Delay-constrained re-entry governs large-scale brain seizures and other network pathologies

TL;DR

This study demonstrates that realistic neural delays can induce self-sustaining re-entrant seizures in a virtual brain model, and proposes targeted interventions to disrupt these pathological loops, advancing understanding of seizure mechanisms and potential treatments.

Contribution

The paper introduces a patient-specific virtual brain model showing how delay-coupling induces re-entry seizures and identifies precise intervention strategies for disruption.

Findings

Re-entry loops can be generated by realistic cortico-cortical delays.

A narrow delay-coupling window predicts seizure characteristics.

Targeted stimuli or virtual lesions can abort re-entry in the model.

Abstract

Re-entry of travelling excitation loops is a long-suspected driver of human seizures, yet how such loops arise in patient brain networks -- and how susceptible they are to targeted disruption -- remains unclear. We reconstruct a millimetre-scale virtual brain from diffusion MRI of a drug-resistant epilepsy patient, embed excitable Epileptor neural fields, and show that realistic cortico-cortical delays are sufficient to generate self-sustaining re-entry. Systematic parameter sweeps reveal a narrow delay-coupling window that predicts oscillation frequency and seizure duration across 184 recorded seizures. Precisely timed biphasic stimuli or sub-millimetre virtual lesions abort re-entry in silico, yielding phase-dependent termination rules validated in intracranial recordings. Our framework exposes delay-constrained re-entry as a generic dynamical mechanism for large-scale brain synchrony and provides a patient-specific testbed for precision neuromodulation and minimally invasive disconnection.