A general framework for characterizing optimal communication in brain networks
Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a...
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eLife Sciences Publications Ltd
2025-04-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/101780 |
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| author | Kayson Fakhar Fatemeh Hadaeghi Caio Seguin Shrey Dixit Arnaud Messé Gorka Zamora-López Bratislav Misic Claus C Hilgetag |
| author_facet | Kayson Fakhar Fatemeh Hadaeghi Caio Seguin Shrey Dixit Arnaud Messé Gorka Zamora-López Bratislav Misic Claus C Hilgetag |
| author_sort | Kayson Fakhar |
| collection | DOAJ |
| description | Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain’s most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence. |
| format | Article |
| id | doaj-art-d0c3eeb043ca4954acb25b4d3bdef44a |
| institution | OA Journals |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-d0c3eeb043ca4954acb25b4d3bdef44a2025-08-20T02:13:06ZengeLife Sciences Publications LtdeLife2050-084X2025-04-011310.7554/eLife.101780A general framework for characterizing optimal communication in brain networksKayson Fakhar0https://orcid.org/0000-0003-0615-1777Fatemeh Hadaeghi1Caio Seguin2Shrey Dixit3Arnaud Messé4https://orcid.org/0000-0001-9081-3088Gorka Zamora-López5Bratislav Misic6https://orcid.org/0000-0003-0307-2862Claus C Hilgetag7MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom; Institute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of Neuroscience, Hamburg, GermanyInstitute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of Neuroscience, Hamburg, GermanyDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, United StatesInstitute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of Neuroscience, Hamburg, Germany; Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; International Max Planck Research School on Cognitive Neuroimaging, Barcelona, SpainInstitute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of Neuroscience, Hamburg, GermanyCenter for Brain and Cognition, Pompeu Fabra University, Barcelona, Spain; Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, SpainMcConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, CanadaInstitute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of Neuroscience, Hamburg, Germany; Department of Health Sciences, Boston University, Boston, United StatesEfficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain’s most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence.https://elifesciences.org/articles/101780brain networkscommunicationnetwork dynamicspropagationcommunication efficiency |
| spellingShingle | Kayson Fakhar Fatemeh Hadaeghi Caio Seguin Shrey Dixit Arnaud Messé Gorka Zamora-López Bratislav Misic Claus C Hilgetag A general framework for characterizing optimal communication in brain networks eLife brain networks communication network dynamics propagation communication efficiency |
| title | A general framework for characterizing optimal communication in brain networks |
| title_full | A general framework for characterizing optimal communication in brain networks |
| title_fullStr | A general framework for characterizing optimal communication in brain networks |
| title_full_unstemmed | A general framework for characterizing optimal communication in brain networks |
| title_short | A general framework for characterizing optimal communication in brain networks |
| title_sort | general framework for characterizing optimal communication in brain networks |
| topic | brain networks communication network dynamics propagation communication efficiency |
| url | https://elifesciences.org/articles/101780 |
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