Differential nodal topology in resting-state networks as a potential imaging marker for adolescent bipolar and depressive disorders

Abstract

Overlapping clinical features and the absence of objective diagnostic markers frequently lead to the misdiagnosis of adolescent bipolar disorder (BD) as major depressive disorder (MDD). However, direct comparisons of functional brain network topology between adolescents with MDD and BD remain limited, particularly regarding their associations with clinical symptom dimensions. A total of 55 adolescents with MDD, 35 with BD, and 44 healthy controls (HC) were recruited. We hypothesized that adolescents with MDD and BD would exhibit distinct patterns of functional brain network organization associated with specific clinical symptoms. Graph-theoretical analyses were used to identify disorder-specific topological alterations, and support vector machine (SVM) models were constructed using significantly altered nodal metrics as classification features, with model performance evaluated using a nested cross-validation framework. Associations between altered nodal metrics and clinical measures were also examined. Compared with MDD patients, adolescents with BD exhibited higher nodal metrics in specific nodes within the default mode network (DMN) and prefrontal regions. Relative to HC, MDD patients showed reduced nodal connectivity and efficiency in visual cortical regions. Correlation analyses revealed that the clustering coefficients of the right dorsolateral superior frontal gyrus and right orbital superior frontal gyrus were positively associated with attention/vigilance performance, whereas the clustering coefficient of the right cuneus was associated with depressive and anxiety symptoms ( p < 0.05). The linear-kernel SVM achieved a mean classification accuracy of 78.5%, a balanced accuracy of 74.0%, and an AUC of 0.739 in distinguishing BD from MDD. Adolescents with MDD and BD exhibited distinct patterns of nodal functional brain network organization, particularly within the default mode, visual, and prefrontal systems. Altered network topology was associated with cognitive and affective symptom dimensions. SVM analyses further suggested that these topological features contain information relevant to differentiating adolescent MDD from BD. These findings provide further insight into the neural mechanisms underlying adolescent affective disorders. Not applicable.