Franco Pestilli, Ph.D. - US grants

Novel neuroscience tools and techniques are necessary to enable insight into the building blocks of neural circuits, the interactions between these circuits that underpin the functions of the human brain, and modulation of these circuits that affect our behavior. To leverage rapid technological development in sensing, imaging, and data analysis new ground breaking advances in neuroscience are necessary to facilitate knowledge discovery using data science methods. To address this societal grand challenge, the project will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral science disciplines together with partnerships in academia, industry, and government at multiple levels. The Big Data Neuroscience Spoke titled Midwest: Advanced Computational Neuroscience Network (ACNN) is strongly aligned with the national priority area of neuroscience and brings together a diverse set of committed regional partners to enable the Midwest region to realize the promise of Big Data for neuroscience. The ACNN Spoke will build broad consensus on the core requirements, infrastructure, and components needed to develop a new generation of sustainable interdisciplinary Neuroscience Big Data research. ACNN will leverage the strengths and resources in the Midwest region to increase innovation and collaboration for the understanding of the structure, physiology, and function of the human brain through partnerships and services in education, tools, and best practices.

The ACNN will design, pilot and support powerful neuroscientific computational resources for high-throughput, collaborative, and service-oriented data aggregation, processing and open-reproducible science. The ACNN Spoke framework will address three specific problems related to neuroscience Big Data: (1) data capture, organization, and management involving multiple centers and research groups, (2) quality assurance, preprocessing and analysis that incorporates contextual metadata, and (3) data communication to software and hardware computational resources that can scale with the volume, velocity, and variety of neuroscience datasets. The ACNN will build a sustainable ecosystem of neuroscience community partners in both academia and industry using existing technologies for collaboration and virtual meeting together with face-to-face group meetings. The planned activities of the ACNN Spoke will also allow the Midwest Big Data Hub to disseminate additional Big Data technologies resources to the neuroscience community, including access to supercomputing facilities, best practices, and platforms.

This award received co-funding from CISE Divisions of Advanced Cyberinfrastructure (ACI) and Information and Intelligent Systems (IIS).

Neuroscience is advancing by dissolving disciplinary boundaries and promoting transdisciplinary research between psychologists, cognitive neuroscientists, computer scientists, and engineers, to name a few. The success of this scientific endeavor would be enhanced by establishing software mechanisms to improve reproducibility of scientific results. This project develops a software platform that facilitates publication of publicly-accessible data and implementation of data-analysis algorithms. Both functions will be achievable within high-performance computing environments. The platform will enable publication of reproducible code, and access to national supercomputers. It will also make available reference datasets for validating results and data quality. It is expected that the open online platform will promote voluntary data submissions in exchange for access to the system. In addition, this platform will provide a reusable database of "data derivatives," which are data at different stages of preprocessing, including cortical segmentations, meshes, functional maps, brain connectivity matrices, or white-matter tracts. This open-derivatives database will allow computer scientists, mathematical scientists and engineers to use these data to develop and improve methods in their domains. Most generally, providing easy-to-use published data and methods will promote understanding the brain and allow diverse communities of scientists to use reproducible methods, and reuse the "long tail" of neuroimaging data.

The project focuses on providing seamless public access to data, computing, and reproducible algorithms, while promoting code sharing and upcycling the long tail of neuroscience data. It has three main objectives. First, to develop a platform to capture brain data, publish algorithms as reproducible applications, and perform data-intensive computing on high-performance compute clusters, as well as public clouds. Second, to develop novel algorithms for mapping brain-connectome individuality and variability. The algorithms will enhance discovery by leveraging the online platform for data intensive processing of large datasets. Third, to collate a large data set of brain data and data derivatives (processed data), such as connectome matrices, multi-parameters tractography models, cortical segmentation and functional maps. These derivatives will benefit scientists to develop algorithms for functional mapping, anatomical computing, and model optimization. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE). It has also received funding from the CISE Office of Advanced Cyberinfrastructure.

This project builds on a prior Midwest Big Data Hub effort. In 2015 stakeholders in the Midwest region of the United States formed a consortium of partners and working groups called the Midwest Big Data Hub (MBDH). MBDH aimed to help member organizations working in Big Data coordinate current activities and launch new collaborative projects. The project included stakeholders in the twelve states of the Midwest Census region (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin) and six leading universities that support hundreds of researchers, technologists, and students. This hub provides a basis for collaboration and outreach that increases the potential for benefitting society.

