The balance between integration and segregation drives network dynamics maximizing multistability and metastability
Abstract The brain’s ability to switch between functional states while maintaining both flexibility and stability is shaped by its structural connectivity. Understanding the relationship between brain structure and neural dynamics is a central challenge in neuroscience. Prior studies link neural dyn...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01612-z |
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| author | Javier Palma-Espinosa Sebastián Orellana-Villota Carlos Coronel-Oliveros Jean Paul Maidana Patricio Orio |
| author_facet | Javier Palma-Espinosa Sebastián Orellana-Villota Carlos Coronel-Oliveros Jean Paul Maidana Patricio Orio |
| author_sort | Javier Palma-Espinosa |
| collection | DOAJ |
| description | Abstract The brain’s ability to switch between functional states while maintaining both flexibility and stability is shaped by its structural connectivity. Understanding the relationship between brain structure and neural dynamics is a central challenge in neuroscience. Prior studies link neural dynamics to local noisy activity and mesoscale coupling mechanisms, but causal links at the whole-brain scale remain elusive. This study investigates how the balance between integration and segregation in brain networks influences their dynamical properties, focusing on multistability (switching between stable states) and metastability (transient stability over time). We analyzed a spectrum of network models, from highly segregated to highly integrated, using structural metrics like modularity, efficiency, and small-worldness. By simulating neural activity with a neural mass model, and analyzing Functional Connectivity Dynamics (FCD), we found that segregated networks sustain dynamic synchronization patterns, while small-world networks, which balance local clustering and global efficiency, exhibit the richest dynamical behavior. Networks with intermediate small-worldness ( $$\omega$$ ) values showed peak dynamical richness, measured by variance in FCD and metastability. Using Mutual Information (MI), we quantified the structure-dynamics relationship, revealing that modularity is the strongest predictor of network dynamics, as modular architectures support transitions between dynamical states. These findings underscore the importance of the balance between local specialization, global integration, and network’s modularity, which fosters the dynamic complexity necessary for cognitive functions. Our study enhances the understanding of how structural features shape neural dynamics. |
| format | Article |
| id | doaj-art-5d12ed3dbaab4c4dbfd27377cdd0c62b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-5d12ed3dbaab4c4dbfd27377cdd0c62b2025-08-20T03:03:40ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01612-zThe balance between integration and segregation drives network dynamics maximizing multistability and metastabilityJavier Palma-Espinosa0Sebastián Orellana-Villota1Carlos Coronel-Oliveros2Jean Paul Maidana3Patricio Orio4Centro Interdisciplinario de Neurociencia de ValparaísoCentro Interdisciplinario de Neurociencia de ValparaísoLatin American Brain Health Institute (BrainLat), Universidad Adolfo IbañezFacultad de Ingeniería, Universidad Andres BelloInstituto de Neurociencia, Facultad de Ciencias, Universidad de ValparaísoAbstract The brain’s ability to switch between functional states while maintaining both flexibility and stability is shaped by its structural connectivity. Understanding the relationship between brain structure and neural dynamics is a central challenge in neuroscience. Prior studies link neural dynamics to local noisy activity and mesoscale coupling mechanisms, but causal links at the whole-brain scale remain elusive. This study investigates how the balance between integration and segregation in brain networks influences their dynamical properties, focusing on multistability (switching between stable states) and metastability (transient stability over time). We analyzed a spectrum of network models, from highly segregated to highly integrated, using structural metrics like modularity, efficiency, and small-worldness. By simulating neural activity with a neural mass model, and analyzing Functional Connectivity Dynamics (FCD), we found that segregated networks sustain dynamic synchronization patterns, while small-world networks, which balance local clustering and global efficiency, exhibit the richest dynamical behavior. Networks with intermediate small-worldness ( $$\omega$$ ) values showed peak dynamical richness, measured by variance in FCD and metastability. Using Mutual Information (MI), we quantified the structure-dynamics relationship, revealing that modularity is the strongest predictor of network dynamics, as modular architectures support transitions between dynamical states. These findings underscore the importance of the balance between local specialization, global integration, and network’s modularity, which fosters the dynamic complexity necessary for cognitive functions. Our study enhances the understanding of how structural features shape neural dynamics.https://doi.org/10.1038/s41598-025-01612-zSmall-world networksModularityBrain dynamicsIntegration and segregationNeural connectivityMetastability |
| spellingShingle | Javier Palma-Espinosa Sebastián Orellana-Villota Carlos Coronel-Oliveros Jean Paul Maidana Patricio Orio The balance between integration and segregation drives network dynamics maximizing multistability and metastability Scientific Reports Small-world networks Modularity Brain dynamics Integration and segregation Neural connectivity Metastability |
| title | The balance between integration and segregation drives network dynamics maximizing multistability and metastability |
| title_full | The balance between integration and segregation drives network dynamics maximizing multistability and metastability |
| title_fullStr | The balance between integration and segregation drives network dynamics maximizing multistability and metastability |
| title_full_unstemmed | The balance between integration and segregation drives network dynamics maximizing multistability and metastability |
| title_short | The balance between integration and segregation drives network dynamics maximizing multistability and metastability |
| title_sort | balance between integration and segregation drives network dynamics maximizing multistability and metastability |
| topic | Small-world networks Modularity Brain dynamics Integration and segregation Neural connectivity Metastability |
| url | https://doi.org/10.1038/s41598-025-01612-z |
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