1995 — 2005 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Anatomic Morphologic Analysis of Mr Brain Images @ Massachusetts General Hospital
[unreadable] DESCRIPTION (provided by applicant): The morphology of the human brain is exceptionally complex; reflecting a myriad of inextricably intertwined systems of neuronal cell bodies, axons, and other components. Groupings of neural components that share common structural or functional properties comprise the structural and functional neuroanatomic framework of the brain. Characterization of the morphologic properties of the brain and its component parts, as enabled by state-of-the-art magnetic resonance imaging (MRI) is exceptionally well suited to permit a quantitative study of the parameters relevant to the structural and functional makeup of the human brain in vivo. The goal of this grant is to continue to develop tools and methods for the precise quantitative analysis of brain morphology in health and disease, and to disseminate the tools and results of the application of these tools to the neuroscience community as a whole. Specifically, we will 1) extend our previously developed pixel segmentation and morphological quantification methods, continuing our efforts to develop a unified neuroanatomic segmentation framework and transition these tools to clinical applications on a routinely available software platform; 2) continue our previously developed methods to characterize shape and shape change metrics in normal subjects and pathological patient populations; and 3) dissemination of segmentation tools and comparison methods, as well as the results of image segmentation and volumetric analysis to the community as a whole using the World Wide Web.This application continues to take advantage of several unique aspects that distinguishes it from other related work. First, a unified framework for segmentation and classification in support of a neurologically-based anatomic morphology has emerged. Second, this unified framework incorporates the multispectral nature of MRI data. Third, this framework intrinsically includes estimates of the underlying uncertainty associated with the segmentation and classification process, which supports a rational assessment of sensitivity of a given method. Fourth, this approach expands upon traditional "static" image analysis by incorporation of shape-based analysis for anomaly detection. In addition, we have identified a number of clinical application areas which, in addition to fostering enhanced analytic capabilities to studies in these areas, permits us to optimize the operational efficiency of the resulting analysis. Specifically, the segmentation, classification and shape analysis of MRI data in patients with stroke and Huntington's disease, as welt as the appropriate normative populations, provide a vital testbed for the evaluation of the clinical utility of these morphological analysis techniques.
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0.946 |
1995 |
Kennedy, David Nelson |
S10Activity Code Description: To make available to institutions with a high concentration of NIH extramural research awards, research instruments which will be used on a shared basis. |
Hierachical Distributed Storage System For Ultrafast Mri @ Massachusetts General Hospital |
0.946 |
1999 — 2002 |
Kennedy, David Nelson |
P01Activity Code Description: For the support of a broadly based, multidisciplinary, often long-term research program which has a specific major objective or a basic theme. A program project generally involves the organized efforts of relatively large groups, members of which are conducting research projects designed to elucidate the various aspects or components of this objective. Each research project is usually under the leadership of an established investigator. The grant can provide support for certain basic resources used by these groups in the program, including clinical components, the sharing of which facilitates the total research effort. A program project is directed toward a range of problems having a central research focus, in contrast to the usually narrower thrust of the traditional research project. Each project supported through this mechanism should contribute or be directly related to the common theme of the total research effort. These scientifically meritorious projects should demonstrate an essential element of unity and interdependence, i.e., a system of research activities and projects directed toward a well-defined research program goal. |
Core--Acquisition and Analysis @ Massachusetts General Hospital
This Core facility is charged with providing a comprehensive computational environment to perform neuroanatomic analysis, image registration and functional-anatomic database development in support of the specific hypotheses regarding anatomic specificity of drug-related changes in fMRI signal presented in the Projects. This task is divided into the following three general processing domains: 1) neuroanatomic analysis of human and rodent subjects, 2) integration of structural and functional maps within a subject, and 3) data management to support within individual or between group hypothesis testing for use in each of the Projects. Functional MRI (fMRI) can provide information about both the location of cortical and subcortical areas involved in the response to drug administration, and dynamic information relating to the temporal interrelationships of these areas. Therefore, this Core must provide a reproducible and efficient means for obtaining, analyzing, testing and reporting the resultant structural and functional data. The non-invasive nature of MRI allows multiple experiments to be repeated on an individual subject, both human and rodent, during the course of one examination period as well as on multiple occasions. These data must be registered accurately, in both space and time, and interpreted to isolate physiological phenomena of interest from extraneous sources. Three- dimensional, high-resolution, anatomic MRI images will serve as the basis for interrelating this information. These intersubject comparisons are required to understand the spatial distribution of brain activation within and between groups of subjects and the relationship of this pattern of activation to drug-related alterations. The required computational operations for these tasks are already developed and in daily use. Continued development effort is necessary to optimize these tasks for proposed projects in order to improve processing efficiency and maximize the specificity of the observed functional changes.
