On spatial and temporal multilevel dynamics and scaling effects in epileptic seizures

TL;DR

This paper investigates multilevel spatial and temporal dynamics in epileptic seizures, revealing novel scaling laws, early-warning signs, and synchronization measures across different brain regions and scales.

Contribution

It introduces new multiscale analysis methods and theoretical insights into seizure dynamics, including critical transitions, oscillatory behavior, and phase-locking measures.

Findings

Identification of early-warning signs of seizure onset.

Discovery of new scaling laws near seizure initiation.

Comparison of wavelet-based and traditional synchronization measures.

Abstract

Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures.