Funded Awards

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Title Investigator Institute Fiscal Year FOA Number Status Project Number Priority Area Summary
A BRAIN Initiative Resource: The Neuroscience Multi-omic Data Archive White, Owen R University Of Maryland Baltimore 2017 RFA-MH-17-255 Active
  • Cell Type
  • Circuit Diagrams
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools

A thorough understanding of the complexities of the brain’s different cell types requires the sharing and integration of myriad genomic information generated from various data sources. Owen White proposes creating a Neuroscience Multi-Omic (NeMO) Archive, a cloud-based data repository for -omic data. White and his team of researchers will establish an archive for multi-omic data and metadata of the BRAIN Initiative. The group will document and archive data processing workflows to ensure standardization, as well as create resources for user engagement and data visualization. The NeMO Archive will provide an accessible community resource for raw -omics data and for other BRAIN Initiative project data, making them available for computation by the general research community.

A Confocal Fluorescence Microscopy Brain Data Archive Bruchez, Marcel P Ropelewski, Alexander J (contact) Watkins, Simon C Carnegie-mellon University 2017 RFA-MH-17-255 Active
  • Cell Type
  • Circuit Diagrams
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools

Advances in microscopy and imaging have created new possibilities in many fields of research, but these advances have also generated large amounts of data that can overwhelm traditional data management systems. Along with collaborators at Carnegie Mellon University and the University of Pittsburgh, Alexander Ropelewski plans to establish a BRAIN Imaging Archive that takes advantages of infrastructure and personnel resources at the Pittsburgh Supercomputing Center. The Archive will include a pipeline for data submission, user access and support, and BRAIN Initiative community engagement through an online presence, workshops, and hackathons. This unique resource will provide an accessible and cost-effective way for the research community to analyze, share, and interact with large image datasets of the BRAIN Initiative.

A Fast, Accurate and Cloud-based Data Processing Pipeline for High-Density, High-Site-Count Electrophysiology Kimmel, Bruce VIDRIO TECHNOLOGIES, LLC 2018 RFA-MH-17-257 Active
  • Cell Type
  • Circuit Diagrams
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools

The community’s need for an integrated open-source analysis platform is rapidly growing due to the increasing capacity of extracellular electrodes and the limited number of new and validated spike- sorting methods. JRCLUST, a free, open-source, standalone spike sorting software, offers a scalable, automated and well-validated spike sorting workflow for analysis of data generated by large multielectrode arrays. The software can tolerate experimental recording conditions from behaving animals, and it can handle a wide range of datasets using a set of pre-optimized parameters making it practical for wide use in the community. JRCLUST has been adopted in 20+ labs worldwide since its inception less than a year ago. Drs. Kimmel and Nathan seek to expand and maintain JRCLUST, thus empowering researchers to elucidate how functionally defined subpopulations of neurons mediate specific information-processing functions at key moments during behavior.

BIDS-Derivatives: A data standard for derived data and models in the BRAIN Initiative Poldrack, Russell A Stanford University 2017 RFA-MH-17-256 Active
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  • Circuit Diagrams
  • Human Neuroscience
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  • Monitor Neural Activity
  • Theory & Data Analysis Tools

The proliferation and heterogeneity of magnetic resonance imaging (MRI) experiments, data analysis pipelines, and statistical modeling procedures presents a challenge for effective data sharing and collaboration. Russell Poldrack and colleagues propose expansion of the Brain Imaging Data Structure (BIDS), which standardizes the description and collection of imaging data/metadata for MRI, with development plans for other neuroimaging types as well. Under BIDS, the group will develop standards for pre-processing data pipelines, computational modeling results, and statistical modeling, using quick validation of any implemented standard so that researchers can assess whether their data fit within BIDS guidelines. These standardization goals will facilitate sharing of data, modeling, and results, ensuring their usability and engaging the greater research community in developing highly useable data standards.

Boss: A cloud-based data archive for electron microscopy and x-ray microtomography Wester, Brock A. Johns Hopkins University 2018 RFA-MH-17-255 Active
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  • Circuit Diagrams
  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools

Technological advancements in high-resolution imaging of brain volumes permits the accumulation of huge quantities of data that requires solution for storage and archiving. Dr. Brock’s project develops an open, accessible, and cloud-based data archive for electron microscopy and X-ray microtomography data by leveraging the proven architecture of the existing BossDB database. Allowing for petabyte scale data storage, curation, sharing, visualization and analysis, the archive is scalable and allows for a fast in- memory spatial data store, seamless migration of data between low cost and durable object storage (i.e. S3), and rapid access to the enormous datasets. The system enables computing data quality metrics on large datasets and metadata stores through a standardized interface. The archive is developed through an agile process that actively folds in community stakeholders for regular reviews and continuous opportunities for design input.

