Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals
The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2021.698386/full |
| _version_ | 1818723975439581184 |
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| author | Nadia Youssef Shasha Xiao Meng Liu Haipeng Lian Renren Li Xi Chen Wei Zhang Xiaoran Zheng Yunxia Li Yingjie Li Yingjie Li |
| author_facet | Nadia Youssef Shasha Xiao Meng Liu Haipeng Lian Renren Li Xi Chen Wei Zhang Xiaoran Zheng Yunxia Li Yingjie Li Yingjie Li |
| author_sort | Nadia Youssef |
| collection | DOAJ |
| description | The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (−3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients. |
| first_indexed | 2024-12-17T21:19:03Z |
| format | Article |
| id | doaj.art-0aa9f1b3387843a59e00d418c3a7e375 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-17T21:19:03Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-0aa9f1b3387843a59e00d418c3a7e3752022-12-21T21:32:15ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882021-10-011510.3389/fncom.2021.698386698386Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography SignalsNadia Youssef0Shasha Xiao1Meng Liu2Haipeng Lian3Renren Li4Xi Chen5Wei Zhang6Xiaoran Zheng7Yunxia Li8Yingjie Li9Yingjie Li10School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaSchool of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaDepartment of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, ChinaSchool of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaDepartment of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, ChinaSchool of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaDepartment of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, ChinaSchool of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaSchool of Life Sciences, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaThe oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (−3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.https://www.frontiersin.org/articles/10.3389/fncom.2021.698386/fullconnectivitygraph theoryresting EEGmild cognitive impairmentminimum spanning treemachine learning |
| spellingShingle | Nadia Youssef Shasha Xiao Meng Liu Haipeng Lian Renren Li Xi Chen Wei Zhang Xiaoran Zheng Yunxia Li Yingjie Li Yingjie Li Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals Frontiers in Computational Neuroscience connectivity graph theory resting EEG mild cognitive impairment minimum spanning tree machine learning |
| title | Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals |
| title_full | Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals |
| title_fullStr | Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals |
| title_full_unstemmed | Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals |
| title_short | Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals |
| title_sort | functional brain networks in mild cognitive impairment based on resting electroencephalography signals |
| topic | connectivity graph theory resting EEG mild cognitive impairment minimum spanning tree machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2021.698386/full |
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