The current award is a collaboration among five academic sites (Indiana University, Iowa State University, UIUC/NCSA, the University of Michigan, the University of North Dakota, and the University of Minnesota - Twin Cities). The project focuses on priority areas that are important to the region and can also be influential on the national stage.
- The five thematic areas of focus, and the institutional partner leading that thematic area, are: Digital Agriculture (led by Iowa State); Smart, Connected, and Resilient Communities (Indiana University); Water Quality (University of Minnesota); Advanced Materials and Manufacturing (UIUC); and Health and Biomedicine (University of Michigan).
- Three cross-cutting areas that are emphasized across the project are: data science education and workforce development; cyberinfrastructure, data access and use; and communication and community development.
The priority areas have regional relevance and also have the prospect for integration into societal contexts at the national level. The overall goal is to enable the use of existing and emerging cyberinfrastructure and best practices to improve access to and use of data. The project plans to reach out to the Midwest community at large and to connect people, resources, and organizations. Ties to Big Data Hubs in three other regions provide a means to advance knowledge across these fields at the national level.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Scientists collect terabytes of critical data every year. Recently a strong open science movement has generated traction for the beneficial practice of sharing data across laboratories, universities and research institutions. Yet, sharing data is not enough. Data must be shared using standardized formats and accompanied by curated metadata to allow for tracking, search, and organization. Metadata are essential for scientific discovery, as they are routinely used to complete all data analyses. However, to date, most brain projects focus on collecting or analyzing data, not on metadata management. Typical metadata records consist of heterogeneous study descriptions, developed at study release stage, without consistency across records or standard mechanisms to track changes.
This project will increase access to brain data and improve metadata handling by combining two NSF-funded projects. It will develop a first-of-its-kind metadata management system able to track data and metadata distributed across heterogeneous geographical locations, storage systems and data formats. This portion of the project will expand the functionality of a previously funded NSF project DataLad. DataLad will also be enhanced to interoperate with major data repositories such as OSF and Figshare. Furthermore, the project will use the NSF-funded cloud computing platform brainlife.io to create a data and metadata marketplace by gathering data from multiple currently separated repositories into a single ecosystem . The goal is to improve interoperability across open science projects and make data and metadata easily searchable and available for computing on national cyberinfrastructure systems, ultimately advancing scientific discovery by increasing data discoverability, utilization, and publication.

This project will generate various technological advances. The core target will be an extensible system capable of automated gathering of metadata from various domains. It will be comprised of two major components: 1) a set of metadata parser algorithms that extract metadata from datasets and individual files using a flexible JSON-LD based data structure (with the ability to encode controlled vocabularies where available) and 2) an aggregation procedure that merges the aggregated metadata across parsers and stores them into compressed files that are optimized for bandwidth-efficient exchange and can be queried directly, or used as input into SQL or graph databases for data discovery applications. Extracted metadata will be included within the same datasets under Git and git-annex version control for unambiguous referencing and versatile data logistics. In parallel development we will improve interoperability of DataLad with existing data publishing portals (such as Figshare and OSF) by taking advantage of extracted metadata (e.g., Author, Description) to prefill required fields, and also by bundling the entire Git object store within the publication to make such published datasets installable back by DataLad without any loss of information. To make such published datasets discoverable, we will establish a crowd-sourced registry (with a RESTful API) which will get announcements on the availability of new datasets upon publication and aggregate their metadata to enable querying across datasets and data hosting providers. The final development will be the integration of DataLad within the brainlife.io data marketplace. This will make it possible to search and install datasets on brainlife.io as well as to process the data utilizing the brainlife.io analyses Apps on various NSF-funded national cyberinfrastructure high-throughput computer systems.

A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The field of network neuroscience has developed powerful analysis tools for studying brain networks and holds promise for deepening our understanding of the role played by brain networks in health, disease, development, and cognition. Despite widespread interest, barriers exist that prevent these tools from having broader impact. These include (1) unstandardized practices for sharing and documenting software, (2) long delays from when a method is first introduced to when it becomes publicly available, and (3) gaps in theoretic knowledge and understanding leading to incorrect, delays due to mistakes, and errors in reported results. These barriers ultimately slow the rate of neuroscientific discovery and stall progress in applied domains. To overcome these challenges, we will use open science methods and cloud-computing, to increase the availability of network neuroscience tools. We will use the platform brainlife.io for sharing these tools, which will be packaged into self-contained, standardized, reproducible Apps, shared with and modified by a community of users, and integrated into existing brainlife.io analysis pipelines. Apps will also be accompanied by links to primary sources, in-depth tutorials, and documentation, and worked-through examples, highlighting their correct usage and offering solutions for mitigating possible pitfalls. In standardizing and packaging network neuroscience tools as Apps, this proposed research will engage a new generation of neuroscientists, providing them powerful new and leading to new discoveries. Second, the proposed research will contribute growing suite of modeling analysis that can be modified to suit specialized purposes. Finally, the Brainlife.io platform will serve as part of the infrastructure supporting neuroscience research. Altogether, these advances will lead to new opportunities in network neuroscience research and further stimulate its growth while increasing synergies with other domains in neuroscience.

