CAREER: Small Data in a Big World: Balancing Interpretability and Generalizability for Data Integration in Clinical Neuroscience

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Neurological and neuropsychiatric disorders affect millions of people worldwide and carry a staggering societal cost. Despite ongoing efforts, clinicians have a bare-bones understanding of these disorders, and hence, a limited ability to treat them. From an analytics perspective, clinical neuroscience is a field of high-dimensional datasets, small sample sizes, massive patient variability, and most importantly, an arguable lack of ground truth information. These challenges have led to a trade-off between the interpretability of a given model and its generalizability to new data. At one extreme, classical statistics allows us to formulate and test interpretable hypotheses about the brain, but it cannot make patient-specific generalizations. At the other extreme, conventional machine learning algorithms are geared towards patient generalizability but rarely illuminate a brain-basis for the prediction. This CAREER program will develop a Coupled Network Optimization (CNO) framework that balances the two analytical extremes. The resulting algorithms will reveal interpretable system-level interactions in the brain that can predict the behavioral and cognitive deficits of a given disorder. In parallel, the investigators have formulated a diverse range of educational initiatives to train the next generation of interdisciplinary data scientists.Mathematically, the CNO framework estimates a low-dimensional network manifold for functional neuroimaging data. The elemental bases of this manifold will correspond to interpretable group-level features, whereas the patient-specific projections will capture predictive information. The technical exploration of this award will unfold in three modular stages, each of which tackles an open challenge in the field. Thrust I will improve the CNO interpretability by imposing a patient-specific graph topology to guide the salient functional interactions. Thrust II will advance the CNO generalizability by introducing nonlinear and nonparametric regression models. Finally, Thrust III will leverage an equivalent Bayesian representation to tackle the challenges of multisite analysis. The CNO framework will be applied to two markedly different application testbeds: a large multi-site repository of neuroimaging, behavioral and genetic data for autism, and a focused clinical trial of functional electrical stimulation for spinal cord injury rehabilitation. Beyond the scientific goals, this award includes a three-pronged educational plan to build strong technical foundations, foster interdisciplinary collaborations through engagement and communication, and finally, motivate young women into the STEM fields. The investigators have outlined a comprehensive schedule of activities, ranging from curriculum development at the high school, undergraduate and graduate levels, to organizing student networking events, to mentoring high school women through the Johns Hopkins Women in Science and Engineering outreach.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.