Diagnosis of adolescent depression with sleep disorder based on network topological attributes and functional connectivity

Background Sleep disorders are common among adolescents with depression, yet lack reliable neuroimaging diagnostic techniques. This study aimed to predict sleep disorders in depressed adolescents using brain network features, including betweenness centrality (BC) and functional connectivity (FC). Methods 117 adolescents diagnosed with depression underwent resting-state fMRI. Whole-brain FC and BC were analyzed. Differences in FC and BC between depressed adolescents with sleep disorders and those without were compared using two-sample t-tests in a discovery dataset (n=86). A support vector machine (SVM) classifier was trained to differentiate these groups. Validation employed leave-one-out cross-validation (LOOCV) internally and an independent dataset (n=31). Results Depressed adolescents with sleep disorders showed elevated BC in the right middle temporal gyrus (MTG.R) and decreased BC in the left median cingulate and paracingulate gyri (DCG.L) and left caudate nucleus (CAU.L). Alterations in FC were observed across several regions, with pronounced changes between the left middle occipital gyrus and MTG.R (MOG.L-MTG.R). The SVM model using combined BC and FC features achieved 81.40% accuracy during LOOCV and 74.19% accuracy in external validation. Conclusions Significant functional brain network alterations occur in depressed adolescents with sleep disorders. Integrating brain network analysis (BC and FC) with machine learning offers a promising approach to identifying neuroimaging markers for diagnosing sleep disorders in depressed adolescents.

Songhao Hu, Xingyue Zuo, Dairui Yu, Jiaqi Huang, Shukun Zhu, Li Xu, Ming Wu, Dandan Liu, Jiping Xiao, Mian Zhang, Yifei Li, Daomin Zhu, Li Zhu