Linking brain activity during naturalistic tasks to individual phenotypes on the depression spectrum

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PROJECT SUMMARY / ABSTRACT Rather than a dichotomy between health and pathology, many mental illnesses—especially depression and other mood disorders—are best conceptualized as the far end of a phenotypic spectrum, suggesting that characterizing individual differences in brain function and behavior will help further our understanding of disease. Existing work suggests that fMRI has the potential to predict individual behaviors from brain function, yet progress has been hindered by an overreliance on group studies (i.e., patients versus controls) and limited paradigms (i.e., either highly controlled tasks that risk being artificial, or at the other extreme, resting state, which is entirely unconstrained and difficult to interpret). Naturalistic fMRI, in which subjects do complex, engaging tasks such as watching films or listening to stories, offers an alternative that more closely mimics real-world cognition and may allow researchers to extract richer, more meaningful information from a single individual’s scan. As such, these paradigms are promising candidates for brain “stress tests” that would elicit patterns of brain activity that predict present or future behaviors. The specific aims of this project are: (1) to leverage existing large-scale datasets to develop methods to predict phenotypes from naturalistic fMRI data; (2) to design and conduct an fMRI study using targeted film stimuli to draw out individual variability of interest, specifically in traits related to depression; and (3) to extend the newly developed paradigms and analyses to a longitudinal study of a population at risk for depression and/or other mood disorders. Several innovative approaches to data analysis will be investigated. The central hypothesis is that brain activity evoked by these paradigms will vary across individuals in a continuous, multidimensional space that covaries with phenotype strength, that these relationships will be strong enough to predict phenotypes in unseen individuals, and that modified (e.g., attenuated) versions of patterns associated with illness will be detectable via these paradigms in those at risk before the emergence of symptoms. The long-term goal of the PI is to become an independent NIH-funded faculty member at a research-intensive university, with a research program exploring the basic cognitive neuroscience of individual differences in personality and cognition, as well as developing translational applications for psychiatry. To reach this goal, the training objectives for this award are to enhance the PI’s skills in the following areas: (1) applying machine learning techniques to predict individual-subject behavior from fMRI data; (2) conducting neuroimaging and behavioral research on depression and mood disorders with clinical and at-risk populations; and (3) gaining professional skills essential for a successful independent research career. The environment in which the career development will take place is the Intramural Research Program of the National Institute of Mental Health, a vibrant community with outstanding resources to support the proposed project, including relevant courses and seminars, state-of-the-art facilities for MRI data acquisition and analysis, computing power, and expertise and guidance from senior scientists in neuroscience, engineering, machine learning and psychiatry.