{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T11:11:16Z","timestamp":1793445076927,"version":"4.5.0"},"reference-count":97,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Gouvernement du Canada | Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Biol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The relationship between structural and functional connectivity in the brain is a key question in systems neuroscience. Modern accounts assume a single global structure-function relationship that persists over time. Here we study structure-function coupling from a dynamic perspective, and show that it is regionally heterogeneous. We use a temporal unwrapping procedure to identify moment-to-moment co-fluctuations in neural activity, and reconstruct time-resolved structure-function coupling patterns. We find that patterns of dynamic structure-function coupling are region-specific. We observe stable coupling in unimodal and transmodal cortex, and dynamic coupling in intermediate regions, particularly in insular cortex (salience network) and frontal eye fields (dorsal attention network). Finally, we show that the variability of a region\u2019s structure-function coupling is related to the distribution of its connection lengths. Collectively, our findings provide a way to study structure-function relationships from a dynamic perspective.<\/jats:p>","DOI":"10.1038\/s42003-022-03466-x","type":"journal-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T06:07:35Z","timestamp":1654150055000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Time-resolved structure-function coupling in brain networks"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0917-7905","authenticated-orcid":false,"given":"Zhen-Qi","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2912-3595","authenticated-orcid":false,"given":"Bertha","family":"V\u00e1zquez-Rodr\u00edguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1530-8916","authenticated-orcid":false,"given":"R. Nathan","family":"Spreng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9256-6041","authenticated-orcid":false,"given":"Boris C.","family":"Bernhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9200-1681","authenticated-orcid":false,"given":"Richard F.","family":"Betzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0307-2862","authenticated-orcid":false,"given":"Bratislav","family":"Misic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"3466_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/nrn.2017.149","volume":"19","author":"A Avena-Koenigsberger","year":"2018","unstructured":"Avena-Koenigsberger, A., Misic, B. & Sporns, O. Communication dynamics in complex brain networks. Nat. Rev. Neurosci. 19, 17 (2018).","journal-title":"Nat. Rev. Neurosci."},{"key":"3466_CR2","doi-asserted-by":"crossref","unstructured":"Su\u00e1rez, L. E., Markello, R. D., Betzel, R. F. & Misic, B. Linking structure and function in macroscale brain networks. Trends Cogn. Sci. 24, 302\u2013315 (2020).","DOI":"10.1016\/j.tics.2020.01.008"},{"key":"3466_CR3","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1162\/netn_a_00116","volume":"4","author":"DJ Lurie","year":"2020","unstructured":"Lurie, D. J. et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw. Neurosci. 4, 30\u201369 (2020).","journal-title":"Netw. Neurosci."},{"key":"3466_CR4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.neuroimage.2016.12.061","volume":"160","author":"MG Preti","year":"2017","unstructured":"Preti, M. G., Bolton, T. A. & Van De Ville, D. The dynamic functional connectome: State-of-the-art and perspectives. NeuroImage 160, 41\u201354 (2017).","journal-title":"NeuroImage"},{"key":"3466_CR5","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.neuroimage.2017.08.010","volume":"180","author":"JM Shine","year":"2018","unstructured":"Shine, J. M. & Poldrack, R. A. Principles of dynamic network reconfiguration across diverse brain states. NeuroImage 180, 396\u2013405 (2018).","journal-title":"NeuroImage"},{"key":"3466_CR6","doi-asserted-by":"publisher","first-page":"10341","DOI":"10.1073\/pnas.1400181111","volume":"111","author":"A Zalesky","year":"2014","unstructured":"Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L. & Breakspear, M. Time-resolved resting-state brain networks. Proc. Natl. Acad. Sci. USA 111, 10341\u201310346 (2014).