Epilepsy Research

Background Psychiatric comorbidities commonly have a negative impact on epilepsy outcomes. However, they are continuously ignored in routine epilepsy care, with focus directed more towards seizure control. There is paucity of data on the burden of psychiatric morbidity among those living with epilepsy in Kenya. This study sought to determine the prevalence and associated factors of psychiatric morbidity among patients living with epilepsy at a tertiary referral hospital in Western Kenya. Methods This was a descriptive cross-sectional study. Consecutive sampling was used to recruit participants, with a sample size of 278. Data were collected using a structured pretested sociodemographic and clinical characteristics questionnaire, and the Mini International Neuropsychiatric Interview (MINI), and analyzed using STATA version 16. Pearson Chi-square test/Fishers Exact test and logistic regression were used to assess relationships at bivariate and multivariate levels respectively. Results The prevalence of psychiatric morbidity was 52.2%. Major depressive disorder was the most prevalent (36%), followed by anxiety disorders (26.2%), psychotic disorders (16.9%), and suicidality (15.1%). Casual/self-employment (aOR=2.590, p=0.020), seizure-related physical trauma (aOR=4.032, p=0.004), antiepileptic polytherapy (aOR=4.280, p=0.001), frequent seizures (aOR=3.801, p<0.001), and comorbid medical conditions (aOR=5.478, p=0.047) were independent predictors of psychiatric morbidity. Having attained a tertiary level of education was protective against psychiatric morbidity (aOR=0.221, p=0.036). Conclusion More than half of the patients living with epilepsy had at least one psychiatric comorbidity. Routine psychiatric screening and integration of mental health services in epilepsy care is essential to improve clinical outcomes.

Objective Temporal lobe encephaloceles (ENC) are underdiagnosed causes of drug-resistant temporal lobe epilepsy (TLE), frequently associated with idiopathic intracranial hypertension (IIH). Emerging evidence suggests glymphatic system dysfunction in both IIH and TLE. We investigated glymphatic markers in TLE associated with ENC compared with seizure-free postoperative TLE controls of different aetiology. Methods Surgical specimens from 13 patients with TLE-ENC and 12 TLE-control patients were analyzed. Histological glymphatic markers included aquaporin-4 (AQP4), glial fibrillary acidic protein (GFAP), podoplanin (PDPN), perivascular space (PVS) enlargement, and vessel density. High resolution MRI was used to assess a global PVS score. Results Compared with TLE-controls, TLE-ENC specimens showed increased white matter AQP4 expression and AQP4/GFAP ratio, whereas the AQP4/GFAP ratio was reduced in grey matter. PDPN expression was significantly elevated in both grey and white matter in TLE-ENC cases. MRI demonstrated greater supratentorial PVS enlargement in in ENC patients. Radiological features suggestive of IIH were identified in 46.1% of TLE-ENC patients. Compared with controls, TLE-ENC patients had shorter disease duration and lacked association with previous febrile seizures. Surgical treatment achieved seizure freedom in 70% of ENC patients at a median follow-up of 32 months. Interpretation This study provides the first characterization of glymphatic alterations in TLE-ENC-related epilepsy. Dysregulation of AQP4 and PDPN together with increased PVS burden suggests a distinct glymphatic dysfunction pattern in TLE-ENC, supporting a potential pathophysiological link among ENC formation, IIH, and epileptogenesis mediated by altered cerebrospinal fluid dynamics.

