{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T17:53:28Z","timestamp":1794333208029,"version":"4.9.0"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"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. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The Integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has allowed for a novel exploration of the brain\u2019s spatial\u2013temporal resolution. While functional brain networks show variations in both spatial and temporal dimensions, most studies focus on fixed spatial networks that change together over time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, for the first time, we link spatially dynamic brain networks with EEG spectral properties recorded simultaneously, which allows us to concurrently capture high spatial and temporal resolutions offered by these complementary imaging modalities. We estimated time-resolved brain networks using sliding window-based spatially constrained independent component analysis (scICA), producing resting brain networks that evolved over time at the voxel level. Next, we assessed their coupling with four time-varying EEG spectral power (delta, theta, alpha, and beta).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our analysis demonstrated how the networks\u2019 volumes and their voxel-level activities vary over time and revealed significant correlations with time-varying EEG spectral power. For instance, we found a strong association between increasing volume of the primary visual network and alpha band power, consistent with our hypothesis for eyes open resting state scan. Similarly, the alpha, theta, and delta power of the Pz electrode were localized to voxel-level activities of primary visual, cerebellum, and temporal networks, respectively. We also identified a strong correlation between the primary motor network and alpha (mu rhythm) and beta activity. This is consistent with motor tasks during rest, though this remains to be tested directly.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These association between space and frequency observed during rest offer insights into the brain\u2019s spatial\u2013temporal characteristics and enhance our understanding of both spatially varying fMRI networks and EEG band power.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2025.1484954","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T01:42:57Z","timestamp":1738028577000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity"],"prefix":"10.3389","volume":"19","author":[{"given":"Souvik","family":"Phadikar","sequence":"first","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University","place":["Atlanta, GA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna","family":"Pusuluri","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University","place":["Atlanta, GA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armin","family":"Iraji","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University","place":["Atlanta, GA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vince D.","family":"Calhoun","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University","place":["Atlanta, GA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"ref1","author":"Akhonda","year":"2018"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s10548-017-0546-2","article-title":"EEG signatures of dynamic functional network connectivity states","volume":"31","author":"Allen","year":"2018","journal-title":"Brain Topogr."},{"key":"ref3","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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