| The emerging field of "neuroprediction" or "predictive analytics" in mental health has promise for revolutionizing clinical practice by moving towards personalized or precision medicine. The core idea is that brain measures at a given time may predict individual future behavioral outcomes, presumably because specific structural or functional brain characteristics constrain the trajectories of evolving behavior over time. For basic science, discovery of such brain measures identifies particular neural circuits that constrain specific future behaviors. For clinical science, such brain measures may support identification of vulnerabilities that could be treated preventively to minimize poor mental health outcomes.;In this thesis, I first provide an overview of the key questions and challenges in the field of predictive analytics, aiming to (1) propose general guidelines for predictive analytics projects in psychiatry, (2) provide a conceptual introduction to core aspects of predictive modeling technology, and (3) foster a broad and informed discussion involving all stakeholders including researchers, clinicians, patients, funding bodies and policymakers. Next, I discuss two strategies for identifying, in a developmental context with children, brain vulnerabilities for future mental health difficulties. First, I used resting state functional connectivity, measured via functional magnetic resonance imaging (fMRI), to discover whether children without depression but with heightened familial risk for major depression disorder (MDD) had brain differences indicative of risk for depression. At-risk children, compared to children not at familial risk, exhibited significant differences in functional brain connectivity in three brain networks. Classification between at-risk versus control children based on resting-state connectivity yielded high accuracy with high sensitivity and specificity that was superior to traditional clinical rating scales. Second, I examined whether variation in functional connectivity could predict the trajectory of clinical symptomology over the ensuing four years in a longitudinal study with a normative child sample. Variation at age 7 in specific networks predicted individual children's developmental trajectories at age 11 towards attentional problems characteristic of Attention Deficit Hyperactivity Disorder (ADHD) or internalizing problems characteristic of MDD. The predictive network for internalizing problems was one of the networks that had been atypical in children at familial risk for MDD. These studies identify variation in brain networks indicative of risk for two of the most common disorders of adolescent mental health, and suggest that such measures may support targeted early and preventive interventions. The conclusion of the thesis provides a discussion of these findings, future directions, theoretical implications, clinical applications and ethical considerations. |