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0.946 |
2004 — 2008 |
Kennedy, David Nelson |
K12Activity Code Description: For support to a newly trained clinician appointed by an institution for development of independent research skills and experience in a fundamental science within the framework of an interdisciplinary research and development program. |
Neuroimaging Neuroinformatics Training Program @ Massachusetts General Hospital
[unreadable] DESCRIPTION (provided by applicant): This proposal is in response to PAR-03-034 "Neuroinformatics Institutional Mentored Research Scientist Development Award (K12)." The overarching goal of this application is to provide an excellent postdoctoral training program in neuroimaging neuroinformatics that capitalizes on the many strengths of the existing neuroscientists, informatics and imaging resources that our combined resources represent. Our proposed Neuroimaging Neuroinformatics Training Program (NNTP) is based upon a number of important strategic alliances. The first cornerstone of this effort is the existing HBP grants held by Dr. Anders Dale (R01 NS39581: Cortical-Surface-Based Brain Imaging) and Dr. David Kennedy (R01 NS34189: Anatomic Morphologic Analysis of MR Brain Images). These efforts span a wealth of technological developments, research and clinical application areas in the rapidly developing area of quantitative morphometric image analysis. A second and vital cornerstone is our association with the Harvard-MIT Division of Health Sciences and Technology (HST) Biomedical Informatics Program. This existing pre- and post-graduate academic program, within a world class biomedical engineering department, is an ideal setting for the development of a coordinated training effort in Neuroinformatics. The established track record in training skilled scientists in areas of informatics will prove invaluable in this new initiative. The third cornerstone is the combined clinical research opportunities afforded by the Harvard-wide biomedical imaging resources. These include the MGH/MIT/HST Athinoula A. Martinos Center for Biomedical Imaging, the Harvard Neuroimaging Center, the Surgical Planning Lab at Brigham and Women's Hospital, the Brain Morphology BIRN (Biomedical Informatics Research Network) and the MIT Artificial Intelligence Laboratory. Together, these active and vibrant programs provide for the best possible training opportunities in imaging science, computer science, clinical application areas, and cognitive neuroscience. A substantial and successful pool of internationally renowned mentors have agreed to participate in this program, and the combined resources provide the best possible exposure to all neuroimaging procedures and insure the capability to draw the highest caliber trainees. A plan for recruiting, selecting and monitoring trainees is proposed. This program will be an asset to the Neuroinformatics initiatives of the Human Brain Project by helping to prepare future scientists with advanced neuroinformatics skills [unreadable] [unreadable]
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0.946 |
2009 — 2015 |
Frazier, Jean A Kennedy, David Nelson |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
A Knowledge Environment For Neuroimaging in Child Psychiatry @ Univ of Massachusetts Med Sch Worcester
DESCRIPTION (provided by applicant): Numerous psychiatric disorders can plague the development of children. Each of these disorders manifests as a distinct pattern of clinical, behavioral, etiological, neuroanatomic, and neurofunctional characteristics that challenge the management of the individual patient, as well as the development of successful intervention and prevention strategies. In the area of neuroimaging, a substantial number of studies have been performed;while each study produces a wealth a clinical and imaging data, most of this information remains an untapped resource due to ineffective use of the principles of data sharing and integration. This proposal seeks to develop a set of cohesive neuroinformatics resources to foster integrated research efforts in the study of children with mental illness. This effort is designed to capitalize on the extensive database of over 250 high-resolution volumetric MRI data and associated analyses that has been generated by the Child and Adolescent Neuropsychiatric Research Program (CANRP) at Cambridge Health Alliance (CHA), in conjunction with the Center for Morphometric Analysis (CMA) at the MGH, and the integrated web resources of the Internet Brain Segmentation Repository (IBSR) and the Internet Brain Volume Database (IBVD). As such, this will constitute the first known "Knowledge Environment" for the integrated understanding of the quantitative neuroanatomy in this population of children with psychiatric disorders. PUBLIC HEALTH RELEVANCE: Numerous psychiatric disorders can plague the development of children. Each of these disorders manifests as a distinct pattern of clinical, behavioral, and neuroanatomic traits that challenge the management of the individual patient, as well as the development of successful intervention and prevention strategies. This project will develop a set of cohesive neuroinformatics resources that will help to integrate research efforts to advance the diagnosis and treatment of these disorders.
