可解释性
人工神经网络
意识的神经相关物
人口
人工智能
心理学
2019年冠状病毒病(COVID-19)
机器学习
计算机科学
医学
疾病
精神科
认知
环境卫生
病理
传染病(医学专业)
作者
Yu Mao,Dongtao Wei,Wenjing Yang,Qunlin Chen,Jie Sun,Yaxu Yu,Li Yu,Kaixiang Zhuang,Xiaoqin Wang,Li He,Tao Feng,Lei Xu,Qinghua He,Hong Chen,Shaozheng Qin,Yunzhe Liu,Jiang Qiu
标识
DOI:10.1109/taffc.2022.3181671
摘要
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.
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