John Darrell Van Horn - US grants

With National Science Foundation support, Dr. Van Horn will spend a year developing and validating methods and tools needed for examining continuous motor-sensory tasks performed while brain activity is monitored by functional magnetic resonance imaging (fMRI). Current experimental frameworks for monitoring brain activity via fMRI during psychological tasks rely principally upon discretely presented stimulus periods or events. Such approaches are ideal for the study of perception-based cognitive processes, yet many real-world behaviors (such as motor acts) are continuous in nature, suggesting a need for new methodological frameworks designed for use with fMRI. The statistical approaches designed and applied in this project will be used to extend the continuous performance framework into a new class of fMRI paradigm. This project involves the custom design and fabrication of specialized stimulus input devices for the MR environment that will permit a continuously sampled response domain. Moreover, it involves computationally intense statistical modeling of data because the paradigms require measuring concurrent performance variables, potentially confounding variables (e.g. heart rate, respiration, eye-movements, etc.), and fMRI acquisition parameters, in order to include these in the experimental design. To examine the validity of the continuous task methodology, a variety of tasks will be studied.

The techniques developed in this project will assist in developing new approaches to studying brain activation paradigms for fMRI. These new approaches emphasize dynamic aspects of brain-behavior relationships. The work in this project will facilitate the development of computational models of continuously performed psychological processes.

Address; Award; Brain imaging; Clinical; Collaborations; Country; Data; Feedback; Future; Human; Human, General; International; Investigators; Knowledge; Letters; Man (Taxonomy); Man, Modern; Mentors; Mission; Motivation; NIH; National Institutes of Health; National Institutes of Health (U.S.); Neurosciences; North America; Recommendation; Research; Research Personnel; Research Resources; Research Training; Researchers; Resources; Science; Scientist; Students; Support of Research; Technology; Today; United States National Institutes of Health; adjudicate; base; brain visualization; cost; day; desire; experience; interest; neuroimaging; next generation

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (06) Enabling Technologies and specific Challenge Topic, 06-MH-103: New Technologies for Neuroscience Research. This project will provide a powerful, content-driven approach to the identification of brains having similar geometry and shape, the clustering of neuroanatomically similar cases, and the interactive 3D visualization of the large collections contained in neuroimaging archives. Beginning with the example of the LONI Image Data Archive (IDA), we will design automated data processing meta-workflows that will decompose the thousands of whole brain MRI volumes into constituent 3D neuroanatomical regions. We will characterize the geometric properties of these regional parcellations, store these measurements, and systematically assess pair-wise regional "distances" between brains, and decompose the resulting similarity matrix using multidimensional scaling and related approaches. These processes will be automated to accommodate the continuous growth of the archive and be able to include content obtained from other neuroimaging archives as well. We will graphically represent the derived space of brain similarity via an interactive and freely available 3D browser. Finally, we will develop means for users to upload their own MR anatomical volumes for automated processing via this same process using a large grid computational architecture;decompositions of the uploaded data will be compared against the shape statistics derived from the previously processed archival data;content- based search results will be returned to users via the web in the form of a rank ordered list of brain volumes having similar neuroanatomical characteristics;hyperlinks to additional meta-data and online information, as well as a depiction of the position of their data with respect to other derived brain data using the interactive 3D browser. Meta-data concerning each object in the display will be easily available describing subject demographics, diagnostic group, scanning parameters, etc. This project does not seek to advocate or support the development of any new centralized neuroimaging database but will provide an unprecedented service to the neuroscience community for interacting with existing digital brain archives. The tools developed here will be capable of accommodating that of other neuroimaging repositories as well as user's local archives, thus having utility beyond a single data resource. We expect the outcomes of this project to draw considerable interest and excitement from the neuroimaging community in a similar manner to which BLAST has had for the genomics community. Following a two year development timeline, we anticipate that these informatics-based approaches and tool deliverables will be instrumental for researchers as part of their neuroscientific enterprise, helping to guide research directions, enhance education, and will provide significant new insights concerning large-scale neuroimaging repositories of health and disease. H The outcomes of this novel project for informatics and dynamic visualization to are expected to draw considerable interest and excitement from the neuroimaging community. This project will empower content-driven searches in a similar manner to BLAST has provided for genomics and will provide significant new insights concerning large-scale neuroimaging repositories of health and disease. Over a two-year period of development, during which a number of new American jobs will be created, this project will deliver a robust, content-driven informatics approach to the identification of brains having similar geometry and shape, the clustering of neuroanatomically similar cases, and the interactive 3D visualization of the large collections contained in neuroimaging archives.

