Unprecedented technological advances over the last decade have facilitated investigation of the brain at exquisite levels of spatial-temporal detail. Ambitious goals of large-scale efforts, including those supported by the BRAIN Initiative, include simultaneously measuring from thousands of neurons for long periods of time, and generating very high resolution images of the brain and its activity. However, enormous volumes of data will be produced by these technologies, and grand-scale analyses of these large datasets are virtually impossible to accomplish with currently available computational tools. This project is focused on addressing a number of these computational limitations by developing a novel and broadly accessible Next-Generation Neural Data Analysis (NGNDA) platform to analyze and integrate large volumes of heterogeneous brain data. This is a collaborative effort of neuroscience researchers, algorithm developers, and computing technology experts from Harvard Medical School Research Computing, the Laboratory of Cognitive Neuroscience at Boston Children's Hospital/Harvard Medical School, the Sleep and Inflammatory Systems Laboratory at Beth Israel Deaconess Medical Center/Harvard Medical School and the Epilepsy Divisions at Boston Children's Hospital and the Medical University of South Carolina. The ultimate goal is to understand not only the healthy brain but also complex diseases and disorders that are affecting progressively larger populations resulting in enormous socioeconomic costs. This project therefore aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.
This project is a pioneer effort to develop a new computational infrastructure, NGNDA, which will be specifically designed for collaborative research, to facilitate grand-scale analysis, simulation and modeling of brain connectomes across species. The overarching goal is to develop the means for intelligent parallel processing of big neural data, and efficient estimation of connectomes across scales of neural organization, via implementation of innovative algorithms and dynamic leveraging and integrating of shared institutional and national high performance computing (HPC) resources. The NGNDA platform will be implemented on the Orchestra HPC resource provided by Harvard Medical School Research Computing, and on resources of the Extreme Science and Engineering Discovery Environment (XSEDE) national computing consortium supported by the National Science Foundation. NGNDA will be validated with four very high-dimensional neural datasets, each posing a unique computational challenge. NGNDA aims to facilitate a "convergence" approach to Neuroscience research whereby expertise and insights from distinct disciplines are merged with cutting-edge resources and tools for a comprehensive investigation of the brain. The NGNDA computational infrastructure will be accessible to thousands of users and all its algorithms and validation data will be freely available to the community; and NGNDA may also eventually serve as a novel e-learning platform for multifaceted collaborative learning and education of next-generation neuroscientists. NGNDA will also facilitate testing the reproducibility and generalization of research findings.
This Early-concept Grants for Exploratory Research (EAGER) award by the CISE Division of Advanced Cyberinfrastructure is jointly supported by the SBE Division of Behavioral and Cognitive Sciences, with funds associated with the NSF Understanding the Brain activity including for developing national research infrastructure for neuroscience, and alignment with NSF objectives under the National Strategic Computing Initiative.