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0.969 |
2010 — 2014 |
Kennedy, David Nelson |
P30Activity Code Description: To support shared resources and facilities for categorical research by a number of investigators from different disciplines who provide a multidisciplinary approach to a joint research effort or from the same discipline who focus on a common research problem. The core grant is integrated with the center's component projects or program projects, though funded independently from them. This support, by providing more accessible resources, is expected to assure a greater productivity than from the separate projects and program projects. |
Quantitative Methodologies Core @ Univ of Massachusetts Med Sch Worcester
In the last year the UMMS has recruited Dr. David Kennedy, an internationally/recognized authority in Neurolnformatics, to its brain imaging program. As noted in the Introduction, one ofthe specific aims of the current P30 grant is to increase the ability of our IDDRC to execute and support cognitive neuroscience projects of high quality, and his arrival fills an important need in that area. Dr. Kennedy will coordinate Informatics Services within Core C, and with Dr. Curtis Deutsch will serve as Co-Director. Dr. Deutsch will continue to direct Statistical Services for the IDDRC. Also, in this expanded Core, we are joined by faculty from the recently formed Department of Quantitative Health Sciences (QHS) at UMMS. Statistics. With the advent ofthe QHS department, there has been an influx of talented consulting statisticians, epidemiologists, and methodologists at UMMS (see letter of support, Catarina Kiefe, MD, Director, QHS). One important addition is Dr. Arlene Ash, a senior biostatistician and investigator in public health, who heads the QHS Division of Biostatistics and Health Services Research. She coordinates the activities of a number of statisticians under the aegis ofthe QHS, including members who are joining our IDDRC Quantitative Services (see below). Usha Govindarajulu, PhD, a member of the QHS faculty, has recently come on board as a staff statistician to the IDDRC. She will join Dr. Anne Hunt, who has served as consulting statistician over the previous funding period and will continue this work. Informatics. Our Core is also the beneficiary of another recently arrived authority in Informatics, Dr. Thomas Houston, newly appointed Director ofthe Division of Health Informatics and Implementation Science, and his colleague Dr. Rajani Sadasivam. Dr. Houston is also Co-Director ofthe Biomedical Informatics program at UMMS, providing support for clinical and translational research. Further, longstanding UMMS faculty Drs. David Lapointe and Juerg Straubhaar, authorities on bioinformatics, will provide their guidance in computing and software-based applications in molecular genetics. In addition to providing direct service at UMMS, we will continue to provide guidance for investigators seeking specialized expertise within the UMMS and beyond its walls. We have created an IDDRC Quantitative Services Advisory Committee, comprised of Drs. Ash, Houston, Lapointe, Sadasivam, Straubhaar, Weng, and Zottola, and chaired by Drs. Kennedy and Deutsch. This committee will meet with investigators, who will present their problems for review, and committee members will make recommendations on design and methodology in Informatics and Statistics. Committee members' networks of colleagues, along with the extensive group of statistical consultants who have participated over previous funding periods, will provide a broad array of expertise to IDDRC investigators.
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0.969 |
2011 |
Kennedy, David Nelson |
R13Activity Code Description: To support recipient sponsored and directed international, national or regional meetings, conferences and workshops. |
4th Annual Neuroinformatics Congress: Boston 2011 @ Univ of Massachusetts Med Sch Worcester
DESCRIPTION (provided by applicant): The International Neuroinformatics Coordinating Facility (INCF) is an international organization devoted to advancing the field of neuroinformatics. The annual INCF Neuroinformatics Congress provides a meeting place for researchers in this emerging field, including data- and knowledge-bases of the nervous system from molecular to behavioral levels;tools for the acquisition, analysis, and visualization of nervous system data;and theoretical, computational, and simulation environments for modeling the brain. The 4th Annual INCF Neuroinformatics Congress will be held in Boston September 4-6, 2011. This proposal seeks to augment the upcoming 2011 meeting by providing a) student and postdoctoral attendance support and b) child and family care support. With these additional attendee support functions in place, we aim to further reduce barriers to attendance of the meeting for all potential attendees, and thus reach an even broader subset of the neuroinformatics community at critical times in their careers. Better dissemination results in better science, and thus better healthcare, for all. PUBLIC HEALTH RELEVANCE: Scientific meetings are critical to the advance of science and students, recent graduates and professionals who are primary family caregivers are at a disadvantage in attending these meetings. This proposal will augment the 4th Annual INCF Neuroinformatics Congress by providing support to reduce these barriers to attendance.
