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Functional connectivity during positive mood and anxiety treatment response in children
Matthew Mattoni1, Dana L McMakin2, Stefanie Sequeira3
1Temple University, Department of Psychology and Neuroscience, United States of America.
Abstract:
Self-reported low positive mood is common in youth anxiety disorders and has been associated with poorer treatment response. Functioning of neural affective systems may have distinct associations with treatment relative to self-reported measures and be useful for understanding biological processes related to treatment. This study examined associations between self-reported mood and pre-treatment functional connectivity network patterns during positive mood and treatment response in 73 youth (mean age = 11.1) diagnosed with anxiety disorders. Participants engaged in an fMRI autobiographical memory-based mood induction task in the scanner that successfully improved mood. Participants then completed 16 sessions of cognitive-behavioral or child-centered anxiety treatment. We found no significant differences in how mood changed during the induction task between youth who did and did not recover or respond to treatment. We then used a person-centered method to estimate effective connectivity networks at group, subgroup, and individual levels for each participant. No subgroup-level paths were identified that distinguished treatment responder subgroups. However, treatment responders showed stronger homologous connectivity in the bilateral nucleus accumbens and bilateral amygdala. Responders also showed greater centrality (network influence) of the nucleus accumbens, amygdala, and medial prefrontal cortex on the broader connectivity network. Findings suggest that greater influence of the nucleus accumbens and amygdala on distributed reward and emotion systems is associated with better treatment response, supporting a view that treatment responders capitalize on more responsive reward- and emotion-related brain networks. Future studies should replicate this finding in larger samples and investigate implicated networks as possible treatment targets.
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