重性抑郁障碍
胼胝体
顶叶下小叶
听力学
医学
静息状态功能磁共振成像
支持向量机
心情
功能磁共振成像
内科学
心理学
神经科学
精神科
人工智能
计算机科学
作者
Qing Chen,Yanmeng Bi,Xiaohua Zhao,Yuqi Lai,Weixin Yan,Lingpeng Xie,Tingting Gao,Shuwen Xie,Ting Zeng,Jun Li,Shanshan Kuang,Lei Gao,Zhi-Ping Lv
标识
DOI:10.1016/j.jad.2022.03.079
摘要
Major depressive disorder (MDD) is a common mood disorder. However, it still remains challenging to select sensitive biomarkers and establish reliable diagnosis methods currently. This study aimed to investigate the abnormalities of the spontaneous brain activity in the MDD and explore the clinical diagnostic value of three amplitude metrics in altered regions by applying the support vector machine (SVM) method.A total of fifty-two HCs and forty-eight MDD patients were recruited in the study. The amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF) and percent amplitude of fluctuation (PerAF) metrics were calculated to assess local spontaneous brain activity. Then we performed correlation analysis to examine the association between cerebral abnormalities and clinical characteristics. Finally, SVM analysis was applied to conduct the classification model for evaluating the diagnostic value.Two-sample t-test exhibited that MDD patients had increased ALFF value in the right caudate and corpus callosum, increased fALFF value in the same regions and increased PerAF value in the inferior parietal lobule and right caudate compared to HCs. Moreover, PerAF value in the inferior parietal lobule was negatively correlated with the slow factor scores. The SVM results showed that a combination of mean ALFF and fALFF in the right caudate and corpus callosum selected as features achieved a highest area under curve (AUC) value (0.89), accuracy (79.79%), sensitivity (65.12%) and specificity (92.16%).Collectively, we found increased mean ALFF and fALFF may serve as a potential neuroimaging marker to discriminate MDD and HCs.
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