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Associations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average network models between known groups. However, potential neural heterogeneity within groups limits the ability to use this approach to make inferences about the individual 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 (mean age = 11.32) and examines associations between individualized features and reward-related behavioral outcomes, depression, and risk for substance use disorders. 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 each individual’s network, with most individuals sharing less than 50% of the group-level network paths and no individual sharing 75% of the group-network paths. We then used Group Iterative Multiple Model Estimation (GIMME) to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks specific to each adolescent. We also identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Additionally, we found numerous associations between individual-specific connectivity paths and behavioral reward functioning and risk for substance use disorders. However, network features were minimally associated with depression. We suggest that accounting for heterogeneity is necessary to examine qualitatively distinct patterns of adolescent reward networks and will improve precision for testing neural associations with behavioral outcomes at the individual level. |
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