Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.

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Objective: To identify a brain-based predictor of cocaine abstinence using a recently developed machine learning approach, connectome-based predictive modeling (CPM). CPM is a predictive tool and a method of identifying networks that underlie specific behaviors ('neural fingerprints'). Methods: Fifty-three individuals participated in neuroimaging protocols at the start of treatment for cocaine-use disorder, and again at the end of 12-week treatment. CPM with leave-one-out cross-validation was run to identify pre-treatment networks that predicted abstinence (percent cocaine-negative urines during treatment). Networks were applied to post-treatment fMRI data to assess changes over time and ability to predict abstinence during follow-up. The predictive ability of identified networks was then tested in separate, heterogeneous sample of individuals scanned prior to treatment for cocaine use disorder (n=45). Results: CPM predicted abstinence during treatment, as indicated by a high correspondence between predicted and actual abstinence values (r (df=52) =0.42, p=0.001). Identified networks included connections within and between canonical networks implicated in cognitive/ executive control (frontoparietal, medial frontal) and in reward responsiveness (subcortical, salience, motor/ sensory). Connectivity strength did not change with treatment, and strength at post-treatment also predicted abstinence during follow-up (r (df=39) =0.34, p=0.03). Network strength in the independent sample predicted treatment response with 64% accuracy by itself, and with 71% accuracy when combined with baseline cocaine-use.

In adults, different levels of gambling problem severity are differentially associated with measures of health and general functioning, gambling behaviors and gambling-related motivations. Here we present data from a survey of 2,484 Connecticut high school students, and investigate the data stratifying by gambling problem severity based on DSM-IV criteria for pathological gambling. Problem/pathological gambling was associated with a range of negative functions; e.g., poor academic performance, substance use, dysphoria/depression, and aggression. These findings suggest a need for improved interventions related to adolescent gambling and a need for additional research into the relationship (e.g., mediating factors) between gambling and risk and protective behaviors.