The structural grammar of integration and competition in the human connectome.
Overview
- Department of Psychology, University of Kansas, Lawrence, KS, United States
- Edwardson School of Industrial Engineering and Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
Abstract
Introduction: Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC)—the network of white matter pathways-provides the physical substrate for functional connectivity (FC), defined as the correlated activity between brain areas. While structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways. To systematically untangle how structural architecture shapes functional patterns, this work aims to establish a set of rules that decode how direct and indirect structural connections and motifs give rise to FC between brain regions.
Methods: Specifically, using a generative linear model, we derive explicit rules that predict an individual's resting-state fMRI FC from diffusion-weighted imaging-derived SC, validated against topological null models.
Results: Examining the rules reveals distinct classes of brain regions, with integrator hubs acting as structural linchpins promoting synchronization and mediator hubs serving as structural fulcrums orchestrating competing dynamics. Virtual lesion experiments further demonstrate how different cortical and subcortical systems distinctively contribute to global FC.
Discussion: Together, by uncovering how structural architecture governs functional interactions, this framework enables us to predict how alterations in SC, resulting from disease or surgery, propagate through functional networks and contribute to cognitive and behavioral impairments.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
No file of the authors' code could be read here: it is described below, and read at its source.
aaronclauset.github.io/wsbm
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
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Data
Datasets cited
- zenodo:3758534, at Zenodo; found in “Data availability statement”
- zenodo:8158914, at Zenodo; found in “Data availability statement”
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 2, 28 September 2026
- Funding: added Fundação para a Ciência e a Tecnologia: LA/P/0083/2020, UID/50009/2025, 54499/LA/P/0083/2020, 10.54499, 10.54499/LA/P/0083/2020, 50009
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 83 references.
Cite
This paper
Kelemen, S. F., Goñi, J., Pequito, S., & Ashourvan, A. (2026). The structural grammar of integration and competition in the human connectome. Frontiers in computational neuroscience, 20, 1810942. https://
BibTeX
@article{kelemen2026stru
author = {Kelemen, Sam Frank and Goñi, Joaquín and Pequito, Sérgio and Ashourvan, Arian},
title = {{The structural grammar of integration and competition in the human connectome}},
journal = {Frontiers in computational neuroscience},
year = {2026},
month = jun,
volume = {20},
pages = {1810942},
publisher = {Frontiers Media SA},
issn = {1662-5188},
doi = {10.3389/
url = {https://
pmid = {42422232},
pmcid = {PMC13341858}
}
RIS
TY - JOUR
AU - Kelemen, Sam Frank
AU - Goñi, Joaquín
AU - Pequito, Sérgio
AU - Ashourvan, Arian
TI - The structural grammar of integration and competition in the human connectome
T2 - Frontiers in computational neuroscience
J2 - Front Comput Neurosci
PY - 2026
DA - 2026/
VL - 20
SP - 1810942
SN - 1662-5188
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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