Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise
Abstract Objectives To investigate the method of resting EEG assessment of depressive symptoms in college students and to clarify the relationship between physical activity level and depressive symptoms in college students. Methods Using a cross-sectional study design, 140 current full-time college...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Psychiatry |
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| Online Access: | https://doi.org/10.1186/s12888-023-05352-0 |
| _version_ | 1827709142341517312 |
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| author | Lili Li Peng Wang Shufan Li Qun Zhao Zhaosong Yin Wei Guan Sicheng Chen Xing Wang Jinlin Liao |
| author_facet | Lili Li Peng Wang Shufan Li Qun Zhao Zhaosong Yin Wei Guan Sicheng Chen Xing Wang Jinlin Liao |
| author_sort | Lili Li |
| collection | DOAJ |
| description | Abstract Objectives To investigate the method of resting EEG assessment of depressive symptoms in college students and to clarify the relationship between physical activity level and depressive symptoms in college students. Methods Using a cross-sectional study design, 140 current full-time college students were recruited to complete the Self-Rating Depression Scale and the International Physical Activity Questionnaire, and 10-min resting EEGs were obtained. Results 1) The power values of δ and α2 in the central (C3, C4) and parietal (P3, P4) regions of depressed college students were significantly higher than those of normal college students. And the degree of lateralization of δ, θ, α1, and α2 in the prefrontal regions (F3, F4) of depressed college students was significantly higher than that of normal college students (all P < 0. 008). 2) The recall rate of the depression recognition model for college students based on resting EEG was 66.67%, the precision was 65.05%, and the AUCs of the training group and validation group were 0.791 and 0.786, respectively, with better detection effects. 3) The two indicators, δ (C3 + C4) and α1 (F4-F3), are significantly correlated with IPAQ scores, and among college students who engage in ball games most commonly, those with a higher level of physical activity have lower δ (C3 + C4) and higher α1 (F4-F3), while among those who engage in resistance training most commonly, higher levels of physical activity are associated with lower δ (C3 + C4). Conclusion The resting EEG of depressed college students has a certain specificity that can objectively assess the risk of developing depressive symptoms in college students. Physical activity is associated with abnormal EEG signals of depressive symptoms. Different types of physical activity may modulate the relationship between physical activity levels and EEG indicators. |
| first_indexed | 2024-03-10T17:16:49Z |
| format | Article |
| id | doaj.art-faf70eeedc3a4c718e9fb89470cb60d0 |
| institution | Directory Open Access Journal |
| issn | 1471-244X |
| language | English |
| last_indexed | 2024-03-10T17:16:49Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj.art-faf70eeedc3a4c718e9fb89470cb60d02023-11-20T10:29:20ZengBMCBMC Psychiatry1471-244X2023-11-0123111410.1186/s12888-023-05352-0Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exerciseLili Li0Peng Wang1Shufan Li2Qun Zhao3Zhaosong Yin4Wei Guan5Sicheng Chen6Xing Wang7Jinlin Liao8Department of Physical Education, Shanghai University of Engineering ScienceShanghai University of SportShanghai University of SportDepartment of Physical Education, Donghua UniversityShanghai University of SportShanghai University of SportShanghai University of SportShanghai University of SportCollege of Physical Education and Health, Longyan UniversityAbstract Objectives To investigate the method of resting EEG assessment of depressive symptoms in college students and to clarify the relationship between physical activity level and depressive symptoms in college students. Methods Using a cross-sectional study design, 140 current full-time college students were recruited to complete the Self-Rating Depression Scale and the International Physical Activity Questionnaire, and 10-min resting EEGs were obtained. Results 1) The power values of δ and α2 in the central (C3, C4) and parietal (P3, P4) regions of depressed college students were significantly higher than those of normal college students. And the degree of lateralization of δ, θ, α1, and α2 in the prefrontal regions (F3, F4) of depressed college students was significantly higher than that of normal college students (all P < 0. 008). 2) The recall rate of the depression recognition model for college students based on resting EEG was 66.67%, the precision was 65.05%, and the AUCs of the training group and validation group were 0.791 and 0.786, respectively, with better detection effects. 3) The two indicators, δ (C3 + C4) and α1 (F4-F3), are significantly correlated with IPAQ scores, and among college students who engage in ball games most commonly, those with a higher level of physical activity have lower δ (C3 + C4) and higher α1 (F4-F3), while among those who engage in resistance training most commonly, higher levels of physical activity are associated with lower δ (C3 + C4). Conclusion The resting EEG of depressed college students has a certain specificity that can objectively assess the risk of developing depressive symptoms in college students. Physical activity is associated with abnormal EEG signals of depressive symptoms. Different types of physical activity may modulate the relationship between physical activity levels and EEG indicators.https://doi.org/10.1186/s12888-023-05352-0College studentsDepressionEEGExerciseLogistic regressionMechanisms |
| spellingShingle | Lili Li Peng Wang Shufan Li Qun Zhao Zhaosong Yin Wei Guan Sicheng Chen Xing Wang Jinlin Liao Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise BMC Psychiatry College students Depression EEG Exercise Logistic regression Mechanisms |
| title | Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise |
| title_full | Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise |
| title_fullStr | Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise |
| title_full_unstemmed | Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise |
| title_short | Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise |
| title_sort | construction of a resting eeg based depression recognition model for college students and possible mechanisms of action of different types of exercise |
| topic | College students Depression EEG Exercise Logistic regression Mechanisms |
| url | https://doi.org/10.1186/s12888-023-05352-0 |
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