CI 1.80-3.44). Concordance with EEG (OR 2.34, 95% CI 1.43-3.83), MRI (OR 1.69, 95% CI 1.19-2.40), and triple concordance with both (OR 2.20, 95% CI 1.32-3.64) was associated with higher odds of favorable outcome. By contrast, diffuse 18F-FDG-PET hypometabolism was associated with worse outcomes compared with focal hypometabolism (OR 0.34, 95% CI 0.22-0.54). DISCUSSION: Localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery, irrespective of the presence of an epileptogenic lesion on MRI. The extent of 18F-FDG-PET hypometabolism provides additional information, with diffuse hypometabolism associated with worse surgical outcome than focal 18F-FDG-PET hypometabolism. These findings support the incorporation of 18F-FDG-PET into routine noninvasive investigations for patients being evaluated for epilepsy surgery to improve epileptogenic zone localization and to aid patient selection for surgery.

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.

Clinical outcomes among initial survivors of cryptogenic new-onset refractory status epilepsy (NORSE).

OBJECTIVE: New-onset refractory status epilepticus (NORSE) is a rare but severe clinical syndrome. Despite rigorous evaluation, the underlying cause is unknown in 30%-50% of patients and treatment strategies are largely empirical. The aim of this study was to describe clinical outcomes in a cohort of well-phenotyped, thoroughly investigated patients who survived the initial phase of cryptogenic NORSE managed in specialist centers. METHODS: Well-characterized cases of cryptogenic NORSE were identified through the EPIGEN and Critical Care EEG Monitoring Research Consortia (CCEMRC) during the period 2005-2019. Treating epileptologists reported on post-NORSE survival rates and sequelae in patients after discharge from hospital. Among survivors >6 months post-discharge, we report the rates and severity of active epilepsy, global disability, vocational, and global cognitive and mental health outcomes. We attempt to identify determinants of outcome. RESULTS: Among 48 patients who survived the acute phase of NORSE to the point of discharge from hospital, 9 had died at last follow-up, of whom 7 died within 6 months of discharge from the tertiary care center. The remaining 39 patients had high rates of active epilepsy as well as vocational, cognitive, and psychiatric comorbidities. The epilepsy was usually multifocal and typically drug resistant. Only a minority of patients had a good functional outcome. Therapeutic interventions were heterogenous during the acute phase of the illness. There was no clear relationship between the nature of treatment and clinical outcomes. SIGNIFICANCE: Among survivors of cryptogenic NORSE, longer-term outcomes in most patients were life altering and often catastrophic. Treatment remains empirical and variable. There is a pressing need to understand the etiology of cryptogenic NORSE and to develop tailored treatment strategies.

Amygdala enlargement in temporal lobe epilepsy: Histopathology and surgical outcomes.

OBJECTIVES: Amygdala enlargement is detected on magnetic resonance imaging (MRI) in some patients with drug-resistant temporal lobe epilepsy (TLE), but its clinical significance remains uncertain We aimed to assess if the presence of amygdala enlargement (1) predicted seizure outcome following anterior temporal lobectomy with amygdalohippocampectomy (ATL-AH) and (2) was associated with specific histopathological changes. METHODS: This was a case-control study. We included patients with drug-resistant TLE who underwent ATL-AH with and without amygdala enlargement detected on pre-operative MRI. Amygdala volumetry was done using FreeSurfer for patients who had high-resolution T1-weighted images. Mann-Whitney U test was used to compare pre-operative clinical characteristics between the two groups. The amygdala volume on the epileptogenic side was compared to the amygdala volume on the contralateral side among cases and controls. Then, we used a two-sample, independent t test to compare the means of amygdala volume differences between cases and controls. The chi-square test was used to assess the correlation of amygdala enlargement with (1) post-surgical seizure outcomes and (2) histopathological changes. RESULTS: Nineteen patients with and 19 patients without amygdala enlargement were studied. Their median age at surgery was 38 years for cases and 39 years for controls, and 52.6% were male. There were no statistically significant differences between the two groups in their pre-operative clinical characteristics. There were significant differences in the means of volume difference between cases and controls (Diff = 457.2 mm3, 95% confidence interval [CI] 289.6-624.8; p < .001) and in the means of percentage difference (p < .001). However, there was no significant association between amygdala enlargement and surgical outcome (p = .72) or histopathological changes (p = .63). SIGNIFICANCE: The presence of amygdala enlargement on the pre-operative brain MRI in patients with TLE does not affect the surgical outcome following ATL-AH, and it does not necessarily suggest abnormal histopathology. These findings suggest that amygdala enlargement might reflect a secondary reactive process to seizures in the epileptogenic temporal lobe.