DESCRIPTION (provided by applicant): The quest to understand fundamental brain connectivity in the context of neuroimaging has received international levels of interest - most notably from countries around the Pacific. Under this R13 proposal (NIH PA-08-149), we seek support to cover the partial costs of a 3-day international meeting of leading researchers from North America and Pacific Rim regions entitled "New Horizons in Human Brain Imaging: A Focus on Brain Networks and Connectivity". The meeting will be comprised of key themes important to the next era of neuroimaging science and address the research and clinical motivations for the research using rapidly evolving methods for measuring and understanding functional and structural brain connectivity. In particular, the meeting will focus specifically on attainable means by which investigators from these nations may maximize collaborative effort in addressing the scientific and clinical challenges facing this dynamic and multi-disciplinary field. Thirty speakers are expected to provide insight into the basic and clinical neuroscience of brain networks and connectivity. Additionally, two to four small studentships will be award to students in each participating country, adjudicated based on desire to attend, financial need, suitability of the meeting for their research training, and on the basis of letters of recommendation from senior colleagues and mentors. A total of twenty studentships will be awarded. Meeting participants will enjoy formal scientific sessions during the meeting, have the opportunity to interact with the invited speakers during informal functions, and provide valuable feedback on their experiences to meeting organizers. This R13 meeting proposal speaks directly to the NIH's recent interests in the mapping of human brain connectivity, its mission to broaden participation across disciplinary boundaries, its goals in promoting international collaborations, and the open exchange of scientific knowledge, data, and resources. PUBLIC HEALTH RELEVANCE: Brain connectivity as measured using neuroimaging is rapidly becoming a major theme for research that is forming the basis for the next era of neuroscience research into brain health and disease. This interest toward understanding fundamental brain networks exists at an international level - notably from researchers around the Pacific Rim. This meeting seeks to gather leading neuroscientists from Pacific Rim countries to share ideas, data, and potentially resources toward the characterization, analysis, and modeling of brain functional and structural connectivity using neuroimaging. The meeting seeks to provide a vision for the future of human brain imaging in the context of brain networks and connectivity that will have direct impact on brain research, clinical medicine, and public health in the US and abroad.

? DESCRIPTION (provided by applicant): In our rapidly evolving information era, methods for handling large quantities of data obtained in biomedical research have emerged as powerful tools for confronting critical research questions, with significant impacts in diverse domains ranging from genomics to health informatics to environmental research. The NIH's Big Data to Knowledge (BD2K) Training Consortium is expected to empower current and future generations of researchers with a comprehensive understanding of the data science ecosystem: the ability to explore, prepare, analyze, visualize, and interpret Big Data. To these ends, we propose a novel Training Coordinating Center (TCC) to coordinate the diverse activities occurring within the BD2K Training Consortium into a synergistic training effort. The TCC will create an inclusive and collaborative virtual environment - entitled Big Data U - serving trainees from a wide spectrum of educational backgrounds and scientific domains. Big Data U will make personalized educational resources easy accessible and facilitate novel research collaborations through scientific rotations. We will harvest the web to automatically identify, model, and incorporate online resources into an Educational Resource Discovery Index (ERuDIte) and a Big Data U Knowledge Map. This unique system will alleviate the burden of sifting through hundreds of educational resources and searching across multiple research and training program websites, allowing users to easily determine which resources are didactically significant and correspond to the appropriate scientific domain of interest, level of education, and learning objective. Over the long term, our efforts will cultivate a diverse network of data scientists that can propagate their knowledge and experience for generations to come. Our PI and team have a demonstrated commitment to training in biomedical data science. The University of Southern California is ideally suited to host this NIH BD2K effort, having a strong history of data science training and recently founded two new masters programs of relevance to Big Data biomedicine. The TCC is the logical extension of our outstanding track record in data science, and we will leverage our comprehensive experience and infrastructure in developing the TCC.

