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Individually Measured Phenotypes to Advance Computational Translation in Mental Health (IMPACT-MH) (U01 Clinical Trial Optional)

Funder
National Institutes of Health
LOI Deadline
LOI Required
Recommended, but not required
Application Deadline
Maximum Project Duration
5 years
Research Focus Areas
Mental Health Treatments/Interventions
Computational
Research Methods
Randomized Control Trials (RCT)
Computational Biology
Quantitative Research Methods
Secondary Data
Description
This Notice of Funding Opportunity (NOFO) is intended to stimulate and support research that will use behavioral measures and computational methods to define novel clinical signatures that can be used for individual-level prediction and clinical decision making in mental disorders. A multi-component approach is proposed in which grantees will (1) identify and/or develop behavioral tasks (and other types of measures, as appropriate) that are optimized for measurement of individual differences in individuals with or at risk of developing mental disorders; (2) collect the data from novel clinical cohorts and/or identify existing datasets that include behavioral data and other data that are typically available in the clinical record; (3) derive novel clinical signatures that incorporate behavioral measures and information derived from the clinical record, and are informative for clinical purposes; and (4) partner with the existing Data Coordinating Center (DCC) (funded under previous RFA-MH-23-106), which is responsible for coordinating the harmonization of methods, aggregation of data, analysis of combined data, and creation of a data infrastructure to support data sharing with the scientific community. Applicants may propose new cohorts from one or more populations targeted to specific clinical groupings (e.g., mood/anxiety disorders, disorders of behavior regulation, psychosis) and/or care delivery settings, may leverage data from existing clinical research cohorts that have appropriate data structures, or may use a combination of approaches with new and existing data.