Comparative effectiveness of digital mental healthcare models for adults with epilepsy: A study protocol of a randomized controlled trial.

OBJECTIVE: Mental health complaints are prevalent among people with epilepsy, yet there are major barriers that prevent access to psychological care, including high out-of-pocket costs and a lack of accessible specialized services. The purpose of the current study is to examine the comparative efficacy, acceptability, cost-effectiveness, and long-term outcomes of a digital psychological intervention when delivered under two models of care (i.e., guided vs. unguided) in supporting the mental health and functioning of adults with epilepsy. METHOD: Approximately 375 participants across Australia will be enrolled. Eligible participants will have a confirmed diagnosis of epilepsy, experience difficulties with their emotional health, be at least 18 years of age, and live in Australia. Participants will be randomized (2:2:1) to receive the Wellbeing Neuro Course, a 10-week internet-delivered program, with (i.e., guided) or without guidance by a mental health clinician (i.e., unguided), or be allocated to a treatment-as-usual waiting-list control group. Participants will complete online questionnaires at pre-, post-treatment, and 3- and 12-month follow-up and consent to have their data linked to their medical records to capture healthcare system resource use and costs. ANALYSIS: Primary outcome measures will be symptoms of depression and anxiety. A cost-utility analysis will be undertaken using the Australian healthcare system perspective and according to current economic evaluation guidelines. Resource use and costs to the healthcare system during the study period will be captured via data linkage to relevant administrative datasets in Australia. SIGNIFICANCE: The results of this trial will provide important data concerning the relative outcomes of these different models of care and will inform the integration of digital psychological interventions translation into healthcare services. ETHICS AND DISSEMINATION: The Human Research Ethics Committee of Macquarie University approved the proposed study (Reference No: 520231325151475). The results will be disseminated through peer-reviewed publication(s). ANZCTR TRIAL REGISTRATION NUMBER: ACTRN12623001327673. PLAIN LANGUAGE SUMMARY: This study seeks to find out if a 10-week online psychological treatment can improve the mental health and well-being of Australian adults with epilepsy. Around 375 participants will be randomly assigned to different groups: one will receive treatment with guidance from mental health clinician (guided group), one without guidance (unguided group), and one starting later (waiting control group). All participants will fill out the same outcome measures online. The main goal of this research is to compare these groups and assess how well the treatment works in improving mental health outcomes.

Work productivity, quality of life, and care needs: An unfolding epilepsy burden revealed in the Australian Epilepsy Project pilot study.

OBJECTIVE: Epilepsy is a common and serious neurological disorder. This cross-sectional analysis addresses the burden of epilepsy at different stages of the disease. METHODS: This pilot study is embedded within the Australian Epilepsy Project (AEP), aiming to provide epilepsy support through a national network of dedicated sites. For this analysis, adults aged 18-65 years with first unprovoked seizure (FUS), newly diagnosed epilepsy (NDE), or drug-resistant epilepsy (DRE) were recruited between February-August 2022. Baseline clinicodemographic data were collected from the participants who completed questionnaires to assess their quality of life (QOLIE-31, EQ-5D-5L), work productivity (Work Productivity and Activity Impairment [WPAI]), and care needs. Univariate analysis and multivariate regression was performed. RESULTS: 172 participants formed the study cohort (median age 34, interquartile range [IQR]: 26-45), comprising FUS (n = 44), NDE (n = 53), and DRE (n = 75). Mean QOLIE-31 score was 56 (standard deviation [SD] ± 18) and median EQ-5D-5L score was 0.77 (IQR: 0.56-0.92). QOLIE-31 but not EQ-5D-5L scores were significantly lower in the DRE group compared to FUS and NDE groups (p < 0.001). Overall, 64.5% of participants participated in paid work, with fewer DRE (52.0%) compared with FUS (76.7%) and NDE (72.5%) (p < 0.001). Compared to those not in paid employment, those in paid employment had significantly higher quality of life scores (p < 0.001). Almost 5.8% of participants required formal care (median 20 h/week, IQR: 12-55) and 17.7% required informal care (median 16 h/week, IQR: 7-101). SIGNIFICANCE: Epilepsy is associated with a large burden in terms of quality of life, productivity and care needs. PLAIN LANGUAGE SUMMARY: This is a pilot study from the Australian Epilepsy Project (AEP). It reports health economic data for adults of working age who live with epilepsy. It found that people with focal drug-resistant epilepsy had lower quality of life scores and were less likely to participate in paid employment compared to people with new diagnosis epilepsy. This study provides important local data regarding the burden of epilepsy and will help researchers in the future to measure the impact of the AEP on important personal and societal health economic outcomes.

