Using Neuroimaging to Predict Behavioral Outcomes

to Predict Behavioral Outcomes/nBy/nSusan Whitfield-Gabrieli/nDoctor of Philosophy in Psychology/nUniversity of California, Berkeley/nProfessor Silvia Bunge, Chair/nThe emerging field of “neuroprediction” or “predictive analytics” in mental health has promise/nfor revolutionizing clinical practice by moving towards personalized or precision medicine. The/ncore idea is that brain measures at a given time may predict individual future behavioral/noutcomes, presumably because specific structural or functional brain characteristics constrain the/ntrajectories of evolving behavior over time. For basic science, discovery of such brain measures/nidentifies particular neural circuits that constrain specific future behaviors. For clinical science,/nsuch brain measures may support identification of vulnerabilities that could be treated/npreventively to minimize poor mental health outcomes./nIn this thesis, I first provide an overview of the key questions and challenges in the field/nof predictive analytics, aiming to (1) propose general guidelines for predictive analytics projects/nin psychiatry, (2) provide a conceptual introduction to core aspects of predictive modeling/ntechnology, and (3) foster a broad and informed discussion involving all stakeholders including/nresearchers, clinicians, patients, funding bodies and policymakers. Next, I discuss two strategies/nfor identifying, in a developmental context with children, brain vulnerabilities for future mental/nhealth difficulties. First, I used resting state functional connectivity, measured via functional/nmagnetic resonance imaging (fMRI), to discover whether children without depression but with/nheightened familial risk for major depression disorder (MDD) had brain differences indicative of/nrisk for depression. At-risk children, compared to children not at familial risk, exhibited/nsignificant differences in functional brain connectivity in three brain networks. Classification/nbetween at-risk versus control children based on resting-state connectivity yielded high accuracy/nwith high sensitivity and specificity that was superior to traditional clinical rating scales./nSecond, I examined whether variation in functional connectivity could predict the trajectory of/nclinical symptomology over the ensuing four years in a longitudinal study with a normative child/nsample. Variation at age 7 in specific networks predicted individual children’s developmental/ntrajectories at age 11 towards attentional problems characteristic of Attention Deficit/nHyperactivity Disorder (ADHD) or internalizing problems characteristic of MDD. The predictive/nnetwork for internalizing problems was one of the networks that had been atypical in children at/nfamilial risk for MDD. These studies identify variation in brain networks indicative of risk for/ntwo of the most common disorders of adolescent mental health, and suggest that such measures/nmay support targeted early and preventive interventions. The conclusion of the thesis provides a/ndiscussion of these findings, future directions, theoretical implications, clinical applications and/nethical considerations.

Advisor: Bunge, Silvia A