NeuroScout: A cloud-based platform for flexible re-analysis of naturalistic fMRI datasets

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Project Summary The widespread introduction of functional magnetic resonance imaging (fMRI) over the past two decades has revolutionized the study of human brain and cognitive function in healthy and clinical populations. Yet the potential utility of fMRI remains constrained by its resource-intensive nature. Because even small, exploratory studies are expensive to conduct, biomedical researchers can collectively test only a small fraction of the research hypotheses that are in principle amenable to fMRI investigation. There is an urgent need for novel methodological approaches that enable rapid and efficient testing of novel theoretical hypotheses by reusing existing fMRI datasets rather than acquiring new ones. To help achieve this goal, we propose a new platform called NeuroScout that will support rapid and flexible cloud-based analysis of existing functional fMRI datasets. Our approach differs importantly from previous infrastructure projects in that, rather than developing a domain-general neuroimaging platform, we focus on extracting maximum utility from a limited set of fMRI experiments--namely, those that use intrinsically high dimensional stimuli such as movies and audio narratives. The proposed work encompasses three Specific Aims. Aim 1 focuses on reducing the burden of re-analyzing existing fMRI by automating much of the analysis process and allowing researchers to easily execute their analyses in the cloud. Aim 2 increases analytical flexibility by developing highly extensible tools for multimodal stimulus annotation. Aim 3 focuses on incentivizing platform use by integrating NeuroScout outputs with existing data sharing, visualization and interpretation platforms such as NeuroVault and Neurosynth. When fully deployed, the NeuroScout platform will provide a turnkey solution for extremely rapid analysis and visualization of existing fMRI data at a marginal cost very close to zero. Researchers will be able to iteratively test and refine hypotheses in domains ranging from visual word recognition to social cognition, and interactively visualize and share their results with the broader research community at the push of a button.