Role of machine learning in the management of epilepsy: a systematic review protocol.

INTRODUCTION: Machine learning is a rapidly expanding field and is already incorporated into many aspects of medicine including diagnostics, prognostication and clinical decision-support tools. Epilepsy is a common and disabling neurological disorder, however, management remains challenging in many cases, despite expanding therapeutic options. We present a systematic review protocol to explore the role of machine learning in the management of epilepsy. METHODS AND ANALYSIS: This protocol has been drafted with reference to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Protocols. A literature search will be conducted in databases including MEDLINE, Embase, Scopus and Web of Science. A PRISMA flow chart will be constructed to summarise the study workflow. As the scope of this review is the clinical application of machine learning, the selection of papers will be focused on studies directly related to clinical decision-making in management of epilepsy, specifically the prediction of response to antiseizure medications, development of drug-resistant epilepsy, and epilepsy surgery and neuromodulation outcomes. Data will be extracted following the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Prediction model Risk Of Bias ASsessment Tool will be used for the quality assessment of the included studies. Syntheses of quantitative data will be presented in narrative format. ETHICS AND DISSEMINATION: As this study is a systematic review which does not involve patients or animals, ethics approval is not required. The results of the systematic review will be submitted to peer-review journals for publication and presented in academic conferences. PROSPERO REGISTRATION NUMBER: CRD42023442156.

Role of the glymphatic system and perivascular spaces as a potential biomarker for post-stroke epilepsy.

Stroke is one of the most common causes of acquired epilepsy, which can also result in disability and increased mortality rates particularly in elderly patients. No preventive treatment for post-stroke epilepsy is currently available. Development of such treatments has been greatly limited by the lack of biomarkers to reliably identify high-risk patients. The glymphatic system, including perivascular spaces (PVS), is the brain's waste clearance system, and enlargement or asymmetry of PVS (ePVS) is hypothesized to play a significant role in the pathogenesis of several neurological conditions. In this article, we discuss potential mechanisms for the role of perivascular spaces in the development of post-stroke epilepsy. Using advanced MR-imaging techniques, it has been shown that there is asymmetry and impairment of glymphatic function in the setting of ischemic stroke. Furthermore, studies have described a dysfunction of PVS in patients with different focal and generalized epilepsy syndromes. It is thought that inflammatory processes involving PVS and the blood-brain barrier, impairment of waste clearance, and sustained hypertension affecting the glymphatic system during a seizure may play a crucial role in epileptogenesis post-stroke. We hypothesize that impairment of the glymphatic system and asymmetry and dynamics of ePVS in the course of a stroke contribute to the development of PSE. Automated ePVS detection in stroke patients might thus assist in the identification of high-risk patients for post-stroke epilepsy trials. PLAIN LANGUAGE SUMMARY: Stroke often leads to epilepsy and is one of the main causes of epilepsy in elderly patients, with no preventative treatment available. The brain's waste removal system, called the glymphatic system which consists of perivascular spaces, may be involved. Enlargement or asymmetry of perivascular spaces could play a role in this and can be visualised with advanced brain imaging after a stroke. Detecting enlarged perivascular spaces in stroke patients could help identify those at risk for post-stroke epilepsy.

Long-term physical and psychiatric morbidities and mortality of untreated, deferred, and immediately treated epilepsy.

