Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography Signals
Previous findings have suggested that the cortex involved in walking control in freely locomotion rats. Moreover, the spectral characteristics of cortical activity showed significant differences in different walking conditions. However, whether brain connectivity presents a significant difference du...
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MDPI AG
2021-03-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/11/3/345 |
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| author | Bo Li Minjian Zhang Yafei Liu Dingyin Hu Juan Zhao Rongyu Tang Yiran Lang Jiping He |
| author_facet | Bo Li Minjian Zhang Yafei Liu Dingyin Hu Juan Zhao Rongyu Tang Yiran Lang Jiping He |
| author_sort | Bo Li |
| collection | DOAJ |
| description | Previous findings have suggested that the cortex involved in walking control in freely locomotion rats. Moreover, the spectral characteristics of cortical activity showed significant differences in different walking conditions. However, whether brain connectivity presents a significant difference during rats walking under different behavior conditions has yet to be verified. Similarly, whether brain connectivity can be used in locomotion detection remains unknown. To address those concerns, we recorded locomotion and implanted electroencephalography signals in freely moving rats performing two kinds of task conditions (upslope and downslope walking). The Granger causality method was used to determine brain functional directed connectivity in rats during these processes. Machine learning algorithms were then used to categorize the two walking states, based on functional directed connectivity. We found significant differences in brain functional directed connectivity varied between upslope and downslope walking. Moreover, locomotion detection based on brain connectivity achieved the highest accuracy (91.45%), sensitivity (90.93%), specificity (91.3%), and F1-score (91.43%). Specifically, the classification results indicated that connectivity features in the high gamma band contained the most discriminative information with respect to locomotion detection in rats, with the support vector machine classifier exhibiting the most efficient performance. Our study not only suggests that brain functional directed connectivity in rats showed significant differences in various behavioral contexts but also proposed a method for classifying the locomotion states of rat walking, based on brain functional directed connectivity. These findings elucidate the characteristics of neural information interaction between various cortical areas in freely walking rats. |
| first_indexed | 2024-03-10T13:25:22Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T13:25:22Z |
| publishDate | 2021-03-01 |
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| spelling | doaj.art-9f1282f8a25949faaea16faf6cfa34142023-11-21T09:41:24ZengMDPI AGBrain Sciences2076-34252021-03-0111334510.3390/brainsci11030345Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography SignalsBo Li0Minjian Zhang1Yafei Liu2Dingyin Hu3Juan Zhao4Rongyu Tang5Yiran Lang6Jiping He7School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaDepartment of Materials Processing Engineering, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, ChinaBeijing Innovation Centre for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, ChinaBeijing Innovation Centre for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaPrevious findings have suggested that the cortex involved in walking control in freely locomotion rats. Moreover, the spectral characteristics of cortical activity showed significant differences in different walking conditions. However, whether brain connectivity presents a significant difference during rats walking under different behavior conditions has yet to be verified. Similarly, whether brain connectivity can be used in locomotion detection remains unknown. To address those concerns, we recorded locomotion and implanted electroencephalography signals in freely moving rats performing two kinds of task conditions (upslope and downslope walking). The Granger causality method was used to determine brain functional directed connectivity in rats during these processes. Machine learning algorithms were then used to categorize the two walking states, based on functional directed connectivity. We found significant differences in brain functional directed connectivity varied between upslope and downslope walking. Moreover, locomotion detection based on brain connectivity achieved the highest accuracy (91.45%), sensitivity (90.93%), specificity (91.3%), and F1-score (91.43%). Specifically, the classification results indicated that connectivity features in the high gamma band contained the most discriminative information with respect to locomotion detection in rats, with the support vector machine classifier exhibiting the most efficient performance. Our study not only suggests that brain functional directed connectivity in rats showed significant differences in various behavioral contexts but also proposed a method for classifying the locomotion states of rat walking, based on brain functional directed connectivity. These findings elucidate the characteristics of neural information interaction between various cortical areas in freely walking rats.https://www.mdpi.com/2076-3425/11/3/345electroencephalographylocomotion detectiongranger causalitybrain functional directed connectivityfreely walking ratsmachine learning |
| spellingShingle | Bo Li Minjian Zhang Yafei Liu Dingyin Hu Juan Zhao Rongyu Tang Yiran Lang Jiping He Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography Signals Brain Sciences electroencephalography locomotion detection granger causality brain functional directed connectivity freely walking rats machine learning |
| title | Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography Signals |
| title_full | Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography Signals |
| title_fullStr | Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography Signals |
| title_full_unstemmed | Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography Signals |
| title_short | Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography Signals |
| title_sort | rat locomotion detection based on brain functional directed connectivity from implanted electroencephalography signals |
| topic | electroencephalography locomotion detection granger causality brain functional directed connectivity freely walking rats machine learning |
| url | https://www.mdpi.com/2076-3425/11/3/345 |
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