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    BRAIN Initiative: New Concepts and Early-Stage Research for Recording and Modulation in the Nervous System (R21) (Clinical Trial Not Allowed)

    Develop cutting-edge technologies for large-scale neural circuit recording and modulation to unlock the secrets of brain functioning beyond current capabilities.

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    Funder: National Institutes of Health

    Due Dates: June 17, 2025 | June 15, 2026

    Funding Amounts: Up to $400,000 total direct costs over 3 years; no more than $200,000 in any single year.

    Summary: Supports early-stage, high-risk research to develop innovative technologies for large-scale recording and modulation of neural activity in the nervous system.

    Key Information: Clinical trials are not allowed; preliminary data are not required; foreign organizations are eligible.


    Description

    This NIH opportunity, part of the BRAIN Initiative, seeks to accelerate the development of novel, early-stage technologies for recording and/or modulating neural activity in the central nervous system. The focus is on high-risk, high-reward projects that are in the initial stages of conceptualization, including new and untested ideas. The goal is to overcome current limitations in spatial and temporal resolution, scale, and specificity of neural recording and modulation, ultimately enabling experiments in humans or behaving animals that are not currently possible.

    A wide range of modalities are appropriate, including acoustic, chemical, electrical, magnetic, optical, and genetic tools. Both invasive and non-invasive approaches are welcome. The program encourages applications from researchers in fields not traditionally associated with neuroscience, such as engineering, physics, chemistry, materials science, and computational modeling.

    Projects may include experimental approaches, as well as calculations, simulations, computational models, or other mathematical techniques to demonstrate theoretical feasibility. Building and testing phantoms, prototypes, or in vitro/bench-top models to validate underlying assumptions is also supported.


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