Secondary Analysis and Archiving of BRAIN Initiative Data (R01 Clinical Trial Not Allowed)
NIH - National Institutes of Health (RFA-MH-20-120)
- Proposal: 09/06/2019
- Amount: $300,000 per year
- Duration: up to 3 years
This Funding Opportunity Announcement (FOA) encourages secondary analysis of the large amounts of existing data related to the BRAIN Initiative. The data do not need to be held in one of the funded BRAIN Initiative data archives, but the data must be held in a data archive that is readily accessible to the research community. Support will be provided for innovative analysis of relevant existing datasets using conventional or novel analytic methods, data science techniques, and machine learning approaches. Support may also be requested to prepare and submit existing data into any of the BRAIN Initiative data archives. Investigators should not underestimate the time and effort that may be necessary to curate or harmonize data.
Analyzed data, models and analytical tools generated under this FOA are expected to be deposited into an appropriate data archive. Since the BRAIN Initiative data archives are mostly making the data available to the research community through cloud-based storage, depositing the analyzed data, models and tools are expected to enhance opportunities to create a data sandbox where investigators can easily compare the results of their analysis with those from other research groups.
The goal of this FOA is to promote studies that will significantly advance new discoveries and accelerate the pace of research of the BRAIN Initiative through harnessing the big data and machine learning opportunities. Awardees are expected to enhance the value of existing data, improve the overall data integration and analysis capability, and strengthen the statistical power and rigor and reproducibility of BRAIN Initiative related data.
Examples of potential research topics include, but are not limited to:
- Integrative or comparative analysis of diverse datasets from different modalities, spatiotemporal scales or cohorts, or cross-species analysis to
- create a cell taxonomy based on gene expression state transitions, developmental trajectories, or spatial relationships.
- construct neuron morphology or map brain networks.
- explore intrinsic geometry and dynamics of brain networks and explore brain complexity through the course of development, across lifespan or neurobehavioral trajectories, or under different plasticity conditions and homeostasis.
- understand circuit functions at multiple scales and information exchange between different components of the nervous system, and search for emergent principles in complex systems beyond circuits.
- Decipher cell type-specific gene or protein expression or reconstruct neuropathological pathways or gene regulatory networks to
- understand molecular mechanisms underlying cell diversity, lineage differentiation, cognition, or behavior.
- explore how alterations in genes, proteins or genetic networks affect brain connectivity, circuit functions or neuronal activities.
- Multi-scale modeling from gene, neuron, circuit to behavior levels to explore
- how the structure of a neuron affects its integration into a circuit.
- how a circuit affects the neural system that it fits into.
- how the dynamic activity of a neural system drives behavior.
- how circuit dysfunction could manifest in behavior or create a disorder.
- Resolve ambiguities and enhance reproducibility, transparency and statistic power of BRAIN Initiative datasets.
- Format existing data that are highly relevant to the BRAIN Initiative program and deposit those data into a relevant data archive.
- Extract, manipulate or convert existing BRAIN Initiative relevant data and appropriate metadata into a format that can be deposited into one of the BRAIN Initiative data archives. Merge or link BRAIN relevant datasets into a format that will be suitable for further research relevant to the mission of the BRAIN Initiative.
Ming Zhan, Ph.D.
National Institute of Mental Health (NIMH)