White Paper Review of Infrastructure and Personnel Recommendations for Biomedical Informatics at the Florida State University, College of Medicine: Looking Forward August 2017

Summary During Spring of 2017, a the request of Jeffrey N. Joyce, Senior Associate Dean for Research and Graduate Programs in the College of Medicine (CoM), Cynthia Vied (Translational Science Laboratory) (TSL) established a committee to gather information about bioinformatics and biostatistics needs of researchers across several areas. This included those researchers that use the omics (proteomics, genomics, metabolomics) resources in the TSL. A committee was established of faculty at FSU that either have or require informatics expertise to give feedback concerning the broadly defined informatics needs in CoM. Including genomics and proteomics needs, health outcomes research and the MRI facility. These are addressed in this white paper. Key points and recommendations of the committee are summarized below: 1. The use of research methods involving bioinformatics and biostatistics at CoM is likely to grow at a continuous rate over the next few years. FSU must develop these capacities in order to keep pace with the integral role of bioinformatics and advanced biostatistics so that we may compete on the national funding scene. Specific areas of projected growth are described in this white paper. 2. The TSL offers cutting-edge omics platforms to researchers but performs bioinformatics analyses on a limited and informal basis. The growth of research involving omics has resulted in more than 20 CoM faculty requesting bioinformatics support from the TSL in the past three years. 3. The FSU Research Computing Center (RCC) provide high performance computing clusters and data storage but significant problems exist for big data storage and analysis. 4. The Center for Genomics and Personalized Medicine (CGPM) at FSU offers genomic bioinformatics services but does not have proteomics or metabolomics expertise and has limited staff. In addition, this facility is not currently equipped to deal with HIPAA and other federal data regulatory guidelines for the use and storage of patient data.

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5. In addition to omics research, the demand for health outcomes research has increased dramatically in terms of federal health funding priorities and the growth in large multi-center grants at CoM. 6. The opportunity for integrating omics big data, health and health outcomes data, population and geospatial data will require research leadership that is currently underrepresented at FSU 7. Health outcomes research will require continued addition of expertise in the areas of computer programming, scientific data standardization and aggregation, data storage and security, database structure and state-of-the-science analytics. 8. The newly established CoM MRI facility will generate terabytes of data on a regular basis for long-term storage, requires large computing resources, and networked computers with access to revision controlled MRI-specific software packages. 9. The MRI facility, Health outcomes researchers across campus, and TSL have overlapping aspects that could draw from a common pool of expertise and infrastructure to aid in the individual requirements for each group. One immediate scenario to address these needs is CoM centric, which would be to hire additional staff members for each of the groups to provide specific informatics support. Under this scenario resources would be utilized to hire informaticians whose expertise is specific to omics, health outcomes research, and medical imaging to support researchers within the College of Medicine. In addition to personnel, it would be important to provide additional computing and data storage resources. All of this could be accomplished through an Informatics Center with several established researchers with a history of NIH/NSF funding and the ability to coordinate a diverse group of personnel from data management to PhD level researchers. It would be a combined core resource and research focused center within the College. On the other hand that scenario would not address the long-term larger-scale solution needed at the university level that would include the hire of CoM tenure- track faculty in these areas, but as well as in other units. This would address the university-level needs for bioinformatics, computational biology and biostatistics and have several advantages, including FSU maintaining a cutting-edge intellectual base of faculty and unique training experiences for students at every level. Some of the computing infrastructure already exists at the RCC and could be improved through this endeavor. Additional benefits include an increase in the following: increased competitiveness for federal grants in the STEM areas, collaboration amongst FSU researchers across colleges, program project grant opportunities, greater use of core university facilities, less reliance on sub- contracting with other universities for bioinformatics and biostatistical collaborations, and student training opportunities in a rapidly growing area with many future job opportunities. Given the size of the investment for either

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scenario, it is prudent to establish a consulting group to address a university- wide solution.

