Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: recent advances, current challenges, and future directions

Clinical Snapshot

Key Findings

• Effect Size: Not quantified - qualitative synthesis

• Primary Outcome: Network-based approaches show promise for improving epileptogenic zone localization beyond traditional methods

• Nnt Or Sensitivity: Surgical cure rates currently below 70% in DRE patients

Clinical Application

Requires specialized computational infrastructure and expertise; implementation challenges in routine clinical practice Relevant to Australian epilepsy surgery centres; potential integration with existing neurophysiology services; may require additional TGA considerations for AI-based medical devices Patients with drug-resistant epilepsy being evaluated for surgical intervention

Abstract

Drug-resistant epilepsy (DRE) affects approximately 30% of epilepsy patients, with surgical cure rates below 70%. This challenge drives a fundamental paradigm shift from localizing a discrete epileptogenic zone toward characterizing and modulating the dysfunctional brain networks that initiate and propagate seizures. This review critically synthesizes how computational neuroelectrophysiology and artificial intelligence (AI) converge to propel this shift. We chart the trajectory from quantifying local biomarkers, including high-frequency oscillations and excitation-inhibition balance metrics, to mapping the topological properties of epileptic networks through functional and effective connectivity. The integration of these network features with AI techniques, particularly spatiotemporal deep learning architectures, has demonstrated significant potential for enhancing both the localization of the epileptogenic network and the prediction of postoperative outcomes. However, the translation of this network-centric paradigm into clinical practice remains constrained by several challenges, including spatial sampling bias, the inherent label instability of surgical outcomes, and the 'black-box' interpretability crisis. Future directions must emphasize EEG foundation models, causal AI, and the development of interactive multimodal AI agents. The convergence of mechanism-driven network models and data-driven AI frameworks promises a new era of personalized, network-guided therapy for DRE.

References

1. 1.Wang, Y., Yan, Z., Gong, Y., Lin, W., Wang, T., Liu, Y., Han, Y., Yang, M., Liu, M., Chen, W., & Hu, B. (2026). Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: recent advances, current challenges, and future directions. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/ae6bf4