心理学
神经影像学
焦虑
社交焦虑
静息状态功能磁共振成像
模式
大脑活动与冥想
发展心理学
临床心理学
神经科学
精神科
脑电图
社会科学
社会学
作者
Qinjian Zhang,Baobin Li,Shuyu Jin,Wenjing Liu,Jingjing Liu,Shuqi Xie,Lei Zhang,Yinzhi Kang,Yue Ding,Xiaochen Zhang,Wenhong Cheng,Zhi Yang
标识
DOI:10.1016/j.pscychresns.2022.111485
摘要
Social anxiety disorder (SAD) is a common anxiety disorder in childhood and adolescence. Studies on SAD in adults have reported both structural and functional aberrancies of the brain at the group level. However, evidence has shown differences in anxiety-related brain abnormalities between adolescents and adults. Since children and adolescents can afford limited scan time, optimizing the scan tasks is essential for SAD research in children and adolescents. Thus, we need to address whether brain structure, resting-state fMRI, and naturalistic imaging enable individualized identification of SAD in children and adolescents, which measurement is more effective, and whether pooling multi-modal features can improve the identification of SAD. We comprehensively addressed these questions by building machine learning models based on parcel-wise brain features. We found that naturalistic fMRI yielded higher classification accuracy (69.17%) than the other modalities and the classification performance showed dependence on the contents of the movie. The classification models also identified contributing brain regions, some of which exhibited correlations with the symptoms scores of SAD. However, pooling brain features from the three modalities did not help enhance the classification accuracy. These results support the application of carefully designed naturalistic imaging in recognizing children and adolescents at risk of SAD.
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