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"3466_CR7","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neuroimage.2009.12.011","volume":"50","author":"C Chang","year":"2010","unstructured":"Chang, C. & Glover, G. H. Time\u2013frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50, 81\u201398 (2010).","journal-title":"NeuroImage"},{"key":"3466_CR8","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neuroimage.2015.12.001","volume":"127","author":"RF Betzel","year":"2016","unstructured":"Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N. & Sporns, O. Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. NeuroImage 127, 287\u2013297 (2016).","journal-title":"NeuroImage"},{"key":"3466_CR9","doi-asserted-by":"publisher","first-page":"28393","DOI":"10.1073\/pnas.2005531117","volume":"117","author":"FZ Esfahlani","year":"2020","unstructured":"Esfahlani, F. Z. et al. High-amplitude cofluctuations in cortical activity drive functional connectivity. Proc. Natl. Acad. Sci. 117, 28393\u201328401 (2020).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR10","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.neuroimage.2017.09.012","volume":"163","author":"R Li\u00e9geois","year":"2017","unstructured":"Li\u00e9geois, R., Laumann, T. O., Snyder, A. Z., Zhou, J. & Yeo, B. T. T. Interpreting temporal fluctuations in resting-state functional connectivity MRI. NeuroImage 163, 437\u2013455 (2017).","journal-title":"NeuroImage"},{"key":"3466_CR11","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1093\/cercor\/bhs352","volume":"24","author":"EA Allen","year":"2014","unstructured":"Allen, E. A. et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 663\u2013676 (2014).","journal-title":"Cereb. Cortex"},{"key":"3466_CR12","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.neuron.2016.09.018","volume":"92","author":"JM Shine","year":"2016","unstructured":"Shine, J. M. et al. The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92, 544\u2013554 (2016).","journal-title":"Neuron"},{"key":"3466_CR13","doi-asserted-by":"publisher","first-page":"12827","DOI":"10.1073\/pnas.1705120114","volume":"114","author":"D Vidaurre","year":"2017","unstructured":"Vidaurre, D., Smith, S. M. & Woolrich, M. W. Brain network dynamics are hierarchically organized in time. Proc. Natl. Acad. Sci. 114, 12827\u201312832 (2017).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR14","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1038\/s41593-020-00719-y","volume":"23","author":"J Faskowitz","year":"2020","unstructured":"Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O. & Betzel, R. F. Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Natw. Neurosci. 23, 1644\u20131654 (2020).","journal-title":"Natw. Neurosci."},{"key":"3466_CR15","doi-asserted-by":"crossref","unstructured":"Jo, Y., Faskowitz, J., Esfahlani, F. Z., Sporns, O. & Betzel, R. F. Subject identification using edge-centric functional connectivity. NeuroImage 238, 118204 (2021).","DOI":"10.1016\/j.neuroimage.2021.118204"},{"key":"3466_CR16","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1162\/netn_a_00172","volume":"5","author":"A Eichenbaum","year":"2021","unstructured":"Eichenbaum, A., Pappas, I., Lurie, D., Cohen, J. R. & D\u2019Esposito, M. Differential contributions of static and time-varying functional connectivity to human behavior. Netw. Neurosci. 5, 145\u2013165 (2021).","journal-title":"Netw. Neurosci."},{"key":"3466_CR17","doi-asserted-by":"publisher","first-page":"8762","DOI":"10.1073\/pnas.1501242112","volume":"112","author":"J Gonzalez-Castillo","year":"2015","unstructured":"Gonzalez-Castillo, J. et al. Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns. Proc. Natl. Acad. Sci. 112, 8762\u20138767 (2015).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR18","doi-asserted-by":"publisher","first-page":"117156","DOI":"10.1016\/j.neuroimage.2020.117156","volume":"222","author":"D Battaglia","year":"2020","unstructured":"Battaglia, D. et al. Dynamic functional connectivity between order and randomness and its evolution across the human adult lifespan. NeuroImage 222, 117156 (2020).","journal-title":"NeuroImage"},{"key":"3466_CR19","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1007\/s00429-017-1539-3","volume":"223","author":"M Fukushima","year":"2018","unstructured":"Fukushima, M. et al. Structure\u2013function relationships during segregated and integrated network states of human brain functional connectivity. Brain Struct. Funct. 