Introduction: The rationale for establishing the committee to review the “big data analysis” needs for CoM was based on an initial survey by Jeffrey Joyce, the Senior Associate Dean for Research and Graduate Programs in CoM, of the biostatistical needs of the different departments and core resources (e.g. TSL) of the college. The breadth of research interests, potential for increased external funding, and lack of current resources within CoM make it difficult to address the needs of CoM. Moreover, the current relationship with the University of Florida through our role in the NIH (CTSA) and PCORI (DataTrust) funded awards has demonstrated a lack of expertise at COM (and across FSU) to take advantage of the current resources. In order to better address the big data analytic needs of the researchers within CoM and already existing links to faculty outside of the college, a committee was charged with identifying and evaluating the breadth of data analytics within CoM. The committee was headed by Dr. Cynthia Vied but included contributions from 14 members of the FSU community (see page 12).

TSL Bioinformatics: Background / Problems / Projected Growth The TSL is a core facility that provides proteomic, genomic, and metabolomic services for FSU and external researchers. The TSL houses state-of-the art mass spectrometers for proteomic and metabolomic techniques, as well as a next generation sequencing machine for genomic analyses. These omics technologies generate large datasets (~ 35 TB of data per year) that are computationally and statistically demanding to analyze. However, the TSL does not formally offer analysis of the data generated from these services other than basic processing of the data. Researchers that generate data in the TSL need computer software packages and expertise to analyze and visualize these data for publications and grant applications. Most researchers in the biomedical field do not have this expertise and rely on bioinformaticians or biostatisticians to analyze the data. Specific to CoM, approximately 20 BMS faculty have requested or received bioinformatics help for omics data analysis in the past 3 years (Arbeitman, Meckes, Pinto, Horabin, Nowakowski Levenson, Delp, Lee, Wang, Olcese, Bhide, McCarthy, Graham, Stefanovic, Hurt, Pinto, Ren, Kato, Rizkallah, Galasko, Kaplan, Bruck). Some faculty have data analysis expertise within their lab but have requested resources, such as functional analysis software and additional computing capabilities to manage their ongoing needs. These resources are expensive for individual investigators but software site licenses could be purchased so that multiple investigators could benefit. Secure

3 | Page remote login capabilities would allow the researchers to continue the analysis from the laboratory or office. However, most genomic analysis packages never have a graphical- interface software solution, and require cutting edge computer programming skills that will require the hiring of trained personnel. The next generation sequencing machine and one of the mass spectrometers within the TSL operate at maximum capacity with samples waiting in a queue for each machine. A new higher-capacity sequencing machine is expected to be added to the TSL during the 2017-2018 fiscal year, partially funded through startup funds from a new investigator in the Department of Biological Science. This machine can produce up to 50 times more data (as much as 6 TB of data per run) than generated by the current sequencing machine. In addition to a new sequencing machine, the TSL staff will be offering two new research techniques, mass spectrometry imaging and advanced metabolomics capabilities, which will increase the data output and bioinformatics needs substantially within the next year. How have these bioinformatics needs been addressed in the past Faculty members in the TSL have been providing some bioinformatics expertise. Drs. Cynthia Vied and Rakesh Singh have been offering sample preparation and analysis for sequencing and proteomics, respectively, for researchers generating data in the TSL. This system has resulted in more researchers using the TSL than would have done so otherwise. The TSL also offers proteomics software tools on virtual machines accessible to lab users as an additional self-service option. However, bioinformatics and sample preparation are not formally offered by the TSL at this time and the data analysis, particularly for proteomics, is not straight-forward and requires advanced bioinformatics expertise. In addition, the other duties of the TSL faculty preclude them from being available for all data analysis requests. Some faculty have hired data scientists that have specific skills but are not bioinformaticians. Most faculty do not have a continuing need, money to hire, or the skills to train a data scientist. This ad-hoc approach to omics data analysis limits potential research productivity from multiple departments across colleges that utilize the TSL. The Center for Genomics and Personalized Medicine (CGPM) was created just over two years ago by the FSU Office of Research. The CGPM has a small staff (one or two staff/faculty) that are providing genomic data analyses but do not have proteomic or metabolomic data analysis expertise. The high demand for CGPM services has resulted in a proposal-based system to prioritize projects but not all projects can be accommodated. In addition, the CGPM is not a resource specific to CoM researchers, and is housed in the Department of Biology where it currently does not have the infrastructure or expertise to securely manage protected health information. The FSU Research Computing Center (RCC) offers high performance computing clusters and data storage space for researchers. The RCC hosts common open-source