OBJECTIVE: In Australia, 30% of newly diagnosed epilepsy patients were not immediately treated at diagnosis. We explored health outcomes between patients receiving immediate, deferred, or no treatment, and compared them to the general population. METHODS: Adults with newly diagnosed epilepsy in Western Australia between 1999 and 2016 were linked with statewide health care data collections. Health care utilization, comorbidity, and mortality at up to 10 years postdiagnosis were compared between patients receiving immediate, deferred, and no treatment, as well as with age- and sex-matched population controls. RESULTS: Of 603 epilepsy patients (61% male, median age = 40 years) were included, 422 (70%) were treated immediately at diagnosis, 110 (18%) received deferred treatment, and 71 (12%) were untreated at the end of follow-up (median = 6.8 years). Immediately treated patients had a higher 10-year rate of all-cause admissions or emergency department presentations than the untreated (incidence rate ratio [IRR] = 2.0, 95% confidence interval [CI] = 1.4-2.9) and deferred treatment groups (IRR = 1.7, 95% CI = 1.0-2.8). They had similar 10-year risks of mortality and developing new physical and psychiatric comorbidities compared with the deferred and untreated groups. Compared to population controls, epilepsy patients had higher 10-year mortality (hazard ratio = 2.6, 95% CI = 2.1-3.3), hospital admissions (IRR = 2.3, 95% CI = 1.6-3.3), and psychiatric outpatient visits (IRR = 3.2, 95% CI = 1.6-6.3). Patients with epilepsy were also 2.5 (95% CI = 2.1-3.1) and 3.9 (95% CI = 2.6-5.8) times more likely to develop a new physical and psychiatric comorbidity, respectively. SIGNIFICANCE: Newly diagnosed epilepsy patients with deferred or no treatment did not have worse outcomes than those immediately treated. Instead, immediately treated patients had greater health care utilization, likely reflecting more severe underlying epilepsy etiology. Our findings emphasize the importance of individualizing epilepsy treatment and recognition and management of the significant comorbidities, particularly psychiatric, that ensue following a diagnosis of epilepsy.

Applying the ILAE diagnostic criteria for Lennox-Gastaut syndrome in the real-world setting: A multicenter retrospective cohort study.

OBJECTIVE: Lennox-Gastaut syndrome (LGS) is an archetypal developmental and epileptic encephalopathy, for which novel treatments are emerging. Diagnostic criteria for LGS have recently been defined by the International League Against Epilepsy (ILAE). We aimed to apply these criteria in a real-world setting. METHODS: We applied ILAE diagnostic criteria to a cohort of patients diagnosed with LGS by epileptologists following inpatient video-EEG monitoring (VEM) at tertiary comprehensive epilepsy centers between 1995 and 2015. We also assessed mortality in this cohort. RESULTS: Sixty patients diagnosed with LGS and had complete records available for review were identified. Among them, 29 (48%) patients met ILAE diagnostic criteria for LGS (ILAE-DC group). Thirty-one did not meet criteria (non-ILAE-DC) due to the absence of documented tonic seizures (n = 7), EEG features (n = 12), or both tonic seizures and EEG features (n = 10), intellectual disability (n = 1), or drug resistance (n = 1). The ILAE-DC group had a shorter duration of epilepsy at VEM than the non-ILAE-DC group (median = 12.0 years vs. 23.7 years, respectively; p = 0.015). The proportions of patients with multiple seizure types (100% vs. 96.7%), ≤2.5 Hz slow spike-and-wave EEG activity (100% vs. 90%), seizure-related injuries (27.6% vs. 25.8%), and mortality (standardized mortality ratio 4.60 vs. 5.12) were similar between the groups. SIGNIFICANCE: Up to 52% of patients diagnosed with LGS following VEM may not meet recently accepted ILAE criteria for LGS diagnosis. This may reflect both the limitations of retrospective medical record review and a historical tendency of applying the LGS diagnosis to a broad spectrum of severe, early-onset drug-resistant epilepsies with drop attacks. The ILAE criteria allow the delineation of LGS based on distinct electroclinical features, potentiating accurate diagnosis, prognostication, and management formulation. Nonetheless, mortality outcomes between those who did and did not meet ILAE diagnostic criteria for LGS were similarly poor, and both groups suffered high rates of seizure-related injury. PLAIN LANGUAGE SUMMARY: More than half of patients diagnosed with Lennox-Gastaut Syndrome (LGS) at three Australian epilepsy monitoring units between 1995 and 2015 did not meet the recently devised International League Against Epilepsy (ILAE) diagnostic criteria for LGS. Mortality was equally high in those who did and did not meet the ILAE diagnostic criteria, and seizure-related injury was common. The ILAE diagnostic criteria will guide accurate diagnosis, management, prognostication, and research in patients with LGS, however may be limited in their practical application to patients with a longer duration of epilepsy, or to those for whom detailed assessment is difficult.

