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The relative contribution of individual cognitive behavioral therapy (CBT) components to treatment outcomes for child anxiety disorders (CADs) is unclear. Recent meta-analyses suggest that exposure may be the primary active ingredient in CBT for CADs, and that relaxation may be relatively less effective. This brief report tests the hypothesis that exposure-focused CBT (EF-CBT) would outperform a relaxation-based active therapy control (Relaxation Mentorship Training; RMT) for the treatment of CADs. Participants were 102 youth with CADs (mean age = 11.91, 26 males; 76.4% White, 14.7% Multiracial, 3.9% Black, 3.9% Asian, 0.9% other/do not wish to identify) as part of an ongoing neuroimaging randomized controlled trial. Participants were randomly assigned (ratio 2:1) to receive 12 sessions of EF-CBT (n = 70) or RMT (n = 32). Clinical improvement was measured at Week 12 (Clinical Global Impression – Improvement scale; CGI-I); treatment response was defined as receiving a rating of "very much" or "much improved" on the CGI-I. Anxiety severity was measured at Weeks 1, 6, 9, 12 (Pediatric Anxiety Rating Scale; PARS). Outcome measures were completed by an independent evaluator unaware of condition. EF-CBT exhibited 2.98 times higher odds of treatment completion than RMT; 13 treatment non-completers were included in analyses. Estimated treatment response rates were higher for EF-CBT (57.3%) than for RMT (19.2%). Longitudinal analyses indicated that EF-CBT was associated with faster and more pronounced anxiety reductions than RMT on the PARS (Hedges' g =.77). Results suggest that EF-CBT without relaxation is effective for CADs, and more effective than a relaxation-based intervention. [ABSTRACT FROM AUTHOR]

Spatial navigation is a fundamental part of daily life. Humans differ in their individual abilities to flexibly navigate their world, and a critical question is how this variability relates to differences in underlying brain structure. Our experiment examined individual differences in the ability to flexibly navigate routes that overlap with, and must be distinguished from, previously learned trajectories. We related differences in flexible navigation performance to differences in brain morphology in healthy young adults using voxel-based morphometry. Our findings provide novel evidence that individual differences in gray matter volume in the hippocampus and dorsolateral prefrontal cortex correlate with our ability rapidly to learn and flexibly navigate routes through our world. [ABSTRACT FROM AUTHOR]

In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)