@inproceedings{
li2023aberrant,
title={Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional {MRI} Networks},
author={Qiang LI and Vince Calhoun and Adithya Ram Ballem and Shujian Yu and Jesus Malo and Armin Iraji},
booktitle={NeurIPS 2023 workshop: Information-Theoretic Principles in Cognitive Systems},
year={2023},
url={https://openreview.net/forum?id=ZgMRaX02ck}
}
The human brain has a complex, intricate functional architecture. While many studies primarily emphasize pairwise interactions, delving into high-order associations is crucial for a comprehensive understanding of how functional brain networks intricately interact beyond simple pairwise connections. Analyzing high-order statistics allows us to explore the nuanced and complex relationships across the brain, unraveling the heterogeneity and uncovering patterns of multilevel overlap on the psychosis continuum. Here, we employed high-order independent component analysis (ICA) plus multivariate information-theoretical metrics ($O$-information and $S$-information) to estimate high-order interaction to examine schizophrenia using resting-state fMRI. The results show that multiple brain regions networks may be altered in schizophrenia, such as temporal, subcortical, and higher-cognitive brain regions, and meanwhile, it also shows that revealed synergy gives more information than redundancy in diagnosing schizophrenia. All in all, we showed that high-order dependencies were altered in schizophrenia. Identification of these aberrant patterns will give us a new window to diagnose schizophrenia.
Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks
Qiang LI Vince Calhoun Adithya Ram Ballem Shujian Yu Jesus Malo Armin Iraji