Generation of a stably transfected mouse embryonic stem cell line for inducible differentiation to excitatory neurons.

In vitro differentiation of stem cells into various cell lineages is valuable in developmental studies and an important source of cells for modelling physiology and pathology, particularly for complex tissues such as the brain. Conventional protocols for in vitro neuronal differentiation often suffer from complicated procedures, high variability and low reproducibility. Over the last decade, the identification of cell fate-determining transcription factors has provided new tools for cellular studies in neuroscience and enabled rapid differentiation driven by ectopic transcription factor expression. As a proneural transcription factor, Neurogenin 2 (Ngn2) expression alone is sufficient to trigger rapid and robust neurogenesis from pluripotent cells. Here, we established a stable cell line, by piggyBac (PB) transposition, that conditionally expresses Ngn2 for generation of excitatory neurons from mouse embryonic stem cells (ESCs) using an all-in-one PB construct. Our results indicate that Ngn2-induced excitatory neurons have mature and functional characteristics consistent with previous studies using conventional differentiation methods. This approach provides an all-in-one PB construct for rapid and high copy number gene delivery of dox-inducible transcription factors to induce differentiation. This approach is a valuable in vitro cell model for disease modeling, drug screening and cell therapy.

Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients.

PURPOSE: Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. METHODS: Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard. RESULTS: One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst. CONCLUSIONS: Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.

Clinical characteristics and outcomes of patients with post-stroke epilepsy: protocol for an individual patient data meta-analysis from the International Post-stroke Epilepsy Research Repository (IPSERR).

INTRODUCTION: Despite significant advances in managing acute stroke and reducing stroke mortality, preventing complications like post-stroke epilepsy (PSE) has seen limited progress. PSE research has been scattered worldwide with varying methodologies and data reporting. To address this, we established the International Post-stroke Epilepsy Research Consortium (IPSERC) to integrate global PSE research efforts. This protocol outlines an individual patient data meta-analysis (IPD-MA) to determine outcomes in patients with post-stroke seizures (PSS) and develop/validate PSE prediction models, comparing them with existing models. This protocol informs about creating the International Post-stroke Epilepsy Research Repository (IPSERR) to support future collaborative research. METHODS AND ANALYSIS: We utilised a comprehensive search strategy and searched MEDLINE, Embase, PsycInfo, Cochrane, and Web of Science databases until 30 January 2023. We extracted observational studies of stroke patients aged ≥18 years, presenting early or late PSS with data on patient outcome measures, and conducted the risk of bias assessment. We did not apply any restriction based on the date or language of publication. We will invite these study authors and the IPSERC collaborators to contribute IPD to IPSERR. We will review the IPD lodged within IPSERR to identify patients who developed epileptic seizures and those who did not. We will merge the IPD files of individual data and standardise the variables where possible for consistency. We will conduct an IPD-MA to estimate the prognostic value of clinical characteristics in predicting PSE. ETHICS AND DISSEMINATION: Ethics approval is not required for this study. The results will be published in peer-reviewed journals. This study will contribute to IPSERR, which will be available to researchers for future PSE research projects. It will also serve as a platform to anchor future clinical trials. TRIAL REGISTRATION NUMBER: NCT06108102.

EEG based automated seizure detection - A survey of medical professionals.