BRAIN power: expanding reproducibility, quality control, and visualization in AFNI/SUMA COX, ROBERT WILLIAM (contact); NIELSON, DYLAN MILES U.S. NATIONAL INSTITUTE OF MENTAL HEALTH 2018 RFA-MH-17-257 Active
  • Cell Type
  • Circuit Diagrams
  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
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AFNI (Analysis of Functional NeuroImages) is an open-source software package for neuroimaging analysis and visualization of both functional and structural MRI as well as other modalities. Drs. Cox and Nielson propose to extend this widely used software package by offering containerization, cloud accessibility and web-accessible visualization. The software extension could support evolving BRAIN Initiative standards for human neuroimaging data organization and experiment specification. The project makes it possible for public integration testing of the software package, thus enabling end-user feedback and wider adoption and dissemination within the neuroimaging community.

C-PAC: A configurable, compute-optimized, cloud-enabled neuroimaging analysis software for reproducible translational and comparative Craddock, Richard Cameron Milham, Michael Peter (contact) Child Mind Institute, Inc. 2018 RFA-MH-17-257 Active
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  • Circuit Diagrams
  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
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Novel neuromodulation, recording, and imaging techniques applied to human and non- human primate brains generate datasets that require tools for organizing, processing and analyzing data that are widely available and easy to use. Drs. Milham and Craddock plan to extend C-PAC (Configurable Pipeline for the Analysis of Connectomes), building a configurable data analysis pipeline that incorporates various statistical analysis, machine learning, and network analytic techniques. In addition to adapting methods used in human imaging for non-human primate data, the project will implement a toolbox for alignment of electrophysiological data with brain imaging data. The resulting software enables high- throughput, semiautomated and end-to-end processing and analysis of structural and functional MRI data that are accessed locally or via the cloud.

Collaborative Standards for Brain Microscopy Hamilton, Carol M Research Triangle Institute 2018 RFA-MH-17-256 Active
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  • Circuit Diagrams
  • Human Neuroscience
  • Integrated Approaches
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  • Monitor Neural Activity
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Recent tissue-clearing techniques and advances in microscopy have made it possible to produce 3D images of intact brains. to help ensure consistency in data collection and analysis, Dr. Hamilton and her team will develop a set of standards for3D imaging of whole brains for the neuroscience research community.. Dr. Hamilton’s group will convene a Working Group of experts who will work through a consensus process to establish standards that will be distributed to the research community. These standards should help improve the efficiency of imaging research and allow comparisons across studies.

Data Archive for the Brain Initiative (DABI) Duncan, Dominique Pouratian, Nader Toga, Arthur W (contact) University Of Southern California 2018 RFA-MH-17-255 Active
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  • Circuit Diagrams
  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools

This project develops DABI (Data Archive for the Brain Initiative) to aid the dissemination of human neurophysiological data generated through the BRAIN Initiative. Incorporating infrastructure from a pre- existing hub for delivering effective informatics and analytics solutions for major projects in the study of neurological diseases, Drs. Toga, Duncan, and Pouratian will aggregate data related to human electrophysiology, making the data broadly available and accessible to the research community. The group plans to incorporate analysis tools with user interfaces, implement tools for data management and use, and link metadata across different data modalities. The overarching goal of this project is to secure, link, and disseminate BRAIN Initiative data with all pertinent recording and imaging parameters coming from participating sites

Data interface and apps for systems neurophysiology and imaging Van Hooser, Stephen D Brandeis University 2018 RFA-MH-17-256 Active
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  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
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Many labs develop unique software to manage and interpret their findings, but those programs are often specific for certain types of datasets, making them difficult to share among researchers. Dr. Van Hooser’s team plans to create an interface standard that establishes a common set of processes for accessing neurophysiological and imaging data. The standard will be tested, and revised accordingly, based on feedback from graduate students and postdoctoral researchers during data access events, or “hack-a-thons.” The interface standard will help increase the speed of research and make data widely available, allowing individuals outside of the neuroscience and research communities to make discoveries.

