Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.
Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models fo...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314268 |
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| _version_ | 1841533095362166784 |
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| author | Yin-Jui Chang Yuan-I Chen Hannah M Stealey Yi Zhao Hung-Yun Lu Enrique Contreras-Hernandez Megan N Baker Edward Castillo Hsin-Chih Yeh Samantha R Santacruz |
| author_facet | Yin-Jui Chang Yuan-I Chen Hannah M Stealey Yi Zhao Hung-Yun Lu Enrique Contreras-Hernandez Megan N Baker Edward Castillo Hsin-Chih Yeh Samantha R Santacruz |
| author_sort | Yin-Jui Chang |
| collection | DOAJ |
| description | Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes. |
| format | Article |
| id | doaj-art-73f3562ea67d43f38adf6c7737429c51 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-73f3562ea67d43f38adf6c7737429c512025-01-17T05:32:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031426810.1371/journal.pone.0314268Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.Yin-Jui ChangYuan-I ChenHannah M StealeyYi ZhaoHung-Yun LuEnrique Contreras-HernandezMegan N BakerEdward CastilloHsin-Chih YehSamantha R SantacruzNeural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes.https://doi.org/10.1371/journal.pone.0314268 |
| spellingShingle | Yin-Jui Chang Yuan-I Chen Hannah M Stealey Yi Zhao Hung-Yun Lu Enrique Contreras-Hernandez Megan N Baker Edward Castillo Hsin-Chih Yeh Samantha R Santacruz Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations. PLoS ONE |
| title | Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations. |
| title_full | Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations. |
| title_fullStr | Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations. |
| title_full_unstemmed | Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations. |
| title_short | Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations. |
| title_sort | multiscale effective connectivity analysis of brain activity using neural ordinary differential equations |
| url | https://doi.org/10.1371/journal.pone.0314268 |
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