Epilepsia最新文献

Objective: Electroencephalographic seizures (ES) are common in neonates with hypoxic-ischemic encephalopathy (HIE), but identification with continuous electroencephalographic (EEG) monitoring (CEEG) is resource-intensive. We aimed to develop an ES prediction model.

Methods: Using a prospective observational study of 260 neonates with HIE undergoing CEEG, we identified clinical and EEG risk factors for ES, evaluated model performance with area under the receiver operating characteristic curve (AUROC), and calculated test characteristics emphasizing high sensitivity. We assessed ES incidence and timing in neonates subdivided by ES risk group (low, moderate, high) as determined by EEG risk factors.

Results: ES occurred in 32% (83/260) of neonates. Performing CEEG for only 24 h would fail to identify the 7% (17/260) of neonates with later onset ES (20% of all neonates experiencing ES). Identifying 90% or 95% of neonates with ES would require CEEG for 63 or 74 h, respectively. The optimal model included continuity and epileptiform discharges, both assessed in the initial 1 h of CEEG. It yielded an AUROC of .80, and at a cutoff that emphasized sensitivity, had sensitivity of 94%, specificity of 45%, positive predictive value of 44%, and negative predictive value of 95%. The model would avoid CEEG beyond 1 h in 32% (84/260) of neonates, but 6% (5/83) of neonates with ES would not have ES identified. ES incidence was significantly different (p < .01) across ES risk groups (6% low, 40% moderate, and 83% high). Only ~6 h of CEEG would identify all neonates with ES in the low-risk group, whereas 75 and 63 h of CEEG would be required to identify 95% of neonates with ES in the moderate-risk and high-risk groups, respectively.

Significance: Among neonates with HIE, a model employing two EEG variables from a 1-h screening EEG and stratifying neonates into low-, moderate-, and high-risk groups could enable evidence-based strategies for targeted CEEG use.

Objective: Loss-of-function mutations in the GIRDIN/CCDC88A gene cause developmental epileptic encephalopathy (DEE) in humans. However, its pathogenesis is largely unknown. Global knockout mice of the corresponding orthologous gene (gKOs) have a preweaning lethal phenotype with growth failure, preventing longitudinal analysis. We aimed to overcome this lethality and elucidate DEE pathogenesis.

Methods: We developed a novel lifelong feeding regimen (NLFR), which consists of providing mash food from postnatal day 14 (P14) until weaning (P28), followed by agar-bound food exclusively after weaning. Videography, electroencephalography (EEG), and histological analyses were performed. Conditional Girdin/Ccdc88a knockout mice (cKOs) of variable lineages (Nestin, Emx1, or Nkx2-1) were generated to identify the region responsible for epilepsy.

Results: Under the NLFR, gKOs survived beyond 1 year and displayed fully penetrant, robust epileptic phenotypes, including early-onset (P22.3 in average) generalized tonic-clonic seizures (GTCSs) (averaging eight per day), which were completely synchronized with fast rhythms on EEG, frequent interictal electroencephalographic spikes (averaging 430 per hour), and progressive deformation of visceral organs. In addition, gKOs had absence seizures, which were not always time-locked to frequent spike waves on EEG. The frequent GTCSs and interictal spikes in gKOs were suppressed by known antiepileptic drugs. Histologically, bilateral hippocampi in gKOs exhibited congenital cornu-ammonis splitting, granule cell dispersion, and astrogliosis. Furthermore, analysis of conditional knockouts using multiple Cre-deleters identified a defect in the delivery of interneuron precursors from the medial ganglionic eminence into the hippocampal primordium during embryogenesis as a major cause of epileptogenesis.

Significance: These findings give rise to a new approach of lifelong caregiving to overcome the problem of preweaning lethality in animal models. We propose a useful model for studying DEE with hippocampal sclerosis and interneuronopathy. gKOs with NLFR combine the contradictory properties of robust epileptic phenotypes and long-term survivability, which can be used to investigate spontaneous epileptic wave propagation and therapeutic intervention in hippocampal sclerosis.

Objective: To date, the identification of objective biomarkers of neural epileptic activity (EA) remains challenging. We therefore investigated whether neuronal complexity could serve as an interictal electroencephalographic measure of EA, independent of interictal epileptiform discharges (IEDs). By tapering anti-seizure medication (ASM) during video-EEG (electroencephalography) monitoring (VEM), we studied whether changes in neuronal complexity could reliably indicate the increase in EA and identify patients with epilepsy.