? DESCRIPTION (provided by applicant): This is an NIH R13 application to fund travel awards for deserving students and trainees to attend the 2015 annual meeting of the Organization for Human Brain Mapping (OHBM; www.humanbrainmapping.org), to be held in Honolulu, Hawaii. The OHBM is the primary international organization dedicated to non-invasive neuroimaging research and the functional organization of the human brain, and its Annual Meeting is regarded as a premier venue for the integration of innovative brain imaging methods and cognitive neuroscience. It is first and foremost an educational forum for the exchange of up-to-the-moment and groundbreaking research in the area of human brain mapping. The meeting is expected to gather 3000 or more scientists whose work depends upon various brain imaging modalities. Student attendees include medical students, graduate students, residents in clinical neuroscience (neurology, psychiatry, and neurosurgery) and post-doctoral fellows in fields related to human brain mapping. Student attendees are a regular and effective feature of this meeting and, in past years, have typically given more than 1/3 of all oral presentations at the annual meeting. Recent past meetings of OHBM were held in: Melbourne Australia 2007; Chicago, Ill 2008; San Francisco CA 2009, Barcelona Spain 2010, Quebec City, Canada 2011; Beijing China, 2012; Seattle, WA 2013; and Hamburg, Germany 2014. Under this proposal, we will support travel awards for selected US-based students and trainees who are first authors on the most highly ranked submitted conference abstracts. We will also provide support for a small number of students working in developing nations, who might not otherwise be able to cover the costs for attending the meeting. Supporting travel awards for this meeting has relevance for the NIH research and educational mission in the following ways: a) virtually every presentation relates to the function and structure of the human brain, in both health and disease; b) improved understanding of the organization of the human brain is directly relevant to treating neurological disease; and c) the use of non-invasive imaging methods is increasingly important to translational investigation and training in clinical neuroscience. The OHBM conference program will highlight emerging structural and functional MRI imaging approaches to tracking brain systems organization, connectivity and plasticity, and imaging genetics. By providing relief from travel costs, student and trainee scientists interested in basic systems neuroscience, non-invasive imaging technology, and neurologic medicine will be better able to take part in the OHBM's premier international event for outstanding interdisciplinary scientific and educational experience on the mapping of the human brain.

? DESCRIPTION (provided by applicant): Nearly 1.7 million Americans suffer traumatic brain injury (TBI) annually, which constitutes a significant US medical health concern. Although neuroimaging plays an important role in pathology localization and surgical planning, TBI clinical care does not currently take full advantage of neuroimaging computational technology. We propose to develop, validate, and commercialize computational algorithms, based on our methods for image segmentation and registration. These methods 1) can accommodate the presence of large pathologies in TBI cases, 2) can yield quantitative measures from chronic and acute TBI data for research into characterizing injury, monitoring pathology evolution, informing patient prognosis, and 3) can aid clinicians in optimizing TBI patient care workflows. We will accomplish our goal during the proposed Phase II effort by building upon our Phase I successes. Featured in conference and journal publications, during Phase I we devised a novel low-rank+sparse method for registering brain MRI scans from TBI patients with large pathologies to healthy brain atlases, enabling more accurate identification and quantification of anatomic changes. In conjunction with our foundational geometric metamorphosis work into quantifying lesion infiltration and recession over time, our set of methods now address the major hurdles associated with TBI patient understanding. Under this Phase II STTR proposal we will specifically focus on extending our computational methods for multimodal neuroimaging of TBI data processing. We will 1) provide finite element models created over a range of clinical cases of mild-to-severe TBI, 2) determine refined measures of patient change from longitudinal registrations, 3) integrate those methods into local and cloud-based environments that support academic and commercial use, and 4) validate the complete commercial system using extensive TBI data collections including neuropsychological motor, cognitive and behavioral outcome measures, in a customer- oriented study.