Objective: Pediatric epilepsy patients undergoing stereo-electroencephalography (sEEG) for ictal onset evaluation provide a rare window to study the developing brain. While methodological frameworks for task-based sEEG research are well-established in adults, pediatric-specific guidance remains underdeveloped. Furthermore, many pediatric epilepsy patients have comorbidities that might typically exclude them from participating in research. We examine factors that influence research participation and discuss considerations for conducting sEEG research in children. Methods: Here, we present a retrospective analysis of task-based research participation patterns from an NIH-funded study of speech and language representations (1R01DC018579) in 66 patients (ages 4-24) undergoing sEEG monitoring at Texas Children's Hospital to determine whether specific comorbidities influenced research participation. Results: Eighty-nine percent (n=66) of patients approached for consent agreed to participate in the study. Despite high rates of comorbidities including neurocognitive disorder (66.67%), language delay (31.75%), global developmental delay (23.81%), mood disorders (33.33%), ADHD (46.03%), autism spectrum disorder (14.29%) or other cognitive/intellectual disabilities (36.51%), all participants engaged in at least one task. While the majority of these diagnoses did not appear to influence subject participation, global developmental delay was associated with a significant reduction in time spent on active tasks. Discussion: Despite high prevalence of neuropsychological comorbidities among participants, our evidence suggests that these participants contribute meaningfully to studies investigating important developmental questions. We suggest strategies for tailoring task-based research to accommodate the unique needs of individuals in this population. Such practices are important for ensuring that research studies reflect the true diversity of the population.

Many patients with epilepsy have inadequate seizure control using current anti-seizure medications (ASMs), illustrating the need for new treatments. Genetic epilepsy syndromes like pathogenic variants in voltage gated sodium channel SCN2A and SCN8A are often poorly controlled by current medications, highlighting the need for better models. Voltage gated sodium channel pathogenic variants that induce epilepsy are often gain-of-function, producing hyperexcitability. We established a fast and precise zebrafish seizure assay using mRNA overexpression of SCN2A and SCN8A variants, which allows rapid screening of both variants and ASMs. These short-term genetic seizure models are assayed in 3 days postfertilization (dpf) larvae. Pathogenic variants of SCN2A and SCN8A produced sporadic seizure behavior. We tested human SCN2A R1882Q, SCN2A R853Q and SCN8A R1872Q pathogenic variants that were identified in epilepsy syndrome patients. These models were used to evaluate the efficacy of 3 ASMs: Topiramate, GS967 and PF-04856264. All 3 epilepsy-associated variants increased seizure activity, and the ASMs significantly decreased this seizure activity. This mRNA overexpression assay successfully evaluates seizure activity induced by variants in voltage gated sodium channel genes and examines ASM efficacy in patient specific pathogenic variants.

Abstract Objective To validate a neonatal seizure detection algorithm that is based on extracted clinical features of the aEEG and CSA on a cohort of cooled neonatal patients with HIE. Methods A seizure detection algorithm was designed using aEEG margin features, CSA features, trained on a public dataset of 79 neonatal EEGs with three supervised machine learning classifiers. It was subsequently tested on an inhouse cohort of 23 neonates with asphyxia whose EEGs were collected during hypothermia therapy. Results The trained Random Forest Classifier, Support Vector Machines and Artificial Neural Network classifiers had an AUC of 0.76, 0.77, and 0.77 and an average accuracy of 0.85, 0.86, and 0.85 respectively. Finally, the average AUC across the 10 seizure patients included was 0.85. Conclusion A neonatal seizure detection algorithm that uses a combination of aEEG and CSA clinical features can capture seizures in HIE patients. Performance across seizure patients is not correlated with seizure duration.