Illuminating Neurodevelopment through Integrated Analysis and Vizualization of Multi-Omic Data Hertzano, Ronna (contact) White, Owen R University Of Maryland Baltimore 2018 RFA-MH-17-257 Active
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  • Circuit Diagrams
  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
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Molecular and cellular neuroscientists often lack the training in computer programming to fully explore “-omics” data common in the BRAIN Initiative. Drs. Hertzano and White will implement analytic software for visualization and interactive genome browsing of gene expression and RNA-seq data, including simple and complex cross-dataset analysis. These tools will be made available in the BRAIN Initiative funded Neuroscience Multi-Omic Data Archive (NeMO) which hosts multi-omic data. The software will provide an easy-to-use web-based work environment for visualization and analysis of multi-modality and multi- omic data, interrogation of relationships between epigenomic signatures and gene expression, and integration of analytical techniques for multivariate analysis, gene co- expression and other analyses.

Multi-context software for robust and reproducible neuroscience image analysis Papademetris, Xenophon (contact) Scheinost, Dustin Yale University 2017 RFA-MH-17-257 Active
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  • Circuit Diagrams
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
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A thorough understanding of brain function requires the integration of neuroscience data across species and scales. While current software can verify data quality within one or a handful of data sources, reproducibility across multiple data sources is limited. Xenophon Papademetris and colleagues are developing software tools with cross-scale, cross-species reproducibility analysis in mind. By leveraging data created by two other BRAIN Initiative projects at Yale University, Papademetris will extend current software algorithms to incorporate data from multiple sources, design the software to be cross-platform compatible, validate the software through rigorous testing, and finally, distribute it to the community. The potential for a set of software tools to reliably and reproducibly analyze multiple heterogeneous neuroscience data types will help to break down data barriers for the greater neuroscience community.

NWB:N: A Data Standard and Software Ecosystem for Neurophysiology Ng, Lydia Lup-ming Ruebel, Oliver (contact) University Of Calif-lawrenc Berkeley Lab 2018 RFA-MH-17-256 Active
  • Cell Type
  • Circuit Diagrams
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools

Neurophysiology research, which focuses on recording brain cell activity, produces enormous amounts of complex data that are difficult to manage. Drs. Rubel and Ng will build upon the Neurodata Without Borders: Neurophysiology project to create a system that will allow for standardizing, sharing, and reusing neurophysiological data. The team will design an open source software system; develop methods to establish a consistent vocabulary for defining cell types, measurements, and behavioral tasks; and create tools to help the community adopt these new resources and standards. The proposed system will help accelerate neurophysiological discoveries as well as reproducibility studies.

OpenNeuro: An open archive for analysis and sharing of BRAIN Initiative data Poldrack, Russell A Stanford University 2018 RFA-MH-17-255 Active
  • Cell Type
  • Circuit Diagrams
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools

To leverage the public investment in the BRAIN Initiative, the sharing of data produced by its myriad projects is paramount. Dr. Podrack’s project extends the recently released OpenNeuro, which was developed based on the well-established and successful OpenfMRI, for an archive of neuroimaging data. The extended archive encompasses a broader range of neuroimaging data including EEG, MEG, diffusion MRI and others. The archive also implements easy-to-use data submission, semi-automated curation and advanced data processing workflows, which run directly on the cloud platform. The archive allows to share the results alongside the data, federate with other relevant repositories, and accessible to all researchers.

SABER: Scalable Analytics for Brain Exploration Research using X-Ray Microtomography and Electron Microscopy Gray Roncal, William R Johns Hopkins University 2017 RFA-MH-17-257 Active
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  • Circuit Diagrams
  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
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Neuroimaging techniques are advancing at a rapid rate, resulting in high resolution images of brain tissue and large datasets that can be difficult to manage. William Gray Roncal’s team propose an integration framework called SABER: Scalable Analytics for Brain Exploration Research. With a focus on techniques (e.g., electron microscopy) that produce high resolution brain images, SABER will create a unified framework by which these data types are accessible to a broader audience, can be processed in a reproducible, portable way, and can be scaled from small data volumes to large datasets. SABER has the potential to enable discoveries from high-resolution imaging techniques, making the parsing of entire brains a new reality.