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Frontoinsular Network Markers of Current and Future Adolescent Mood Health
Roselinde H Kaiser1, Elena Peterson1, Min Su Kang2
1Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado.
Background:
Adolescence is a developmental period in which depression and related mood syndromes often emerge, but few objective markers exist to guide diagnosis or predict symptoms. One potential mood marker is the functioning of frontoinsular networks, which undergo substantial development in adolescence and have been implicated in adult depression. To test this hypothesis, we used task-based neuroimaging to evaluate whether frontoinsular network dysfunction was linked to current and prospective mood health in adolescents.
Methods:
Adolescents (n = 40, 13-19 years of age) reporting varying levels of depressive symptom severity performed an emotional working memory task with neuroimaging. Next, teens completed a 2-week follow-up consisting of a daily diary report of negative affect and final report of depressive symptoms (n = 28 adherent). Analyses tested associations between task-related functional connectivity in frontoinsular networks and baseline or prospective measures of mood health over 2-week follow-up.
Results:
Frontoinsular task response was associated with higher current depression severity (p = .049, ηp2 = .12), increases in future depression severity (p = .018, ηp2 = .23), and more intense and labile negative affect in daily life (ps = .015 to .040, ηp2 = .22 to .30). In particular, hypoconnectivity between insula and lateral prefrontal regions of the frontoparietal network was related to both baseline and prospective mood health, and hyperconnectivity between insula and midline or temporal regions of the default network was related to prospective mood health.
Conclusions:
These findings indicate that frontoinsular imbalances are related to both current depression and changes in mood health in the near future and suggest that frontoinsular markers may hold promise as translational tools for risk prediction.
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