Circadian rhythms maintain healthy neural function, and their disruption links to pathological brain states including epilepsy. Current diagnostic approaches for epilepsy, which predominantly focus on transient ictal events or static spectral features in intracranial EEG, suffer from a temporal myopia that neglects the rich spatiotemporal dynamics of long-term neural activity. To address this limitation, this study aims to establish multi-band circadian biomarkers as diagnostic signatures for epileptogenic tissue identification and patient subtyping. In this article, we developed a comprehensive biomarker extraction pipeline that analyzes long-term intracranial EEG recordings (72+ h) from 38 drug-resistant epilepsy patients, quantifying multi-band rhythm features from delta to gamma frequencies (1–100 Hz). The pipeline captures three circadian signatures: rhythm amplitude, temporal stability, and cross-frequency coupling. Epileptogenic tissue showed systematic circadian dysregulation: 43.2% reduction in delta band circadian amplitude (p < 0.001), 31.5% impairment in delta–gamma coupling (quantified as a power–envelope correlation proxy for phase–amplitude coupling), and progressive temporal instability across sleep–wake transitions. Using unsupervised clustering, we identified three chronobiological subtypes—Circadian-Preserved (36.8%), Coupling-Deficient (39.5%), and Pan-Dysrhythmic (23.7%)—each with distinct pathophysiological mechanisms and surgical outcomes. Our machine learning classification achieved clinically significant discrimination (AUC = 0.865), with circadian amplitude and coupling strength as the most informative features. These multi-band circadian biomarkers provide interpretable, physiologically grounded signatures for epilepsy diagnosis and subtype stratification, offering a temporal framework for personalized surgical planning and chronotherapy interventions.
Li et al. (2026) studied this question.