Abstract Importance: Drug-resistant focal epilepsy affects approximately 30% of children with epilepsy and carries excess mortality, impaired neurodevelopment, and substantial costs. Epilepsy surgery is underutilized despite proven superiority over medical management. MRI-guided laser interstitial thermal therapy (MRgLITT) is a minimally invasive alternative to open resection, but comparative evidence to guide procedure selection is limited. Objective: To estimate lifetime outcomes and costs of epilepsy surgery versus medical management for pediatric drug-resistant focal epilepsy, and to provide etiology-informed guidance for choosing between open resection and MRgLITT. Design: Markov decision analytic model with a lifetime horizon, parameterized from published systematic reviews, meta-analyses, and cohort studies. Setting: United States, healthcare payer perspective. Participants: Hypothetical cohort of 10-year-old children with drug-resistant focal epilepsy and a seizure focus <3 cm3. Interventions: Best medical management, open resective surgery, or MRgLITT. Main Outcomes and Measures: Quality-adjusted life years (QALYs), lifetime direct medical costs, incremental cost-effectiveness ratios, and lifetime survival. Seizure outcomes were classified as seizure freedom or disabling seizures. Cost-effectiveness was assessed at $100,000/QALY. Results: Both surgical strategies were associated with a 4.6-year survival advantage, 3.6 additional lifetime QALYs, and lower costs than medical management. MRgLITT yielded 22.64 QALYs at $120,943; open resection yielded 22.62 QALYs at $121,650; medical management yielded 19.00 QALYs at $127,471. The difference between MRgLITT and open resection was 0.015 QALYs, reflecting near-equivalent effectiveness; in probabilistic sensitivity analysis, MRgLITT was optimal in 50.3% of iterations and open resection in 38.3%, with neither showing clear superiority. Etiology-specific analyses favored MRgLITT for focal cortical dysplasia and mesial temporal sclerosis, and open resection for tumor-related and cavernoma-related epilepsy. Conclusions and Relevance: Both open resection and MRgLITT were associated with substantially better lifetime outcomes and lower costs than medical management, supporting early surgical referral. Overall effectiveness between surgical approaches was clinically similar, with neither demonstrating clear superiority; the model suggests epilepsy etiology, rather than expected effectiveness alone, should guide procedure selection between MRgLITT and open resection.

Closed-loop neuromodulation via responsive neurostimulation (RNS) of the thalamus has emerged as a promising therapy for drug-resistant epilepsy (DRE), particularly in patients with broad or multifocal onset. However, response to thalamic RNS is inconsistent, and there is a crucial need to identify factors that distinguish responders from non-responders. Given the heterogeneous composition of the thalamus, the specific contributions of individual thalamic nuclei during seizures may explain the variability in outcomes between patients and could potentially serve as biomarkers for guiding target selection. We analyzed 129 seizures from 28 patients with DRE who underwent stereo-EEG monitoring with recordings of the centromedian (CM: n = 15) or pulvinar (PLV: n = 13) thalamic nuclei and were subsequently treated with RNS targeting the corresponding nucleus (CM: 11/15 [73%] responders; PLV: 7/13 [54%] responders). Patients were classified as responders (Engel class I-III) or non-responders (Engel class IV) based on reduction in seizure frequency. For each seizure, we constructed functional connectivity networks spanning seizure onset to termination and quantified the role of the thalamic nucleus by computing its total node strength. We also used an automated detection algorithm to measure the time of seizure spread to each thalamic nucleus relative to seizure onset. Connectivity and spread timing were then compared between responders and non-responders within each nucleus group. The timing of thalamic recruitment following seizure onset did not differ significantly between responders and non-responders in either nucleus, although CM responders showed a non-significant trend toward earlier recruitment. Analysis of functional connectivity revealed nucleus-specific patterns. CM responders exhibited significantly higher thalamic node strength than non-responders during the late-seizure phase, with no significant difference at early- or middle-seizure phases. PLV responders showed significantly higher thalamic node strength during the middle-seizure phase, but there was no significant difference at early- or late-seizure phases. These findings suggest that the degree and timing of thalamic involvement during seizures may serve as biomarkers for predicting response to thalamic RNS in DRE. CM involvement in responders was characterized by stronger connectivity that persisted through seizure termination, whereas PLV involvement in responders was reflected primarily in connectivity during seizure propagation and progression. Incorporating these nucleus-specific ictal network features into pre-surgical evaluation could improve patient selection and guide nucleus-specific targeting for thalamic RNS.

