Frontal Asymmetry as a Prospective Predictor of Transdiagnostic Internalizing Domains: Using the Minnesota Twin Family Study to Predict Outcomes from Childhood to Adolescence and Adulthood
Author
Reznik, Samantha JillIssue Date
2020Advisor
Allen, John J.B.
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The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Abstract
Internalizing psychopathology is the leading cause of disability worldwide (Mathers, Boerma, & Ma Fat, 2004). A large body of research suggests that relative left frontal activity (rLFA) may be a neurophysiological biomarker of internalizing psychopathology (Allen & Reznik, 2015). RLFA has been found to prospectively predict both anxiety and depression in childhood (Mitchell & Pössel, 2012), adolescence (Mitchell & Pössel, 2012), and adulthood (Blackhart, Minnix, & Kline, 2006; Nusslock et al., 2011). Yet, it remains unclear whether rLFA can predict internalizing psychopathology across developmental stages. The present study examined rLFA at age 11 as a predictor of depression and anxiety across early adolescence (11-14), late adolescence (14-17), and late adolescence to early adulthood (17-24). In the longest longitudinal study of rLFA to date, we found that rLFA at age 11 predicted depression status between ages 14-17, 17-24, and by age 14, 17, and 24. These results suggest that rLFA at age 11 can predict depression both during certain developmental stages and across developmental stages. We also found that rLFA predicted anhedonia between ages 17 and 24. ROC curve analysis revealed that rLFA predicted anhedonia at levels slightly above chance but not depression. Given the size of these relationships, rLFA may not be used independently in a clinical setting to predict anhedonia and depression but may lend predictive power that could be used as part of a larger algorithm. This work has significant implications for identification of those at risk for the disorder.Type
textElectronic Dissertation
Degree Name
Ph.D.Degree Level
doctoralDegree Program
Graduate CollegePsychology