Associations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual.

Associations between brain connectivity and behavioral phenotypes are typically examined by comparing group averages (e.g., clinical vs. control). This approach presumes that the group-average network reflects individuals. However, if individuals' network structures are highly heterogenous, averaging across them will create a group-level network that does not generalize to individuals, preventing valid inferences of associations with behavioral differences. Here, we showed that a group-level reward network in early adolescents poorly reflected individuals. We then used GIMME to identify reward network features that were specific to individuals. These network features were associated with multiple reward-related outcomes, including familial risk for substance use disorder (R2 = 31%). A focus on the individual, rather than groups, may be necessary for valid inferences of individual behavioral differences from fMRI connectivity.

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Author notes

Competing Interests: The authors have declared that no competing interests exist.

Handling Editor: Olaf Sporns

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