A hierarchical model of youth internalizing symptoms and associated neurostructural differences

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Internalizing symptoms often have their onset during youth and are associated with differences in neurostructural development. However, most studies conceptualize internalizing disorders categorically, which does not adequately capture the dimensional, transdiagnostic, highly correlated, and hierarchical nature of these symptoms. Prior studies investigating dimensions of internalizing psychopathology in youth are limited, and few have related dimensional models of youth internalizing symptoms to brain structure. The present studies leverage a large sample (N = 11,868) of 9-to-10-year-old children and hierarchical modeling techniques to delineate the underlying structure of youth internalizing problems (Study 1) and identify associated neurostructural differences using regional gray matter volume (GMV), cortical thickness (CT), and cortical surface area (SA) (Study 2). The hierarchical modeling in Study 1 revealed factors for general internalizing, distress, cognitive, fear, and somatic symptoms. After controlling for age, sex, income, parental education level, and MRI device, Study 2 results showed that general internalizing symptoms, distress symptoms, and cognitive symptoms were each associated with relatively widespread smaller regional GMV and SA. Fear symptoms were associated with a more localized pattern of smaller SA in some regions within the parietal, temporal, and insular cortices. No associations were found for CT regions or the somatic symptoms factor. Together, these results suggest that at 9-10 years of age, GMV and SA may be more relevant to internalizing psychopathology than CT; general internalizing, distress, and cognitive symptoms may have a relatively widespread inverse relationship with GMV and SA; and that general internalizing, distress, and cognitive symptoms may have stronger relationships with brain structure than somatic and fear symptoms.

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youth internalizing symptoms, neurostructural development, hierarchical modeling

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