| Autor: |
Mao, Yu, Wei, Dongtao, Yang, Wenjing, Chen, Qunlin, Sun, Jiangzhou, Yu, Yaxu, Li, Yu, Zhuang, Kaixiang, Wang, Xiaoqin, He, Li, Feng, Tingyong, Lei, Xu, He, Qinghua, Chen, Hong, Qin, Shaozheng, Liu, Yunzhe, Qiu, Jiang |
| Zdroj: |
IEEE Transactions on Affective Computing; Oct-Dec2023, Vol. 14 Issue 4, p2646-2656, 11p |
| Abstrakt: |
The long-lasting global pandemic of Coronavirus disease 2019 (COVID-19) has changed our daily life in many ways and put heavy burden on our mental health. Having a predictive model of negative emotions during COVID-19 is of great importance for identifying potential risky population. To establish a neural predictive model achieving both good interpretability and predictivity, we have utilized a large-scale (n = 542) longitudinal dataset, alongside two independent samples for external validation. We built a predictive model based on psychologically meaningful resting state neural activities. The whole-brain resting-state neural activity and social-psychological profile of the subjects were obtained from Sept. to Dec. 2019 (Time 1). Their negative emotions were tracked and re-assessed twice, on Feb 22 (Time 2) and Apr 24 (Time 3), 2020, respectively. We first applied canonical correlation analysis on both the neural profiles and psychological profiles collected on Time 1, this step selects only the psychological meaningful neural patterns for later model construction. We then trained the neural predictive model using those identified features on data obtained on Time 2. It achieved a good prediction performance (r = 0.44, p = 8.13 × 10-27). The two most important neural predictors are associated with self-control and social interaction. This study established an effective neural prediction model of negative emotions, achieving good interpretability and predictivity. It will be useful for identifying potential risky population of emotional disorders related to COVID-19. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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