Roman Kotov1,2, Aprajita Mohanty1, Jingwen Jin1,3, Jared X Van Snellenberg1,2,4, Greg Perlman2, Christine DeLorenzo2, Daniel N Klein1,2. 1. Department of Psychology, Stony Brook University, Stony Brook, NY, USA. 2. Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA. 3. Department of Psychology, The University of Hong Kong, Hong Kong, China. 4. Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
Abstract
BACKGROUND: Adolescence is characterized by affective and cognitive changes that increase vulnerability to depression, especially in females. Neurodevelopmental models attribute adolescent depression to abnormal responses in amygdala, striatum, and prefrontal cortex (PFC). We examined whether the strength of functional brain networks involving these regions predicts depression symptoms in adolescent females. METHODS: In this longitudinal study, we recorded resting-state functional connectivity (RSFC) in 174 adolescent females. Using a cross-validation strategy, we related RSFC profiles that included (a) a network consisting of amygdala, striatum, and PFC (within-circuit model), (b) connectivity of this network to the whole brain (extended-circuit model), and (c) a network consisting of the entire brain (whole-brain model) to depression symptoms assessed concurrently and 18 months later. RESULTS: In testing subsets, the within-circuit RSFC profiles were associated with depression symptoms concurrently and 18 months later, while the extended-circuit and whole-brain model did not explain any additional variance in depression symptoms. Connectivity related to anterior cingulate and ventromedial prefrontal cortex contributed most to the association. CONCLUSIONS: Our results demonstrate that RSFC-based brain networks that include amygdala, striatum, and PFC are stable neural signatures of concurrent and future depression symptoms, representing a significant step toward identifying the neural mechanism of depression in adolescence.
BACKGROUND: Adolescence is characterized by affective and cognitive changes that increase vulnerability to depression, especially in females. Neurodevelopmental models attribute adolescent depression to abnormal responses in amygdala, striatum, and prefrontal cortex (PFC). We examined whether the strength of functional brain networks involving these regions predicts depression symptoms in adolescent females. METHODS: In this longitudinal study, we recorded resting-state functional connectivity (RSFC) in 174 adolescent females. Using a cross-validation strategy, we related RSFC profiles that included (a) a network consisting of amygdala, striatum, and PFC (within-circuit model), (b) connectivity of this network to the whole brain (extended-circuit model), and (c) a network consisting of the entire brain (whole-brain model) to depression symptoms assessed concurrently and 18 months later. RESULTS: In testing subsets, the within-circuit RSFC profiles were associated with depression symptoms concurrently and 18 months later, while the extended-circuit and whole-brain model did not explain any additional variance in depression symptoms. Connectivity related to anterior cingulate and ventromedial prefrontal cortex contributed most to the association. CONCLUSIONS: Our results demonstrate that RSFC-based brain networks that include amygdala, striatum, and PFC are stable neural signatures of concurrent and future depression symptoms, representing a significant step toward identifying the neural mechanism of depression in adolescence.
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