223, 1091\u20131106 (2018).","journal-title":"Brain Struct. Funct."},{"key":"3466_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-020-01331-3","volume":"3","author":"M Fukushima","year":"2020","unstructured":"Fukushima, M. & Sporns, O. Structural determinants of dynamic fluctuations between segregation and integration on the human connectome. Commun. Biol. 3, 1\u201311 (2020).","journal-title":"Commun. Biol."},{"key":"3466_CR21","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1073\/pnas.1912034117","volume":"117","author":"GL Baum","year":"2020","unstructured":"Baum, G. L. et al. Development of structure\u2013function coupling in human brain networks during youth. Proc. Natl. Acad. Sci. 117, 771\u2013778 (2020).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR22","doi-asserted-by":"crossref","unstructured":"Gu, Z., Jamison, K. W., Sabuncu, M. R. & Kuceyeski, A. Heritability and interindividual variability of regional structure-function coupling. Nat. Commun. 12, 4894 (2021).","DOI":"10.1038\/s41467-021-25184-4"},{"key":"3466_CR23","doi-asserted-by":"publisher","first-page":"21219","DOI":"10.1073\/pnas.1903403116","volume":"116","author":"B V\u00e1zquez-Rodr\u00edguez","year":"2019","unstructured":"V\u00e1zquez-Rodr\u00edguez, B. et al. Gradients of structure\u2013function tethering across neocortex. Proc. Natl. Acad. Sci. 116, 21219\u201321227 (2019).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-12765-7","volume":"10","author":"MG Preti","year":"2019","unstructured":"Preti, M. G. & Van De Ville, D. Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nat. Commun. 10, 1\u20137 (2019).","journal-title":"Nat. Commun."},{"key":"3466_CR25","doi-asserted-by":"publisher","first-page":"e3000284","DOI":"10.1371\/journal.pbio.3000284","volume":"17","author":"C Paquola","year":"2019","unstructured":"Paquola, C. et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol. 17, e3000284 (2019).","journal-title":"PLoS Biol."},{"key":"3466_CR26","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.tics.2013.09.017","volume":"17","author":"RL Buckner","year":"2013","unstructured":"Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17, 648\u2013665 (2013).","journal-title":"Trends Cogn. Sci."},{"key":"3466_CR27","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1093\/brain\/121.6.1013","volume":"121","author":"M-M Mesulam","year":"1998","unstructured":"Mesulam, M.-M. From sensation to cognition. Brain J. Neurol. 121, 1013\u20131052 (1998).","journal-title":"Brain J. Neurol."},{"key":"3466_CR28","doi-asserted-by":"publisher","first-page":"e62116","DOI":"10.7554\/eLife.62116","volume":"9","author":"G Shafiei","year":"2020","unstructured":"Shafiei, G. et al. Topographic gradients of intrinsic dynamics across neocortex. Elife 9, e62116 (2020).","journal-title":"Elife"},{"key":"3466_CR29","doi-asserted-by":"crossref","unstructured":"Hansen, J. Y. et al. Mapping gene transcription and neurocognition across human neocortex. Nat Hum Behav 5, 1240\u20131250 (2021).","DOI":"10.1038\/s41562-021-01082-z"},{"key":"3466_CR30","doi-asserted-by":"crossref","unstructured":"Kong, X. et al. Anatomical and functional gradients shape dynamic functional connectivity in the human brain. 2021.03.15.435361, https:\/\/www.biorxiv.org\/content\/10.1101\/2021.03.15.435361v1 (2021).","DOI":"10.1101\/2021.03.15.435361"},{"key":"3466_CR31","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.neuron.2019.01.017","volume":"101","author":"M Demirta\u015f","year":"2019","unstructured":"Demirta\u015f, M. et al. Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics. Neuron 101, 1181\u20131194 (2019).","journal-title":"Neuron"},{"key":"3466_CR32","doi-asserted-by":"publisher","first-page":"eaat7854","DOI":"10.1126\/sciadv.aat7854","volume":"5","author":"P Wang","year":"2019","unstructured":"Wang, P. et al. Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain. Sc. Adv. 5, eaat7854 (2019).","journal-title":"Sc. Adv."