Epilepsia最新文献

Objective: This study aimed to assess the diagnostic value of symmetrical claustrum sign (SCS) in children with febrile infection-related epilepsy syndrome (FIRES), and describe the clinical features, treatment responses, and prognosis of children with FIRES accompanied by symmetrical claustrum lesions (FIRES-C).

Methods: A retrospective cohort study conducted at Beijing Children's Hospital from January 2015 to April 2024 included children with "fever and convulsions" who met the inclusion criteria. The diagnostic efficacy of SCS was calculated by analyzing the sensitivity and specificity.

Results: The study enrolled 1105 patients, including 129 with FIRES, 35 of whom were diagnosed with FIRES-C (median onset age: 8.3 years; 18 male). In the cohort of patients with status epilepticus (n = 347), the sensitivity of SCS was 27.1% (95% confidence interval [CI], 19.8%-35.9%), and the specificity was 95.4% (95% CI, 91.8%-97.4%). The median time to develop bilateral claustrum lesions was 15.8 days (interquartile range [IQR] 11-19). The results of the magnetic resonance imaging (MRI) scans revealed that 7 patients (20%) had isolated claustrum lesions, whereas 28 patients (80%) had extra-claustrum lesions. After a median follow-up of 42 months, 14 of the 29 patients (48.3%) who survived exhibited moderate to severe deficits (modified Rankin scale [mRS] ≥3) and 2 patients died. When compared to FIRES patients without claustrum lesions (n = 94), those with FIRES-C (n = 35) demonstrated a tendency toward milder disease severity, characterized by higher Glasgow Coma Scale (GCS) scores and lower incidence of non-midazolam anesthetic use and respiratory support requirements (p < .001). However, no significant differences in prognosis were observed between the two groups (p = .322).

Significance: SCS exhibit high specificity but limited sensitivity in the diagnosis of FIRES in pediatric patients. SCS may serve as a biomarker to assist in the diagnostic process and is typically associated with a less severe clinical progression.

Objective: Memory impairments are common in neurological disorders, especially drug-resistant epilepsy, where cognitive deficits such as accelerated long-term forgetting may occur before surgery due to interictal/ictal discharges, antiseizure medications, and disease progression. Postsurgical memory decline affects 40%-60% of patients. Understanding the neural mechanisms of memory encoding is essential for identifying critical network hubs and guiding surgical interventions that preserve cognitive function.

Methods: We analyzed intracranial electroencephalographic recordings from epilepsy patients (n = 15) performing a verbal memory encoding task to identify key structures involved in successful memory formation. Functional connectivity was computed using multiple features across several frequency bands. Temporal dynamics of connectivity were examined to characterize pre-encoding and encoding phases.

Results: The middle temporal gyrus (MTG) consistently emerged as a verbal memory hub across features and frequency bands. The inferior frontal gyrus (IFG) was identified as a beta-band-specific hub via phase synchrony. Temporal analyses revealed that MTG exhibited stable and widespread connectivity differences across all encoding phases, suggesting a role in pre-encoding network priming. In contrast, IFG displayed a transient decrease in connectivity and spatial extent during early encoding, indicating a more focal and dynamic contribution.

Significance: These findings suggest that successful memory encoding relies on widespread, frequency-specific connectivity patterns centered on MTG, complemented by transient, localized IFG activity. Identifying such hubs may inform surgical planning by highlighting regions critical for cognitive outcomes, ultimately supporting strategies to preserve memory in epilepsy surgery.

Objectives: Despite providing significant insights into the phenotypic spectrum and genotype-phenotype correlations in SCN8A-related disorders (SCN8A-RD), cohort and registry-based studies typically lack sufficient longitudinal data to characterize disease progression or treatment-response trajectories. Here we utilize data from electronic medical records (EMRs) of 80 patients as a complementary approach to advance understanding of the natural history of SCN8A-RD.

Methods: Longitudinal profiles were constructed by mining text from EMRs and writing code to visualize timelines for individual patients. Age-dependent unsupervised clustering analysis was performed on developmental skills, seizure profiles, electroencephalography (EEG) results, comorbidities, hospitalizations, genetic variant, and sex. Skill acquisition data in four domains (gross motor, fine motor, language, and academic) were used to infer developmental trajectories for the overall cohort and phenotypic subgroups. Features that distinguished clusters at each age group were identified using penalized regression. Antiseizure medication (ASM) effectiveness was inferred by correlating prescriptions taken during periods of seizure reduction.

Results: Cluster analysis identified dynamic phenotypic groupings peaking at four clusters at Year 5, suggesting that the middle childhood years may represent a critical developmental window when phenotypic divergence is most apparent. Developmental trajectories varied significantly by phenotypic subgroup: Patients in the loss-of-function (LoF) subgroup achieved highest gross motor and language skills, whereas patients with developmental and epileptic encephalopathy (DEE) remained the most impaired across all domains. Oxcarbazepine emerged as the most common ASM monotherapy during the seizure-free periods.

