ABSTRACT
Parenting behaviors and neighborhood environment influence the development of adolescents' brains and behaviors. Simultaneous trajectories of brain and behavior, however, are understudied, especially in these environmental contexts. In this four-wave study spanning 9-18 years of age (N=224 at baseline, N=138 at final assessment) we used longitudinal k-means clustering to identify clusters of participants with distinct trajectories of uncinate fasciculus (UF) fractional anisotropy (FA) and anxiety symptoms; we examined behavioral outcomes and identified environmental factors that predicted cluster membership. We identified three clusters of participants: 1) high UF FA and low symptoms ("low-risk"); 2) low UF FA and high symptoms ("high-risk"); and 3) low UF FA and low symptoms ("resilient"). Adolescents in disadvantaged neighborhoods were more likely to be in the resilient than high-risk cluster if they also experienced maternal warmth. Thus, neighborhood disadvantage may confer neural risk for psychopathology that can be buffered by maternal warmth, highlighting the importance of considering multiple environmental influences in understanding emotional and neural development in youth.
ABSTRACT
Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns of the RDoC framework, our study employed a latent variable approach, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. External validation was performed with 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Our results indicate that a bifactor model with a task-general domain and splitting the cognitive systems domain into sub-domains better fits the current corpus of tfMRI data than the current RDoC framework. Our data-driven validation supports revising the RDoC framework to accurately reflect underlying brain circuitry.