Mao Ning, Che Kaili, Chu Tongpeng, Li Yuna, Wang Qinglin, Liu Meijie, Ma Heng, Wang Zhongyi, Lin Fan, Wang Bin, Ji Haixia
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
Medical Imaging Research Institute, Binzhou Medical University, Yantai, China.
Front Psychol. 2020 Jul 21;11:1784. doi: 10.3389/fpsyg.2020.01784. eCollection 2020.
To explore the changes of brain function and conduct clinical differential diagnosis based on support vector machine (SVM) in adolescent patients with depression. A total of 24 adolescent patients with depression according to CCMD-3 and DSM-5 and 23 gender, education level, body mass index, and age matched healthy controls were assessed with 17-item Hamilton Depression Rating Scale (HAMD). HAMD scores were requested from ≥17 of patients. Three-dimensional T1 and resting-state functional magnetic resonance imaging data were acquired from all participants. The data were analyzed using SPM 12 and REST1.8. Two-sample -test was conducted to compare regional homogeneity (ReHo) values among the groups of participants. Finally, based on SVM classification, clinical differential diagnosis of the patients was carried out. The receiver operator characteristic (ROC) curve were used to confirm the performance of the SVM model. An increase ReHo values were observed in the lingual gyrus, middle occipital gyrus, postcentral gyrus, and precentral gyrus, whereas a decrease in ReHo was found in vermis compared with the control group. The SVM model showed good performance in classification prediction of adolescent depression, with an area under curve (AUC) of 0.778 [95% confidence interval (CI), 0.661-0.797]. The changes in the spontaneous neural activity of these regions may play an important role in the neuropathological mechanism of adolescent depression and may provide promising markers for clinical evaluation.
探讨基于支持向量机（SVM）的青少年抑郁症患者脑功能变化及进行临床鉴别诊断。根据中国精神障碍分类与诊断标准第3版（CCMD - 3）和精神疾病诊断与统计手册第5版（DSM - 5），选取24例青少年抑郁症患者，并选取23例性别、教育程度、体重指数和年龄相匹配的健康对照者，采用17项汉密尔顿抑郁量表（HAMD）进行评估。要求患者HAMD得分≥17分。采集所有参与者的三维T1和静息态功能磁共振成像数据。使用SPM 12和REST1.8对数据进行分析。进行两样本t检验以比较参与者组间的局部一致性（ReHo）值。最后，基于SVM分类对患者进行临床鉴别诊断。采用受试者工作特征（ROC）曲线来确认SVM模型的性能。与对照组相比，舌回、枕中回、中央后回和中央前回的ReHo值升高，而小脑蚓部的ReHo值降低。SVM模型在青少年抑郁症的分类预测中表现良好，曲线下面积（AUC）为0.778 [95%置信区间（CI），0.661 - 0.797]。这些区域自发神经活动的变化可能在青少年抑郁症的神经病理机制中起重要作用，并可能为临床评估提供有前景的标志物。
