BMC psychiatry · 2025
Diagnosis of adolescent depression with sleep disorder based on network topological attributes and functional connectivity.
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Hu Songhao, Zuo Xingyue, Yu Dairui, Huang Jiaqi, Zhu Shukun, Xu Li, Wu Ming, Liu Dandan, Xiao Jiping, Zhang Mian, Li Yifei, Zhu Daomin, Zhu Li
Аннотация
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 (reflecting inter-regional relationships) and BC (quantifying a node's importance for network information flow) were analyzed. Differences in FC and BC between depressed adolescents with sleep disorders and depressed adolescents without sleep disorders 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), indicating altered information flow hubs. Alterations in FC were observed across several regions, with the most pronounced changes occurring between the left middle occipital gyrus and MTG.R (MOG.L-MTG.R). The SVM model, using combined whole-brain BC and FC features, achieved 81.40% accuracy during LOOCV and identified discriminative features. Predictive performance was validated externally, yielding 74.19% accuracy.
CONCLUSIONS: Significant functional brain network alterations occur in depressed adolescents with sleep disorders. Integrating brain network analysis(BC and FC analysis) with machine learning techniques offers a promising approach to identifying neuroimaging markers for diagnosing sleep disorders in depressed adolescents.
METHODS: 117 adolescents diagnosed with depression underwent resting-state fMRI. Whole-brain FC (reflecting inter-regional relationships) and BC (quantifying a node's importance for network information flow) were analyzed. Differences in FC and BC between depressed adolescents with sleep disorders and depressed adolescents without sleep disorders 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), indicating altered information flow hubs. Alterations in FC were observed across several regions, with the most pronounced changes occurring between the left middle occipital gyrus and MTG.R (MOG.L-MTG.R). The SVM model, using combined whole-brain BC and FC features, achieved 81.40% accuracy during LOOCV and identified discriminative features. Predictive performance was validated externally, yielding 74.19% accuracy.
CONCLUSIONS: Significant functional brain network alterations occur in depressed adolescents with sleep disorders. Integrating brain network analysis(BC and FC analysis) with machine learning techniques offers a promising approach to identifying neuroimaging markers for diagnosing sleep disorders in depressed adolescents.