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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Neuroscience Methods and Techniques
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1484954

Integrating fMRI Spatial Network Dynamics and EEG Spectral Power: Insights into Resting State Connectivity

Provisionally accepted
Souvik Phadikar Souvik Phadikar 1,2*Krishna Pusuluri Krishna Pusuluri 1,2Armin Iraji Armin Iraji 1,2Vince D Calhoun Vince D Calhoun 1,2,3,4
  • 1 Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, United States
  • 2 Georgia State University, Atlanta, Georgia, United States
  • 3 Georgia Institute of Technology, Atlanta, Georgia, United States
  • 4 Emory University, Atlanta, Georgia, United States

The final, formatted version of the article will be published soon.

    The Integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has allowed for a novel exploration of the brain's spatial-temporal 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. 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). Our analysis demonstrated how the networks' 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. These association between space and frequency observed during rest offer insights into the brain's spatial-temporal characteristics and enhance our understanding of both spatially varying fMRI networks and EEG band power.

    Keywords: multimodal fusion, simultaneous EEG/fMRI, spatial dynamics, resting state networks, EEG spectra

    Received: 22 Aug 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Phadikar, Pusuluri, Iraji and Calhoun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Souvik Phadikar, Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.