},{"key":"3466_CR33","doi-asserted-by":"crossref","unstructured":"Deco, G. et al. Dynamical consequences of regional heterogeneity in the brain\u2019s transcriptional landscape. Sci. Adv. 7, eabf4752 (2021).","DOI":"10.1126\/sciadv.abf4752"},{"key":"3466_CR34","doi-asserted-by":"crossref","unstructured":"Valk, S. L. et al. Genetic and phylogenetic uncoupling of structure and function in human transmodal cortex. Nat Commun 13, 2341 (2022).","DOI":"10.1038\/s41467-022-29886-1"},{"key":"3466_CR35","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.tics.2020.02.002","volume":"24","author":"M Allen","year":"2020","unstructured":"Allen, M. Unravelling the neurobiology of interoceptive inference. Trends Cogn. Sci. 24, 265\u2013266 (2020).","journal-title":"Trends Cogn. Sci."},{"key":"3466_CR36","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/nrn3857","volume":"16","author":"LQ Uddin","year":"2015","unstructured":"Uddin, L. Q. Salience processing and insular cortical function and dysfunction. Nat. Rev. Neurosci. 16, 55\u201361 (2015).","journal-title":"Nat. Rev. Neurosci."},{"key":"3466_CR37","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1093\/cercor\/bhv308","volume":"27","author":"J Ghaziri","year":"2017","unstructured":"Ghaziri, J. et al. The corticocortical structural connectivity of the human insula. Cereb. Cortex 27, 1216\u20131228 (2017).","journal-title":"Cereb. Cortex"},{"key":"3466_CR38","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s00429-010-0255-z","volume":"214","author":"F Kurth","year":"2010","unstructured":"Kurth, F., Zilles, K., Fox, P. T., Laird, A. R. & Eickhoff, S. B. A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct. Funct. 214, 519\u2013534 (2010).","journal-title":"Brain Struct. Funct."},{"key":"3466_CR39","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/j.neuroimage.2018.08.055","volume":"183","author":"Y Tian","year":"2018","unstructured":"Tian, Y. & Zalesky, A. Characterizing the functional connectivity diversity of the insula cortex: Subregions, diversity curves and behavior. NeuroImage 183, 716\u2013733 (2018).","journal-title":"NeuroImage"},{"key":"3466_CR40","doi-asserted-by":"crossref","unstructured":"Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125\u20131165 (2011).","DOI":"10.1152\/jn.00338.2011"},{"key":"3466_CR41","doi-asserted-by":"publisher","first-page":"12574","DOI":"10.1073\/pnas.1608282113","volume":"113","author":"DS Margulies","year":"2016","unstructured":"Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl. Acad. Sci. 113, 12574\u201312579 (2016).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR42","unstructured":"von Economo, C. F. & Koskinas, G. N. Die cytoarchitektonik der hirnrinde des erwachsenen menschen (J. Springer, 1925)."},{"key":"3466_CR43","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","volume":"62","author":"DC Van Essen","year":"2012","unstructured":"Van Essen, D. C. et al. The human connectome project: a data acquisition perspective. Neuroimage 62, 2222\u20132231 (2012).","journal-title":"Neuroimage"},{"key":"3466_CR44","doi-asserted-by":"publisher","first-page":"3095","DOI":"10.1093\/cercor\/bhx179","volume":"28","author":"A Schaefer","year":"2018","unstructured":"Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity mri. Cereb. Cortex 28, 3095\u20133114 (2018).","journal-title":"Cereb. Cortex"},{"key":"3466_CR45","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1073\/pnas.1315529111","volume":"111","author":"J Go\u00f1i","year":"2014","unstructured":"Go\u00f1i, J. et al. Resting-brain functional connectivity predicted by analytic measures of network communication. Proc. Natl. Acad. Sci. 111, 833\u2013838 (2014).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR46","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1162\/netn_a_00161","volume":"4","author":"C Seguin","year":"2020","unstructured":"Seguin, C., Tian, Y. & Zalesky, A. Network communication models improve the behavioral and functional predictive utility of the human structural connectome. Netw. Neurosci. 4, 980\u20131006 (2020).","journal-title":"Netw. Neurosci."},{"key":"3466_CR47","doi-asserted-by":"publisher","first-page":"6297","DOI":"10.1073\/pnas.1801351115","volume":"115","author":"C Seguin","year":"2018","unstructured":"Seguin, C., Van Den Heuvel, M. P. & Zalesky, A. Navigation of brain networks. Proc. Natl. Acad. Sci. 115, 6297\u20136302 (2018).