School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China.
Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Psychiatry Res Neuroimaging. 2022 Jul;323:111485. doi: 10.1016/j.pscychresns.2022.111485. Epub 2022 Apr 26.
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.
社交焦虑障碍(SAD)是儿童和青少年中常见的焦虑障碍。对成年人 SAD 的研究报告称,大脑在群体水平上存在结构和功能异常。然而,有证据表明,青少年和成年人之间的焦虑相关大脑异常存在差异。由于儿童和青少年可承受的扫描时间有限,因此优化扫描任务对于儿童和青少年 SAD 研究至关重要。因此,我们需要确定大脑结构、静息态 fMRI 和自然影像是否能够实现儿童和青少年 SAD 的个体化识别,哪种测量方法更有效,以及是否可以汇集多种模态的特征来提高 SAD 的识别能力。我们通过基于体素的脑特征构建机器学习模型来全面解决这些问题。我们发现,自然影像 fMRI 的分类准确率(69.17%)高于其他模态,且分类性能取决于电影的内容。分类模型还确定了有贡献的脑区,其中一些与 SAD 症状评分存在相关性。然而,汇集来自三种模态的脑特征并不能提高分类准确率。这些结果支持在识别有 SAD 风险的儿童和青少年时应用精心设计的自然影像。