Objective: EEG biomarkers for seizure-generating tissue have historically been identified visually, which lacks objectivity and limits utility of automated approaches. For example, high frequency oscillations and interictal epileptiform discharges were promising markers to improve surgical outcomes for refractory epilepsy, but low specificity has hindered clinical implementation, and automated algorithms have not improved this. Methods: We developed Intracranial EEG Pattern Identification and Categorization, an automated, data-driven time-frequency framework for EEG biomarker discovery. It detects transient high-power intracranial EEG waveforms (1-500 Hz) and characterizes them using eight features. In seizure-free patients, waveforms occurring predominantly in resected intracranial EEG channels are candidate biomarkers. Results: In retrospective data from 14 seizure-free post-surgical patients from University of California, Los Angeles, we identified 9 waveform categories strongly associated with resected intracranial EEG channels. These included beta, gamma, and ripple band bursts, sometimes co-occurring with interictal epileptiform discharges; however, many were visually imperceptible in the broadband EEG. Using a support vector machine, we generated a unified classification metric based on these waveforms and tested it on 87 seizure-free subjects from Detroit Medical Center. This metric achieved higher area under the precision-recall curve than six state-of-the-art benchmark algorithms (p<0.001, corrected) and higher positive predictive value than three algorithms (p<0.01, corrected). Retraining the support vector machine on the Detroit dataset with five-fold cross-validation, the metric outperformed all six benchmarks across performance metrics. Interpretation: Our analysis framework identified novel intracranial EEG biomarkers for seizure-generating tissue, outperforming traditional markers and generalizing across datasets, providing a new avenue for EEG biomarker discovery.

Background The field of implantable Brain-Computer Interfaces (iBCIs) is rapidly advancing, with individuals with amyotrophic lateral sclerosis (ALS) as key beneficiaries. However, ALS-related cortical degeneration may impair iBCI effectiveness. This study investigated whether structural magnetic resonance imaging (MRI) and functional MRI (fMRI) metrics are associated with the quality of electrocorticography (ECoG) signals critical for iBCI use. Methods Six late-stage ALS participants and 76 controls underwent T1-weighted structural MRI and task-based fMRI during right-hand movement or attempts thereof. ECoG data of ALS participants was benchmarked using ECoG data acquired in epilepsy patients. Grey matter thickness in the sensorimotor cortex and fMRI activation in the motor-hand area were measured. Results Four ALS participants showed >0.4 mm thinning in the precentral gyrus, while the postcentral gyrus was spared. ECoG signal quality was significantly associated with precentral grey matter thickness, but not with fMRI activity. Conclusions These findings suggest that presurgical assessment of precentral grey matter thickness could potentially prove useful for iBCI candidate selection in advanced ALS.

Background To build a clinically translatable neonatal seizure detection algorithm using amplitude-integrated electroencephalography (aEEG) and compressed spectral array (CSA). Methods Using a public dataset of annotated neonatal EEGs, features of the aEEG and CSA were extracted from the left and right centroparietal electrodes. These features were then used to train and test three machine learning classifiers, Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Results The trained RF, SVM, and ANN classifiers had areas under the curve (AUC) of 0.80, 0.69, and 0.79 for capturing seizure time periods and an average accuracy of 0.91, 0.90, and 0.92 respectively for capturing seizure and non-seizure time periods. Median accuracy scores were higher among patients without hypoxic-ischemic encephalopathy (HIE; median = 1 for all three classifiers) than HIE patients (median = 0.92, 0.93, 0.93). Conclusion A clinically interpretable aEEG-CSA algorithm is feasible for neonatal seizure detection by extracting standard EEG features and coupling these features with a supervised ML classifier.

