Topological Analysis of Seizure-Induced Changes in Brain Hierarchy Through Effective Connectivity

TL;DR

This paper introduces a novel causality-based topological method, CBTR, for analyzing effective brain connectivity and hierarchy changes during seizures, overcoming limitations of traditional symmetric TDA techniques.

Contribution

The paper presents CBTR, a new method combining causal inference and topological ranking to analyze directional brain connectivity and seizure-related hierarchy alterations.

Findings

CBTR accurately identifies hierarchical brain structures in simulated data.

CBTR detects significant interaction changes in EEG data during seizures.

The method offers new insights into seizure impact on brain dynamics.

Abstract

Traditional Topological Data Analysis (TDA) methods, such as Persistent Homology (PH), rely on distance measures (e.g., cross-correlation, partial correlation, coherence, and partial coherence) that are symmetric by definition. While useful for studying topological patterns in functional brain connectivity, the main limitation of these methods is their inability to capture the directional dynamics - which is crucial for understanding effective brain connectivity. We propose the Causality-Based Topological Ranking (CBTR) method, which integrates Causal Inference (CI) to assess effective brain connectivity with Hodge Decomposition (HD) to rank brain regions based on their mutual influence. Our simulations confirm that the CBTR method accurately and consistently identifies hierarchical structures in multivariate time series data. Moreover, this method effectively identifies brain regions showing the most significant interaction changes with other regions during seizures using electroencephalogram (EEG) data. These results provide novel insights into the brain's hierarchical organization and illuminate the impact of seizures on its dynamics.