Psychological medicine · 2026
Transcriptional signatures associated with cortical similarity network changes in adolescents with major depressive disorder.
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Wu Baolin, Zhang Xun, Zhang Qian, Xiao Dan, Gong Qiyong
Аннотация
BACKGROUND: Adolescent major depressive disorder (MDD) has been associated with cortical structural abnormalities. However, whether these structural changes correlate with gene expression profiles remains unclear. Here, we investigated the cortical similarity network alterations and the associated transcriptional patterns in adolescents with MDD using a novel Morphometric Inverse Divergence (MIND) network model.
METHODS: Using structural images from 174 adolescents with MDD and 82 healthy controls (HCs), we constructed individual MIND networks based on multiple morphological features. We compared regional MIND between groups and developed machine learning models to assess the potential of MIND for distinguishing patients from HCs. Spatial associations between MIND differences and gene expression profiles were assessed using partial least squares regression, followed by gene enrichment analyses to identify associated biological pathways, cell types, and developmental trajectories.
RESULTS: Regional MIND abnormalities in adolescent MDD patients were primarily located in the orbitofrontal cortex, as well as in sensorimotor and visual network regions. Using MIND features, the optimal machine learning model achieved a high area under the curve (AUC) of 0.824 and generalized well to the external independent dataset (AUC = 0.724). MIND changes were associated with gene expression related to synaptic signaling, neurodevelopment, metabolism, and specific cell types. Developmental trajectory analysis suggested distinct, region-specific susceptibility windows for adolescent MDD.
CONCLUSIONS: By linking MIND network alterations and transcriptional expression profiles, this study provides new insights into the neural basis and associated molecular mechanisms underlying adolescent MDD and underscores the diagnostic potential of MIND.
METHODS: Using structural images from 174 adolescents with MDD and 82 healthy controls (HCs), we constructed individual MIND networks based on multiple morphological features. We compared regional MIND between groups and developed machine learning models to assess the potential of MIND for distinguishing patients from HCs. Spatial associations between MIND differences and gene expression profiles were assessed using partial least squares regression, followed by gene enrichment analyses to identify associated biological pathways, cell types, and developmental trajectories.
RESULTS: Regional MIND abnormalities in adolescent MDD patients were primarily located in the orbitofrontal cortex, as well as in sensorimotor and visual network regions. Using MIND features, the optimal machine learning model achieved a high area under the curve (AUC) of 0.824 and generalized well to the external independent dataset (AUC = 0.724). MIND changes were associated with gene expression related to synaptic signaling, neurodevelopment, metabolism, and specific cell types. Developmental trajectory analysis suggested distinct, region-specific susceptibility windows for adolescent MDD.
CONCLUSIONS: By linking MIND network alterations and transcriptional expression profiles, this study provides new insights into the neural basis and associated molecular mechanisms underlying adolescent MDD and underscores the diagnostic potential of MIND.