4 | Page software, including genomic data analysis software, but the software often requires coding skills and/or specialized knowledge that many individual researchers do not have. The RCC briefly hosted a web-based genomic and proteomic analysis platform, known as Galaxy. This application would be useful, but it would need more implementation input from researchers along with researcher training in order to be successfully maintained. Other universities that maintain this platform offer training and frequent assistance for researchers. In addition to software, the RCC provides long-term storage for a fee. A portion of the TSL budget is dedicated to the long-term storage of the approximate 35 TB of data that are generated each year (~160 TB total at present). Some individual campus investigators have found the RCC storage options unaffordable for large datasets, which will be compounded by the addition of the higher output sequencing machine for the researchers and the TSL. An additional issue is an annual RCC maintenance shut down that suspends services and access to stored data for up to two weeks. This is a problem, specifically for the TSL, because the sequencing data are stored at the RCC as they are generated. Two weeks of down time for the sequencing machine(s) is an issue for researchers and often occurs just before an important grant cycle. The maintenance period also means that omics data analyses that occurs on the high performance computer clusters at the RCC are on hold during this period. It has been pointed out that the RCC is short on staff and cannot accommodate all needs. While new hires for the RCC would be beneficial, particularly a bioinformatician to provide training of software hosted on the RCC, CoM does not control RCC hiring decisions and this would not address all CoM bioinformatics needs. Solutions An option for immediate relief of the TSL needs would be to hire several computational biologists with expertise in bioinformatics for omics data analysis. This team would maintain a virtual machine(s) with software site licenses for downstream analyses by individual researchers. The team would assist with data analysis for publications and grant applications, as well as train researchers and students in the analyses. The limit to this plan is that few bioinformaticians have expertise in all omics analyses, but with two individuals there could be coverage of a broad range of research areas. Also, without additional storage options and computing solutions, the maintenance downtime and storage issues with the RCC will not be addressed, so we propose the purchase of additional computer resources to meet this need. Long term, we propose that CoM hire at least three tenure-track computational biologists, quantitative biologists and/or biostatisticians with research programs in proteomics, genomics, and metabolomics that are developing cutting-edge algorithms and analysis tools for these technologies, such as through a cluster-hiring initiative for Biomedical Computational Sciences in the Biomedical Sciences Department. This would be particularly beneficial to genomic data analyses, proteomic data analysis and mass spectrometry imaging that is currently being developed in the TSL and in individual faculty laboratories. These faculty would consult with and provide training to

5 | Page the TSL faculty for advanced data analysis. In addition, their students and post-docs could collaborate with scientists at FSU and other institutions to further stimulate research in these areas.