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR48","unstructured":"Fornito, A., Zalesky, A. & Bullmore, E. Fundamentals of Brain Network Analysis (Academic Press, 2016)."},{"key":"3466_CR49","doi-asserted-by":"publisher","first-page":"036111","DOI":"10.1103\/PhysRevE.77.036111","volume":"77","author":"E Estrada","year":"2008","unstructured":"Estrada, E. & Hatano, N. Communicability in complex networks. Phys. Rev. E 77, 036111 (2008).","journal-title":"Phys. Rev. E"},{"key":"3466_CR50","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1098\/rsif.2008.0484","volume":"6","author":"JJ Crofts","year":"2009","unstructured":"Crofts, J. J. & Higham, D. J. A weighted communicability measure applied to complex brain networks. J. R. Soc. Interface 6, 411\u2013414 (2009).","journal-title":"J. R. Soc. Interface"},{"key":"3466_CR51","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/s00429-016-1238-5","volume":"222","author":"A Avena-Koenigsberger","year":"2017","unstructured":"Avena-Koenigsberger, A. et al. Path ensembles and a tradeoff between communication efficiency and resilience in the human connectome. Brain Struct. Funct. 222, 603\u2013618 (2017).","journal-title":"Brain Struct. Funct."},{"key":"3466_CR52","doi-asserted-by":"publisher","first-page":"e1000808","DOI":"10.1371\/journal.pcbi.1000808","volume":"6","author":"J Sepulcre","year":"2010","unstructured":"Sepulcre, J. et al. The organization of local and distant functional connectivity in the human brain. PLoS Comput. Biol. 6, e1000808 (2010).","journal-title":"PLoS Comput. Biol."},{"key":"3466_CR53","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1007\/s00429-016-1333-7","volume":"222","author":"S Oligschl\u00e4ger","year":"2017","unstructured":"Oligschl\u00e4ger, S. et al. Gradients of connectivity distance are anchored in primary cortex. Brain Struct. Funct. 222, 2173\u20132182 (2017).","journal-title":"Brain Struct. Funct."},{"key":"3466_CR54","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1007\/s00429-018-1811-1","volume":"224","author":"S Oligschl\u00e4ger","year":"2019","unstructured":"Oligschl\u00e4ger, S. et al. Gradients of connectivity distance in the cerebral cortex of the macaque monkey. Brain Struct. Funct. 224, 925\u2013935 (2019).","journal-title":"Brain Struct. Funct."},{"key":"3466_CR55","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.21105\/joss.01026","volume":"3","author":"R Vallat","year":"2018","unstructured":"Vallat, R. Pingouin: statistics in python. J. Open Source Softw. 3, 1026 (2018).","journal-title":"J. Open Source Softw."},{"key":"3466_CR56","doi-asserted-by":"publisher","first-page":"116859","DOI":"10.1016\/j.neuroimage.2020.116859","volume":"216","author":"J Royer","year":"2020","unstructured":"Royer, J. et al. Myeloarchitecture gradients in the human insula: Histological underpinnings and association to intrinsic functional connectivity. NeuroImage 216, 116859 (2020).","journal-title":"NeuroImage"},{"key":"3466_CR57","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1038\/nrn755","volume":"3","author":"M Corbetta","year":"2002","unstructured":"Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201\u2013215 (2002).","journal-title":"Nat. Rev. Neurosci."},{"key":"3466_CR58","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.neuron.2008.04.017","volume":"58","author":"M Corbetta","year":"2008","unstructured":"Corbetta, M., Patel, G. & Shulman, G. L. The reorienting system of the human brain: from environment to theory of mind. Neuron 58, 306\u2013324 (2008).","journal-title":"Neuron"},{"key":"3466_CR59","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1016\/j.tics.2018.09.007","volume":"22","author":"J Stiso","year":"2018","unstructured":"Stiso, J. & Bassett, D. S. Spatial embedding imposes constraints on neuronal network architectures. Trends Cogn. Sci. 22, 1127\u20131142 (2018).","journal-title":"Trends Cogn. Sci."