High-Order Triadic Functional Connectivity in the Brain and Beyond
Year: 2026
Type: preprint
Abstract: Here, we report high-order functional network connectivity as a promising way for studying the brain connectome. Traditional functional connectivity approaches capture only pairwise relationships between brain regions, overlooking complex multivariate dependencies that underlie cognition and behavior. First, we demonstrated that high-order interactions capture more information and can distinguish between resting-state and task-state brain activity. Second, we introduce a matrix-based entropy-functional method for estimating triadic interactions, which are statistical dependencies among triplets of brain regions, and apply it to large-scale functional brain networks. The resulting triadic networks revealed distinct community patterns that complement those observed in traditional pairwise functional connectivity analyses and simultaneously capture additional connection information. Despite the potential combinatorial explosion of triadic configurations, the networks exhibited constrained and hierarchical structures that allowed computation and interpretation. These findings position triadic connectivity as a promising next-step functional connectivity framework for probing brain network organization and high-order neural interactions, while also highlighting key biological and technical challenges that require careful consideration. Functional connectivity is widely used in brain imaging but typically captures only second-order, pairwise interactions. Large-scale brain networks, however, exhibit high-order interactions that go beyond such analyses. Here, we introduce a high-order triadic functional connectivity matrix, enabling the characterization of three-way interactions in fMRI brain signals. We highlight emerging opportunities and discuss both biological and methodological challenges. High-order triadic functional connectivity provides a novel, informative framework for advancing brain imaging and multimodal signal analysis beyond traditional pairwise measures.
Source: PubMed
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7208724254