Data Science & Informatics

Project Summary: A Data-driven Framework for Brain Transcriptomic Cell Type Definition, Ontology, and Nomenclature Defining the complete census of neuronal and non-neuronal cell types in the brain is a major priority for the

There is an increasing need for efficient and robust software to process, integrate, and offer insight across the diversity of population imaging efforts underway across the BRAIN Initiative and other projects.

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 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 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.

Project Summary The proposed work will address a critical gap in our understanding of neuronal phenotypes and cell types by developing machine learning algorithms and cloud-based software for the integration of multiple modality characterizations large and growing datasets of cortical neurons in mouse and human.

PROJECT SUMMARY/ABSTRACT Modern experimental approaches allow researchers to collect a variety of whole-brain data from the same animal via different anatomical labels, including tracers, genetic markers, and fiducial marks from recording electrodes. Unfortunately, viewing and analysis methods have not kept pace with the complexity of these datasets, which can be as large as several terabytes. This limitation makes it time- and resource-intensive to view and manipulate light-microscopy data or to share these datasets with distant laboratories.

The increasing availability and ease of use of confocal, two-photon, and light-sheet microscopes coupled with rapid developments in fluorescent protein reporters have made 3D and functional imaging and its analysis a central component of modern Neuroscience research. Yet, the ease of acquiring 3D and functional images is creating progressively larger datasets, prompting the need for high-throughput image analysis algorithms and software that can be both rapid and accurate.

Project Summary/Abstract The effective sharing of data requires the development and broad adoption of standards for the organization of data and metadata. Within the field of neuroimaging, there is an emerging standard for data/metadata organization, the Brain Imaging Data Structure (BIDS), which is currently implemented for MRI and is under development for PET and MEG.