Methods: The study included 27 patients with unilateral mesial temporal lobe epilepsy (TLE) and 24 control patients with non-epileptic episodes (NEEs) only, each undergoing ASM reduction during VEM. Thirteen additional patients undergoing intracranial recordings during VEM were included to study the relation of surface EEG complexity to intracranial IED. Neuronal complexity was quantified using sample entropy. Delta power served as a control parameter. Receiver-operating characteristic (ROC) analysis was used to evaluate diagnostic performance.

Results: As ASM was reduced, patients with epilepsy showed a significant decrease in neuronal complexity over consecutive days (p = .0008). In contrast, patients with NEE showed no significant change in neuronal complexity (p = .78). Delta power in contrast increased and did not differ significantly between patients with TLE and patients with NEE (p = 1). ROC analysis demonstrated that neuronal complexity effectively distinguished between patients with epilepsy and patients with NEE (area under the curve [AUC] = .76), whereas delta power performed at chance level (AUC = .5). Analysis of simultaneously recorded surface and intracranial EEG showed that hippocampal IEDs are followed by an increase in surface EEG delta power (p = 1.8 × 10^-18) without any significant change in complexity (p = .39).

Significance: An increase in EA caused by ASM reduction resulted in a loss of neuronal complexity in surface EEG recordings of patients with epilepsy, independent of IEDs. These findings suggest that neuronal complexity could serve as a potential biomarker to differentiate between epilepsy patients and those with NEEs only. This holds promise for improving the clinical evaluation of EA in epilepsy, addressing the limitations of seizure frequency and IED identification.

Objective: The discovery and validation of electroencephalography (EEG) biomarkers often rely on visual identification of waveforms. However, bias toward visually striking events restricts the search space for new biomarkers, and low interrater reliability can limit rigorous validation. We present a data-driven approach to biomarker discovery called scalp EEG Pattern Identification and Categorization (s-EPIC), which enables automated, unsupervised identification of EEG waveforms. S-EPIC is validated on Lennox-Gastaut syndrome (LGS), an epilepsy that is difficult to diagnose and assess due to its variable presentation and insidious evolution of symptoms.

Methods: We retrospectively collected 10-min scalp EEG clips during non-rapid eye movement (NREM) sleep from 20 subjects with LGS and 20 approximately age-matched healthy controls. For s-EPIC, EEG events of interest (EOIs) were detected in all subjects using time-frequency analysis. The 11 705 EOIs were characterized based on 11 features and were collectively grouped using both k-means clustering and feature categorization. To provide clinical context, 1350 EOIs were visually reviewed and classified by three epileptologists.

Results: s-EPIC identified four clusters as candidate biomarkers of LGS, each having significantly more LGS EOIs than control EOIs. Two clusters contained EOIs resembling known LGS biomarkers such as interictal epileptiform discharges and generalized paroxysmal fast activity. The other two LGS-associated EEG clusters contained short bursts of power in beta and gamma frequency bands that were primarily unrecognized by epileptologists. This approach also uncovered significant differences in sleep spindles between LGS and control cohorts.

Significance: s-EPIC provides a quantitative approach to waveform identification that could be broadly applied to EEG from both healthy subjects and those with suspected pathology. s-EPIC can objectively identify and characterize relevant EEG waveforms without visual review or assumptions about the waveform's morphology and could therefore be a powerful tool for the discovery and refinement of EEG biomarkers.

Objective: To test a hypothesis that acutely regulated plasma microRNAs (miRNAs) can serve as prognostic biomarkers for the development of post-traumatic epilepsy (PTE).

Methods: Adult male Sprague-Dawley rats (n = 245) were randomized to lateral fluid-percussion-induced traumatic brain injury (TBI) or sham operation at three study sites (Finland, Australia, United States). Video-electroencephalography (vEEG) was performed on the seventh post-injury month to detect spontaneous seizures. Tail vein plasma collected 48 h after TBI for miRNA analysis was available from 209 vEEG monitored animals (45 sham, 164 TBI [32 with epilepsy]). Based on small RNA sequencing and previous data, the seven most promising brain enriched miRNAs (miR-183-5p, miR-323-3p, miR-434-3p, miR-9a-3p, miR-124-3p, miR-132-3p, and miR-212-3p) were validated by droplet digital polymerase chain reaction (ddPCR).

