Symptom-specific neural circuits and corresponding transcriptomic profiles in depression.

BACKGROUND

Depression is a clinically heterogeneous disorder with symptom-specific neural correlates. However, systematic mapping of brain networks underlying individual symptoms remains limited, hindering deeper neurobiological understanding.

METHODS

This study used the functional connectivity network mapping method to explore neural circuits associated with specific depressive symptoms, integrating resting-state functional magnetic resonance imaging data from multiple studies. Additionally, transcriptomic analysis was performed to examine the relationship between symptom-specific functional connectivity networks and gene expression.

RESULTS

This study identified distinct neural circuits associated with specific depressive symptoms. Damage networks related to anxiety, cognitive impairment, and rumination were primarily localized within the limbic network or default mode network, while anhedonia and suicide ideation/attempt were mainly linked to the visual network. Transcriptomic analysis further identified genes linked to these networks, highlighting synaptic function, neural plasticity, and neurotransmission as key processes driven by gene expression underlying depressive symptoms. Functional enrichment analysis also revealed distinct neurobiological profiles for different symptoms, including specific immune-related processes for cognitive impairment and neurodevelopmental features for sleep disturbance.

CONCLUSIONS

This study identified neural circuits associated with specific depressive symptoms and their associated brain gene expression. The findings revealed symptom-specific damage networks, highlighting key biological processes including synaptic signaling, neural plasticity, and neurotransmission. Additionally, distinct neurobiological features were observed, including immune-related processes linked to cognitive impairment and neurodevelopmental characteristics related to sleep disturbance. These findings may provide new insights into the neurobiological mechanisms of depression.