Project Summary/Abstract Cloud-based analyses and easily shared data formats are revolutionizing neuroimaging. We will develop software to fill gaps created by these two trends. We will develop and refine software to visualize and analyze the data from the BRAIN Initiative.
Project Summary Many advances in modern neuroscience rely on electrophysiological recordings of large neural populations (e.g. many hundreds of cells) or high-resolution measurements of animal behavior (e.g. from video). These datasets have unlocked a wide range of genuinely transformational scientific opportunities, as they enable us to draw reliable statistical inferences about individual animal subjects at precisely encapsulated moments in time. However, these statistical models are complex and non-trivial to implement in computer software.
Project Summary Categorizing individual neurons into different groups, or cell types, is a classical approach to studying the nervous system. With increasingly more tools being invented to observe the neurons, new criteria were created to characterize different aspects, or modalities, of the cells. While these modality-specific categorizations have enabled in-depth knowledge in neuroscience, the inconsistencies across different criteria leave data integration across modalities technically difficult.
Abstract In response to the BRAIN Initiative “Integration and Analysis of BRAIN Initiative Data” FOA, we propose further developing nTracer2, a cloud-based platform for brain image visualization and neuron tracing in the web browser.
ABSTRACT The generation of scientifically rich, high resolution neuroimaging volumes continues to increase in extent and rate due to the advancement of new Electron Microscopy (EM) and X-ray Microtomography (XRM) imaging sys- tems and data processing methodologies.
Project Summary/Abstract The BRAIN Initiative is supporting a broad portfolio of neuroscience research aimed at revolutionizing our understanding of the brain.
Summary The goal of this proposal is to develop DDALAB, a software platform that will make it possible for researchers to identify latent cortical states and analyze the flow of information in large populations of neurons using Delay Differential Analysis (DDA). Although DDA can be used to analyze any time series data, we will initially focus on EEG recordings from the scalp and iEEG data recordings directly from the brain.
PROJECT SUMMARY / ABSTRACT The BRAIN Initiative’s -omics data archive NeMO contains all the BICCN single cell single cell data, more than one million files at the time of writing.
cloudSLEAP – PROJECT SUMMARY/ABSTRACT Understanding how the brain produces complex behavior is a central goal of neuroscience, but quantifying behavior is technically challenging, particularly in unrestrained and naturalistic settings. Tools that are able to overcome these limitations leverage deep learning to achieve robust markerless motion capture, enabling characterization of behavior through precise positional tracking of body parts from standard videos of behavior.
Summary: Behavioral data is central to biomedical research, including both synchronous measures (e.g. brain activation and button-presses from a reading task in an fMRI scan), and those performed independently (e.g. a literacy questionnaire.) Compared to neurophysiology and brain imaging data, behavioral data is often relatively small, with file sizes in the megabytes rather than terabytes for both experimental scripts and resulting datasets.
