Arousal impacts distributed hubs modulating the integration of brain functional connectivity
Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922004839 |
| _version_ | 1818114551254614016 |
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| author | Kangjoo Lee Corey Horien David O'Connor Bronwen Garand-Sheridan Fuyuze Tokoglu Dustin Scheinost Evelyn M.R. Lake R. Todd Constable |
| author_facet | Kangjoo Lee Corey Horien David O'Connor Bronwen Garand-Sheridan Fuyuze Tokoglu Dustin Scheinost Evelyn M.R. Lake R. Todd Constable |
| author_sort | Kangjoo Lee |
| collection | DOAJ |
| description | Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity. |
| first_indexed | 2024-12-11T03:52:31Z |
| format | Article |
| id | doaj.art-c5ceac572fef480c87d3be513048db91 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-11T03:52:31Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-c5ceac572fef480c87d3be513048db912022-12-22T01:21:52ZengElsevierNeuroImage1095-95722022-09-01258119364Arousal impacts distributed hubs modulating the integration of brain functional connectivityKangjoo Lee0Corey Horien1David O'Connor2Bronwen Garand-Sheridan3Fuyuze Tokoglu4Dustin Scheinost5Evelyn M.R. Lake6R. Todd Constable7Department of Radiology and Bioimaging Sciences, Yale University School of Medicine, New Haven, CT 06520, United States; Corresponding author.Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT 06520, United StatesDepartment of Biomedical Engineering, Yale University, New Haven, CT 06520, United StatesDepartment of Music, Yale University, New Haven, CT 06520, United StatesDepartment of Radiology and Bioimaging Sciences, Yale University School of Medicine, New Haven, CT 06520, United StatesDepartment of Radiology and Bioimaging Sciences, Yale University School of Medicine, New Haven, CT 06520, United States; Department of Biomedical Engineering, Yale University, New Haven, CT 06520, United States; The Child Study Center, Yale University School of Medicine, New Haven, CT 06520, United States; Department of Statistics and Data Science, Yale University, New Haven, CT 06511, United StatesDepartment of Radiology and Bioimaging Sciences, Yale University School of Medicine, New Haven, CT 06520, United StatesDepartment of Radiology and Bioimaging Sciences, Yale University School of Medicine, New Haven, CT 06520, United States; Department of Biomedical Engineering, Yale University, New Haven, CT 06520, United States; Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06520, United StatesEven when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.http://www.sciencedirect.com/science/article/pii/S1053811922004839ArousalNetwork hubsResting statefMRIPupillometry |
| spellingShingle | Kangjoo Lee Corey Horien David O'Connor Bronwen Garand-Sheridan Fuyuze Tokoglu Dustin Scheinost Evelyn M.R. Lake R. Todd Constable Arousal impacts distributed hubs modulating the integration of brain functional connectivity NeuroImage Arousal Network hubs Resting state fMRI Pupillometry |
| title | Arousal impacts distributed hubs modulating the integration of brain functional connectivity |
| title_full | Arousal impacts distributed hubs modulating the integration of brain functional connectivity |
| title_fullStr | Arousal impacts distributed hubs modulating the integration of brain functional connectivity |
| title_full_unstemmed | Arousal impacts distributed hubs modulating the integration of brain functional connectivity |
| title_short | Arousal impacts distributed hubs modulating the integration of brain functional connectivity |
| title_sort | arousal impacts distributed hubs modulating the integration of brain functional connectivity |
| topic | Arousal Network hubs Resting state fMRI Pupillometry |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922004839 |
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