MRI Facility Bioinformatics: Background / Problems / Projected Growth Beyond the needs for high level expertise in big data analysis for omics is the expected need for mass image data acquisition, storage and analysis from the MRI facility that will be accommodating up to 6 primary investigators in the several years. The FSU MRI Facility (MRIF) was established at CoM in 2016 for the study of intact biological systems to understand the function and disorders of specific tissues, with an emphasis on functional MRI (fMRI) of the brain. MRIF is a fee for service facility for FSU and external researchers and features a 3 Tesla Siemens Prisma (whole body) human scanner, which delivers high resolution detail. This system includes multiple imaging techniques, such as EPI BOLD, DWI, and DTI. In addition to the MRI, MRIF also features a high density EEG/ERP system, full psychophysiology recording suite (BrainAmp Exg MR 16), eye tracker, behavioral testing space, and computer workstations for data acquisition and processing. The combined fMRI and EEG capability allows researchers to simultaneously study functional and structural MRI data with EEG – maximizing the spatial resolution of fMRI and temporal resolution of EEG. Most individuals will undergo multiple MRI scan sequences, and some sequences can produce more than a TB of data. The long-term storage of these scans, along with the psychophysiological and behavioral testing data, is critical for comparison across tissues, disease states, and participants. In addition to storage, the data analysis requires enormous computer processing capabilities to not only analyze the individual datasets (MRI, psychophysiology, behavioral testing) but also for larger scale analyses across datasets. Centralized data analytic capabilities would also facilitate MRI processing across labs, for instance, for the uniform upgrading of software and analytic pipelines. Statistical approaches to MRI and related data are constantly evolving, especially in the context of large and growing data sets – and bioinformatics advances in image storage and processing are a growth area in the field. MRIF is expected to expand quickly with the addition of 3-5 faculty members through the Department of Psychology and 1 faculty member through CoM in the next few years. One research area that is expected to be added through these hires is computational psychiatry. This type of research utilizes advanced computational approaches with novel bioinformatics solutions to better define individual differences and mental illness. The addition of as many as 6 faculty members in the near future will greatly increase the computational and bioinformatics requirements within MRIF.

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How have these informatics needs been addressed in the past As MRIF is a new facility the informatics are in the beginning stages. Dr. Greg Hajcak is the Scientific Director of MRIF and has years of experience with EEG, fMRI, and the statistical analyses associated with these techniques. His focus has been on the intersection between neuroscience and psychopathology, particularly on neural measures of reward in pubertal development. In addition to Greg, MRIF has recently hired Dr. Colm Connolly, who has a PhD in computer science and has since been studying the neural basis of human psychological processes. His research has focused on using fMRI to identify biomarkers of major depressive disorder (MDD) in adolescents. Colm’s background in computer science and fMRI data analysis along with Greg’s expertise will provide much of the scientific and technical leadership required for the MRIF, however, there is a large unmet need with regard to bioinformatics and biostatistics. Solutions The RCC has the computer processing capabilities and long-term data storage capabilities. However, the current long-term data storage option is slow for retrieval. Ideally, a centralized data storage option would exist for rapid comparison between datasets and centralized data processing and analyses, in addition to the long-term storage at the RCC. Furthermore, centralized data analysis compute nodes and networked computers with access to revision controlled MRI-specific software packages are essential. Currently, compute nodes and software are controlled almost exclusively by RCC staff. The software hosted at the RCC are updated by the staff and not controlled by researchers. MRIF faculty and staff will need administrative control of the software and compute nodes whether hosted by the RCC or at the facility. MRIF has the state-of-the-art 3T MRI scanner, which will allow the university to attract additional top scholars in this field of research. In order to advance developments in the clinical understanding of disease and molecular imaging, we propose to hire a tenure- track faculty member, either as part of or in addition to the proposed 6 hires, in the area of multivariate analyses (MVA) techniques. MVA is a class of advanced statistical, computational, and pattern recognition technologies that can be used to address a wide variety of medical imaging challenges. The use of MVA technologies in MRI is a relatively young research field with potential for significant research growth, particularly with MRIF capabilities.

Health Outcomes Research Bioinformatics: Background / Problems / Projected Growth Electronic health record (EHR) data and other key sources of digitized health data contain a wide variety of patient data, for example demographics, lab results,

