Circuit Diagrams

Abstract: Many cell types together assemble the functional circuitry of the human brain. For over a century, neuroscientists have categorized brain cell types by their features, including shape, position, physiology, molecules, and function. Single cell transcriptomics studies are now defining molecular cell types at a resolution not previously possible, uncovering a taxonomy of hundreds to thousands of brain cell types.

Project Summary The function of the nervous system is dependent on complex interactions between networks of neurons composed of multiple neuron types. Understanding how these networks function both in health and disease is dependent on understanding the precise connectivity between specific neuron types and their functional interactions in the intact brain.

Project Summary: (30 lines of text max) We propose to create an open-source cloud-based software system for real-time fMRI neurofeedback experiments. Our goal is to make real-time fMRI neurofeedback broadly accessible for both the scientific and clinical communities, in order to accelerate both basic research and the development and deployment of clinical treatments. Real-time fMRI neurofeedback (RT-fMRI) is an increasingly important area of research.

Two major recent advances have raised the possibility of fundamental breakthroughs in both basic and clinical neuroscience: the development of new tools to probe the nervous system with single-cell resolution as well as brain-wide scope, and breakthroughs in machine learning methods for handling complex data. Yet there remain crucial barriers to progress: while data acquisition tools are now broadly within the grasp of neuroscience researchers, the same cannot be said about data analytical tools that can tackle the complexities of the new data sets being gathered.

PROJECT SUMMARY A goal of the BRAIN initiative is to develop and validate novel tools to map and manipulate neural circuits. The definition and control of behaviorally relevant circuits requires both retrograde and anterograde trans-synaptic technologies that perform well in vivo.

Project Summary Neuroscience stands at the precipice of a new depth of understanding about how the brain works thanks to recent advances in imaging data acquisition technologies such as light-sheet fluorescence microscopy (LSFM). How- ever, the lack of analytic tooling to mine this rich information's relationship across samples, timepoints, and data acquisition technologies prevents researchers from unlocking quantitative relationships. We propose the creation of an easy-to-use, distributed-computation image registration tools that will map large images into a common reference frame.

PROJECT SUMMARY Tools for exclusively targeting neuronal and glial subtypes are needed to advance our understanding of the brain. “Intersectional” systems improve targeting by restricting “reporter/effector” transgenes to a subdomain defined by the expression overlap between two activating factors. “Split-driver” systems have enhanced targeting precision in flies and are operable in fish, but have yet to be systematically deployed in vertebrate systems.

Abstract The brain is remarkably complex, and our understanding of this organ is still in its infancy. Many fundamental questions about brain development and function remain. Single cell genomics promises new ways to answer these questions, but nearly all single-cell methods share shortcoming – cells are destroyed when their molecular states are measured.

PROJECT SUMMARY Methods for regulating cellular processes within distinct populations of neurons are needed to elucidate relationships between molecular mechanisms, circuits, and behavior; and to develop cell type- or circuit- selective treatments for neurological disorders. We propose a novel, non-genetic, small molecule method— transcription factor-chemically induced proximity (TF-CiP)—that harnesses the cell type- and circuit-specificity of endogenous transcription factors to regulate gene expression in subsets of neurons.

Project Summary Despite the rapid advances in the neuroimaging research workflow over the last decade, the enormous variability between and within data types and specimens impedes integrated analyses. Moreover, the availability of a comprehensive portfolio of software libraries and tools has also resulted in a concerning degree of analytical variability.