Epilepsia

ObjectivesWith the expected demographic shift toward those [≥]65 years of age in the United States, late-onset epilepsy (LOE) poses a significant public health issue, yet it has been historically understudied. We are undertaking an effort in the Epilepsy-Cog study to pool individual participant data from six US-based prospective cohort studies. In this paper, we outline the process for ascertaining epilepsy, harmonizing, and pooling individual participant data across the six cohorts. Methods...

BackgroundEpilepsy affects around 50 million people worldwide and remains a major diagnostic challenge, particularly in resource-limited settings. Electroencephalography (EEG) is essential for diagnosis but relies heavily on expert interpretation, often limited by workforce shortages. Artificial intelligence (AI) offers a promising solution to automate EEG interpretation, enhance diagnostic accuracy, and improving diagnostic efficiency. MethodsThis retrospective diagnostic validation study was ...

BackgroundDifferentiating between motor functional dissociative seizures (FDS) and motor epileptic seizures (ES) is a common diagnostic challenge, requiring video electroencephalography (vEEG) as gold standard. However, vEEG requires specialized technicians and clinical experts to set up and interpret and oftentimes fails to capture events. We sought to develop machine-learning (ML) tools to carry out this diagnostic task independently of vEEG or human review by a neurologist. MethodsIn this re...

ObjectiveDetecting epileptic seizures in real-world environments remains challenging, as electroencephalography (EEG) is often impractical in chronic ambulatory monitoring. Heart rate and accelerometry, measurable from wearable devices, provide a less obtrusive alternative. Although some studies explored multimodal wearable-based seizure detection, few have been validated on long-term ambulatory datasets reflecting real-world variability. This study investigated the added value of accelerometry ...

ImportanceLarge language models are increasingly used for clinical decision support yet may perpetuate socioeconomic biases. Whether simple prompt-based interventions can mitigate such biases remains unknown. ObjectiveTo determine whether a prompt-based inoculation instructing large-language-models (LLMs) to disregard clinically irrelevant information can reduce bias and improve accuracy in recommendations. DesignExperimental study conducted November 21 to December 11, 2025. Each clinical vig...

Epilepsy is one of the most prevalent neurological diseases, with 25-33% of patients developing drug-resistant epilepsy (DRE). The precise etiology of DRE remains unidentified. Recent studies have revealed an increase in tetraploid astrocytes in drug-resistant temporal lobe epilepsy (DR-TLE), a common subtype of DRE. This study aims to characterize the function of tetraploid astrocytes in the brain of subjects without central nervous system diseases and in DR-TLE. Cortical samples adjacent to th...

BackgroundEpilepsy is a common neurologic disorder characterized by recurrent, unprovoked seizures. Epilepsy manifests as different seizure types and epilepsy types, which have important implications for treatment and prognosis. Electronic health record systems containing longitudinal data on large epilepsy cohorts can be valuable resources for clinical research. However, detailed epilepsy phenotypes are poorly captured by structured data such as diagnostic codes and are instead buried in unstru...

Automated seizure detection and localization from intracranial EEG requires validated benchmark datasets with expert annotations, yet existing open datasets lack multi-expert consensus annotations and exclude stimulation-induced seizures. We present stereotactic EEG recordings from 83 seizures (46 spontaneous, 37 stimulation-induced) across 32 patients (19 from the University of Pennsylvania, 13 from the Childrens Hospital of Philadelphia) with drug-resistant epilepsy. Three board-certified epil...

One-third of the worlds 70 million people with epilepsy have seizures that are not controlled by medication; and implantable devices are an exciting option for treatment. These devices improve seizure control and can detect impending attacks, missed medication, and impaired cognition. Unfortunately, they have no way to share this information with their hosts in real-time - a limitation common to most medical devices. This is a missed opportunity for implants and wearables to learn from patients,...

BackgroundEpisodic memory complaints are frequently reported by people with epilepsy (PWE), and often contrast with normal performance on standard neuropsychological assessment using short retention intervals (20-30 minutes). This discrepancy is consistent with the concept of accelerated long-term forgetting (ALF). However, the objective assessment of ALF remains challenging in clinical practice, and the underlying mechanisms, whether related to encoding, early or late consolidation, are still d...