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0.969 |
2016 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Data Model and Integration @ Univ of Massachusetts Med Sch Worcester
TR&D Project 2: Data Model, Provenance and Integration Abstract Our proposed BTRC resource, the Center for Reproducible Neuroimaging Computation (CRNC), seeks to implement a shift in the way neuroimaging research is performed and reported. Through the development and implementation of technology that supports a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. Reproducibility is critical because the current literature is fraught with published results that are due to mistakes (occasionally misconduct); or turn out to be false positive (contributed to by the lack of statistical power). More importantly, given the current publication system, it is exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate. In this Technology Research and Development Project, TR&D 2: Data Models and Integration, we will establish the technology for implementation of standardized workflow description and development of machine- readable markup and storage of the results of these workflows. The goals of this project are to encapsulate in a structured form the set of software tools utilized in a set of targeted workflows, to enable the markup of such analyses with important metadata, to determine the factors that contribute to the reproducibility of results or lack thereof, and to provide an application for scientists to interact with data models. To accomplish this, we will: 1) Use Linked Data Models to markup Data, Workflows, and Results; 2) Create reusable workflows with provenance tracking; and 3) Create BrainVerse: a suite of services for NIDM and computational reproducibility. We will work in partnership with the other TR&D projects, the Training and Dissemination Core and the Collaborator and Service Project users to deploy, test and validate the reproducible analysis system and to foster use of the reproducible framework in the neuroimaging research community.
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0.969 |
2016 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Execution Environments @ Univ of Massachusetts Med Sch Worcester
TR&D Project 3: Standardized Execution Environments Abstract Our proposed BTRC resource, the Center for Reproducible Neuroimaging Computation (CRNC), seeks to implement a shift in the way neuroimaging research is performed and reported. Through the development and implementation of technology that supports a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. Reproducibility is critical because the current literature is fraught with published results that are due to mistakes (occasionally misconduct); or turn out to be false positive (contributed to by the lack of statistical power). More importantly, given the current publication system, it is exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate. In this Technology Research and Development Project, TR&D 3: Execution Environments, we will establish the technology for development of execution options that facilitates operation in multiple computational environments and reduces barriers to scale and reliability. Centralized clearinghouses address only one necessary aspect of a sustainable software ecosystem: availability. Unfortunately, even given a detailed specification of provenance information, mere availability of software and data does not provide accessibility. Accessibility requires that software and tools are deployed in computing environments available to end users without technical system and software installation knowledge. This deployment needs to be well specified (e.g., exactly which tools are installed), automated (so it can be reconstructed later on), and controlled (e.g., the environment is tested). To accomplish this, we will develop NeuroImaging Computation Environments MANager (NICEMAN) which will provide: 1) computational platform with automatic version-based provisioning; 2) tools for turnkey execution of computational workflows; and 3) web service for instantiation of arbitrary computation environments locally or in the cloud through interactive web interface. We will work in partnership with the other TR&D projects, the Training and Dissemination Core and the Collaborator and Service Project users to deploy, test and validate the reproducible analysis system and to foster use of the reproducible framework in the neuroimaging research community.