Significance: This EMR analysis represents the first integration of long-term developmental trajectories that encompasses both gain-of-function (GoF) and LoF subgroups. Patients carrying both types of variants cluster together at different ages in early childhood. Several sodium channel blockers were effective as monotherapies during seizure-freedom periods. Despite intermittent periods of reduced seizure activity, persistent developmental delays across all subgroups highlight the need for neurodevelopmental support beyond seizure control. EMR analysis reveals an evolving phenotypic landscape with distinct developmental outcomes for SCN8A-RD patients in different subgroups.

Objective: Thalamic responsive neurostimulation (RNS) is a surgical option for patients with drug-refractory epilepsy. However, it is unclear whether thalamic connectivity with the seizure onset zone (SOZ) has a role in clinical outcomes. Here, we aim to investigate the clinical utility of the connectivity between the SOZ and the thalamus for thalamic RNS targeting.

Methods: Retrospective analysis was made of 12 patients treated with thalamic RNS. Clinical features and Engel scores were recorded. Patients were divided into responders, partial responders, and nonresponders based on seizure frequency reduction at last follow-up. Structural connectivity between the SOZ and the whole thalamus was calculated using patient-specific tractography. RNS electrodes were used to model the volume of tissue activated (VTA) with stimulation parameters at last follow-up based on individualized electrode locations. The patient's VTAs were then used to identify thalamic areas with high or low probability of connectivity with the SOZ and how they were associated and correlated with clinical outcomes using nonparametric Mann-Whitney U and Spearman correlation tests.

Results: Seven patients were responders, three nonresponders, and two partial responders. Thalamic nuclei targeted included anterior nucleus of thalamus and centromedian nucleus. Cortical areas of the SOZs included medial prefrontal, supplementary motor, cingulate, orbitofrontal, insular, mesial temporal, and lateral temporal cortices. Stimulation of thalamic areas with higher connectivity between the SOZ and the thalamus was associated with a clinical response of >50% reduction in seizures (p = .017). Furthermore, higher degree of tract activation between the SOZ and the thalamus was correlated with better seizure outcomes at last follow-up (r = .78, p = .004).

Significance: Greater recruitment of white matter connections between the SOZ and thalamus is associated with clinical response and may correlate with improved seizure outcomes during thalamic RNS. Using tractography to map the patient-specific "thalamic seizure network" and the surgical targeting of these connections may result in improved clinical outcomes in patients treated with thalamic RNS.

Objective: Epilepsy patients face significantly elevated cardiovascular risks, with cardiac arrhythmias occurring 2-3 times more frequently than in the general population. Current knowledge of brain-heart functional coupling abnormalities in epilepsy, particularly during interictal periods, remains limited. We investigated brain-heart interplay characteristics in temporal lobe epilepsy through synchronized electroencephalographic-electrocardiographic analysis using a synthetic data generation model and microstate analysis.

Methods: We enrolled 52 patients with temporal lobe epilepsy (mean age = 33.4 ± 12.7 years) and 42 age-matched healthy controls (mean age = 31.95 ± 11.03 years). Twenty-minute artifact-free electroencephalographic segments were analyzed during resting states. Four directional brain-heart coupling sequences were extracted: C_Brain→HF, C_Brain→LF, C_HF→Brain, and C_LF→Brain, representing bidirectional interactions between brain activity and cardiac components.

Results: Six microstate topologies were consistently identified across all brain-heart interplay sequences, with temporal lobe epilepsy patients demonstrating significantly more complex and unstable topological characteristics compared to healthy controls. For C_Brain→HF coupling, patients exhibited significantly reduced mean duration of microstate 3 (.34 ± .09 s vs. .38 ± .07 s, p = .02), increased occurrence rates of microstates 3 and 4 (both p < .001), and altered temporal coverage patterns. Similar abnormalities were observed across all sequences, with patients showing shortened microstate durations, altered occurrence rates, and disrupted temporal coverage. Spatial dissimilarity analysis revealed significant topological abnormalities across all microstates. A logistic regression model incorporating microstate parameters achieved 94.7% diagnostic accuracy for temporal lobe epilepsy, with an F1 score of .952 and an area under the curve of .932.

Significance: Temporal lobe epilepsy is characterized by profound disruptions in brain-heart functional coupling during interictal periods, manifesting as altered microstate topographies and temporal dynamics. These findings establish microstate-based analysis as a promising framework for characterizing brain-heart axis dysfunction in epilepsy.

Background and objective: Lafora disease (LD) is a rare progressive disorder caused by mutations in the EPM2A or EPM2B genes, characterized by the accumulation of Lafora bodies, drug-resistant epilepsy, and cognitive decline. To investigate the early molecular mechanisms of LD, we studied electrophysiological changes in the dentate gyrus (DG) of the Epm2a^R240X knock-in mouse model at various ages.