},{"key":"3466_CR60","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.neuroimage.2015.09.009","volume":"124","author":"JA Roberts","year":"2016","unstructured":"Roberts, J. A. et al. The contribution of geometry to the human connectome. NeuroImage 124, 379\u2013393 (2016).","journal-title":"NeuroImage"},{"key":"3466_CR61","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.neuron.2013.07.036","volume":"80","author":"M Ercsey-Ravasz","year":"2013","unstructured":"Ercsey-Ravasz, M. et al. A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80, 184\u2013197 (2013).","journal-title":"Neuron"},{"key":"3466_CR62","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1016\/j.neuroimage.2015.09.041","volume":"124","author":"RF Betzel","year":"2016","unstructured":"Betzel, R. F. et al. Generative models of the human connectome. NeuroImage 124, 1054\u20131064 (2016).","journal-title":"NeuroImage"},{"key":"3466_CR63","doi-asserted-by":"publisher","first-page":"e111007","DOI":"10.1371\/journal.pone.0111007","volume":"9","author":"B Mi\u0161i\u0107","year":"2014","unstructured":"Mi\u0161i\u0107, B. et al. The functional connectivity landscape of the human brain. PLoS One 9, e111007 (2014).","journal-title":"PLoS One"},{"key":"3466_CR64","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1162\/netn_a_00075","volume":"3","author":"RF Betzel","year":"2019","unstructured":"Betzel, R. F., Griffa, A., Hagmann, P. & Mi\u0161i\u0107, B. Distance-dependent consensus thresholds for generating group-representative structural brain networks. Net. Neurosci. 3, 475\u2013496 (2019).","journal-title":"Net. Neurosci."},{"key":"3466_CR65","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1111\/epi.16540","volume":"61","author":"S Larivi\u00e8re","year":"2020","unstructured":"Larivi\u00e8re, S. et al. Functional connectome contractions in temporal lobe epilepsy: Microstructural underpinnings and predictors of surgical outcome. Epilepsia 61, 1221\u20131233 (2020).","journal-title":"Epilepsia"},{"key":"3466_CR66","doi-asserted-by":"publisher","first-page":"10240","DOI":"10.1073\/pnas.0701519104","volume":"104","author":"CJ Honey","year":"2007","unstructured":"Honey, C. J., K\u00f6tter, R., Breakspear, M. & Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. USA 104, 10240\u201310245 (2007).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"3466_CR67","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.1073\/pnas.0811168106","volume":"106","author":"CJ Honey","year":"2009","unstructured":"Honey, C. J. et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. USA 106, 2035\u20132040 (2009).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"3466_CR68","doi-asserted-by":"publisher","first-page":"3285","DOI":"10.1093\/cercor\/bhw089","volume":"26","author":"B Mi\u0161i\u0107","year":"2016","unstructured":"Mi\u0161i\u0107, B. et al. Network-level structure-function relationships in human neocortex. Cereb. Cortex 26, 3285\u20133296 (2016).","journal-title":"Cereb. Cortex"},{"key":"3466_CR69","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1016\/j.neuron.2015.05.035","volume":"86","author":"B Mi\u0161i\u0107","year":"2015","unstructured":"Mi\u0161i\u0107, B. et al. Cooperative and competitive spreading dynamics on the human connectome. Neuron 86, 1518\u20131529 (2015).","journal-title":"Neuron"},{"key":"3466_CR70","doi-asserted-by":"crossref","unstructured":"Zamani Esfahlani, F., Faskowitz, J., Slack, J., Mi\u0161i\u0107, B. & Betzel, R. F. Local structure-function relationships in human brain networks across the lifespan. Nat. Commun. 13, 2053 (2022).","DOI":"10.1038\/s41467-022-29770-y"},{"key":"3466_CR71","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neuroimage.2012.06.081","volume":"73","author":"DK Jones","year":"2013","unstructured":"Jones, D. K., Kn\u00f6sche, T. R. & Turner, R. White matter integrity, fiber count, and other fallacies: the do\u2019s and don\u2019ts of diffusion mri. NeuroImage 73, 239\u2013254 (2013).","journal-title":"NeuroImage"},{"key":"3466_CR72","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.neuroimage.2012.12.066","volume":"70","author":"MA de Reus","year":"2013","unstructured":"de Reus, M. A. & van den Heuvel, M. P. Estimating false positives and negatives in brain networks. NeuroImage 70, 402\u2013409 (2013).","journal-title":"NeuroImage"},{"key":"3466_CR73","doi-asserted-by":"publisher","first-page":"16574","DOI":"10.1073\/pnas.1405672111","volume":"111","author":"C Thomas","year":"2014","unstructured":"Thomas, C. et al. Anatomical accuracy of brain connections derived from diffusion mri tractography is inherently limited. Proc. Natl. Acad. Sci. USA 111, 16574\u201316579 (2014).