Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents  

作　　者：Zhi-Hui Yu Ren-Qiang Yu Xing-Yu Wang Wen-Yu Ren Xiao-Qin Zhang Wei Wu Xiao Li Lin-Qi Dai Ya-Lan Lv 

机构地区：[1]Department of Radiology,The First Affiliated Hospital of Chongqing Medical University,Chongqing 400016,China [2]Department of Psychiatry,The First Affiliated Hospital of Chongqing Medical University,Chongqing 400016,China [3]School of Medical Informatics,Chongqing Medical University,Chongqing 400016,China

出　　处：《World Journal of Psychiatry》2024年第11期1696-1707,共12页世界精神病学杂志（英文）

摘　　要：BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity(FC).AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study.Using resting-state functional magnetic resonance imaging,the FC was compared between the adolescents with MDD and the healthy controls,with the bilateral amygdala serving as the seed point,followed by statistical analysis of the results.The support vector machine(SVM)method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.RESULTS Compared to the controls and using the bilateral amygdala as the region of interest,patients with MDD showed significantly lower FC values in the left inferior temporal gyrus,bilateral calcarine,right lingual gyrus,and left superior occipital gyrus.However,there was an increase in the FC value in Vermis-10.The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls,achieving a diagnostic accuracy of 83.91%,sensitivity of 79.55%,specificity of 88.37%,and an area under the curve of 67.65%.CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

关 键 词：Major depressive disorder ADOLESCENT Support vector machine Machine learning Resting-state functional magnetic resonance imaging NEUROIMAGING BIOMARKER 

分 类 号：R749.4[医药卫生—神经病学与精神病学]