BRAIN Initiative: New Technologies and Novel Approaches for Recording and Modulation in the Nervous System (R01 Clinical Trial Not Allowed)

This Notice of Funding Opportunity (NOFO) seeks applications for proof-of-concept testing and development of new technologies and novel approaches for recording and modulation of neural cells and circuits, to address major challenges and enable transformative understanding of dynamic signaling in the central nervous system. Preliminary feasibility data are not required, and it is expected that the proposed research may be high-risk, but if successful could profoundly change the course of neuroscience research. Applications may propose development of instrumentation hardware and/or devices and associated software, and/or molecular constructs for sensing and manipulating neural activity. Approaches may utilize any modality such as optical, electrical, magnetic, or acoustic recording/manipulation, to target neuronal electrical signals or other forms of neural activity, including intracellular signaling and engagement of non-neuronal cells in circuit function. The aim of the proposed technologies should be to reduce major barriers to conducting neurobiological experiments, including considerations of cost and ease of access, and to enable new discoveries for understanding neural circuit function. Technologies should address major challenges associated with recording and modulating CNS activity, at cellular or circuit resolution, and should contribute to an overall ecosystem of technologies spanning multiple spatial and temporal scales in any region throughout the CNS. The approaches should be compatible with experiments in behaving animals, with an expectation that they will be validated with in vivo experiments during the course of the project. Proposed validation experiments must focus on demonstrating the capabilities and potential impact of the technology, rather than advancing the state of biological knowledge as the primary project goal. Applications are encouraged to integrate multiple approaches, and where appropriate, to leverage a variety of domains of expertise from biological, chemical, and physical sciences, engineering, computational modeling, and statistical analysis.