Methods: Electrophysiological recordings measured neuronal membrane properties, epileptic-like activity, epileptic thresholds, and synaptic plasticity in Epm2a^R240X mice at 1, 3, and 12 months. We also employed Periodic Acid-Schiff (PAS) diastase staining, immunofluorescence, and Western blotting to detect Lafora bodies, amyloid beta deposition, and the expression of glutamate receptor subunits.

Results: Epileptic-like activity began at 1 month and intensified with age. Aberrant long-term potentiation (LTP) appeared at 3 months and worsened by 12 months. Notably, cannabidiol treatment reduced excitability and restored LTP in older mice, suggesting its potential therapeutic value.

Significance: The reversibility of synaptopathy, even at advanced stages, reinforces the importance of early detection of hyperexcitability and the development of effective therapeutic approaches.

Consensus-based recommendations (CBRs) are essential for health care decision-making when evidence is limited or conflicting. They can be developed using established methodologies such as the Delphi technique, the nominal group technique (NGT), and the RAND Corporation/University of California Los Angeles (UCLA) Appropriateness Method (RAM). This review explores the advantages and disadvantages of these methods and their applications in epilepsy. A narrative review using PubMed (November 22, 2017 to November 22, 2022) was undertaken examining publications describing Delphi, NGT, and RAM. The frequency of use of each method and their applications in epilepsy care were also reviewed (1966 to October 31, 2023). Sixteen articles were included describing the different consensus-based methods. The advantages and disadvantages of each method varied widely. The Delphi technique, the most widely used method, emerged as adaptable for instances with limited evidence or impracticality of face-to-face interactions. Although NGT favors prompt consensus in single-session formats, the RAM offers a balanced approach with its hybrid structure. We identified 64 epilepsy studies that used consensus methods, with 58 utilizing the most widely used technique, the Delphi. The Delphi guided consensus mostly for management including for individuals with rare conditions such as myoclonic-atonic seizures or in those with epilepsy and pregnancy. The NGT guided expert consensus on the use of cannabidiol for Dravet and Lennox-Gastaut syndromes and facilitated decision-making among pharmacy students addressing ethical issues related to patients with epilepsy who drive. The RAM was applied in four studies for its combined individual and group evaluative approach. It was used to develop recommendations or imaging, create quality-of-care indicators for infantile spasms, and establish a web-based tool for assessing surgical candidacy. Consensus methodologies are crucial to inform robust CBR for epilepsy management where clinical practice guidelines are not possible due to limited evidence. The best method depends on the study goal and available resources.

Objective: Sudden unexpected death in epilepsy (SUDEP) often follows generalized tonic-clonic seizures during sleep, likely resulting from impaired brainstem cardiorespiratory function. We used ictal electrocardiogram (ECG)-based cross-frequency phase-amplitude coupling (PAC) to detect cardiorespiratory disruptions, comparing SUDEP to non-SUDEP cohorts. Leveraging respiratory modulation of ECG signals can provide a robust indirect proxy of respiratory monitoring despite high-amplitude noise.

Methods: We analyzed ictal ECG and electroencephalographic recordings in 21 SUDEP cases and 21 non-SUDEP epilepsy controls. Ictal ECG segments from 76 seizures (38 SUDEP, 38 non-SUDEP) were processed using continuous wavelet transformation to compute PAC between respiratory (.1-.55 Hz, 6-33 breaths per minute) and cardiac (.7-3.7 Hz, 42-222 beats per minute) frequencies. Relative PAC coupling strength was evaluated for respiratory frequencies > .25 Hz (15 breaths per minute) and cardiac frequencies > 1.7 Hz (102 beats per minute). Furthermore, a 3 × 3 grid of PAC ranges was derived for each 20-s window, yielding 18 features (mean and SD) as inputs to a logistic regression model.

Results: Elevated ictal PAC at higher respiratory (>.25 Hz, p < .0001) and cardiac (>1.7 Hz, p < .0142) frequencies in SUDEP patients suggests ictal respiration modulates ictal tachycardia, leading to cardiorespiratory dysfunction, probably brainstem-mediated. The logistic model accurately distinguished 38 seizures in SUDEP cases from 38 seizures in non-SUDEP cases (receiver operating characteristic area under the curve = 91%). Seizures in SUDEP patients had higher propensity scores (p < .001) both per seizure and per patient. All six test seizures (three SUDEP, three non-SUDEP) were correctly classified using the optimal threshold.

Significance: Ictal ECG-based PAC analysis is a potential noninvasive biomarker for SUDEP risk, capturing cardiorespiratory dysregulation during seizures. Its integration into wearable ECG devices could enable real-time risk assessment, informing clinical interventions such as rescue medications, antiseizure medication adjustments, or surgical evaluations.