EEG dynamical network analysis method reveals the neural signature of visual-motor coordination.
Human visual-motor coordination is an essential function of movement control, which requires interactions of multiple brain regions. Understanding the cortical-motor coordination is important for improving physical therapy for motor disabilities. However, its underlying transient neural dynamics is...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0231767 |
| _version_ | 1818916789918105600 |
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| author | Xinzhe Li Bruno Mota Toshiyuki Kondo Slawomir Nasuto Yoshikatsu Hayashi |
| author_facet | Xinzhe Li Bruno Mota Toshiyuki Kondo Slawomir Nasuto Yoshikatsu Hayashi |
| author_sort | Xinzhe Li |
| collection | DOAJ |
| description | Human visual-motor coordination is an essential function of movement control, which requires interactions of multiple brain regions. Understanding the cortical-motor coordination is important for improving physical therapy for motor disabilities. However, its underlying transient neural dynamics is still largely unknown. In this study, we applied an eigenvector-based dynamical network analysis method to investigate the functional connectivity calculated from electroencephalography (EEG) signals under visual-motor coordination task and to identify its meta-stable states dynamics. We first tested this signal processing on a simulated network to evaluate it in comparison with other dynamical methods, demonstrating that the eigenvector-based dynamical network analysis was able to correctly extract the dynamical features of the evolving networks. Subsequently, the eigenvector-based analysis was applied to EEG data collected under a visual-motor coordination experiment. In the EEG study with participants, the results of both topological analysis and the eigenvector-based dynamical analysis were able to distinguish different experimental conditions of visual tracking task. With the dynamical analysis, we showed that different visual-motor coordination states can be distinguished by investigating the meta-stable states dynamics of the functional connectivity. |
| first_indexed | 2024-12-20T00:23:46Z |
| format | Article |
| id | doaj.art-dd21e0e848534348a6e5dda078747682 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T00:23:46Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-dd21e0e848534348a6e5dda0787476822022-12-21T20:00:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01155e023176710.1371/journal.pone.0231767EEG dynamical network analysis method reveals the neural signature of visual-motor coordination.Xinzhe LiBruno MotaToshiyuki KondoSlawomir NasutoYoshikatsu HayashiHuman visual-motor coordination is an essential function of movement control, which requires interactions of multiple brain regions. Understanding the cortical-motor coordination is important for improving physical therapy for motor disabilities. However, its underlying transient neural dynamics is still largely unknown. In this study, we applied an eigenvector-based dynamical network analysis method to investigate the functional connectivity calculated from electroencephalography (EEG) signals under visual-motor coordination task and to identify its meta-stable states dynamics. We first tested this signal processing on a simulated network to evaluate it in comparison with other dynamical methods, demonstrating that the eigenvector-based dynamical network analysis was able to correctly extract the dynamical features of the evolving networks. Subsequently, the eigenvector-based analysis was applied to EEG data collected under a visual-motor coordination experiment. In the EEG study with participants, the results of both topological analysis and the eigenvector-based dynamical analysis were able to distinguish different experimental conditions of visual tracking task. With the dynamical analysis, we showed that different visual-motor coordination states can be distinguished by investigating the meta-stable states dynamics of the functional connectivity.https://doi.org/10.1371/journal.pone.0231767 |
| spellingShingle | Xinzhe Li Bruno Mota Toshiyuki Kondo Slawomir Nasuto Yoshikatsu Hayashi EEG dynamical network analysis method reveals the neural signature of visual-motor coordination. PLoS ONE |
| title | EEG dynamical network analysis method reveals the neural signature of visual-motor coordination. |
| title_full | EEG dynamical network analysis method reveals the neural signature of visual-motor coordination. |
| title_fullStr | EEG dynamical network analysis method reveals the neural signature of visual-motor coordination. |
| title_full_unstemmed | EEG dynamical network analysis method reveals the neural signature of visual-motor coordination. |
| title_short | EEG dynamical network analysis method reveals the neural signature of visual-motor coordination. |
| title_sort | eeg dynamical network analysis method reveals the neural signature of visual motor coordination |
| url | https://doi.org/10.1371/journal.pone.0231767 |
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