? DESCRIPTION (provided by applicant): In our rapidly evolving information era, methods for handling large quantities of data obtained in biomedical research have emerged as powerful tools for confronting critical research questions, with significant impacts in diverse domains ranging from genomics to health informatics to environmental research. The NIH's Big Data to Knowledge (BD2K) Training Consortium is expected to empower current and future generations of researchers with a comprehensive understanding of the data science ecosystem: the ability to explore, prepare, analyze, visualize, and interpret Big Data. To these ends, we propose a novel Training Coordinating Center (TCC) to coordinate the diverse activities occurring within the BD2K Training Consortium into a synergistic training effort. The TCC will create an inclusive and collaborative virtual environment - entitled Big Data U - serving trainees from a wide spectrum of educational backgrounds and scientific domains. Big Data U will make personalized educational resources easy accessible and facilitate novel research collaborations through scientific rotations. We will harvest the web to automatically identify, model, and incorporate online resources into an Educational Resource Discovery Index (ERuDIte) and a Big Data U Knowledge Map. This unique system will alleviate the burden of sifting through hundreds of educational resources and searching across multiple research and training program websites, allowing users to easily determine which resources are didactically significant and correspond to the appropriate scientific domain of interest, level of education, and learning objective. Over the long term, our efforts will cultivate a diverse network of data scientists that can propagate their knowledge and experience for generations to come. Our PI and team have a demonstrated commitment to training in biomedical data science. The University of Southern California is ideally suited to host this NIH BD2K effort, having a strong history of data science training and recently founded two new masters programs of relevance to Big Data biomedicine. The TCC is the logical extension of our outstanding track record in data science, and we will leverage our comprehensive experience and infrastructure in developing the TCC.

? DESCRIPTION (provided by applicant): In our rapidly evolving information era, methods for handling large quantities of data obtained in biomedical research have emerged as powerful tools for confronting critical research questions, with significant impacts in diverse domains ranging from genomics to health informatics to environmental research. The NIH's Big Data to Knowledge (BD2K) Training Consortium is expected to empower current and future generations of researchers with a comprehensive understanding of the data science ecosystem: the ability to explore, prepare, analyze, visualize, and interpret Big Data. To these ends, we propose a novel Training Coordinating Center (TCC) to coordinate the diverse activities occurring within the BD2K Training Consortium into a synergistic training effort. The TCC will create an inclusive and collaborative virtual environment - entitled Big Data U - serving trainees from a wide spectrum of educational backgrounds and scientific domains. Big Data U will make personalized educational resources easy accessible and facilitate novel research collaborations through scientific rotations. We will harvest the web to automatically identify, model, and incorporate online resources into an Educational Resource Discovery Index (ERuDIte) and a Big Data U Knowledge Map. This unique system will alleviate the burden of sifting through hundreds of educational resources and searching across multiple research and training program websites, allowing users to easily determine which resources are didactically significant and correspond to the appropriate scientific domain of interest, level of education, and learning objective. Over the long term, our efforts will cultivate a diverse network of data scientists that can propagate their knowledge and experience for generations to come. Our PI and team have a demonstrated commitment to training in biomedical data science. The University of Southern California is ideally suited to host this NIH BD2K effort, having a strong history of data science training and recently founded two new masters programs of relevance to Big Data biomedicine. The TCC is the logical extension of our outstanding track record in data science, and we will leverage our comprehensive experience and infrastructure in developing the TCC.

