BRAIN Initiative: Tools to Facilitate High-Throughput Microconnectivity Analysis (R01)
NIH - National Institutes of Health (RFA-MH-20-135)
- Proposal: 09/27/2019
- Duration: up to 3 years
The purpose of this Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative is to encourage applications that will develop and validate tools and resources to facilitate the detailed analysis of brain microconnectivity. Novel and augmented techniques are sought that will ultimately be broadly accessible to the neuroscience community for the interrogation of microconnectivity in healthy and diseased brains of model organisms and humans. Development of technologies that will significantly drive down the cost of connectomics would enable routine mapping of the microconnectivity on the same individuals that have been analyzed physiologically, or to compare normal and pathological tissues in substantial numbers of multiple individuals to assess variability. Advancements in both electron microscopy (EM) and super resolution light microscopic approaches are sought. Applications that propose to develop approaches that break through existing technical barriers to substantially improve current capabilities are highly encouraged. Proof-of-principle demonstrations and/or reference datasets enabling future development are welcome, as are improved approaches for automated segmentation and analysis strategies of neuronal structures in EM images.
This FOA seeks applications in areas including, but not limited to:
- Novel methods for tagging individual neurons such that cellular components of a functional circuit can be explored.
- Novel trans-synaptic tracers that can be used both at the EM and light-microscopic level.
- Innovative approaches to reduce the time and cost of determining high resolution synaptic connectivity by electron microscopy or other approaches.
- Novel computational approaches to analyze and segment neuronal connections from various imaging modalities.
- Novel techniques for integrating micro-scale connectivity data with cellular or synaptic phenotypic information.
- Novel uses of super-resolution light microscopic approaches for identifying synaptic connections and mapping micro-circuits.
- Tools to identify gap junctions and characterize electrical synapses.
- Software tools for enhancing and scaling automated image processing, connectivity analysis, and data interpretation, including algorithms, information extraction routines, and user interfaces
- Datasets to serve as ground-truth for algorithm development and testing
- Develop a high-quality toolbox of methods for efficiently mapping and annotating projections in experimental animals, including non-human primates, as well as in human tissue blocks.
- Methods to reduce the time needed to segment and/or analyze images from volume EM data sets
- Proof-of-principle demonstrations or reference datasets consisting of reconstructions of micro-connectomes of individual animals, for example, demonstrating microconnectivity of cells that have been studied using optical physiology during specific behaviors.
- Techniques for using electron and/or super-resolution light microscopy to integrate molecular signatures of cells and synapses with their nanoscale connectivity.