Brain-age prediction has emerged as a novel approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. This staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. We propose a multidimensional brain-age index (MBAI) that provides regional age predictions. Using a database of 556 individuals, we identified clusters of imaging features with distinct developmental trajectories and built machine learning models to obtain brain-age predictions from each of the clusters. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age. Importantly, brain-ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to distinguish disorder groups (e.g., depression and oppositional defiant disorder) from healthy controls. In summary, we show that MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns that may serve as biomarkers that contribute to our understanding of healthy and pathological brain development and the characterization and diagnosis of psychiatric disorders.

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The purpose of this study it to build a machine learning model to predict dietary lapses with comparable accuracy, sensitivity, and specificity to previous literature while recovering predictor interactions. The sample for the current study consisted of merged data from two separate studies of individuals with obesity/overweight (total N = 87). Participants completed six ecological momentary assessment surveys per day where they were asked about 16 risk factors of lapse and if they had lapsed from their dietary prescriptions since the previous survey. Alcohol consumption and self‐efficacy were the most prevalent in the top 10 stable interactions. Alcohol consumption decreased the protective effect of self‐efficacy, motivation, and planning. Higher planning predicted higher risk for lapse only when consuming alcohol. Low motivation, hunger, cravings, and lack of healthy food availability increased the protective effect of self‐efficacy. Higher self‐efficacy increased risk effect of positive mood and having recently eaten a meal on lapse. For individuals with lower levels of self‐efficacy, planning increased the risk of lapse. Alcohol intake and self‐efficacy interact with several variables to predict dietary lapses, and these interactions should be targeted in just‐in‐time adaptive interventions that deliver interventions for lapses.

In response to school-based arrests representing a growing proportion of youth arrests nationwide, several programs have emerged to divert youth from school-based arrests. However, few such initiatives have undergone empirical evaluation, and none have been evaluated with a focus on long-term (i.e., 4-to 5-year) youth outcomes. To address this gap, this study compared long-term recidivism arrest and school outcomes (i.e., out-of-school suspension, dropout, and on-time graduation) among students diverted through the Philadelphia Police School Diversion Program (n = 427) and comparable students arrested in Philadelphia schools (n = 531). Mixed-effects logistic regression results revealed that diverted youth were significantly less likely than matched arrested youth to experience a recidivism arrest within 5 years of their initial school-based incident. However, we did not observe significant between-group differences for school-related outcomes once relevant covariates were considered. Findings indicate small yet significant long-term program effects on public safety and potential time-limited effects on exclusionary discipline.