Theory & Data Analysis Tools

This funding opportunity announcement (FOA), in support of the NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, is one of several FOAs aimed at supporting transformative discoveries that will lead to breakthroughs in understanding human brain function. Guided by the long-term scientific plan, “BRAIN 2025: A Scientific Vision,” this FOA specifically seeks to support efforts addressing core ethical issues associated with research focused on the human brain and resulting from emerging technologies and advancements supported by The BRAIN Initiative®.

This funding opportunity announcement (FOA), in support of the NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, is one of several FOAs aimed at supporting transformative discoveries that will lead to breakthroughs in understanding human brain function.

This funding opportunity announcement (FOA) invites applications for mentored career enhancement (K18) awards in research areas that are highly relevant to the NIH BRAIN Initiative.

The purpose of The BRAIN Initiative® Fellows (F32) program is to enhance the research training of promising postdoctorates, early in their postdoctoral training period, who have the potential to become productive investigators in research areas that will advance the goals of The BRAIN Initiative®. Applications are encouraged in any research area that is aligned with The BRAIN Initiative®, including neuroethics. Applicants are expected to propose research training in an area that complements their predoctoral research.

This Funding Opportunity Announcement (FOA) solicits applications to develop informatics tools for analyzing, visualizing, and integrating data related to The BRAIN Initiative® or to enhance our understanding of the brain.

This FOA solicits new theories, computational models, and statistical methods to derive understanding of brain function from complex neuroscience data. Approaches could include the creation of new theories, ideas, and conceptual frameworks to organize/unify data and infer general principles of brain function; new computational models to develop testable hypotheses and design/drive experiments; and new mathematical and statistical methods to support or refute a stated hypothesis about brain function, and/or assist in detecting features in complex brain data.

This Funding Opportunity Announcement (FOA) solicits applications to develop web-accessible data archives to capture, store, and curate data related to BRAIN Initiative activities. The data archives will work with the research community to incorporate tools that allow users to analyze and visualize the data, but the creation of such tools is not part of this FOA. The data archives will use appropriate standards to describe the data, but the creation of such standards is not part of this FOA.

focuses onexploratory studies that use new and emerging methods for large scale recording and manipulation to elucidate the contributions of dynamic circuit activity to a specific behavioral or neural system. Applications should propose teams of investigators that seek to cross boundaries of interdisciplinary collaboration, for integrated development of experimental, analytic and theoretical capabilities in preparation for a future competition for large-scale awards.

The purpose of this FOA is to promote the integration of experimental, analytic, and theoretical capabilities for large-scale analysis of neural systems and circuits. This FOA seeks applications for exploratory research studies that use new and emerging methods for large scale recording and manipulation of neural circuits across multiple brain regions. Applications should propose to elucidate the contributions of dynamic circuit activity to a specific behavioral or neural system.

This Funding Opportunity Announcement (FOA) intends to support a Brain Cell Data Center (BCDC) that will work with other BICCN Centers and interested researchers to establish a web-accessible information system to capture, store, analyze, curate, and display all data and metadata on brain cell types, and their connectivity.