Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity
IntroductionThe 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...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1484954/full |
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| author | Souvik Phadikar Krishna Pusuluri Armin Iraji Vince D. Calhoun |
| author_facet | Souvik Phadikar Krishna Pusuluri Armin Iraji Vince D. Calhoun |
| author_sort | Souvik Phadikar |
| collection | DOAJ |
| description | IntroductionThe 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.MethodsIn 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).ResultsOur 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.DiscussionThese 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. |
| format | Article |
| id | doaj-art-ca98aa0037dd4dcc9baaec9e13ef36a9 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-ca98aa0037dd4dcc9baaec9e13ef36a92025-01-28T06:41:10ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-01-011910.3389/fnins.2025.14849541484954Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivitySouvik PhadikarKrishna PusuluriArmin IrajiVince D. CalhounIntroductionThe 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.MethodsIn 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).ResultsOur 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.DiscussionThese 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.https://www.frontiersin.org/articles/10.3389/fnins.2025.1484954/fullmultimodal fusionsimultaneous EEG/fMRIspatial dynamicsresting state networksEEG spectra |
| spellingShingle | Souvik Phadikar Krishna Pusuluri Armin Iraji Vince D. Calhoun Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity Frontiers in Neuroscience multimodal fusion simultaneous EEG/fMRI spatial dynamics resting state networks EEG spectra |
| title | Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity |
| title_full | Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity |
| title_fullStr | Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity |
| title_full_unstemmed | Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity |
| title_short | Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity |
| title_sort | integrating fmri spatial network dynamics and eeg spectral power insights into resting state connectivity |
| topic | multimodal fusion simultaneous EEG/fMRI spatial dynamics resting state networks EEG spectra |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1484954/full |
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