The aim of this work is to validate patient-specific EEG baseline establishment using the e-norms method as a screening and retrospective-review tool for seizure detection in pediatric epilepsy. The method was applied to 247 seizure-free EEG recordings (263.92 hours) from 10 patients in the CHB-MIT Scalp EEG Database (ages 3-18). A composite stability metric combining first-derivative dynamics, spectral entropy, variance, and line length was computed per 2-second epoch across 23 channels. Patient-specific detection thresholds were derived from each patient's seizure-free baseline using a weighted statistical procedure. Performance was validated against 72 expert-annotated seizures (2,705 epochs) across 62 seizure files, with durations spanning 6 to 264 seconds (44-fold range). The results show that detection achieved 94.4% event-level sensitivity (68 of 72 seizures; 95% CI 86.6-97.8%) and 81.5% epoch-level sensitivity (2,204 of 2,705 epochs; 95% CI 80.0-82.9%). Eight of ten patients achieved 100% event-level sensitivity with epoch-level sensitivity ranging from 58.7% to 100.0%. Two patients showed partial event-level failures (CHB-15: 17 of 20; CHB-18: 5 of 6), with the four missed events attributable to two characterizable failure modes. Patient-specific thresholds ranged from 4.06 to 4.81 (mean 4.51 +/- 0.25); threshold variation did not correlate reliably with age or sex, confirming that no universal threshold could achieve comparable performance. Detection margins ranged from 0.88 to 1.24 times. Patient-specific e-norms achieves 94.4% event-level sensitivity for pediatric EEG seizure detection without requiring labeled seizure training data, exceeding published human expert inter-rater agreement (50-76%) and recent automated approaches in adult cohorts using behind-the-ear EEG and wearable ECG. Two characterizable failure modes account for the four missed events and inform appropriate clinical use. As a high-sensitivity screening tool complementary to real-time alarm systems, the method is ready for adult validation, prospective deployment, and head-to-head benchmarking.

Autistic psychiatrists occupy a paradoxical position: trained to recognise and assess autism in others, yet navigating a professional culture in which their own autistic identity remains largely concealed. Despite growing visibility of autistic clinicians, the barriers autistic psychiatrists face to formal diagnosis and professional disclosure remain unexplored. This study used interpretive phenomenological analysis to examine the experiences of seven autistic psychiatrists in relation to diagnosis and disclosure. Data were generated through in-depth interviews and Retzinger's framework for identifying shame in discourse was applied as an analytical tool within the interpretive process. Shame emerged as the overarching theme across the dataset, operating through four group experiential themes. Its origins lay in childhood experiences of difference and perceived defectiveness, transmitted through family, peers, and the broader social environment. In professional life, shame was sustained and amplified by colleagues' misconceptions about autism, anticipated loss of credibility, and the deficit-based diagnostic criteria - which rendered self-recognition difficult and made formal diagnosis a perceived professional liability. Critically, shame did not only create barriers: it functioned as an override mechanism, rendering the known benefits of disclosure - to participants themselves, to colleagues, and to patients - insufficient to translate into action. This override function was not explained by fear of discrimination or rational career protection alone; it reflected shame's operation as an internal prohibition, dissociated from its original social source and persisting even where stigma had been intellectually processed and rejected. These findings reposition shame not as one barrier among many but as the organising force through which all barriers operate. Interventions aimed at increasing disclosure by raising awareness of its benefits misread the operative mechanism. Creating conditions in which autistic psychiatrists can make decisions about their identities freely requires naming and addressing shame - in research, in clinical training, and in the culture of psychiatry.

