  Discovering reliable and informative interactions among brain regions from
functional magnetic resonance imaging (fMRI) signals is essential in
neuroscientific predictions of cognition. Most of the current methods fail to
accurately characterize those interactions because they only focus on pairwise
connections and overlook the high-order relationships of brain regions. We
delve into this problem and argue that these high-order relationships should be
maximally informative and minimally redundant (MIMR). However, identifying such
high-order relationships is challenging and highly under-explored. Methods that
can be tailored to our context are also non-existent. In response to this gap,
we propose a novel method named HyBRiD that aims to extract MIMR high-order
relationships from fMRI data. HyBRiD employs a Constructor to identify
hyperedge structures, and a Weighter to compute a weight for each hyperedge.
HyBRiD achieves the MIMR objective through an innovative information bottleneck
framework named multi-head drop-bottleneck with theoretical guarantees. Our
comprehensive experiments demonstrate the effectiveness of our model. Our model
outperforms the state-of-the-art predictive model by an average of 12.1%,
regarding the quality of hyperedges measured by CPM, a standard protocol for
studying brain connections.

Neurons and Cognition;Machine Learning