? DESCRIPTION (provided by applicant): In our rapidly evolving information era, methods for handling large quantities of data obtained in biomedical research have emerged as powerful tools for confronting critical research questions, with significant impacts in diverse domains ranging from genomics to health informatics to environmental research. The NIH's Big Data to Knowledge (BD2K) Training Consortium is expected to empower current and future generations of researchers with a comprehensive understanding of the data science ecosystem: the ability to explore, prepare, analyze, visualize, and interpret Big Data. To these ends, we propose a novel Training Coordinating Center (TCC) to coordinate the diverse activities occurring within the BD2K Training Consortium into a synergistic training effort. The TCC will create an inclusive and collaborative virtual environment - entitled Big Data U - serving trainees from a wide spectrum of educational backgrounds and scientific domains. Big Data U will make personalized educational resources easy accessible and facilitate novel research collaborations through scientific rotations. We will harvest the web to automatically identify, model, and incorporate online resources into an Educational Resource Discovery Index (ERuDIte) and a Big Data U Knowledge Map. This unique system will alleviate the burden of sifting through hundreds of educational resources and searching across multiple research and training program websites, allowing users to easily determine which resources are didactically significant and correspond to the appropriate scientific domain of interest, level of education, and learning objective. Over the long term, our efforts will cultivate a diverse network of data scientists that can propagate their knowledge and experience for generations to come. Our PI and team have a demonstrated commitment to training in biomedical data science. The University of Southern California is ideally suited to host this NIH BD2K effort, having a strong history of data science training and recently founded two new masters programs of relevance to Big Data biomedicine. The TCC is the logical extension of our outstanding track record in data science, and we will leverage our comprehensive experience and infrastructure in developing the TCC.

? DESCRIPTION (provided by applicant): In our rapidly evolving information era, methods for handling large quantities of data obtained in biomedical research have emerged as powerful tools for confronting critical research questions, with significant impacts in diverse domains ranging from genomics to health informatics to environmental research. The NIH's Big Data to Knowledge (BD2K) Training Consortium is expected to empower current and future generations of researchers with a comprehensive understanding of the data science ecosystem: the ability to explore, prepare, analyze, visualize, and interpret Big Data. To these ends, we propose a novel Training Coordinating Center (TCC) to coordinate the diverse activities occurring within the BD2K Training Consortium into a synergistic training effort. The TCC will create an inclusive and collaborative virtual environment - entitled Big Data U - serving trainees from a wide spectrum of educational backgrounds and scientific domains. Big Data U will make personalized educational resources easy accessible and facilitate novel research collaborations through scientific rotations. We will harvest the web to automatically identify, model, and incorporate online resources into an Educational Resource Discovery Index (ERuDIte) and a Big Data U Knowledge Map. This unique system will alleviate the burden of sifting through hundreds of educational resources and searching across multiple research and training program websites, allowing users to easily determine which resources are didactically significant and correspond to the appropriate scientific domain of interest, level of education, and learning objective. Over the long term, our efforts will cultivate a diverse network of data scientists that can propagate their knowledge and experience for generations to come. Our PI and team have a demonstrated commitment to training in biomedical data science. The University of Southern California is ideally suited to host this NIH BD2K effort, having a strong history of data science training and recently founded two new masters programs of relevance to Big Data biomedicine. The TCC is the logical extension of our outstanding track record in data science, and we will leverage our comprehensive experience and infrastructure in developing the TCC.

A Data Science Innovation Lab workshop focused on Interdisciplinary Approaches to Biomedical Data Science will be held at the UCLA Lake Arrowhead Conference Center, June 15-19, 2016. (See http://www.bigdatau.org/innovationlab/.) This workshop will help generate research ideas and build teams of researchers to explore the challenges associated with big data generated by mobile sensors. The development of these teams has the potential to transform the use of mobile devices in the biomedical sciences using science driven knowledge.

The 2016 Innovation Lab on Interdisciplinary Approaches to Biomedical Data Science will bring together researchers in quantitative and biomedical sciences with a goal of developing new research teams to work on problems in biomedical data science. The teams that are developed will pursue research in the use of mobile devices to collect health data, to deliver health care data to patients and practitioners, and to assist in the monitoring of health care patients. Funds from this award will support travel for participants to attend the workshop.

A Data Science Innovation Lab workshop, "Quantitative Approaches to Biomedical Data Science Challenges in Our Understanding of the Microbiome", will be held at the Wylie Inn and Conference Center, Boston, MA, June 19-23, 2017. (See http://www.bigdatau.org/innovationlab2017/.) The Innovation Lab will bring together early-career biomedical and quantitative investigators to engage in intensive multidisciplinary interactions, with a goal of developing new and bold approaches to address data science challenges involving the microbiome.

