Adolescence is characterized by changes in decision-making, accompanied by the progressive development of the prefrontal cortex and reconfiguration of brain networks that support goal-directed decision-making. Adolescence is also the typical age of clinical onset and peak prevalence for many forms of mental illness. Recent advances in computational modeling of cognitive processes have enabled the quantification of parameters that govern learning and decision and characterization of how they differ in mental illnesses. There are several differentiating properties of learning and decision making processes in the brain: learning can be model-free (based on past trial and error) vs. model-based (learning the structure of a task and computing a best course of action given that structure), Pavlovian (with innate sensitivities to different motivationally relevant outcomes) vs. instrumental (arbitrarily adaptive), and learning occurs from positive and negative consequences. Furthermore, responses can be biased toward action or inaction, and can be more or less exploratory (variable). We will use three reinforcement-learning tasks that, together with computational models, index these multiple differentiable features of learning and decision making, in order to jointly define an individual “computational phenotype” of learning and decision processes. In Aim 1 this computational phenotype will be defined in a large online sample age 10-25 in order to map changes in symptom dimensions across adolescent development.
In Aim 2 we will use neuroimaging to characterize the relationship between decision-making phenotypes and neural connectivity in children, adolescents, and young adults. In Aim 3 we will characterize the relation between decision-making phenotypes and clinical symptomatology in a diagnostically heterogeneous sample of adolescents with generalized anxiety, depression, ADHD or OCD. Throughout, computational modeling of task behavior and self-reported symptom dimensions will build on state-of-the-art hierarchical modeling of multimodal and multi-task data. The research activities described in this proposal hold the potential to improve our understanding of the cognitive and neural mechanisms that underpin adolescent psychopathology, a question of broad societal impact given the prevalence and cost of mental illness, and the super-additive benefits of early detection and treatment.