• Energy Research

Ubiquitously expressed genes have been implicated in a variety of specific behaviors, including responses to ethanol. However, the mechanisms that confer this behavioral specificity have remained elusive. Previously, we showed that the ubiquitously expressed small GTPase Arf6 is required for normal ethanol-induced sedation in adult Drosophila. Here, we show that this behavioral response also requires Efa6, one of (at least) three Drosophila Arf6 guanine exchange factors. Ethanol-naive Arf6 and Efa6 mutants were sensitive to ethanol-induced sedation and lacked rapid tolerance upon re-exposure to ethanol, when compared with wild-type flies. In contrast to wild-type flies, both Arf6 and Efa6 mutants preferred alcohol-containing food without prior ethanol experience. An analysis of the human ortholog of Arf6 and orthologs of Efa6 (PSD1-4) revealed that the minor G allele of single nucleotide polymorphism (SNP) rs13265422 in PSD3, as well as a haplotype containing rs13265422, was associated with an increased frequency of drinking and binge drinking episodes in adolescents. The same haplotype was also associated with increased alcohol dependence in an independent European cohort. Unlike the ubiquitously expressed human Arf6 GTPase, PSD3 localization is restricted to the brain, particularly the prefrontal cortex (PFC). Functional magnetic resonance imaging revealed that the same PSD3 haplotype was also associated with a differential functional magnetic resonance imaging signal in the PFC during a Go/No-Go task, which engages PFC-mediated executive control. Our translational analysis, therefore, suggests that PSD3 confers regional specificity to ubiquitous Arf6 in the PFC to modulate human alcohol-drinking behaviors.

AbstractWith the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 -78% in the IMAGEN dataset (n∼1182). Our results not only show that structural differences in brain can predict AAM, but also suggests that such differences might precede AAM behavior in the data. We predicted 10 phenotypes of AAM at age 22 using brain MRI features at ages 14, 19, and 22. Binge drinking was found to be the most predictable phenotype. The most informative brain features were located in the ventricular CSF, and in white matter tracts of the corpus callosum, internal capsule, and brain stem. In the cortex, they were spread across the occipital, frontal, and temporal lobes and in the cingulate cortex. We also experimented with four different ML models and several confound control techniques. Support Vector Machine (SVM) with rbf kernel and Gradient Boosting consistently performed better than the linear models, linear SVM and Logistic Regression. Our study also demonstrates how the choice of the predicted phenotype, ML model, and confound correction technique are all crucial decisions in an explorative ML study analyzing psychiatric disorders with small effect sizes such as AAM.