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0.969 |
2016 — 2020 |
Frazier, Jean A Kennedy, David Nelson |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Exposing the Deep Content of the Publication: Knowledge Extraction For Neuroimaging in Child Psychiatry @ Univ of Massachusetts Med Sch Worcester
Abstract Numerous psychiatric disorders can plague the development of children. Each of these disorders manifests as a distinct pattern of clinical, behavioral, etiological, neuroanatomic, and neurofunctional characteristics that challenge the management of the individual patient, as well as the development of successful intervention and prevention strategies. In the area of neuroimaging, a substantial number of studies have been performed and published in the literature; while each study produces a wealth a clinical and imaging data, most of the detailed knowledge information in the manuscript (detailed findings, methods, raw and derived data, etc.) remains an untapped resource due to limitations of the current publication format related to standards of human and machine-readable knowledge expression. Following upon the successful development of numerous data sharing resources and mandates, this proposal seeks to develop the tools necessary to liberate the detailed knowledge expressions form the publication, enhance our ability to query, drive inference and act upon these published knowledge elements via the Child Psychiatry Portal, and embark on a research plan that highlights the utility of these combined data resources to solve specific problems in the child psychiatry domain related to reproducibility of findings and amplification of RDoC constructs. Successful execution of this program of social, technical and biological study will move the field forward by providing additional methods for all investigators to fulfill their NIH obligation to participate in open, reproducible science. As such, this fosters a bolder ?discovery- mode? data interrogation designed to capture the richness of the neuroimaging data landscape and provide better directed hypotheses for future study into diagnosis, prediction, monitoring of therapeutic intervention.
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0.969 |
2016 — 2020 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Center For Reproducible Neuroimaging Computation (Crnc) @ Univ of Massachusetts Med Sch Worcester
? DESCRIPTION (provided by applicant): Over the last two decades a vast technological, computational and societal infrastructure has emerged and transformed how information is collected and knowledge is gathered in all facets of science. Within the medical community, in response to numerous NIH data sharing initiatives and mandates, as well as through grassroots efforts, the community has succeeded in accumulating an extensive array of shared data. In the scientific process, methods should be reproducible. In the economics of science, data and methods should be maximally reusable in order to maximize the scientific return on the data acquisition investment. Data and analytic processing methods reuse have become a focal point in a growing concern about the replicability and power of many of today's scientific studies. The magnitude of this reproducibility issue indicates that a paradigm shift may be in order as to how we generate and report knowledge from our mounting public and private neuroimaging repositories. These factors impede scientific discovery and is ultimately a disservice to all the stakeholders, including the investigators themselves, their peers and colleagues, their institution, and their funding agencies. Our proposed BTRC resource, the Center for Reproducible Neuroimaging Computation (CRNC), seeks to implement a shift in the way neuroimaging research is performed. Through the development of technology that supports a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. Reproducibility is critical because the current literature contains large numbers of erroneous conclusions (due to limited power, false positive, publication bias and occasionally mistakes). Given a neuroimaging study, it is exceedingly difficult to discern between false positive and true positive findings as data is hard to aggregate, and exact methods are hard to replicate. In order to advance the field in terms of analysis and publication in a way that embraces reproducibility, the overall Center will have the following aims: A) Deliver a reproducible analysis system comprised of components that include data and software discovery (TR&D 1), implementation of standardized workflow description and development of machine-readable markup and storage of the results of these workflows (TR&D 2) and development of execution options that facilitates operation in multiple computational environments and reduces barriers to scale and reliability (TR&D 3); B) Working with a community of collaborator and service users, deploy, test and validate the reproducible analysis system with a wide variety of use cases ranging from software developers to applied scientists that support the archiving and reuse of raw data and the archival and reuse of derived results to promote reproducible clinical research (and its publication) in multiple different application areas; and C) Provide training and education to the community to foster continued use and development of the reproducible framework in neuroimaging research.
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0.969 |
2016 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Resource Discovery @ Univ of Massachusetts Med Sch Worcester
TR&D Project 1: Resource Discovery Abstract Our proposed BTRC resource, the Center for Reproducible Neuroimaging Computation (CRNC), seeks to implement a shift in the way neuroimaging research is performed and reported. Through the development and implementation of technology that supports a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. Reproducibility is critical because the current literature is fraught with published results that are due to mistakes (occasionally misconduct); or turn out to be false positive (contributed to by the lack of statistical power). More importantly, given the current publication system, it is exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate. In this Technology Research and Development Project, TR&D 1: Resource Discovery, we will establish the technology for comprehensive data and software discovery. We will integrate existing successful community platforms, extend existing data and search services, and develop new search and discovery tools to create a sophisticated, comprehensive and dynamic search environment for working with distributed neuroimaging data, tools, workflows and execution environments. This work will support users in discovery and also assist the end user with a specific analytic goal in finding the appropriate available data and software that can subsequently be submitted to the specified workflows and executions environments. To accomplish this, we will: 1) Establish a comprehensive environment for search and discovery of Neuroimaging data and tools; 2) Provide automatic search and retrieval of Neuroimaging data for repeatable processing workflows; and 3) Implement ?NeuroBLAST?, a tool that allows users to find matching/similar studies based on a combination of task, analysis, and activation patterns. We will work in partnership with the other TR&D projects, the Training and Dissemination Core and the Collaborator and Service Project users to deploy, test and validate the reproducible analysis system and to foster use of the reproducible framework in the neuroimaging research community.
