A Fast Detection Method of Break Points in Effective Connectivity Networks

TL;DR

This paper introduces a rapid, scalable method for detecting change points in large neuroimaging time series data, enabling better understanding of brain network dynamics and their underlying mechanisms.

Contribution

It proposes a novel multi-step approach using regularized objectives and clustering to efficiently identify and validate break points in Granger causal networks from neuroimaging data.

Findings

Effective detection of break points in synthetic data of various sizes

Successful application to EEG data during visual tasks

Controlled false positive rate in network change detection

Abstract

There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop fast, scalable methods for detecting and localizing in time such changes and also identify their drivers, thus enabling neuroscientists to hypothesize about potential mechanisms. This paper presents a fast method for detecting break points in exceedingly long time series neuroimaging data, based on vector autoregressive (Granger causal) models. It uses a multi-step strategy based on a regularized objective function that leads to fast identification of candidate break points, followed by clustering steps to select the final set of break points and subsequent estimation with false positives control of the underlying Granger causal networks. The latter provides insights into key changes in network connectivity that led to the presence of break points. The proposed methodology is illustrated on synthetic data varying in their length, dimensionality, number of break points, strength of the signal, and also applied to EEG data related to visual tasks.