Results: All seven plasma miRNAs differentiated between TBI and sham-operated rats. None of the seven miRNAs differentiated TBI rats that did and did not develop epilepsy (p > .05), or rats with ≥3 vs <3 seizures in a month (p > .05). However, miR-212-3p differentiated rats that developed epilepsy with seizure clusters (i.e., ≥3 seizures within 24 h) from those without seizure clusters (.34 ± .14 vs .60 ± .34, adj. p < .05) with an area under the curve (AUC) of .81 (95% confidence interval [CI] .65-.97, p < .01, 64% sensitivity, 95% specificity). Lack of elevation in miR-212-3p also differentiated rats that developed epilepsy with seizure clusters from all other TBI rats (n = 146, .34 ± .14 vs .55 ± .31, p < .01) with an AUC of .74 (95% CI .61-.87, p < .01, 82% sensitivity, 62% specificity). Glmnet analysis identified a combination of miR-212-3p and miR-132-3p as an optimal set to differentiate TBI rats with vs without seizure clusters (cross-validated AUC .75, 95% CI .47-.92, p < .05).

Significance: miR-212-3p alone or in combination with miR-132-3p shows promise as a translational prognostic biomarker for the development of severe PTE with seizure clusters.

Objective: Although traumatic brain injury (TBI) and posttraumatic epilepsy (PTE) are common, there are no prospective models quantifying individual epilepsy risk after moderate-to-severe TBI (msTBI). We generated parsimonious prediction models to quantify individual epilepsy risk between acute inpatient rehabilitation for individuals 2 years after msTBI.

Methods: We used data from 6089 prospectively enrolled participants (≥16 years) in the TBI Model Systems National Database. Of these, 4126 individuals had complete seizure data collected over a 2-year period post-injury. We performed a case-complete analysis to generate multiple prediction models using least absolute shrinkage and selection operator logistic regression. Baseline predictors were used to assess 2-year seizure risk (Model 1). Then a 2-year seizure risk was assessed excluding the acute care variables (Model 2). In addition, we generated prognostic models predicting new/recurrent seizures during Year 2 post-msTBI (Model 3) and predicting new seizures only during Year 2 (Model 4). We assessed model sensitivity when keeping specificity ≥.60, area under the receiver-operating characteristic curve (AUROC), and AUROC model performance through 5-fold cross-validation (CV).

Results: Model 1 (73.8% men, 44.1 ± 19.7 years, 76.1% moderate TBI) had a model sensitivity = 76.00% and average AUROC = .73 ± .02 in 5-fold CV. Model 2 had a model sensitivity = 72.16% and average AUROC = .70 ± .02 in 5-fold CV. Model 3 had a sensitivity = 86.63% and average AUROC = .84 ± .03 in 5-fold CV. Model 4 had a sensitivity = 73.68% and average AUROC = .67 ± .03 in 5-fold CV. Cranial surgeries, acute care seizures, intracranial fragments, and traumatic hemorrhages were consistent predictors across all models. Demographic and mental health variables contributed to some models. Simulated, clinical examples model individual PTE predictions.

Significance: Using information available, acute-care, and year-1 post-injury data, parsimonious quantitative epilepsy prediction models following msTBI may facilitate timely evidence-based PTE prognostication within a 2-year period. We developed interactive web-based tools for testing prediction model external validity among independent cohorts. Individualized PTE risk may inform clinical trial development/design and clinical decision support tools for this population.

Objective: This study was undertaken to explore the experiences and concerns of people living with epilepsy by analyzing discussions in an online epilepsy community, using large language models (LLMs) to identify themes, demographic patterns, and associations with emotional distress, substance use, and suicidal ideation.

Methods: We analyzed 56 970 posts and responses to them from 21 906 users on the epilepsy forum (subreddit) of Reddit and 768 504 posts from the same users in other subreddits, between 2010 and 2023. LLMs, validated against human labeling, were used to identify 23 recurring themes, assess demographic differences, and examine cross-posting to depression- and suicide-related subreddits. Hazard ratios (HRs) were calculated to assess the association between specific themes and activity in mental health forums.

