Higher-Order Interactions in Brain Connectomics: Implicit versus Explicit Modeling Approaches
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908641217478656 |
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| author | Salehi, Mohamma Reza BashirGonbadi, Ali Soltanian-Zadeh, Hamid |
| author_facet | Salehi, Mohamma Reza BashirGonbadi, Ali Soltanian-Zadeh, Hamid |
| contents | The human brain is a complex system defined by multi-way, higher-order interactions invisible to traditional pairwise network models. Although a diverse array of analytical methods has been developed to address this shortcoming, the field remains fragmented, lacking a unifying conceptual framework that integrates and organizes the rapidly expanding methodological landscape of higher-order brain connectivity. This review provides a synthesis of the methodologies for studying higher-order brain connectivity. We propose a fundamental distinction between implicit paradigms, which quantify the statistical strength of group interactions, and explicit paradigms, which construct higher-order structural representations like hypergraphs and topological data analysis. We trace the evolution of each approach, from early Correlation-of-Correlations and information-theoretic concepts of synergy/redundancy, to the edge-centric paradigm and advanced topological methods. Through a critical analysis of conceptual, statistical, and computational challenges, we argue that the future of the field lies not in a single best method, but in a principled integration of these complementary approaches. This manuscript aims to provide a unified map and a critical perspective to guide researchers toward a robust and insightful understanding of the brain's complex, multi-level architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Higher-Order Interactions in Brain Connectomics: Implicit versus Explicit Modeling Approaches Salehi, Mohamma Reza BashirGonbadi, Ali Soltanian-Zadeh, Hamid Quantitative Methods The human brain is a complex system defined by multi-way, higher-order interactions invisible to traditional pairwise network models. Although a diverse array of analytical methods has been developed to address this shortcoming, the field remains fragmented, lacking a unifying conceptual framework that integrates and organizes the rapidly expanding methodological landscape of higher-order brain connectivity. This review provides a synthesis of the methodologies for studying higher-order brain connectivity. We propose a fundamental distinction between implicit paradigms, which quantify the statistical strength of group interactions, and explicit paradigms, which construct higher-order structural representations like hypergraphs and topological data analysis. We trace the evolution of each approach, from early Correlation-of-Correlations and information-theoretic concepts of synergy/redundancy, to the edge-centric paradigm and advanced topological methods. Through a critical analysis of conceptual, statistical, and computational challenges, we argue that the future of the field lies not in a single best method, but in a principled integration of these complementary approaches. This manuscript aims to provide a unified map and a critical perspective to guide researchers toward a robust and insightful understanding of the brain's complex, multi-level architecture. |
| title | Higher-Order Interactions in Brain Connectomics: Implicit versus Explicit Modeling Approaches |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2511.07344 |