The functional magnetic resonance imaging (FMRI) community is shifting its interest from localizing functions to capturing functional brain networks. To investigate the functional network architecture of the brain neuroimagers are studying the brain’s spontaneous activity using resting state FMRI (R-FMRI). Both ease-of-use and the distributed nature of the network readouts are contributing to the popularity of R-FMRI for studying clinical populations. Studies increasingly report aberrant functional connectivity within and between specific networks of interest, but with a vast variety of metrics. Such scattered diagnostic findings rarely translate into clinically useful biomarkers. By contrast, I expect that providing an integrated assessment of the brain’s functional architecture will extend our ability to stratify participants beyond diagnostic categories. Increasing our understanding of clinically relevant subtypes, we ultimately create possibilities for tailored treatment protocols. Therefore, this proposal aims to construct a functional fingerprint of the brain that is sensitive to subtle variations, inclusive in what it captures, specific when distinguishing subgroups, and well validated for clinical use. Specifically, I will construct functional fingerprints that capture the richness of functional connectivity interactions between brain regions, augmented with a behavioral profile and informed by brain structure. I will test the ability of the functional fingerprint to stratify a population into subgroups using a large ADHD-related cohort available at the host institution. Finally, I will cross-validate the functional fingerprints in two independent datasets. My proposal aims to devise integrative methods that yield results that are interpretable in a clinical setting. My R-FMRI expertise and the host institutions expertise in data-driven FMRI methodology and ADHD pathophysiology provide excellent grounds for successful completion of the objectives.
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