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0.969 |
2020 — 2021 |
Kennedy, David Nelson Laird, Angela R [⬀] |
R25Activity Code Description: For support to develop and/or implement a program as it relates to a category in one or more of the areas of education, information, training, technical assistance, coordination, or evaluation. |
Abcd Course On Reproducible Data Analyses @ Florida International University
PROJECT SUMMARY/ABSTRACT The ABCD-ReproNim Course (1R25-DA051675) is a collaborative partnership to provide research educational training in reproducible analyses of data from the ABCD Study. The course integrates curriculum from ReproNim: A Center for Reproducible Neuroimaging Computation, which is a NIBIB-funded P41 Biomedical Technology Resource Center (BTRC) whose vision is to help neuroimaging researchers achieve more reproducible data analysis workflows and outcomes. The ReproNim approach relies on both technical development of readily accessible, user-friendly computational tools and services that can be readily integrated into current research practices, as well as a broad educational outreach about reproducibility to the neuroimaging community at large, including developers as well as applied researchers across basic sciences and clinical disciplines. The current project proposes an administrative supplement to provide dedicated research training on making data from the Adolescent Brain Cognitive Development (ABCD) Study FAIR (i.e., Findable, Accessible, Interoperable, and Reusable) and AI/ML (i.e., Artificial Intelligence and Machine Learning) ready. ML/AI applications have increased relevance in the discovery of biomarkers, predicting intervention outcomes, and integrating information across datasets. However, the knowledge required to perform effective biomedical ML research spans knowledge about data, scientific questions, computing technologies alongside ML/AI platforms and tools. The ABCD-ReproNim AI/ML Course will extend the current training to make trainees aware of the tools, concepts, and caveats for multimodal ML/AI processing of ABCD data. Students will first receive training across a 5-week online course that includes lectures, readings, and ABCD data exercises on topics that include: (1) FAIR for and FAIRness in ML/AI Applications, (2) Core Concepts in ML, (3), Neuroimaging ML, (4) Interpretable/Explainable ML, and (5) Introduction to Deep Learning. Competencies and skills addressed will include training and publishing ML models, organizing and evaluating data for ML applications, and reusing existing models efficiently. Didactic instruction will be followed by a 5-day remote Project Week, where students will apply the skills learned and work towards completion of AI/ML data analysis projects. Success will result in well-trained researchers who are able to apply reproducible AI/ML practices to test generalizability of AI/ML models for cross-sectional and longitudinal prediction across the ABCD dataset.
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0.922 |
2020 — 2021 |
Haselgrove, Christian Kennedy, David Nelson Preuss, Nina |
R24Activity Code Description: Undocumented code - click on the grant title for more information. |
Neuroimaging Informatics Tools and Resources Collaboratory: Outreach, Infrastructure and Maintece @ Univ of Massachusetts Med Sch Worcester
Abstract The NeuroImaging Tools and Resources Collaboratory (NITRC: www.nitrc.org) is an established and award- winning (2009 Excellence.gov award and 2015 HHS Innovates award) repository of information and support for resources relevant to a broad set of neuroimaging domains and their communities of developers and users. Resources hosted on NITRC Resources (NITRC-R) include software, hardware, and datasets, among other assets. The purpose of NITRC is to provide neuroscience researchers with a complete solution to the problem of finding, developing, and sharing neuroimaging and neuroinformatics software tools, finding and sharing large-scale resting state and structural imaging datasets, and manipulating the software and the data in high- performance computing environments. NITRC will continue to be a framework where researchers can identify software and data and seamlessly deliver them to a cloud-based computational resource for processing. This will be accomplished through the execution of aims designed to provide: 1) Content and user maintenance for the established NITRC-R, IR and CE environments; 2) infrastructure maintenance for the complete development and deployment environments; 3) content expansion in the existing domains, and identification of new areas that are amenable to NITRC support; and 4) outreach, user documentation, training, and support for the established NITRC user base. This continued effort will greatly enhance the delivery of high-performance computing to the large-scale datasets that are becoming the mainstay of neuroscience research and promote more reproducible publication practices through enhanced resource sharing. !
