Energy landscape analysis based on the Ising model: Tutorial review

TL;DR

This tutorial review explains how energy landscape analysis using the Ising model can interpret multivariate time series data, with applications mainly in neuroscience and emerging uses in other fields.

Contribution

It provides a comprehensive tutorial on the Ising model-based energy landscape analysis, including methodology, validation, and recent extensions, for diverse research applications.

Findings

Applicable to fMRI data and beyond

Captures data dynamics as trajectories on an energy landscape

Includes recent methodological developments

Abstract

We review a class of energy landscape analysis method that uses the Ising model and takes multivariate time series data as input. The method allows one to capture dynamics of the data as trajectories of a ball from one basin to a different basin to yet another, constrained on the energy landscape specified by the estimated Ising model. While this energy landscape analysis has mostly been applied to functional magnetic resonance imaging (fMRI) data from the brain for historical reasons, there are emerging applications outside fMRI data and neuroscience. To inform such applications in various research fields, this review paper provides a detailed tutorial on each step of the analysis, terminologies, concepts underlying the method, and validation, as well as recent developments of extended and related methods.