Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest
Previous studies have shown that a re-organization of the brain's functional connectome expressed in terms of network integration and segregation may play a pivotal role for brain function. However, it has been proven difficult to fully capture both processes independently in a single methodolo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923000393 |
| _version_ | 1811169475667427328 |
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| author | Peter Fransson Marika Strindberg |
| author_facet | Peter Fransson Marika Strindberg |
| author_sort | Peter Fransson |
| collection | DOAJ |
| description | Previous studies have shown that a re-organization of the brain's functional connectome expressed in terms of network integration and segregation may play a pivotal role for brain function. However, it has been proven difficult to fully capture both processes independently in a single methodological framework. In this study, by starting from pair-wise assessments of instantaneous phase synchronization and community membership, we assemble spatiotemporally flexible networks that reflect changes in integration/segregation that occur at a spectrum of spatial as well as temporal scales. This is achieved by iteratively assembling smaller networks into larger units under the constraint that the smaller units should be internally integrated, i.e. belong to the same community. The assembled subnetworks can be partly overlapping and differ in size across time. Our results show that subnetwork integration and segregation occur simultaneously in the brain. During task performance, global changes in synchronization between networks arise that are tied to the underlying temporal design of the experiment. We show that a hallmark property of the dynamics of the brain's functional connectome is a presence of quasi-periodic patterns of network activation and deactivation, which during task performance becomes intertwined with the underlying temporal structure of the experimental paradigm. Additionally, we show that the degree of network integration throughout a n-back working memory task is correlated to performance. |
| first_indexed | 2024-04-10T16:43:02Z |
| format | Article |
| id | doaj.art-2ec029297e8d4aa197431a75d093eff1 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-10T16:43:02Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-2ec029297e8d4aa197431a75d093eff12023-02-08T04:16:32ZengElsevierNeuroImage1095-95722023-03-01268119890Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and restPeter Fransson0Marika Strindberg1Corresponding author.; Department of Clinical Neuroscience, Karolinska Institutet, SwedenDepartment of Clinical Neuroscience, Karolinska Institutet, SwedenPrevious studies have shown that a re-organization of the brain's functional connectome expressed in terms of network integration and segregation may play a pivotal role for brain function. However, it has been proven difficult to fully capture both processes independently in a single methodological framework. In this study, by starting from pair-wise assessments of instantaneous phase synchronization and community membership, we assemble spatiotemporally flexible networks that reflect changes in integration/segregation that occur at a spectrum of spatial as well as temporal scales. This is achieved by iteratively assembling smaller networks into larger units under the constraint that the smaller units should be internally integrated, i.e. belong to the same community. The assembled subnetworks can be partly overlapping and differ in size across time. Our results show that subnetwork integration and segregation occur simultaneously in the brain. During task performance, global changes in synchronization between networks arise that are tied to the underlying temporal design of the experiment. We show that a hallmark property of the dynamics of the brain's functional connectome is a presence of quasi-periodic patterns of network activation and deactivation, which during task performance becomes intertwined with the underlying temporal structure of the experimental paradigm. Additionally, we show that the degree of network integration throughout a n-back working memory task is correlated to performance.http://www.sciencedirect.com/science/article/pii/S1053811923000393BrainModularitySpatiotemporal networksFlexibilityWorking memoryResting-state |
| spellingShingle | Peter Fransson Marika Strindberg Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest NeuroImage Brain Modularity Spatiotemporal networks Flexibility Working memory Resting-state |
| title | Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest |
| title_full | Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest |
| title_fullStr | Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest |
| title_full_unstemmed | Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest |
| title_short | Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest |
| title_sort | brain network integration segregation and quasi periodic activation and deactivation during tasks and rest |
| topic | Brain Modularity Spatiotemporal networks Flexibility Working memory Resting-state |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923000393 |
| work_keys_str_mv | AT peterfransson brainnetworkintegrationsegregationandquasiperiodicactivationanddeactivationduringtasksandrest AT marikastrindberg brainnetworkintegrationsegregationandquasiperiodicactivationanddeactivationduringtasksandrest |