7 | Page diagnoses, assessments, procedures, medications, unique patient identifiers, and links to administrative and clinical data from both government (e.g. Medicaid) and private entities. Analyses of health datasets is vital for most cutting-edge health-related translational research including comparative effectiveness, treatment, and health services research. The technological and social systems for acquiring, storing, analyzing and reporting on electronic health data are both advancing rapidly and woefully inadequate. In particular, data acquisition and is limited by privacy issues and by the commercial interests of providers and insurers. Trust relationships between data users and data providers are crucial to advances in these systems. As one example, As an example, FSU CoM has a partnership with UF to access to the OneFlorida Data Trust [onefloridaconsortium.org/], which contains EHR data that can be used for certain types of clinical research. The Data Trust is a central repository of patient-level, HIPAA-limited electronic health record and health insurance data transformed into a common data model. It contains records on more than 10 million patients and is particularly useful for finding cohorts for clinical trials and for observational analyses of clinical activities across providers. However, higher impact and more complex biostatistical analysis of the effects of interventions on patients is not well supported by the OneFlorida Data Trust. It is not possible to link all records for individual patients and not possible to evaluate providers. Much of the crucial demographic information is missing. To evaluate patient outcomes, a researcher might begin by querying the Data Trust to identify potential patients. The next step would be to use honest brokers to gather additional data from providers that would supplement the Data Trust records for these patients. This process would require a HIPAA as well as other federal privacy regulatory bodies for compliance with data storage facility. The analysis of the resulting data sets requires not only high-performance computing systems, but systems that may be certified for federal security compliance. Faculty and staff with special methodological expertise are required for handling of complex data ascertainment, aggregation and standardization, as well as advanced biostatistical analyses. In addition, research compliance expertise in data use agreements, honest broker processes and IRB approval of coordinated databases needs advancing at FSU. Additional health outcomes research requiring data management and biostatistics is conducted by faculty throughout the College of Medicine and these faculty often seek assistance in data analysis for their manuscripts, contracts, and grants. Over a dozen CoM faculty have requested quantitative assistance for health outcomes research over the past two years, including Campbell, Flynn, Harman, Glueckauf, Sutin, Beitsch, Brown-Speights, Rust, Goldfarb, Nair-Collins, Geletko, Kabbaj, Laywell, Levenson, Livingston, Meckes, Myers, Nowakowski, Oviedo, Reyes, Rodriguez, and Rosado. The Health Outcomes Research Group is expanding through a recent hire; Dr. Sylvie Naar and her group that includes the expansion by hiring of three faculty members. Dr. Naar’s research focuses on translational behavioral research to reduce early mortality

8 | Page and health disparities in youth. Her bioinformatics needs and computational requirements will increase the demand that already exists within the Department. BSSM faculty members are in various stages of conducting innovative research in line with federal funding priorities at NIH and PCORI that involve the acquisition, merging and complex biostatistical analyses of data from EHR’s across the state and nationally, Medicaid, large state agencies and organizations including the Department of Health, The Department of Children and Families, the Florida Council for Community Mental Health, as well as COM’s large network of health care practices across the state. Many of these project require sub-contracting with other Universities or companies that have the required expertise or resources. The opportunities for collaboration across FSU, such as with the Center for Indigenous Nursing Research for Health Equity (INRHE) is limited by our current resources.This is an urgent time for FSU to further develop our own capacity for these vital supports and expertise in order to maintain resources at FSU and to increase our capacity to compete for federal health research grants. How have these informatics needs been addressed in the past CoM efforts in health data acquisition and analysis have concentrated on building a trust relationship with specific sites, including TMH and with the Immokalee Health Education Site. Interactions with the One Florida Data Trust have been limited to storing a copy of the TMH data in the Trust and loading that data into a locally managed instance of I2B2, a software framework for managing, discovering and analyzing clinical data (https://www.i2b2.org/). To date, the best outcomes have been from data analysis projects with small groups within BSSM supporting clinical health outcomes research. Typically they ascertain, aggregate, standardize and perform advanced, multivariate statistical analyses based on large and diverse sources of health data. Examples of faculty data sources for these projects include multi-state Medicaid data, patient-reported data, (captured either electronically or in paper form or both), large, publically availably epidemiological data, EMR, state and local agency administrative and clinical data, and secondary data collected and aggregated from completed research studies of various types nationally and internationally. In most cases, faculty working on these projects rely on data supports and infrastructure from either outside COM or outside of FSU in the form of sub-contracts or other partnership agreements. Outside expertise used has included data aggregation and secure warehousing, data queries, advanced biostatistical analyses, and development of computerized assessments and tools. There is a clear need for FSU to develop these supports and infrastructures here as clinical health outcomes research continues to grow exponentially at FSU. Trends in federal funding show a clear emphasis on clinical health research that utilizes large, non-coordinated date sources that may also be combined with patient-reported primary data, such as clinical trial data. In addition, health centers and agencies, such as Departments of health and the Florida Legislature, are increasingly looking to partner with Universities