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0.969 |
2021 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
A Fair Data and Metadata Foundation For Reproducible Research @ Univ of Massachusetts Med Sch Worcester
TR&D Project 1: A FAIR Data and Metadata Foundation for Reproducible Research (DISCOVER) SUMMARY Our NCBIB resource, ReproNim: A Center for Reproducible Neuroimaging Computation, seeks to continue to drive a shift in the way neuroimaging research is performed and reported to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. In this Technology Research and Development Project, TR&D 1 - A FAIR Data and Metadata Foundation for Reproducible Research, we focus on the necessary tools and best practices to enable the efficient annotation of scientific data and the effective search for and discovery of this data and its associated workflows and software. During the current period, we have developed robust data annotation tools for raw and derived data and associated tools for discovery. The data annotation tools are supported by an infrastructure for managing the necessary terminologies required for annotation. Our tools and procedures support the ?FAIR Data Principles? which describe a set of key principles that will ensure data?s value to the research community such that the data are Findable (with sufficient explicit metadata), Accessible (for humans and machines), Interoperable (using standard definitions and Common Data Elements), and Reusable (meeting community standards, and sufficiently documented). The Office of Data Science at NIH has endorsed these principles and NIH has recently incorporated them in their most recent policy for data management and sharing (NOT-OD-21-013) that requires the preservation and sharing of scientific data from all research, funded or conducted in whole or in part by NIH. The tools and services provided by TR&D1 will therefore not only assist researchers in performing reproducible neuroimaging, but also in the utilization of the increased amounts of data being made available as part of this data sharing policy. Support for researchers will be accomplished via two specific aims: 1) Production of FAIR data through metadata annotation and alignment allowing for the sharing and publication of these data; and 2) Enabling data discovery and cohort generation for researchers to be able to effectively re-use FAIR data for re-analysis or re-execution. These two complementary aims will be supported by a third aim focused on support and training: 3) Extend and harden existing ReproNim software for FAIR data publication and discovery in coordination with the community. This aim will ensure that the tools we develop will be more accessible to those who have limited technical experience and will be complemented by training modules and support for different user experience levels and use-cases. This suite of tools, part of the larger ReproNim toolset, enables researchers to work within a FAIR data ecosystem. We will carry out this work in collaboration with the other ReproNim technology research and development projects and our Collaborative and Service projects. Together, we will help researchers become more efficient in the production and sharing of FAIR data, promoting the ability of these researchers to utilize a growing collection of well described data and to advance knowledge and explore the generalizability of scientific claims.
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0.969 |
2021 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Repronim: a Center For Reproducible Neuroimaging Computation @ Univ of Massachusetts Med Sch Worcester
SUMMARY Our proposed NCBIB resource, ReproNim: A Center for Reproducible Neuroimaging Computation, seeks to continue to drive a shift in the way neuroimaging research is performed and reported. In the first award period, we have successfully created a substantial set of tools, developed best practices and trained a cadre of users that improves the baseline reproducibility of many studies. But there is more to do. Through the continued development and implementation of technology that supports more efficiency and effectiveness for more users, we extend our comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities. Our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research, while making the process easier and more efficient for investigators. Reproducibility is critical because the current literature is fraught with published results that are due to mistakes (occasionally misconduct); or turn out to be false positives (contributed to by the lack of statistical power). More importantly, given the current publication system, it is exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate. In this Administration Core, we seek to continue to seamlessly and efficiently administer the overall operations of the Center. To accomplish this, we will: 1) Provide overall management of the day-to-day operations of the Center; 2) Provide continuous self-monitoring and external verification of our progress and direction; and 3) Promote our technological developments with a voice that is heard within the national and international neuroimaging, neuroinformatics and reproducibility research communities. We will work in partnership with the other TR&D projects and the Collaborative and Service Project users and the Training and Dissemination Core to foster knowledge of and use of the reproducible framework in the neuroimaging research community.
