Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains.

With 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 exploratory 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 proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

Predictors of Five-year Readmission to an Inpatient Service among Patients with Opioid Use Disorders.

Background Opioid use disorder (OUD), a relapsing-remitting chronic medical disease, accounts for a sizable proportion of all-cause adult inpatient stays. We evaluated the incidence and predictors of any and multiple readmissions to inpatient care for OUD. Methods This retrospective, register-based cohort study assessed consecutive patients with OUD admitted to a federally-funded inpatient service of an addiction treatment center in North India between January 2007 and December 2014. Binary logistic regression was used to determine independent readmission predictors based on demographic, clinical, and treatment variables that significantly differed in bivariate analysis. Results Among 908 patients, 306 (33.7%) and 106 (11.7%) had any and multiple readmissions, respectively. Injection drug use (Odds ratio [OR] 2.92, 95% confidence interval [CI] 1.90-4.49), comorbid severe mental illness (OR 2.80, 95% CI 1.42-5.55) and common mental disorder (OR 3.4 95% CI 1.65-6.95), antagonist treatment (OR 1.6 95% CI 1.14-2.27), and urban residence (OR 1.38 95% CI 1.01-1.90) increased odds of readmission. 'Improved' discharge status (OR 0.48 95% CI 0.34-0.70) in first admissions reduced odds of any readmission. Similar risk factors also influenced multiple readmissions with higher odds ratios. Conclusions Identification and adequate treatment of risk factors may reduce the chances of readmission.