The 2017 Innovation Lab will focus on novel approaches for analyzing high-dimensional big data derived from microbiota associated with a health or biomedical research objective. While many approaches have been applied to identify and explore interactions and potential causal relationships between the human microbiome and the health of the human host, mechanistic modeling of gut interactions largely remains an unfulfilled goal. New mathematical frameworks, analytic tools and computational models will allow for a deeper quantitative understanding of these mechanisms, leading to improved treatment of microbiome-related illness. Funds from this award will support travel for participants to attend the workshop.

PROJECT SUMMARY - TRAINING AND DISSEMINATION The goals of training and dissemination under LONIR are to provide a comprehensive arsenal of materials needed to instruct researchers both on the theory and philosophy of image analysis, computational anatomy and multidimensional modeling, as well as the specific applications of our sophisticated software tools. LONIR has enjoyed a rich history of successful training efforts and the dissemination of information on the use of our high- caliber software solutions. Under our new LONIR award period, we will continue with these essential efforts, remaining responsive to our constituency as well as incorporating the new advances to be developed as described in the Specific Aims our overall and TR&D research plans. Our LONI P41 Resource activities in training and dissemination will specifically involve: 1) Workshops, both international and domestic; 2) Connection to formal university training programs; 3) An enhanced visiting scholar series; 4) Enrichment of LONIR website content; and 5) undertake the dissemination of graphical and video media, lectures, and scientific posters, tools, and protocols. Moreover, we will integrate our activities with several appropriate T32 Training Programs to include the activities of the LONI P41 Resource. All in all, our LONIR training and dissemination plans are ideally suited to providing the neuroimaging community with instruction on the leading-edge of brain data science research.

Abstract: This IPERT R25 application will formalize and expand upon a series of unique Biomedical Data Science Innovation Lab online and in-person workshops whose immersive and hands-on approach will accelerate the ability of early-career biomedical and quantitative investigators to form new collaborations around biomedical science projects. Each year a distinct biomedical topic will be specifically identified which would benefit from a fresh or divergent data science perspective. During the Biomedical Data Science Innovation Lab events, professional facilitators and senior scientist mentors (with relevant expertise and exposure to the topic area) assist the participants - guiding the development of innovative research project proposals. The primary purpose of the facilitators is to build the community, strengthen the bonds between the participants, and help the teams to form. Program directorship will provide leadership, continuity, and logistical support, while other stakeholders provide vision, expertise, and ongoing program feedback. Senior faculty mentors will contribute topic background and data science applications while also providing oversight and feedback on the development of new projects. Early-career investigators (at the post-doc, assistant to newly associate professor level, research and tenure track) from quantitative (including but not limited to applied and/or theoretical mathematics, statistics, computer science, physics, and engineering) and biomedical fields (including but not limited to biological, biophysical, epidemiological, and clinical disciplines) will be welcomed to submit applications. A broad diversity of backgrounds is desired with women and members of underrepresented communities encouraged to apply. Throughout the year-long program, participants will benefit directly from explicit skills training in data science methods, reproducibility, Team Science, responsible conduct in research, and will learn about research funding mechanisms directly through interaction with NIH and NSF program officers. Participants will form multidisciplinary teams during the in-person workshop to develop biomedical topic domain projects involving mathematical modeling, statistical analytics, and other quantitative methods. Upon completion of the workshops, follow-up virtual activities will solidify teams and their research proposals as grant applications suitable for submission to federal and private funding agencies. Rigorous assessment and characterization of skills training activities as well as team effectiveness using emerging Science of Team Science-based approaches will strictly pursued. A rich program for dissemination of workshop activities includes broad-based outreach, a detailed website, and the development of professional-grade visual media and materials. With a well-suited team and a track record of prior success, this is a novel program for research community building, fully aligned with the goals of the IPERT R25 program. 1