9 | Page for data-related clinical health and health outcomes projects that center on data coordination and analyses. CoM is using computing and data storage facilities at the RCC to manage and analyze EHR data. RCC has sufficient capacity to scale the CoM research efforts to much larger datasets and more complex analyses but they do not provide the software, and the interface with RCC can be dynamic and time consuming. In the past, CoM researchers have sought assistance with data management and analysis on an ad-hoc basis. Similar to those requiring bioinformatics assistance, many researchers lack this expertise and instead consult external biostatisticians to conduct their analyses and serve on their grant proposals. A small group of data analysts and one biostatistician, Dr. Naomi Brownstein, have provided assistance when available. However, the volume of CoM requests continues to grow and already extends far beyond the time current staff have available. Solutions The ability to work with providers to extract data from EHR and other health information systems requires navigation of a complex and often unique record storage system as well as an array of legal and political issues. Providers facing the risk of legal exposure will incur additional expenses in participating in a data-based research collaboration with CoM. We must have the ability to document our data security capacities to outside entities in order to facilitate this research. In addition, we are contractually obligated to federal and state funding agencies to abide by the highest level of data security. The I2B2 program, for example, has been successful in many instances at other institutions because the medical facilities were able to use the data they provided to identify cohorts for clinical trials with significantly less effort than without the I2B2 system. A separate document describes the staffing roles that are required for management and analysis of EHR data. However, to meet some of these needs we have utilize PCORI (UF-FSU) cost share budget dollars to support data management (Grant Macdonnell) and software expertise (Greg Riccardi) to provide some of the skills and time required for these roles. This is limited solution and additional staff is needed to make the efforts sustainable and scalable. A data concierge service provided by CoM to researchers and provider partners will find and develop solutions to address their data needs. Services would include a buffered interface to RCC, HIPAA compliant data storage and access, data analysis using tools such as the R programming system and SAS software, and assistance with complex biostatistical support for grant proposals. The data concierge service would give researchers access to more complex custom solutions and a more effective path from research ideas to data-driven solutions. It would, however, be limited in support to researchers outside of CoM.

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Student Training Opportunities: All the proposed tenure-track hires could provide training for graduate, medical, post- doctoral and undergraduate students. Bioinformatics courses have been an ongoing request from graduate students, which could be provided by a combined effort of new and current faculty members. Medical student summer research projects could have a bioinformatics component, particularly in health outcomes research through analysis of EHR. For undergraduates, a capstone course in bioinformatics could be developed for the newly established Interdisciplinary Medical Sciences (IMS) Degree program offered through CoM. In addition, the white paper committee proposes and others commented/elaborated on an application for a cross-disciplinary graduate student training grant. In fact, David Gilbert in the Department of Biology is submitting a T32 training grant entitled “The Integrating Genotype and Phenotype Training Program “ that identifies many of our current training needs, but is limited in the number of graduate students to support. The predominate idea for a broader approach is to build an Information Sciences Partnership across several FSU Departments, such as Statistics, Computer Science, Scientific Computing, Biology, Psychology, Math (and others) with a senior faculty member taking the lead on this initiative. This could provide a platform for co-mentorship of graduate students across the Departments that will result in PhD Scientists with a degree from their respective department/program, with an emphasis in Biomedical Informatics, Computation and Statistics that are highly marketable. Conclusions: In this document we have addressed the broad informatics, including bioinformatics and biostatistics, needs of the College of Medicine, particularly of the TSL and faculty that use that facility, health outcomes research group, and the MRI facility. To address these needs we have proposed staff hires for each concentration, addition of computing and software capabilities, HIPAA compliant data storage, and several tenure-track faculty hires in the different fields of research. It is likely that a larger-scale solution may be needed at the university level to incorporate the bioinformatics and biostatistics needs of all researchers that utilize the resources housed within CoM and the university as a whole. This larger-scale solution would be a combination of service oriented staff and senior research faculty hiring mechanisms. It would have training opportunities, grant funding mechanisms, and cost- recovery options. A university-wide Information Sciences Partnership solution would provide additional benefits by increasing the following; collaboration amongst researchers, program project grant opportunities, use of core university facilities, and student training opportunities Definitions: • Biomedical Informatics (Bioinformatics) deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of data in biomedical/translational research.