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"3466_CR74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-017-01285-x","volume":"8","author":"KH Maier-Hein","year":"2017","unstructured":"Maier-Hein, K. H. et al. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8, 1\u201313 (2017).","journal-title":"Nat. Commun."},{"key":"3466_CR75","doi-asserted-by":"publisher","first-page":"116699","DOI":"10.1016\/j.neuroimage.2020.116699","volume":"213","author":"N Colenbier","year":"2020","unstructured":"Colenbier, N. et al. Disambiguating the role of blood flow and global signal with partial information decomposition. NeuroImage 213, 116699 (2020).","journal-title":"NeuroImage"},{"key":"3466_CR76","doi-asserted-by":"publisher","first-page":"e13714","DOI":"10.1111\/psyp.13714","volume":"58","author":"KA Tsvetanov","year":"2021","unstructured":"Tsvetanov, K. A. et al. The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors. Psychophysiology 58, e13714 (2021).","journal-title":"Psychophysiology"},{"key":"3466_CR77","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3389\/fphys.2012.00015","volume":"3","author":"E Tagliazucchi","year":"2012","unstructured":"Tagliazucchi, E., Balenzuela, P., Fraiman, D. & Chialvo, D. Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis. Front. Physiol. 3, 15 (2012).","journal-title":"Front. Physiol."},{"key":"3466_CR78","doi-asserted-by":"publisher","first-page":"381","DOI":"10.3389\/fnins.2016.00381","volume":"10","author":"E Tagliazucchi","year":"2016","unstructured":"Tagliazucchi, E., Siniatchkin, M., Laufs, H. & Chialvo, D. R. The voxel-wise functional connectome can be efficiently derived from co-activations in a sparse spatio-temporal point-process. Front. Neurosci. 10, 381 (2016).","journal-title":"Front. Neurosci."},{"key":"3466_CR79","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1002\/hbm.21513","volume":"34","author":"N Petridou","year":"2013","unstructured":"Petridou, N., Gaudes, C. C., Dryden, I. L., Francis, S. T. & Gowland, P. A. Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity. Hum. Brain Mapp. 34, 1319\u20131329 (2013).","journal-title":"Hum. Brain Mapp."},{"key":"3466_CR80","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.media.2013.01.003","volume":"17","author":"G-R Wu","year":"2013","unstructured":"Wu, G.-R. et al. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med. Image Anal. 17, 365\u2013374 (2013).","journal-title":"Med. Image Anal."},{"key":"3466_CR81","doi-asserted-by":"publisher","first-page":"4392","DOI":"10.1073\/pnas.1216856110","volume":"110","author":"X Liu","year":"2013","unstructured":"Liu, X. & Duyn, J. H. Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc. Natl. Acad. Sci. 110, 4392\u20134397 (2013).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR82","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms8751","volume":"6","author":"FI Karahano\u011flu","year":"2015","unstructured":"Karahano\u011flu, F. I. & Van De Ville, D. Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks. Nat. Commun. 6, 7751 (2015).","journal-title":"Nat. Commun."},{"key":"3466_CR83","doi-asserted-by":"crossref","unstructured":"Novelli, L. & Razi, A. A mathematical perspective on edge-centric brain functional connectivity. Nat. Commun. 13, 2693 (2022).","DOI":"10.1038\/s41467-022-29775-7"},{"key":"3466_CR84","doi-asserted-by":"crossref","unstructured":"Pope, M., Fukushima, M., Betzel, R. F. & Sporns, O. Modular origins of high-amplitude co-fluctuations in fine-scale functional connectivity dynamics. Proc. Natl. Acad. Sci. U.S.A. 118, e2109380118 (2021).","DOI":"10.1073\/pnas.2109380118"},{"key":"3466_CR85","doi-asserted-by":"publisher","first-page":"117429","DOI":"10.1016\/j.neuroimage.2020.117429","volume":"224","author":"B-y Park","year":"2021","unstructured":"Park, B.-y et al. Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function. NeuroImage 224, 117429 (2021).","journal-title":"NeuroImage"},{"key":"3466_CR86","unstructured":"Dhollander, T., Raffelt, D. & Connelly, A. Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion mr data without a co-registered t1 image. In ISMRM Workshop on Breaking the Barriers of Diffusion MRI, vol. 5, 5 (2016)."