Results: Prominent topics included seizure descriptions, medication management, stigma, drug and alcohol use, and emotional well-being. The posts on topics less likely to be discussed in clinical settings had the highest engagement. Younger users focused on stigma and emotional issues, whereas older users discussed medical treatments. Posts about emotional distress (HR = 1.3), postictal state (HR = 1.4), surgical treatment (HR = .7), and work challenges (HR = 1.6) predicted activity in a subreddit associated with suicidal ideation, whereas emotional distress (HR = 1.5), surgical treatment (HR = .6), and stigma (HR = 1.3) predicted activity in the depression subreddit. Substance use discussions showed a temporal pattern of association with seizure descriptions, implying possible opportunities for intervention.

Significance: LLM analysis of online epilepsy communities provides novel insights into patient concerns often overlooked in clinical settings. These findings may improve patient-provider communication, inform personalized interventions, and support the development of patient-reported outcome measures. Additionally, hazard models can help identify at-risk individuals, offering opportunities for early mental health interventions.

Objective: This study was undertaken to anatomically categorize insulo-opercular focal cortical dysplasia (FCD) lesions according to their location and extent, and to summarize corresponding stereoelectroencephalographic (SEEG) patterns to guide preoperative evaluation and surgical planning.

Methods: Patients who underwent epilepsy surgery for insulo-opercular FCD between 2015 and 2022 were enrolled. FCD lesions were categorized into insular, peri-insular, opercular, and complex types based on their location and extent, as ascertained from electroclinical and neuroimaging data. SEEG signals from the seizure onset electrodes were collected for quantitative analysis. The normalized interictal spike counts, high-frequency oscillation (HFO) counts, and ictal epileptogenicity index (EI) values of the insular and opercular channels were calculated. The spatial patterns of the spike counts, HFO counts, and EI values were analyzed. Cluster analyses utilizing spike counts, HFO counts, and EI values were performed for automatic categorization, and the results were compared with the manual categorization from the preoperative evaluations.

Results: A total of 53 patients were included, comprising 10 insular, 17 peri-insular, 24 opercular, and two complex cases. Thirty-eight patients were included in the quantitative SEEG analysis. Spike, HFO, and EI analyses indicated that in insular FCDs, the values of the three parameters were higher in insular channels than in opercular channels. In peri-insular FCDs, the values in insular and opercular channels were comparable, whereas in opercular FCDs, the values were higher in opercular channels than in insular channels. The accuracies of the cluster analysis based on the spike counts, HFO counts, and EI values were 71.05% (27/38), 76.32% (29/38), and 86.84% (33/38), respectively. Surgical strategies were proposed according to the anatomical categorization, achieving a favorable postoperative seizure-free rate of 84.91%.

Significance: Insulo-opercular FCDs can be categorized into insular, peri-insular, opercular, and complex types. SEEG patterns can facilitate the automatic categorization of insulo-opercular FCDs, thereby enhancing preoperative planning and surgical outcomes.

Objective: The piriform cortex (PC) plays a critical role in ictogenesis, where an excitation/inhibition imbalance contributes to epilepsy etiology. However, the epileptic dynamics of the gamma-aminobutyric acid (GABA) system and the precise role of GABAergic neurons within the PC in epilepsy remain unclear.

Methods: We combined Ca²⁺ and GABA sensors to investigate the dynamics of Gad2-expressing neurons and GABA levels, and selectively manipulated GABAergic neurons in the PC through chemogenetic inhibition and caspase3-mediated apoptosis targeting Gad2 interneurons.

Results: GABAergic system dynamics in the PC were bidirectional and asymmetric, accompanied by PC optokindling-induced seizures, notably characterized by a robust response of Gad2 neurons but a rapid descent of GABA content during seizures. Chemogenetic inhibition of PC Gad2 neurons induced seizure-like behavior, with a discrepancy between the GABAergic neuron activities and GABA levels, signifying a transition from interictal to ictal states. Surprisingly, selective inhibition of Gad2 neurons in the PC produced paradoxical activation in a subset of Gad2 neurons. Moreover, the chronic deficiency of PC Gad2 neurons triggered spontaneous recurrent seizures.

Significance: Our findings uncover the dynamic interplay within PC inhibitory components and elaborate counteractive mechanisms in seizure regulation. These insights could inform future therapeutic strategies targeting GABAergic neurons to control epileptic activity.