Low-dimensional controllability of brain networks.
Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012691 |
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| _version_ | 1841533227285610496 |
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| author | Remy Ben Messaoud Vincent Le Du Camile Bousfiha Marie-Constance Corsi Juliana Gonzalez-Astudillo Brigitte Charlotte Kaufmann Tristan Venot Baptiste Couvy-Duchesne Lara Migliaccio Charlotte Rosso Paolo Bartolomeo Mario Chavez Fabrizio De Vico Fallani |
| author_facet | Remy Ben Messaoud Vincent Le Du Camile Bousfiha Marie-Constance Corsi Juliana Gonzalez-Astudillo Brigitte Charlotte Kaufmann Tristan Venot Baptiste Couvy-Duchesne Lara Migliaccio Charlotte Rosso Paolo Bartolomeo Mario Chavez Fabrizio De Vico Fallani |
| author_sort | Remy Ben Messaoud |
| collection | DOAJ |
| description | Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain. |
| format | Article |
| id | doaj-art-42dbaec7270f4186af862782343c1ed7 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-42dbaec7270f4186af862782343c1ed72025-01-17T05:30:55ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101269110.1371/journal.pcbi.1012691Low-dimensional controllability of brain networks.Remy Ben MessaoudVincent Le DuCamile BousfihaMarie-Constance CorsiJuliana Gonzalez-AstudilloBrigitte Charlotte KaufmannTristan VenotBaptiste Couvy-DuchesneLara MigliaccioCharlotte RossoPaolo BartolomeoMario ChavezFabrizio De Vico FallaniIdentifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain.https://doi.org/10.1371/journal.pcbi.1012691 |
| spellingShingle | Remy Ben Messaoud Vincent Le Du Camile Bousfiha Marie-Constance Corsi Juliana Gonzalez-Astudillo Brigitte Charlotte Kaufmann Tristan Venot Baptiste Couvy-Duchesne Lara Migliaccio Charlotte Rosso Paolo Bartolomeo Mario Chavez Fabrizio De Vico Fallani Low-dimensional controllability of brain networks. PLoS Computational Biology |
| title | Low-dimensional controllability of brain networks. |
| title_full | Low-dimensional controllability of brain networks. |
| title_fullStr | Low-dimensional controllability of brain networks. |
| title_full_unstemmed | Low-dimensional controllability of brain networks. |
| title_short | Low-dimensional controllability of brain networks. |
| title_sort | low dimensional controllability of brain networks |
| url | https://doi.org/10.1371/journal.pcbi.1012691 |
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