Monogenic epilepsies are 1.6 times more likely to be treatment-resistant compared to other epilepsies, emphasizing the need for additional therapeutic strategies. Sleep dysfunction beyond sleep-related breathing disorders is common yet insufficiently characterized and treated in monogenic epilepsies. We therefore sought to study sleep phenotypes across these epilepsies, examine associations with seizure severity, and assess the diagnostic rate of sleep disorders. From 2,519 individuals enrolled in the Epilepsy Genetics Research Project at Children's Hospital of Philadelphia, we identified the monogenic epilepsies most frequently associated with sleep-related diagnoses, yielding 252 individuals across nine genetic diagnoses (STXBP1, n = 79; SCN1A, n = 57; SCN2A, n = 34; KCNQ2, n = 21; SLC6A1, n = 14; SYNGAP1, n = 13; WDR45, n = 13; KCNT1, n = 11; PCDH19, n = 10). Monogenic epilepsies exhibited distinct sleep endophenotypes, including insomnia, parasomnia, and sleep-related movement disorders in SCN1A-related disorders; frequent epileptiform discharges in sleep with insomnia symptoms in SCN2A-related disorders; sleep dysfunction restricted to the developmental and epileptic encephalopathy subtype in KCNQ2-related disorders; and insomnia without nocturnal seizure involvement in SYNGAP1-related disorders. Formal sleep diagnoses were present in only 25% of individuals (63/252), yet 58% (145/252) reported sleep difficulties, suggesting substantial underdiagnosis. Persistent seizures were associated with higher odds of sleep disorder diagnoses (OR 2.87, 95% CrI 1.57-5.36), disrupted sleep architecture (OR 2.06, 95% CrI 1.08-4.16), nocturnal seizures (OR 4.47, 95% CrI 2.50-8.28), hypersomnolence (OR 2.38, 95% CrI 1.27-4.58) and insomnia (OR 1.80, 95% CrI 1.06-3.05). Neuropsychiatric comorbidities were independently associated with sleep burden after adjustment for seizure severity (OR 2.49, 95% CrI 1.40-4.49). We find that monogenic epilepsies exhibit distinct, gene-specific sleep endophenotypes that are underdiagnosed. Treating sleep difficulties beyond obstructive sleep apnoea may improve seizure control and developmental outcomes, highlighting the need for timely diagnosis of co-occurring sleep disorders.

Dravet syndrome (DS) is a severe developmental and epileptic encephalopathy whose clinical and research representation requires integration of heterogeneous knowledge spanning seizures, development, behavior, SUDEP/autonomic risk, genetics, comorbidities, electrophysiology, pharmacology, and drug responsiveness. We report the development of a DS-focused ontology created by expert-guided specialization of a previously published epilepsy ontology. Scope expansion was defined through a scientific advisory board, structured review meetings, and iterative ontology curation in OWL. The resulting resource reorganized DS content across nine major domains and expanded the publicly released ontology from the pre-extension baseline to the current BioPortal version. Beyond structural growth, the ontology was assessed through expert-guided curation and downstream task-based reuse, including two published ontology-enabled LLM studies and an ongoing ontology-derived DS knowledge graph and AI assistant platform. These results suggest that disease-focused ontology specialization can provide durable infrastructure for DS data harmonization, knowledge representation, and AI-enabled translational informatics.

Background Functional neurological disorder (FND), one of the most common neurological conditions, affects women almost twice as frequently as men. The reasons for this are unknown, and there has been minimal research into how physiological and pathological features of women's health interact with symptoms of FND. Methods We conducted an online survey assessing the effect of several aspects of women's health with the severity of symptoms of FND. Results 484 people completed the survey. Among the 223 who had regular or fairly regular menstrual cycles, a strong difference across the menstrual cycle was seen, with symptoms at their best in the follicular phase, worsening in the luteal phase, and worst in the pre-menstrual period and the menses. This effect was not moderated by a proxy measure of pre-menstrual dysphoric disorder (PMDD). Participants who were taking the combined oral contraceptive (COC, n=43) and progesterone-based contraception (n=80) were more likely to report symptom improvement from starting the medication than worsening. When compared to menstruating participants who were not taking the COC, participants taking the COC reported less worsening in their symptoms of FND in the luteal, pre-menstrual, and menstrual phases. Of the 99 women who had passed menopause since developing FND, 76% reported worsening of their FND symptoms after menopause. Discussion This study demonstrates interactions between several aspects of women's health and symptoms of FND. The observed pattern of symptom fluctuation across hormonal states suggests a potential modulatory role of oestrogen, warranting further targeted investigation.

