{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:47:26Z","timestamp":1782283646080,"version":"3.54.5"},"reference-count":85,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC)\u2014the 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\n                      <jats:italic>rules<\/jats:italic>\n                      that decode how direct and indirect structural connections and motifs give rise to FC between brain regions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Examining the rules reveals distinct classes of brain regions, with\n                      <jats:italic>integrator<\/jats:italic>\n                      hubs acting as structural linchpins promoting synchronization and\n                      <jats:italic>mediator<\/jats:italic>\n                      hubs serving as structural fulcrums orchestrating competing dynamics. Virtual lesion experiments further demonstrate how different cortical and subcortical systems distinctively contribute to global FC.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fncom.2026.1810942","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:55:48Z","timestamp":1782280548000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The structural grammar of integration and competition in the human connectome"],"prefix":"10.3389","volume":"20","author":[{"given":"Sam Frank","family":"Kelemen","sequence":"first","affiliation":[{"name":"Department of Psychology, University of Kansas","place":["Lawrence, KS, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joaqu\u00edn","family":"Go\u00f1i","sequence":"additional","affiliation":[{"name":"Edwardson School of Industrial Engineering and Weldon School of Biomedical Engineering, Purdue University","place":["West Lafayette, IN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00e9rgio","family":"Pequito","sequence":"additional","affiliation":[{"name":"Institute for Systems and Robotics, Instituto Superior T\u00e9cnico, Universidade de Lisboa","place":["Lisbon, Portugal"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arian","family":"Ashourvan","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of Kansas","place":["Lawrence, KS, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.neuroimage.2013.12.039","article-title":"Network diffusion accurately models the relationship between structural and functional brain connectivity networks","volume":"90","author":"Abdelnour","year":"2014","journal-title":"Neuroimage"},{"key":"B2","doi-asserted-by":"publisher","first-page":"116738","DOI":"10.1016\/j.neuroimage.2020.116738","article-title":"Modeling brain dynamics after tumor resection using the virtual brain","volume":"213","author":"Aerts","year":"2020","journal-title":"Neuroimage"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1305.5782","article-title":"Adapting the stochastic block model to edge-weighted networks","author":"Aicher","year":"2013","journal-title":"arXiv"},{"key":"B4","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1093\/cercor\/bhs352","article-title":"Tracking whole-brain connectivity dynamics in the resting state","volume":"24","author":"Allen","year":"2014","journal-title":"Cereb. 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