Adolescence is a critical neurodevelopmental period in which life decisions start to be taken over by the adolescent. However, decision making ability may be limited. For instance, reward and executive functions may be modulated differently, or by different factors, during adolescence and as a result may be imbalanced, leading to risky or non-optimal decisions. Adolescents are also at risk for psychiatric illness, including schizophrenia (SZ) and bipolar (BP) disorder. Individuals with SZ and BP show decreased decision making ability, although deficits appear to be driven by executive dysfunction in SZ and reward dysfunction in BP. Given the demonstrated importance of executive and reward networks to decision making in adolescence as well as SZ and BP, we take the novel approach of deconstructing this complex process and testing the independent contributions of reward and executive networks to decision making behavior. We will longitudinally assess a sample of adolescents with affective and non-affective psychosis and bipolar disorder, as well as healthy controls, using multimodal neuroimaging. To functionally define the two circuits of interest, we will use a working memory task to localize a fronto-parietal executive network and a reward responsivity task to localize a ventrostriatal-orbitofrontal network. Task-based regions of interes (ROIs) will be used as seeds for diffusion tensor imaging tractography and as ROIs in which to probe grey matter (GM) thickness. To assess structural development we will use a combination of within and between subject data to plot the trajectory of structural and behavioral change from age 12-21 in patients and controls, as well as whether structural change (white matter (WM) and GM) in each circuit mediates behavior. Then, we will test whether there is a differential impact of illness on development of the two circuits, such that mood symptom severity (across all diagnoses) is associated with reward circuit alterations while severity of positive symptoms is associated with changes in executive networks. We will further test whether a traditional categorical analysis or our symptom spectrum analysis can account for more of the variance in the structural measures. Finally, we will determine the neural biosignatures that describe individuals at high and low ends of the symptom spectrums by performing a profile analysis based on data from ROIs in both networks. We will also test the degree to which baseline lab-based decision making performance longitudinally predicts real life behavior, as well as if WM and GM maturation between baseline and 1 year follow up can predict behavior at the 2 year follow up. This work may help identify novel treatment opportunities specifically appropriate for developing adolescents, who may be amenable to treatments that would not be as effective in adults. Further, if we can identify specific aspects o decision making that are affected in individuals with different symptom profiles, that may provide important traction on how to uniquely tailor cognitive remediation approaches. Finally, if we can predict later real life behavior with measures from laboratory visits, we can identify individuals n need of social or cognitive intervention.
Adolescence is a period of ongoing development of both complex cognitive functions, such as decision making, and the neural circuits underlying these functions;it is also an important period of risk for a variety of neuropsychiatric disorders. Our project proposes the critical step of taking a longitudinal approach to understanding how neurodevelopmental changes impact complex, real-life behavior in both healthy individuals and those with severe psychiatric disorders. Furthermore, we propose to take a spectrum based approach to understanding differences in decision making in adolescence by relating specific symptom domains (regardless of diagnosis) to changes in specific neural circuits, an approach which ultimately may help us develop targeted treatments specifically tailored to the dynamic and developing brains of young adult patients.
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