Evaluating efficiency of evidence accumulation as a neurodevelopmental mechanism of transdiagnostic risk for psychopathology: a computational model-based approach

About This Grant

PROJECT SUMMARY/ABSTRACT Neurocognitive abilities relevant to adaptive decision making and self-regulation (e.g., inhibition, working memory) mature from childhood through adolescence and play a critical role in the emergence of psychopathology. However, one-to-one mappings between these abilities and risk for specific psychiatric diagnoses have been elusive. The field’s struggle to identify such mappings is nonetheless consistent with high rates of comorbidity among disorders and the presence of a general factor for psychopathology (“p-factor”) suggesting many etiological mechanisms have a broad influence across diagnostic boundaries. Common factors also account for a substantial proportion of developmental change and individual differences in specific neurocognitive abilities, suggesting many of the associations between neurocognition and psychopathology are mediated through general processes that drive adaptive functioning across many tasks, contexts, and diagnoses. Efficiency of evidence accumulation (EEA)—a biologically plausible cognitive mechanism that has been well-characterized in computational modeling and neurophysiological research—is a compelling candidate for a general factor that can explain neurocognitive contributions to transdiagnostic psychopathology risk. EEA is a reliable higher-order factor that accounts for performance across a wide variety of cognitive functions—from simple decisions to complex executive tasks—and is impaired in multiple disorders linked to self-regulatory difficulties and maladaptive decision making. We posit that EEA is a developmental catalyst that supports adaptive decision making across many contexts, that EEA’s development is strongly influenced by the interplay between environmental factors (e.g., family conflict, socioeconomic resources) and the maturation of largescale brain networks, and that aberrant development of EEA conveys transdiagnostic risk for psychopathology. EEA’s well-characterized computational definition and biological underpinnings promise to provide a novel bridge between computational neuroscience and developmental psychopathology research. Furthermore, recent indications that EEA’s development may be impeded by specific adverse environments suggest that knowledge about EEA could inform targeted prevention efforts. To spur a program of computationally rigorous research on EEA as a general, and potentially malleable, neurodevelopmental influence on psychopathology, we will leverage four large longitudinal neuroimaging data sets to accomplish the following aims: 1) map the canonical maturational trajectory and nomological network of EEA across development, 2) quantify the influence of genetic and diverse environmental influences on the development of EEA, and 3) examine EEA’s co- development with psychopathology, environmental adversity, and neuroimaging metrics. Knowledge gained through this project will allow the field of developmental psychopathology to leverage key benefits of well- established computational models to establish precise and biologically plausible accounts of neurocognitive risk factors for psychopathology, their environmental determinants, and potential translational applications.