A Domain-Knowledge-Informed Bayesian Approach for Reliable Disentangling of the Brain Connectome

Music City Center 

The brain is a network of interconnected neural circuits. Numerous studies show that neural circuits are key to understanding brain function, development and aging, and neuropsychiatric disorders. However, delineating neural circuits from complex, high-dimensional brain networks is challenging. Blind source separation (BSS) offers a powerful, data-driven approach to uncover neural circuits from brain data, with each latent source corresponding to one circuit. Recent advances in BSS allow direct decomposition of connectivity data instead of raw imaging, yielding new insights into the brain connectome. However, a crucial limitation is that these methods are typically developed without integrating any domain knowledge such as neuroanatomical information. While their status as independent statistical tools demonstrates statistical effectiveness, they miss the opportunity to reveal more reliable scientific findings. To address this gap, we propose a Bayesian hierarchical decomposition model with a novel domain-knowledge-informed prior for brain connectivity data. Our method further enables joint analysis of data from multiple subject groups, facilitating the identification of differences in neural circuits across groups with varying covariates, such as age or sex. Applied to functional connectivity data from the Lifespan Human Connectome Project in Development (HCP-D) study, our method identifies several scientific meaningful and reliable neural circuits, along with age- and sex-related variations within them. Our method also uncovers novel neural circuits that have not been previously reported, offering new insights into the development of the functional connectome.