Diagnosing and managing seizures presents substantial challenges for clinicians caring for patients with epilepsy. Although machine learning (ML) has been proposed for automated seizure detection using EEG data, there is little evidence of these technologies being broadly adopted in clinical practice. Moreover, there is a noticeable lack of surveys investigating this topic from the perspective of medical practitioners, which limits the understanding of the obstacles for the development of effective automated seizure detection. Besides the issue of generalisability and replicability seen in a small amount of studies, obstacles to the adoption of automated seizure detection remain largely unknown. To understand the obstacles preventing the application of seizure detection tools in clinical practice, we conducted a survey targeting medical professionals involved in the management of epilepsy. Our study aimed to gather insights on various factors such as the clinical utility, professional sentiment, benchmark requirements, and perceived barriers associated with the use of automated seizure detection tools. Our key findings are: I) The minimum acceptable sensitivity reported by most of our respondents (80%) seems achievable based on studies reported from most currently available ML-based EEG seizure detection algorithms, but replication studies often fail to meet this minimum. II) Respondents are receptive to the adoption of ML seizure detection tools and willing to spend time in training. III) The top three barriers for usage of such tools in clinical practice are related to availability, lack of training, and the blackbox nature of ML algorithms. Based on our findings, we developed a guide that can serve as a basis for developing ML-based seizure detection tools that meet the requirements of medical professionals, and foster the integration of these tools into clinical practice.

An integrated in vitro human iPSCs-derived neuron and in vivo animal approach for preclinical screening of anti-seizure compounds.

INTRODUCTION: One-third of people with epilepsy continue to experience seizures despite treatment with existing anti-seizure medications (ASMs). The failure of modern ASMs to substantially improve epilepsy prognosis has been partly attributed to overreliance on acute rodent models in preclinical drug development as they do not adequately recapitulate the mechanisms of human epilepsy, are labor-intensive and unsuitable for high-throughput screening (HTS). There is an urgent need to find human-relevant HTS models in preclinical drug development to identify novel anti-seizure compounds. OBJECTIVES: This paper developed high-throughput preclinical screening models to identify new ASMs. METHODS: 14 natural compounds (α-asarone, curcumin, vinpocetine, magnolol, ligustrazine, osthole, tanshinone IIA, piperine, gastrodin, quercetin, berberine, chrysin, schizandrin A and resveratrol) were assessed for their ability to suppress epileptiform activity as measured by multi-electrode arrays (MEA) in neural cultures derived from human induced pluripotent stem cells (iPSCs). In parallel, they were tested for anti-seizure effects in zebrafish and mouse models, which have been widely used in development of modern ASMs. The effects of the compounds in these models were compared. Two approved ASMs were used as positive controls. RESULTS: Epileptiform activity could be induced in iPSCs-derived neurons following treatment with 4-aminopyridine (4-AP) and inhibited by standard ASMs, carbamazepine, and phenytoin. Eight of the 14 natural compounds significantly inhibited the epileptiform activity in iPSCs-derived neurons. Among them, piperine, magnolol, α-asarone, and osthole showed significant anti-seizure effects both in zebrafish and mice. Comparative analysis showed that compounds ineffective in the iPSCs-derived neural model also showed no anti-seizure effects in the zebrafish or mouse models. CONCLUSION: Our findings support the use of iPSCs-derived human neurons for first-line high-throughput screening to identify compounds with anti-seizure properties and exclude ineffective compounds. Effective compounds may then be selected for animal evaluation before clinical testing. This integrated approach may improve the efficiency of developing novel ASMs.

Reduced total number of enlarged perivascular spaces in post-traumatic epilepsy patients with unilateral lesions - a feasibility study.

BACKGROUND: We investigated the value of automated enlarged perivascular spaces (ePVS) quantification to distinguish chronic traumatic brain injury (TBI) patients with post-traumatic epilepsy (PTE+) from chronic TBI patients without PTE (PTE-) in a feasibility study. METHODS: Patients with and without PTE were recruited and underwent an MRI post-TBI. Multimodal auto identification of ePVS algorithm was applied to T1-weighted MRIs to segment ePVS. The total number of ePVS was calculated and corrected for white matter volume, and an asymmetry index (AI) derived. RESULTS: PTE was diagnosed in 7 out of the 99 participants (male=69) after a median time of less than one year since injury (range 10-22). Brain lesions were observed in all 7 PTE+ cases (unilateral=4, 57%; bilateral=3, 43%) as compared to 40 PTE- cases (total 44%; unilateral=17, 42%; bilateral=23, 58%). There was a significant difference between PTE+ (M=1.21e-4, IQR [8.89e-5]) and PTE- cases (M=2.79e-4, IQR [6.25e-5]) in total corrected numbers of ePVS in patients with unilateral lesions (p=0.024). No differences in AI, trauma severity and lesion volume were seen between groups. CONCLUSION: This study has shown that automated quantification of ePVS is feasible and provided initial evidence that individuals with PTE with unilateral lesions may have fewer ePVS compared to TBI patients without epilepsy. Further studies with larger sample sizes should be conducted to determine the value of ePVS quantification as a PTE-biomarker.