Many forms of memory are thought to rely on visual imagery, but individuals who report lacking visual imagery (aphantasia) can still perform various memory tasks. There is, however, evidence that aphantasia may lead to less detailed autobiographical memories, suggesting there may be deficits in the underlying cognitive processes that support personal memories. One such process is associative memory, which requires binding of different types of information. Here, we tested whether associative visual memory is intact in aphantasia. We assessed 72 self-identified individuals with aphantasia and 77 controls who reported having visual imagery. Participants completed an associative memory task which involved memorising displays where a unique object in a specific location was associated with a particular colour fixation point. Individuals with aphantasia performed equivalently to controls for object locations and outperformed controls on the associated object-identity. In addition, whereas controls were significantly worse at remembering associated object-identity than object-location, individuals with aphantasia showed no such difference. Both groups showed good metacognitive performance evidenced by a positive correlation between confidence and accuracy; there were no significant differences in confidence between the groups. Reported strategies varied between groups: a large proportion of control participants reported using visual imagery and self-reported use of imagery positively correlated with performance. Conversely, individuals with aphantasia mostly reported using nonvisual strategies to remember the associations. Overall, the findings suggest that individuals with aphantasia can form associative memories using nonvisual strategies. Thus, difficulties with autobiographical memory in aphantasia seem unlikely to be due to fundamental issues with associative memory.

Background and Objectives: Stereoelectroencephalography (SEEG) involves the implantation of intracerebral electrodes to investigate drug-resistant epilepsy. SEEG requires millimetric accuracy to ensure safety and optimal mapping. Although studies have evaluated SEEG accuracy, there is substantial variability in reporting. Here we report on implantation accuracy in a large series using the most common accuracy metrics described in the literature and perform a detailed analysis of contributing factors. Methods: SEEG implantations between 2013 and 2025 were included. Application accuracy was computed for each implanted electrode. Specifically, Euclidean, radial, depth, and angle error were calculated at both target and entry points. Correlative and multivariable analyses were conducted between each variable and error metric. Trajectories were also grouped by atlas-derived lobar target. Results: No metrics met assumptions of normality and thus we report accuracy using median with interquartile range (IQR). In a series of 3176 trajectories, median Euclidean target and entry errors were lower for robot-assisted electrodes (n=2858) at 2.19 (IQR: 1.54-2.98) mm and 1.38 (IQR: 0.89-2.01) mm respectively, compared to frame-based (n=318, p<.001) at 2.76 (IQR:1.79-3.76) mm and 2.21 (IQR: 1.42-3.32) mm. Correlation and multivariable regression analysis showed target error was positively correlated with implantation angle, scalp thickness, skull thickness, and trajectory length. Target error was also higher in obese patients. On lobar analysis, parietal lobe trajectories were the most accurate and frontal lobe trajectories were the least accurate. On temporal lobe trajectory analysis, posterior hippocampus trajectories were the most accurate and temporal pole trajectories were the least accurate. Presence of mesial temporal sclerosis also impacted accuracy. Conclusions: We present a detailed description of SEEG implantation accuracy, demonstrating the superior accuracy and speed of robot-assisted to frame-based methods. Furthermore, we analyzed how accuracy varies with specific factors from a global to trajectory level, which can be accounted for when planning SEEG implantations.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSIn temporal lobe epilepsy (TLE), the thalamus acts as a nexus in a pathophysiological network that implicates mesiotemporal, subcortical, and neocortical regions. Studying a large multimodal and multicentre dataset, we profiled thalamic, hippocampal, and neocortical functional connectivity (FC), assessed structural mediators, and examined clinical associations. MethodsWe studied resting-state FC alongside structural and diffusion MRI data in 250 unilateral TLE patients and 259 healthy controls, with measures aggregated across four independent datasets. Data were processed using open-access neuroinformatics workflows and analyzed at a subregional level to maximize anatomical precision. Statistical analysis and mediation models assessed between-group FC changes, structural contributors, and clinical correlations. ResultsCompared to controls, TLE patients presented with reduced thalamo-cortical FC, which was most marked in mesiotemporal, fronto-central, and occipital regions. Thalamo-hippocampal FC was also reduced, with effects seen in all CA subfields. In the thalamus, FC reductions peaked in the ventral posterior nucleus when considering neocortical target regions and in the mediodorsal nucleus when considering hippocampal target regions. While ipsilateral hippocampal volume and diffusion changes mediated thalamo-hippocampal FC, thalamo-cortical FC appeared decoupled from structural alterations. Findings were consistent in left and right TLE patients, in patients with short and long disease duration, and across imaging sites, suggesting that thalamo-cortical FC imbalances are a consistent signature of TLE. Conversely, thalamo-hippocampal FC was elevated in patients with focal-to-bilateral-tonic-clonic seizures and FC alterations were more marked in the subgroup of operated patients that became seizure-free after surgery. ConclusionOur multi-site findings demonstrate marked thalamic circuit fragmentation in TLE. Ipsilateral findings robustly showed subdivision-specific effects, which point to both mesiotemporal co-lateralization as well as broader system-level involvement. Mediation analyses furthermore confirmed a key role of hippocampal pathology in disrupted thalamo-hippocampal connectivity in TLE, while broader thalamo-cortical fragmentation becomes increasingly independent of mesiotemporal compromise. Critically, thalamic FC represents a network substrate for seizure generalization and can serve as a prognostic indicator for surgical outcome. These results underscore the contribution of the thalamus as a hub in macroscale dysfunction in TLE.

