Project Summary Population neuroscience requires investigation of brain-behavioral relationships within epidemiological samples studied in longitudinal designs, including a large number of assessment modalities and with subjects enrolled at many data collection sites to achieve a large sample and broad representation of the population.
PROJECT SUMMARY Overview: We will extend and develop implementations of foundational methods for analyzing populations of attributed connectomes. Our toolbox will enable brain scientists to (1) infer latent structure from individual connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships between connectomes and multivariate phenotypes. The methods we develop and extend will naturally overcome the challenges inherent in connectomics: high-dimensional non-Euclidean data with multi-level nonlinear interactions.
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.
