# Inference of Dynamic Graph Changes for Functional Connectome

## TL;DR

This paper introduces a new statistical method to detect and analyze dynamic changes in brain networks over time using high-dimensional graphical models, enhancing understanding of brain responses to stimuli.

## Contribution

A novel hypothesis testing framework using bootstrap statistics for detecting change points in dynamic brain connectivity networks.

## Key findings

- Successfully identified change points in simulated data.
- Detected meaningful temporal brain network changes during stimuli.
- Correlated network changes with emotional annotations.

## Abstract

Dynamic functional connectivity is an effective measure for the brain's responses to continuous stimuli. We propose an inferential method to detect the dynamic changes of brain networks based on time-varying graphical models. Whereas most existing methods focus on testing the existence of change points, the dynamics in the brain network offer more signals in many neuroscience studies. We propose a novel method to conduct hypothesis testing on changes in dynamic brain networks. We introduce a bootstrap statistic to approximate the supreme of the high-dimensional empirical processes over dynamically changing edges. Our simulations show that this framework can capture the change points with changed connectivity. Finally, we apply our method to a brain imaging dataset under a natural audio-video stimulus and illustrate that we are able to detect temporal changes in brain networks. The functions of the identified regions are consistent with specific emotional annotations, which are closely associated with changes inferred by our method.