Beyond Pairwise Connections in Complex Systems: Insights into the Human Multiscale Psychotic Brain

Abstract

Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-order interactions critical for understanding mental disorders. Conventional brain network studies focus on pairwise links, offering insights into basic connectivity but failing to capture the complexity of neural dysfunction in psychiatric conditions. This study aims to bridge this gap by applying a matrix-based entropy functional to estimate total correlation, a mathematical framework that incorporates multivariate information measures extending beyond pairwise interactions. We apply this framework to fMRI-ICA-derived multiscale brain networks, enabling the investigation of interactions beyond pairwise relationships in the human multiscale brain. Additionally, this approach holds promise for psychiatric studies, providing a new lens through which to explore beyond pairwise brain network interactions. By examining both triple interactions and the latent factors underlying the triadic relationships among intrinsic brain connectivity networks through tensor decomposition, our study presents a novel approach to understanding higher-order brain dynamics. This framework not only enhances our understanding of complex brain functions but also offers new opportunities for investigating pathophysiology, potentially informing more targeted diagnostic and therapeutic strategies. Moreover, the methodology of analyzing multiway interactions beyond pairwise connections can be applied to any signal analysis. In this study, we specifically explore its application to neural signals, demonstrating its power in uncovering complex multiway interaction patterns of brain activity, which provide a window to explore connectivity beyond pairwise interactions in the multiscale functionality of the brain.