Widespread decoupling of spindles and slow waves in temporal lobe epilepsy

Epilepsia, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Memory impairment is common in people with temporal lobe epilepsy (TLE). Recent studies healthy subjects showed a positive correlation between sleep spindles coupled to slow waves (SWs) and memory performance. We aimed determine differences spindle-SW coupling TLE patients compared controls using combined high-density electroencephalography polysomnography. The study population consisted of 20 (12 female, 36.5 ± 9.9 years old) unilateral drug-resistant (10 left temporal) age- sex-matched 31.2 6.3 old). Spindles (10-16 Hz, .5-3 s) SWs (.5-4 Hz) were automatically detected during all N2 N3 epochs validated detectors. Coupling was defined as overlap both events. Coupled rates (per minute) globally reduced (median = .18 [interquartile range (IQR) .08-.36] vs. .35 [IQR .24-.46], p .014, d -.46). This reduction also found for fast spindle (12-16 Hz)-SW (.06 .02-.13] .07-.25], .013, -.46) (10-12 (.11 .04-.23] .19 .13-.27], .034, -.40). Within patients, there no local difference the epileptic focus contralateral side (.09 .07 .02-.13], .18). effect size stronger early than late (early -.50 -.39; -.53 -.47). Despite focal generator, widespread decoupling that most prominent sleep. As shown be associated neuropsychological performance people, this global may constitute one potential mechanism poor TLE.

Language: Английский

Discovering Neurophysiological Characteristics of Pathological High-Frequency Oscillations in Epilepsy with an Explainable Deep Generative Model

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 11, 2024

ABSTRACT Objective Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether distinct mechanisms underlying and encapsulated in their signal morphology intracranial EEG (iEEG) recordings this mechanism-driven distinction could be simulated by deep generative model. Methods In retrospective cohort 185 epilepsy patients who underwent iEEG monitoring, we analyzed 686,410 across 18,265 brain contacts. To learn morphological characteristics, each event was transformed into time-frequency plot input variational autoencoder. characterized latent space clusters containing morphologically defined putative (mpHFOs) using interpretability analysis, including disentanglement time-domain perturbation. Results mpHFOs showed strong associations with expert-defined spikes were predominantly located within seizure onset (SOZ). Discovered novel features included high power gamma (30–80 Hz) ripple (>80 bands centered on event. These characteristics consistent multiple variables, institution, electrode type, patient demographics. Predicting 12-month postoperative outcomes resection ratio outperformed unclassified (F1=0.72 vs. 0.68) matched current standards SOZ (F1=0.74). Combining mpHFO data demographic status further improved prediction accuracy (F1=0.83). Interpretation Our data-driven approach yielded novel, explainable definition HFOs, which has potential to enhance use EZ delineation.

Language: Английский

Quantifying epileptic networks: every data point brings us a step closer to an optimized surgery

Brain Communications, Journal Year: 2024, Volume and Issue: 6(5)

Published: Jan. 1, 2024

This scientific commentary refers to ‘The sixth sense: how much does interictal intracranial EEG add determining the focality of epileptic networks?’, by Gallagher et al. (https://doi.org/10.1093/braincomms/fcae320).

Language: Английский