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• Biostatistics: statistical processes and methods applied to the collection, analysis and interpretation of biological data, particularly data relating to human biology, health and medicine. • Proteomics: Large-scale study of the structure, function, and interactions of the proteins produced by the genes of a particular cell, tissue, or organism. In the TSL, this refers to mass spectrometry data. • Genomics: The branch of molecular biology concerned with the structure, function, evolution, and mapping of genes. The TSL has an Illumina HiSeq 2500 for genomic analyses. • Metabolomics: The scientific study of the set of metabolites present within a cell, tissue, or organism. Metabolites are analyzed through mass spectrometric techniques in the TSL. • Omics: Informal reference to a field of study in biology ending in –omics. • I2B2, a software framework for managing, discovering and analyzing clinical data (https://www.i2b2.org/). • fMRI: Functional MRI is used in biomedical and clinical research to map more complex functions (e.g. emotions, face recognition, complex motor control, specialized language functions, etc.) in normal and pathological conditions. • EPI BOLD: Echo planar imaging blood oxygenation level dependent effect. This fMRI technique is used to acquire sequential brain images with increased signal intensity in areas of activated neurons. Specific brain regions involved in a task, process or emotion can be identified with BOLD imaging. • DWI: Diffusion-weighted MRI. Imaging method that uses the diffusion of water to generate contrast in MRI to reveal microscopic details of tissue architecture. • DTI: diffusion tensor imaging. Specific type of DWI to map white matter tracts in the brain or muscle fibers in the heart. • EEG/ERP: Electroencephalography/event related potentials. Non-invasive methods of measuring brain activity during cognitive processing. • BrainAmp ExG MR: An EEG amplifier that can be placed inside the MRI scanner, which allows simultaneous acquisition of EEG and MRI (BOLD) data.

Committee Members: Elizabeth Slate (Department of Statistics), Debajyoti Sinhad (Department of Statistics), Darin Rokyta (Department of Biological Science), Brian Inouye ((Department of Biological Science), Naomi Brownstein (Department of Behavioral Sciences and Social Medicine), Grant Macdonnell (College of Medicine), Greg Riccardi (School of Information), David Meckes (Department of Biomedical Sciences), Florian Duclot (Department of Biomedical Sciences), Greg Hajcak (Department of Biomedical Sciences & Department of Psychology), Yanming Yang (Translational Science Laboratory), Rakesh Singh (Translational Science Laboratory), Roger Mercer (Translational Science Laboratory), and Cynthia Vied (Translational Science Laboratory). Heather Flynn (Department of Behavioral Sciences and Social

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Medicine) and Michelle Arbeitman (Department of Biomedical Sciences) provided external reviews of the initial report. Authors: Cynthia Vied, Grant Macdonnell, Greg Riccardi, Greg Hajcak, Roger Mercer, Michelle Arbeitman and Heather Flynn. Final authorship of the report was the responsibility of Jeffrey Joyce (Senior Associate Dean for Research and Graduate Studies, CoM).

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