Sunday, June 1, 2025
6/1/2025
Differentiating Primary and Secondary Subtypes of Callous Unemotional Traits During Early Childhood - PROJECT SUMMARY Childhood disruptive behavior disorders (DBDs) are one of the most common forms of psychopathology in children, with a worldwide pooled prevalence of nearly 6%. Much research has linked the presence of callous- unemotional (CU) traits (i.e., low empathy, guilt, and prosociality) to increased risk for severe DBDs across development. Moreover, children with DBDs and CU traits show poorer treatment outcomes when compared to children with DBDs alone. Heterogeneity within the clinical phenotype of CU traits is thought to compound this treatment gap. In particular, children with CU traits can be further differentiated into primary (i.e., low fear, low anxiety) and secondary (i.e., anxiety and trauma) subtypes. Theoretical models suggest that primary and secondary CU traits develop through distinct etiological pathways. However, the physiological mechanisms which underlie the development of these subtypes are not well understood and studies have yet to use advanced machine learning techniques to derive biologically informed subtypes. Here, I propose that the development of CU traits occurs via two distinct etiological pathways underpinned by unique profiles of physiological reactivity and exposure to adversity. As part of an ongoing, longitudinal study, preschool aged children (n=500) complete a variety of behavioral tasks while parasympathetic nervous system (PNS) function, as indexed by respiratory sinus arrythmia (RSA), is measured. Parent-reported questionnaires assessing CU traits, DBDs, anxiety, and exposure to adversity are also collected. I hypothesize that RSA will differentially predict CU trait at baseline and 2-year follow-up, such that higher RSA will be related to CU traits for children exposed to more adversity, and lower RSA will be related to CU traits for children regardless of exposure to adversity (Aim 1). Additionally, I hypothesize that a novel semi-supervised machine learning algorithm will identify “biotypes” of CU traits based on patterns of PNS function. Specifically, I expect to find a primary profile characterized by low levels of RSA across tasks, associated with lower anxiety and fearlessness, and a secondary profile characterized by high levels of RSA, associated with anxiety and emotion dysregulation (Aim 2). The overarching goal of this proposal is to gain a better understanding of how subtypes of CU traits develop based on distinct patterns of physiological reactivity in the context of differential exposure to adversity. These findings can inform etiological models of CU traits, while simultaneously informing early identification and precision treatment of childhood DBDs. With guidance from a strong mentorship team with relevant expertise (Drs. Waller, Wagner, Kimonis, Davatzikos, and Zisser), my proposal and the accompanying training plan provide an ideal foundation for my planned career as an independently funded, leading clinical scientist studying biomarkers of externalizing psychopathology.
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