Brain Structure Shapes Function through higher-order Functional Interactions

Authors

Su, S., Zhuang, M., Palombo, M., Liu, M., Jiang, X., Zhang, T., Wang, H., Zhang, S.

Abstract

Brain function is deeply embedded within multiscale structural architecture. Conventional studies predominantly utilize pairwise connectivity networks to investigate structure-function relationships. However, this low-dimensional perspective overlooks multi-region collaborations for complex cognition. Consequently, whether and how anatomy constrains such higher-order functional networks remains unresolved. To address this pivotal question, we utilize an information-theoretic O-information approach to characterize higher-order functional interactions (HOIs). By reconstructing individual-level HOIs from multimodal structural networks, we directly validate the structural constraint on HOIs. The resulting reconstruction coefficients are defined as structural-functional constraint strength (SFCS), serving as a quantitative vehicle to decipher how anatomy shapes these higher-order networks. SFCS uncovers a highly heterogeneous structural constraint landscape across data modalities, spatial regions, and informational interaction modes. Crucially, individualized SFCS robustly predicts multi-domain cognitive phenotypes, showing higher sensitivity for informant-reported than patient-reported assessments. Finally, we show that this landscape undergoes pathological, mode-specific reorganization in Alzheimers disease. Cross-scale alignment with spatial transcriptomics further demonstrates that this macroscale network remodeling is coupled with microscale metabolic and regulatory gene pathways. Collectively, our findings not only validate the structural constraint on higher-order functional networks but also decipher its precise underlying mechanisms. This constraint paradigm plays a pivotal role in shaping diverse cognitive capabilities, while its pathological disruption in Alzheimers disease highlights the potential of SFCS as a biomarker for tracking neurodegenerative network impairments.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 29 Jun 2026.

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