Neural Netw. 2026 May 19;203:109151. doi: 10.1016/j.neunet.2026.109151. Online ahead of print.
ABSTRACT
Accurate identification of the seizure onset zone (SOZ) is critical for the diagnosis and treatment of drug-resistant epilepsy (DRE). In recent years, although phase-amplitude coupling (PAC) has played an important role in epilepsy-related studies, few investigations have focused on applying PAC methods to SOZ identification. To this end, leveraging the capability of PAC to characterize neural interactions within the brain, this study computes the modulation index (MI) from clinical electrocorticography (ECoG) recordings of DRE patients. Subsequently, a statistical analysis of temporally evolving distributions of MI values across multiple frequency bands is conducted to analyze the differences in MI distribution features between SOZ and non-seizure onset zone (NSOZ) regions. Finally, distribution features of MI values are integrated with machine learning techniques to systematically evaluate the influence of different frequency bands and time windows on SOZ identification performance. The results demonstrate that MI distribution features can achieve accurate SOZ identification, with classification accuracy reaching 90.69%, indicating their potential as biomarkers for SOZ identification.
PMID:42184467 | DOI:10.1016/j.neunet.2026.109151
