Energy landscape analysis of neuroimaging data

TL;DR

This paper reviews and develops energy landscape analysis methods rooted in statistical physics, applying them to neuroimaging data to understand brain dynamics and their alterations.

Contribution

It introduces a data-driven approach to neuroimaging analysis based on the energy landscape framework, extending methods from electrophysiology to fMRI data.

Findings

Fitting accuracy depends on system size and data length.

Energy landscape methods reveal brain state transitions.

Application to fMRI data demonstrates method feasibility.

Abstract

Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analyzed, and the data length.