Neural Topologies of Reinforcement Sensitivity Theory: A Latent Variable Approach to Magnetic Resonance Imaging Data

BACKGROUND: The reinforcement sensitivity theory (RST) proposes 3 neurobiological systems that underlie individual differences in sensitivity to reward, punishment, and motivational conflicts. From a latent variable perspective, theoretical model structures can be identified based on empirical data. We applied exploratory and confirmatory factor analyses as well as structural equation modeling (SEM) with the aim of evaluating the RST neurobiological systems from biological phenotype indicators based on brain morphological organization.

METHODS: We analyzed magnetic resonance imaging (MRI) data from 300 healthy adults (128 female, 172 male) using gray matter volumes extracted through the Neuromorphometrics atlas, targeting RST-related brain systems. To assess the underlying structure of RST neurobiological systems, we used principal component analysis, confirmatory factor analysis, exploratory factor analysis, and exploratory SEM, as well as its model hierarchy. All analyses were enhanced by advanced techniques such as parallel analysis and exploratory graph analysis.

RESULTS: The findings reveal a robust 4-factor model: the behavioral activation system, the combined behavioral inhibition and fight-flight-freeze system, and a dual constraint system with dorsal cortical stream and ventral cortical stream. The dorsal cortical stream exhibited significant integrative capacity, impacting the model hierarchy through top-down projections on all the other systems. Exploratory SEM provided the best fit to the MRI data, underscoring its suitability for summarizing neural substrate data.

CONCLUSIONS: This study provides insights into the neurobiological foundations of RST, proposing a structural brain topology that is consistent with the theoretical proposal and emerging empirical evidence in human research. The results support the integration of psychological constructs with biological phenotypes.