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program and SBE's Science of Learning and Augmented Intelligence Program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Franco Pestilli at Indiana University, this postdoctoral fellowship award supports an early career scientist investigating the role of white matter communication pathways in the human brain in learning and generalization. Prior work has related individual differences in human white matter to current abilities as a measurement of past learning; the proposed work will, instead, use individual differences in human white matter to predict future learning. The hypothesis addressed in the proposed work is that sensorimotor training changes white matter communication pathways in ways that allow for generalization to untrained behaviors. The investigators will test this hypothesis by using machine-learning methods and implementing an explicit model testing approach. This research will provide the field with important information concerning learning-related changes in the brain that will be applicable to educational and neuro-rehabilitation practices.

This project integrates cutting-edge measurements of white matter communication pathways in the brain with novel behavioral assessments. The proposed work builds from a well-documented and repeatable finding: sensorimotor learning leads to learning that generalizes (e.g., handwriting increases letter recognition). The project has three goals. The first goal is to demonstrate that training on a sensorimotor task (i.e., drawing novel symbols) leads to task-specific changes in the tissue properties of white matter communication pathways. We will employ a between-participants training manipulation and assess differences in learning-related white matter microstructure among training groups. The second goal is to demonstrate that the white matter changes associated with sensorimotor learning support generalization to an untrained behavior. We will use machine-learning to build a model of the relationship between learning-related changes in white matter tissue microstructure and sensorimotor learning. We will then quantify how well that model predicts visual recognition learning (i.e., learning to recognize the novel symbols). The expectation is that individual variability in global white matter tissue properties will predict sensorimotor learning and generalization. The final goal of the work is to leverage the cloud computing platform?brainlife.io?to deliver open-science and reproducible methods as well as publicly available analyses and services. Data, analyses, and results will be shared on brainlife.io with the potential to impact multiple communities of scientists interested in learning: behavioral scientists, computer scientists, and neuroscientists.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Summary/Abstract The Brain Imaging Data Structure (BIDS) is a BRAIN initiative (R24 MH114705) community-driven standard meant to maximize neuroimaging data sharing, and facilitate analysis tool development. We propose to extend the standard to encompass derivatives resulting from experiments related to both functional as well as structural magnetic resonance imaging data that describe macroscopic brain connectivity estimates. The focus of this proposal is to advance BIDS to describe the entire experimental workflow?from minimally processed anatomical, functional and diffusion MRI data through connectivity matrices and tractometry features?in service of supporting BRAIN initiative studies of large-scale connectivity of human and nonhuman brains. BIDS was initially scoped to MRI data of the brain, but the standard has set up a solid infrastructure to steer the community and has been extended to cover a range of other modalities (PET, EEG, MEG, ECoG). Since its first announcement, BIDS has evolved to become an organized community with shared governance and a strong impact well beyond the U.S. BRAIN initiative. To date, 131 individuals among faculty, students, and postdocs contributed to the development of the standard and the article describing BIDS has been cited 277 times. Current gaps exist in developing BIDS to effectively support the process of scientific results generation. This is because the standard does not yet describe brain features that can be extracted from MRI data and that are routinely used to perform statistical tests and complete scientific studies. These features comprise connectivity maps, structural and functional connections, major white matter tracts, diffusion signal models as well as white matter tractograms and tractometry. Sharing processed data and features in addition to raw and minimally-processed data is critical to accelerating scientific discovery. This is because substantial effort, software, and hardware instrumentation, and know-how are required to bring raw data to a usable state. One previous project (R24 MH114705) laid the foundations for the BIDS derivatives standard, ultimately leading to the existing Common Derivatives standard. However, the current BIDS derivative standard does not cover advanced data derivatives that describe brain connectivity experiments. The current proposal is to advance the BIDS standard beyond preprocessed data to describe data products generated from experiments and models fit after preprocessing. The project will deliver a community-developed standard describing brain connectivity experiments. The standard will be accompanied by software to validate the datasets.