Responsive neurostimulation (RNS) is an implanted device that delivers direct brain stimulation for drug-resistant focal epilepsy. Individual responses are highly variable, and no validated framework exists to predict outcome or guide lead placement before implantation. We hypothesized that this variability is partly explained by lead placement in relation to patterns of functional connectivity in brain networks. Fourty-nine patients with drug-resistant focal epilepsy who underwent pre-implantation intracranial EEG (iEEG) and RNS implantation across three independent epilepsy centers were retrospectively studied. We developed a composite functional connectivity score, based on simple Spearman correlation, combining the standard deviation and kurtosis of interictal iEEG connectivity distributions to predict the response outcome in a training cohort (HUP, n=18) and validated in two independent cohorts (NYU, n=17; UCSF, n=14). We accounted for a spatial mismatch between iEEG and RNS electrodes with a distance-based correction. The score was extended to generate patient-specific 3D maps of predicted RNS efficacy across 200 simulated, or virtual RNS, lead configurations. Accuracy of the score in predicting clinical outcome was 72% at the group level, 61% at the individual patient level, and, after distance-based optimization, 100% in patients with RNS electrodes placed close to location of iEEG electrodes. Applied to the validation cohort, the same score reached 68% accuracy (71% balanced accuracy, 55% sensitivity, 88% specificity). The spatial combination of the scores at different SEEG contacts localization gives a spatial score for each patient. Responders showed significantly higher spatial scores than non-responders, supporting that actual RNS lead placement in responders was located in map-identified favorable regions. Interictal iEEG functional connectivity predicts individual RNS response across independent epilepsy centers, and patient-specific 3D maps derived from this biomarker could prospectively guide lead implantation toward favorable network regions, opening a promising avenue toward network-informed RNS surgical planning.

Heterozygous de novo missense mutations in the EEF1A2 gene encoding translation elongation factor eEF1A2 result in neurodevelopmental disorders, typically characterised by early onset epilepsy and intellectual disability (ID). The E122K mutation is the most commonly reported missense mutation and is amongst the more severe in terms of epilepsy and ID. Here we made use of a recently developed mouse model which recapitulates the E122K mutation to examine how mutations in EEF1A2 might disrupt neuronal gene expression. Primary neurons from mutant mice and transfected HEK293T cells were used to examine effects on global protein synthesis. In contrast to previous reports, we were unable to detect a change in global protein synthesis using either of two different assay systems. TRAP-seq and mass spectrometry were then employed to study the effects of the mutation on the translatome and proteome respectively. These analyses revealed perturbation of expression of a subset of genes, with a slight skew towards downregulation, particularly for longer transcripts. Further analysis indicated a down regulation of proteins involved in synaptic function in both the translatomic and proteomic datasets.