  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.
