Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke
Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-fre...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.785397/full |
| _version_ | 1819157056646545408 |
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| author | Bin Ren Bin Ren Kun Yang Kun Yang Li Zhu Li Zhu Lang Hu Lang Hu Tao Qiu Wanzeng Kong Wanzeng Kong Jianhai Zhang Jianhai Zhang |
| author_facet | Bin Ren Bin Ren Kun Yang Kun Yang Li Zhu Li Zhu Lang Hu Lang Hu Tao Qiu Wanzeng Kong Wanzeng Kong Jianhai Zhang Jianhai Zhang |
| author_sort | Bin Ren |
| collection | DOAJ |
| description | Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500–800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke. |
| first_indexed | 2024-12-22T16:02:42Z |
| format | Article |
| id | doaj.art-9a6ba44f96f04766836840a00e6c2beb |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-22T16:02:42Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-9a6ba44f96f04766836840a00e6c2beb2022-12-21T18:20:39ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882022-03-011610.3389/fncom.2022.785397785397Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After StrokeBin Ren0Bin Ren1Kun Yang2Kun Yang3Li Zhu4Li Zhu5Lang Hu6Lang Hu7Tao Qiu8Wanzeng Kong9Wanzeng Kong10Jianhai Zhang11Jianhai Zhang12College of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaKey Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, ChinaCollege of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaKey Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, ChinaCollege of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaKey Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, ChinaCollege of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaKey Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, ChinaDepartment of Neurology, Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, ChinaCollege of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaKey Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, ChinaCollege of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaKey Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, ChinaEvaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500–800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.https://www.frontiersin.org/articles/10.3389/fncom.2022.785397/fullstrokecross-frequency couplingfunctional connectivitybrain networkmental rotation |
| spellingShingle | Bin Ren Bin Ren Kun Yang Kun Yang Li Zhu Li Zhu Lang Hu Lang Hu Tao Qiu Wanzeng Kong Wanzeng Kong Jianhai Zhang Jianhai Zhang Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke Frontiers in Computational Neuroscience stroke cross-frequency coupling functional connectivity brain network mental rotation |
| title | Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke |
| title_full | Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke |
| title_fullStr | Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke |
| title_full_unstemmed | Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke |
| title_short | Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke |
| title_sort | multi granularity analysis of brain networks assembled with intra frequency and cross frequency phase coupling for human eeg after stroke |
| topic | stroke cross-frequency coupling functional connectivity brain network mental rotation |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.785397/full |
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