A multiplex of connectome trajectories enables several connectivity patterns in parallel
Complex brain function comprises a multitude of neural operations in parallel and often at different speeds. Each of these operations is carried out across a network of distributed brain regions. How multiple distributed processes are facilitated in parallel is largely unknown. We postulate that suc...
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eLife Sciences Publications Ltd
2025-06-01
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| Online Access: | https://elifesciences.org/articles/98777 |
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| author | Parham Mostame Jonathan Wirsich Thomas Alderson Ben Ridley Anne-Lise Giraud David W Carmichael Serge Vulliemoz Maxime Guye Louis Lemieux Sepideh Sadaghiani |
| author_facet | Parham Mostame Jonathan Wirsich Thomas Alderson Ben Ridley Anne-Lise Giraud David W Carmichael Serge Vulliemoz Maxime Guye Louis Lemieux Sepideh Sadaghiani |
| author_sort | Parham Mostame |
| collection | DOAJ |
| description | Complex brain function comprises a multitude of neural operations in parallel and often at different speeds. Each of these operations is carried out across a network of distributed brain regions. How multiple distributed processes are facilitated in parallel is largely unknown. We postulate that such processing relies on a multiplex of dynamic network patterns emerging in parallel but from different functional connectivity (FC) timescales. Given the dominance of inherently slow fMRI in network science, it is unknown whether the brain leverages such multi-timescale network dynamics. We studied FC dynamics concurrently across a breadth of timescales (from infraslow to γ-range) in rare, simultaneously recorded intracranial EEG and fMRI in humans, and source-localized scalp EEG-fMRI data in humans. We examined spatial and temporal convergence of connectome trajectories across timescales. ‘Spatial convergence’ refers to spatially similar EEG and fMRI connectome patterns, while ‘temporal convergence’ signifies the more specific case of spatial convergence at corresponding timepoints in EEG and fMRI. We observed spatial convergence but temporal divergence across FC timescales; connectome states (recurrent FC patterns) with partial spatial similarity were found in fMRI and all EEG frequency bands, but these occurred asynchronously across FC timescales. Our findings suggest that hemodynamic and frequency-specific electrophysiological signals, while involving similar large-scale networks, represent functionally distinct connectome trajectories that operate at different FC speeds and in parallel. This multiplex is poised to enable concurrent connectivity across multiple sets of brain regions independently. |
| format | Article |
| id | doaj-art-e13610da9a7249ebaf73074375b5b170 |
| institution | DOAJ |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | eLife Sciences Publications Ltd |
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| series | eLife |
| spelling | doaj-art-e13610da9a7249ebaf73074375b5b1702025-08-20T03:21:17ZengeLife Sciences Publications LtdeLife2050-084X2025-06-011310.7554/eLife.98777A multiplex of connectome trajectories enables several connectivity patterns in parallelParham Mostame0https://orcid.org/0000-0002-1353-1551Jonathan Wirsich1https://orcid.org/0000-0003-0588-9710Thomas Alderson2Ben Ridley3Anne-Lise Giraud4David W Carmichael5Serge Vulliemoz6Maxime Guye7Louis Lemieux8Sepideh Sadaghiani9Department of psychology, University of Illinois at Urbana-Champaign, Champaign, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, United StatesEEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, SwitzerlandDepartment of psychology, University of Illinois at Urbana-Champaign, Champaign, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, United StatesAix Marseille Univ, CNRS, CRMBM, Marseille, France; IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, ItalyDepartment of Neuroscience, University of Geneva, Geneva, SwitzerlandDevelopmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United KingdomEEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, SwitzerlandAix Marseille Univ, CNRS, CRMBM, Marseille, France; AP-HM, Hôpital Universitaire Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, FranceDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, United KingdomDepartment of psychology, University of Illinois at Urbana-Champaign, Champaign, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, United StatesComplex brain function comprises a multitude of neural operations in parallel and often at different speeds. Each of these operations is carried out across a network of distributed brain regions. How multiple distributed processes are facilitated in parallel is largely unknown. We postulate that such processing relies on a multiplex of dynamic network patterns emerging in parallel but from different functional connectivity (FC) timescales. Given the dominance of inherently slow fMRI in network science, it is unknown whether the brain leverages such multi-timescale network dynamics. We studied FC dynamics concurrently across a breadth of timescales (from infraslow to γ-range) in rare, simultaneously recorded intracranial EEG and fMRI in humans, and source-localized scalp EEG-fMRI data in humans. We examined spatial and temporal convergence of connectome trajectories across timescales. ‘Spatial convergence’ refers to spatially similar EEG and fMRI connectome patterns, while ‘temporal convergence’ signifies the more specific case of spatial convergence at corresponding timepoints in EEG and fMRI. We observed spatial convergence but temporal divergence across FC timescales; connectome states (recurrent FC patterns) with partial spatial similarity were found in fMRI and all EEG frequency bands, but these occurred asynchronously across FC timescales. Our findings suggest that hemodynamic and frequency-specific electrophysiological signals, while involving similar large-scale networks, represent functionally distinct connectome trajectories that operate at different FC speeds and in parallel. This multiplex is poised to enable concurrent connectivity across multiple sets of brain regions independently.https://elifesciences.org/articles/98777functinoal connectomeintracranial EEGfMRIconcurrentconnectome dynamicsmultiplex |
| spellingShingle | Parham Mostame Jonathan Wirsich Thomas Alderson Ben Ridley Anne-Lise Giraud David W Carmichael Serge Vulliemoz Maxime Guye Louis Lemieux Sepideh Sadaghiani A multiplex of connectome trajectories enables several connectivity patterns in parallel eLife functinoal connectome intracranial EEG fMRI concurrent connectome dynamics multiplex |
| title | A multiplex of connectome trajectories enables several connectivity patterns in parallel |
| title_full | A multiplex of connectome trajectories enables several connectivity patterns in parallel |
| title_fullStr | A multiplex of connectome trajectories enables several connectivity patterns in parallel |
| title_full_unstemmed | A multiplex of connectome trajectories enables several connectivity patterns in parallel |
| title_short | A multiplex of connectome trajectories enables several connectivity patterns in parallel |
| title_sort | multiplex of connectome trajectories enables several connectivity patterns in parallel |
| topic | functinoal connectome intracranial EEG fMRI concurrent connectome dynamics multiplex |
| url | https://elifesciences.org/articles/98777 |
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