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0.969 |
2021 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Improving Research Efficiency Through Better Descriptors @ Univ of Massachusetts Med Sch Worcester
TR&D Project 2: Improving Research Efficiency through Better Descriptors (DESCRIBE) SUMMARY: The scale and complexity of neuroimaging research have grown exponentially over the last three decades and have enabled new insights into human cognition in health and disease and development of new imaging hardware, processing, and informatics technologies. As new information has proliferated into the research ecosystem, there is a need to integrate this knowledge from publications, data sources, and analysis tools. This integration has been hampered by limited harmonization of description across these digital outputs. During the current period, this Technology Research and Development Project, TR&D2, has addressed some of these challenges. We extended the Neuroimaging Data Model (NIDM) - a descriptor framework built on top of the World Wide Web Consortium's Provenance Data Model (W3C-PROV) and backed by community- developed ontologies. Using such standards we also created a set of technologies with our ReproNim projects and partners to enable reproducible analytics, to harmonize data and results, and to gather standardized provenance. This proposal aims to increase research efficiency and overall trust in scientific findings through better description of digital objects and better provenance of analytics. To accomplish these overarching goals, we will: 1) Formalize detailed and structured descriptors of all stages of a neuroimaging research workflow. This is critical for interpreting and trusting scientific results. 2) Develop a resource to create and disseminate Findable, Accessible, Interoperable, Reusable (FAIR) and robust scientific workflows. This will enable users to trust and reuse existing and well-tested analyses, as well as disseminate their own scripts when such analyses are not available. 3) Extend and harden existing ReproNim technologies in coordination with the community. We will integrate our technologies through developers of other tools, thus making our technologies more accessible to those who have limited technical experience. This effort will be complemented by training and support for different user experience levels and use cases. We will deliver a set of technologies that allows researchers to harmonize their output by design, from assessment and imaging data collection to final results. These technologies will also support consolidation and reuse of existing workflows, with new processes being developed only when necessary. Finally, our tools will support community-based generation, curation, and management of standardized information. We will carry out this work in collaboration with the other ReproNim technology research and development projects, and our collaborative and service projects. Together, we will help researchers become more effective through increased efficiency in every facet of the research lifecycle. TR&D2 technologies support the overall mission of ReproNim to improve the way neuroimaging research is performed and reported, to enable a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, and to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research.
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0.969 |
2021 |
Kennedy, David Nelson |
P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Efficient and Reproducible Execution From Data Collection to Processing @ Univ of Massachusetts Med Sch Worcester
TR&D Project 3: Efficient and reproducible execution from data collection to processing (DO) SUMMARY: The ReproNim project seeks to transform neuroimaging practice, to make research more efficient and effective in such a way that also makes it reproducible as a result. As more data, metadata, and computing resources become available to the neuroimaging community, tools and frameworks for managing data and processing workflows that ensure consistent control over all of the digital objects of science become increasingly important. Such tools should assist in obtaining valid results while establishing their provenance and minimizing the need for manual curation and intervention; they should not get in the way of doing research. In this Technology Research and Development Project, TR&D 3, we establish new approaches, as well as adopt and contribute back to existing tools, to automate many stages of data collection and analysis, making efficient use of local or remote computing resources that are available to the researchers. In particular, we aim to 1) Automate ?Doing (execution of) an experiment? through collection and representation of data, metadata, and provenance across all stages of a neuroimaging acquisition, including all the data types that could be important for quality assurance and proper accounting for possible confounding factors, such as audio/video stimuli, physiological recordings, details of the experimental design. Automated integration of imaging and non-imaging data not only makes research more efficient and labor saving, it also makes collected and shared data more comprehensive, accurate, and reproducible. 2) Make computational resources (GPUs, local High Performance Computing centers, and cloud computing resources) conveniently and efficiently available to researchers to perform execution of needed data transformations (conversion, analysis, etc.). While orchestrating execution we will record detailed provenance information, sufficient for re-execution of any stage of the research process, and make it available to the researcher alongside with the produced results. Efficient use of computational resources and collection of detailed provenance will facilitate experimentation and application of bleeding edge analysis workflows, while reducing necessary technological know-how. 3) Maintain, support, and extend existing ReproNim and related software and data resources that we and our partners have made available openly to the community. This effort will be complemented by training modules and support for different user experience levels and use cases. Ensuring such continuity in availability and robust operation of tools, computing environments, and data resources is essential for any effort aiming to support efficient and reproducible computation. We will carry out this work in collaboration with the other ReproNim technology research and development projects, our collaborative and service projects, and the neuroimaging community at large. This work will automate and conveniently interface complex technologies while facilitating use of established data standards and provenance recording, lowering the technological expertise necessary for neuroimaging scientists to advance knowledge.
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0.969 |