Applications for Deep Learning in Epilepsy Genetic Research.

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.

Phase II randomised placebo-controlled trial of sodium selenate as a disease-modifying treatment in chronic drug-resistant temporal lobe epilepsy: the SeLECT study protocol.

INTRODUCTION: Epilepsy is one of the most common neurological conditions worldwide. Despite many antiseizure medications (ASMs) being available, up to one-third of patients do not achieve seizure control. Preclinical studies have shown treatment with sodium selenate to have a disease-modifying effect in a rat model of chronic temporal lobe epilepsy (TLE). AIM: This randomised placebo-controlled trial aims to evaluate the antiseizure and disease-modifying effects of sodium selenate in people with drug-resistant TLE. METHODS: This will be a randomised placebo-controlled trial of sodium selenate. One hundred and twenty-four adults with drug-resistant TLE and ≥4 countable seizures/month will be recruited. Outcomes of interest will be measured at baseline, week 26 and week 52 and include an 8-week seizure diary, 24-hour electroencephalogram and cognitive, neuropsychiatric and quality of life measures. Participants will then be randomised to receive a sustained release formulation of sodium selenate (initially 10 mg three times a day, increasing to 15 mg three times a day at week 4 if tolerated) or a matching placebo for 26 weeks. OUTCOMES: The primary outcome will be a consumer codesigned epilepsy-Desirability of Outcome Rank (DOOR), combining change in seizure frequency, adverse events, quality of life and ASM burden measures into a single outcome measure, compared between treatment arms over the whole 52-week period. Secondary outcomes will compare baseline measures to week 26 (antiseizure) and week 52 (disease modification). Exploratory measures will include biomarkers of treatment response. ETHICS AND DISSEMINATION: The study has been approved by the lead site, Alfred Hospital Ethics Committee (594/20). Each participant will provide written informed consent prior to any trial procedures. The results of the study will be presented at national and international conferences, published in peer-reviewed journals and disseminated through consumer organisations. CONCLUSION: This study will be the first disease-modification randomised controlled trial in patients with drug-resistant TLE. TRIAL REGISTRATION NUMBER: ANZCTR; ACTRN12623000446662.

Neuropsychological function in psychosis of epilepsy.

OBJECTIVE: The neuropsychological profile of patients with psychosis of epilepsy (POE) has received limited research attention. Recent neuroimaging work in POE has identified structural network pathology in the default mode network and the cognitive control network. This study examined the neuropsychological profile of POE focusing on cognitive domains subserved by these networks. METHODS: Twelve consecutive patients with a diagnosis of POE were prospectively recruited from the Comprehensive Epilepsy Programmes at The Royal Melbourne, Austin and St Vincent's Hospitals, Melbourne, Australia between January 2015 and February 2017. They were compared to 12 matched patients with epilepsy but no psychosis and 42 healthy controls on standardised neuropsychological tests of memory and executive functioning in a case-control design. RESULTS: Mean scores across all cognitive tasks showed a graded pattern of impairment, with the POE group showing the poorest performance, followed by the epilepsy without psychosis and the healthy control groups. This was associated with significant group-level differences on measures of working memory (p = < 0.01); immediate (p = < 0.01) and delayed verbal recall (p = < 0.01); visual memory (p < 0.001); and verbal fluency (p = 0.02). In particular, patients with POE performed significantly worse than the healthy control group on measures of both cognitive control (p = .005) and memory (p < .001), whereas the epilepsy without psychosis group showed only memory difficulties (delayed verbal recall) compared to healthy controls (p = .001). CONCLUSION: People with POE show reduced performance in neuropsychological functions supported by the default mode and cognitive control networks, when compared to both healthy participants and people with epilepsy without psychosis.