},{"key":"3466_CR87","doi-asserted-by":"publisher","first-page":"116137","DOI":"10.1016\/j.neuroimage.2019.116137","volume":"202","author":"J-D Tournier","year":"2019","unstructured":"Tournier, J.-D. et al. Mrtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202, 116137 (2019).","journal-title":"NeuroImage"},{"key":"3466_CR88","doi-asserted-by":"publisher","first-page":"10154","DOI":"10.1073\/pnas.1803667115","volume":"115","author":"RV de Wael","year":"2018","unstructured":"de Wael, R. V. et al. Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding. Proc. Natl. Acad. Sci. 115, 10154\u201310159 (2018).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3466_CR89","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.neuroimage.2013.11.046","volume":"90","author":"G Salimi-Khorshidi","year":"2014","unstructured":"Salimi-Khorshidi, G. et al. Automatic denoising of functional mri data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 90, 449\u2013468 (2014).","journal-title":"Neuroimage"},{"key":"3466_CR90","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neuroimage.2016.12.069","volume":"170","author":"LH Scholtens","year":"2018","unstructured":"Scholtens, L. H., de Reus, M. A., de Lange, S. C., Schmidt, R. & van den Heuvel, M. P. An mri von economo\u2013koskinas atlas. Neuroimage 170, 249\u2013256 (2018).","journal-title":"Neuroimage"},{"key":"3466_CR91","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.tics.2017.11.002","volume":"22","author":"JM Huntenburg","year":"2018","unstructured":"Huntenburg, J. M., Bazin, P.-L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21\u201331 (2018).","journal-title":"Trends Cogn. Sci."},{"key":"3466_CR92","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1016\/j.neuroimage.2018.05.070","volume":"178","author":"AF Alexander-Bloch","year":"2018","unstructured":"Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 178, 540\u2013551 (2018).","journal-title":"Neuroimage"},{"key":"3466_CR93","doi-asserted-by":"crossref","unstructured":"Markello, R. D. & Misic, B. Comparing spatial null models for brain maps. NeuroImage 236, 118052 (2021).","DOI":"10.1016\/j.neuroimage.2021.118052"},{"key":"3466_CR94","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/j.biopsych.2019.09.031","volume":"87","author":"G Shafiei","year":"2020","unstructured":"Shafiei, G. et al. Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture. Biol. Psychiat. 87, 727\u2013735 (2020).","journal-title":"Biol. Psychiat."},{"key":"3466_CR95","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1037\/0033-2909.114.3.542","volume":"114","author":"DV Budescu","year":"1993","unstructured":"Budescu, D. V. Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychol. Bull. 114, 542\u2013551 (1993).","journal-title":"Psychol. Bull."},{"key":"3466_CR96","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1037\/1082-989X.8.2.129","volume":"8","author":"R Azen","year":"2003","unstructured":"Azen, R. & Budescu, D. V. The dominance analysis approach for comparing predictors in multiple regression. Psychol. Methods 8, 129\u2013148 (2003).","journal-title":"Psychol. Methods"},{"key":"3466_CR97","doi-asserted-by":"crossref","unstructured":"Kraha, A., Turner, H., Nimon, K., Zientek, L. & Henson, R. Tools to support interpreting multiple regression in the face of multicollinearity. Front. Psychol. 3, 44 (2012).","DOI":"10.3389\/fpsyg.2012.00044"}],"container-title":["Communications Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42003-022-03466-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42003-022-03466-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42003-022-03466-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T03:36:57Z","timestamp":1669347417000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42003-022-03466-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,2]]},"references-count":97,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["3466"],"URL":"https:\/\/doi.org\/10.1038\/s42003-022-03466-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.07.08.451672","asserted-by":"object"}]},"ISSN":["2399-3642"],"issn-type":[{"value":"2399-3642","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,2]]},"assertion":[{"value":"9 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"532"}}