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Peer-reviewedCC BY-NC-NDNature Communications

Charting higher-order models of brain function beyond pairwise interactions

  • Andrea Santoro1
  • Matteo Neri2
  • Simone Poetto3,4
  • Davide Orsenigo5
  • Matteo Diano5
  • Marilyn Gatica6,7
  • Giovanni Petri8,9
9 affiliations
  1. 1ISI Foundation, Torino, Italy. [email protected]
  2. 2Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille, France
  3. 3Intesa Sanpaolo A.I. Research, Torino, Italy
  4. 4Nicolaus Copernicus University, Toruń, Poland
  5. 5Department of Psychology and Neuroscience Institute of Turin, University of Torino, Torino, Italy
  6. 6Centre for Apprenticeships, Northeastern University London, London, UK
  7. 7NPLab, Network Science Institute, Northeastern University London, London, UK
  8. 8Department of Physics, Northeastern University, Boston, MA, USA. [email protected]
  9. 9NPLab, Network Science Institute, Northeastern University London, London, UK. [email protected]
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Abstract

Traditional models of brain connectivity primarily focus on pairwise interactions, overlooking collective dynamics among multiple regions. Although many higher-order interaction (HOI) metrics have been proposed, their comparative properties and utility remain unclear. Here, we systematically compare information-theoretic and topological HOI metrics using resting-state and task fMRI data from 100 unrelated Human Connectome Project subjects. We identify a taxonomy of redundant, synergistic, and topological metrics, with the latter bridging the redundancy-synergy continuum. Despite methodological differences, all HOI metrics align with the brain’s sensorimotor-to-association hierarchy, yet reveal distinct molecular signatures linking redundancy to metabolic profiles and synergy/topological scaffolds to receptor architecture. HOI metrics outperform pairwise connectivity in brain fingerprinting, whereas pairwise models remain competitive for task decoding. Multivariate analyses further identify topological descriptors as key links between brain function and behavioral variability. Overall, our findings provide a conceptual map of HOI methods for studying multi-regional brain interactions across cognitive and clinical neuroscience. This study benchmarks ten higher-order interaction metrics on fMRI data, revealing distinct patterns of brain functional organization. Higher-order models outperform pairwise methods in fingerprinting individuals and predicting cognitive performance.

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Cite

BibTeXRIS
BibTeX
@article{santoro2026charting,
  title = {{Charting higher-order models of brain function beyond pairwise interactions}},
  author = {Andrea Santoro and Matteo Neri and Simone Poetto and Davide Orsenigo and Matteo Diano and Marilyn Gatica and Giovanni Petri},
  journal = {Nature Communications},
  year = {2026},
  month = jul,
  doi = {10.1038/s41467-026-75959-w},
  url = {https://doi.org/10.1038/s41467-026-75959-w},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Charting higher-order models of brain function beyond pairwise interactions
AU  - Andrea Santoro
AU  - Matteo Neri
AU  - Simone Poetto
AU  - Davide Orsenigo
AU  - Matteo Diano
AU  - Marilyn Gatica
AU  - Giovanni Petri
PY  - 2026
DA  - 2026/07/29
JO  - Nature Communications
DO  - 10.1038/s41467-026-75959-w
UR  - https://doi.org/10.1038/s41467-026-75959-w
AB  - Traditional models of brain connectivity primarily focus on pairwise interactions, overlooking collective dynamics among multiple regions. Although many higher-order interaction (HOI) metrics have been proposed, their comparative properties and utility remain unclear. Here, we systematically compare information-theoretic and topological HOI metrics using resting-state and task fMRI data from 100 unrelated Human Connectome Project subjects. We identify a taxonomy of redundant, synergistic, and topological metrics, with the latter bridging the redundancy-synergy continuum. Despite methodological differences, all HOI metrics align with the brain’s sensorimotor-to-association hierarchy, yet reveal distinct molecular signatures linking redundancy to metabolic profiles and synergy/topological scaffolds to receptor architecture. HOI metrics outperform pairwise connectivity in brain fingerprinting, whereas pairwise models remain competitive for task decoding. Multivariate analyses further identify topological descriptors as key links between brain function and behavioral variability. Overall, our findings provide a conceptual map of HOI methods for studying multi-regional brain interactions across cognitive and clinical neuroscience. This study benchmarks ten higher-order interaction metrics on fMRI data, revealing distinct patterns of brain functional organization. Higher-order models outperform pairwise methods in fingerprinting individuals and predicting cognitive performance.
SN  - 2041-1723
ER  -