Purpose:
This Funding Opportunity Announcement (FOA) supports research to understand causal linkages across different scales (e.g., molecular, cellular, circuit, behavioral, clinical) relevant to neuropsychiatric disorders. The Convergent Neuroscience (CN) program goal is to establish theoretical models to show how specific constituent processes at one level of analysis contribute to quantifiable properties at other levels, either directly or as emergent phenomenon. Studies should incorporate four key features: (1) a premise on identifying the causal and disease-relevant relationships between objective genetic/biological/clinical criteria at two or more contiguous levels of analysis, whether involving human subjects or experimentally tractable in vivo or in vitro paradigms (genetic variation can be one of the contiguous levels of analysis or can be the context in which other, higher levels of analysis are linked); (2) use of the large and diverse datasets existing or generated at these levels of analysis to develop testable theoretical models within three years of award; (3) experimental testing of the model-based predictions to confirm or reject their validity, (4) preferential utilization of scalable approaches to maximize analytic throughput, content, sensitivity, selectivity, spatiotemporal resolution and robustness. Results should yield causal linkages between levels of analysis and mechanistically explain key functional dimension(s) relevant to mental illness pathophysiology. Leadership of research teams will include expertise in experimental neurobiology or clinical research paired with orthogonal theoretical disciplines (e.g., mathematics, computation, physics). This endeavor will be facilitated by active participation in a community-driven manner through the CN Consortium. Groups will manage their data and analysis methods using a harmonized framework with other U19 awardees through a CN Consortium Data Commons structure.
Background:
The Convergent Neuroscience (CN) program aims to exploit a wealth of unbiased, large scale data being generated from genetic, neurobiological and clinical research by facilitating collaboration with experts from orthogonal disciplines (e.g., mathematics, computation, physics, engineering) who use multi-scale modeling and related computational methods such as machine learning. The objective of research under this FOA is to understand causal linkages across different scales (e.g., molecular, cellular, circuit, behavioral, clinical) relevant to neuropsychiatric disorders, using theoretical frameworks to model how specific constituent processes at one level of analysis contribute to quantifiable properties at other levels, either directly or as emergent phenomenon.
Applications responding to this FOA should incorporate four key features:
(1) A premise on identifying the causal and disease-relevant relationships between objective genetic/biological/clinical criteria at two or more contiguous levels of analysis, whether involving human subjects or experimentally tractable in vivo or in vitro paradigms (genetic variation can be one of the contiguous levels of analysis or can be the context in which other, higher levels of analysis are linked);
(2) Use of the large and diverse datasets existing or generated at these levels of analysis to develop testable theoretical models within three years of award;
(3) Experimental testing of the model-based predictions to confirm or reject their validity;
(4) Preferential utilization of scalable approaches to maximize analytic throughput, content, sensitivity, selectivity, spatiotemporal resolution and robustness.
Key Dates:
URL for more information:
https://grants.nih.gov/grants/guide/rfa-files/RFA-MH-21-165.html
Filed Under: Funding Opportunities