ImportanceTracking and predicting seizure frequency in patients with epilepsy is important for prognostication and therapy management. Interictal spikes have been proposed as a biomarker of seizure burden, but their association with seizure frequency has not been well quantified across epilepsy subtypes. ObjectiveTo measure the association between spike rate and seizure frequency and how this varies by epilepsy subtype. Design, Setting and ParticipantsWe studied 3,614 consecutive routine outpa...

BackgroundPrevious studies have suggested that social cognition abilities do not change following surgical treatment for temporal lobe epilepsy (TLE). However, the follow-up period in these studies was no longer than 14 months. The present study investigated the long-term effects of epilepsy surgery on social cognition, extending the follow-up period to an average of 12 years (range 7-15 years). MethodsWe assessed 24 patients with drug-resistant TLE (mean age = 37 {+/-} 11 years; 14 males) who ...

Background and PurposeInterictal cerebral blood flow (CBF) may be useful for seizure focus localization. However, its accuracy is debatable. Studies suggested interictal hypoperfusion at the seizure focus, yet others suggested hyperperfusion. This study aims to investigate the patterns of interictal perfusion in epilepsy subjects compared to healthy controls using multiple labeling-delays arterial spin labeling MRI, and to explore the accuracy of perfusion estimation using single post-labeling d...

Annotating seizure onset and spread in intracranial EEG is essential for epilepsy surgical planning, yet manual annotation is unreliable and cannot scale to large datasets. We introduce Neural Dynamic Divergence (NDD), an unsupervised framework that detects seizure activity by measuring deviation from patient-specific baseline neural dynamics using autoregressive models. NDD requires no labeled training data and adapts to individual patients, channels, and brain states. Validating against expert...

Although epilepsy surgery studies have proposed intracranial EEG-derived biomarkers for localizing seizure onset and anticipating postoperative outcomes, evaluation has often been limited to derivation cohorts using internal cross-validation. An influential notion holds that neurons distributed within the seizure onset zone (SOZ) frequently generate high-frequency activity (HFA) and that resection of such sites is associated with favorable postoperative seizure control. However, the extent to wh...

Neuromodulation targeting thalamic nuclei is increasingly used to treat drug-resistant focal epilepsy, yet human intracranial EEG studies describing how thalamocortical interactions evolve across seizures remain limited. We aimed to define frequency-specific thalamocortical network dynamics from seizure onset to termination, compare thalamocortical and cortico-cortical network activation, and test whether thalamic EEG features can classify seizure state to inform closed-loop or adaptive thalamic...

About half of patients who undergo epilepsy surgery for drug-resistant epilepsy have seizure recurrence, supporting the need for approaches that more accurately identify the epileptogenic zone, defined as the brain areas whose removal causes cessation of seizures. Altered network connectivity has emerged as a candidate biomarker of the epileptogenic zone, but how connectivity is altered in the epileptogenic zone remains uncertain, with prior studies reporting inconsistent results. We hypothesize...

BackgroundPathogenic KCNQ2 variants are the most common genetic cause of neonatal-onset epilepsies, with phenotypes ranging from self-limited (familial) neonatal epilepsy (SeL(F)NE) to severe developmental and epileptic encephalopathy (KCNQ2-DEE). Sodium channel blockers (SCBs) have shown promise for seizure control in these disorders, but their impact on neurodevelopmental outcomes and possible relationship with timing of initiation remain incompletely understood. MethodsWe leveraged a large, ...

Epilepsy diagnosis and treatment monitoring are hindered by the episodic, heterogeneous expression of seizures and by normal-appearing scalp EEG in many patients. We previously described paroxysmal slow-wave events (PSWEs)--brief epochs of broadband slowing detectable on EEG. Here, using intracerebral and epidural recordings in a paraoxon rat model of temporal lobe epilepsy, we show that PSWEs arise preferentially in temporo-frontal networks, co-occur with global slowing, and increase during bot...

Epilepsy is among the most prevalent neurological disorders, affecting millions of individuals worldwide at every stage of life. Characterised by recurrent seizures, epilepsy can significantly disrupt daily functioning, education, employment, and overall quality of life. Despite advances in neuroimaging, current approaches often overlook the individualised nature of brain disruptions in epilepsy. Here, we introduce an individualised functional Magnetic Resonance Imaging (fMRI) framework, Adjuste...