Angela R. Laird - US grants

DESCRIPTION (provided by applicant): The objective of this proposal is to develop, evaluate, and distribute a domain ontology of cognitive paradigms for application and use in the functional neuroimaging community. This cognitive paradigm ontology, CogPO, will be developed through the integration of two well known and established databases, the Functional Imaging Biomedical Informatics Research Network (FBIRN) Human Imaging Database (HID) and the BrainMap database. The design of CogPO concentrates on what can be observed directly: categorization of each paradigm in terms of (1) the stimulus presented to the subjects, (2) the requested instructions, and (3) the returned response. All paradigms are essentially comprised of these three orthogonal components, and formalizing an ontology around them is a clear and direct approach to describing paradigms. This structured, well-defined, common and controlled vocabulary will be capable of representing the cognitive paradigms in the FBIRN Data Repository, which stores structural and functional imaging datasets for later analysis, and in BrainMap, which stores analyzed results from the functional imaging literature. This proposal has been designed to include strong collaboration with the National Center for Biomedical Ontology (NCBO) and the Neuroinformatics Information Framework (NIF). Neither the NCBO nor the NIF currently seeks to develop an ontology of cognitive paradigms;thus, the current proposal is novel and does not conflict with any existing ontology efforts, but is a complementary endeavor. Through a series of workshops, CogPO developers will consult both the domain experts (the neuroimaging research community) and the ontological experts (NCBO) and the BIRN Ontology Task Force (OTF). CogPO developers also plan to allow user input and feedback from the entire neuroimaging community through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC). This proposal includes a formal plan for community acceptance of the developed ontology, complete with a list of concrete deliverables in Protigi-OWL format to be distributed by the end of the funding period, which can be utilized not only by FBIRN and BrainMap users, but also by the entire neuroimaging community. It is our ultimate aim that CogPO be designed to enable its extension to a broader context in cognitive sciences. PUBLIC HEALTH RELEVANCE: CogPO, a domain ontology of cognitive paradigms, would facilitate descriptions of cognitive experimental paradigms in ways that are machine-readable and machine-interpretable, to allow communication and automated data sharing across diverse databases and data sources. This ontology will be made available for adoption not only by other fMRI databases, but also for archives of other neuroimaging modalities (e.g., EEG or MEG data), such as the Neural ElectroMagnetic Ontologies (NEMO), and literature neuroinformatics efforts such as the Society for Neuroscience's PubMed Plus. It is our ultimate aim that CogPO be designed to enable its extension to a broader context in cognitive sciences.

DESCRIPTION (provided by applicant): The overall objective of the present proposal is to develop, evaluate, distribute, and apply tools for the BrainMap Project, which provides the human brain mapping (HBM) community with data sets, computational tools, and informatics resources for quantitative meta-analyses and meta-analysis-based data interpretation. The development of coordinate-based, voxel-wise meta-analysis (CVM) has been a breakthrough for HBM, enabling statistically rigorous meta-analysis and systems-level modeling of published results. The HBM literature suitable for CVM meta-analysis was estimated in 2007 to be ~6,000 papers, with ~1000 new conforming papers being published each year. In this renewal, we propose numerous enhancements to widely used coordinate-based meta-analysis tools (Aim 1). Meta-analytic connectivity mapping (MACM) is a new application of CVM, which derives inter-regional connectivity maps from inter-study co-occurrence patterns. In this renewal, we propose to extend the functionality of our MACM tools, construct connectivity atlases through data mining, and validate these atlases by comparison to non-meta-analytic approaches (Aim 2). In BrainMap, behavioral meta-data are coded using a taxonomy developed and progressively refined by the BrainMap team. Recent application of independent component analysis (ICA) to BrainMap extracted intrinsic neural systems that were well, but not perfectly, discriminated by the BrainMap coding scheme. This observation suggests robust computational approaches both for providing a behavioral ontology for intrinsically connected networks and also for programmatic refinement of the BrainMap coding scheme (Aim 3). Finally, it has recently been demonstrated that CVM can be applied to voxel-based morphometry (VBM) structural neuroimaging studies. In Aim 4, we anticipate the rapid growth of VBM research by creating a BrainMap-like database of the standards- compliant VBM literature. These proposed enhancements and extensions of the BrainMap Project will allow this rich set of neuroinformatics tools to continue to meet the research and educational needs for knowledge discovery and data mining in the neuroimaging community. 1 PUBLIC HEALTH RELEVANCE: An extraordinary amount of neuroimaging data has been acquired and analyzed over the last two decades in both healthy subjects and patients diagnosed with various neurologic or psychiatric diseases and disorders. The BrainMap Project aims to improve public health by developing methods that will enable more informed cognitive, perceptual, and motor models of brain function and dysfunction. The proposed research will allow 2

? DESCRIPTION (provided by applicant): Despite significant recent breakthroughs in our understanding of the neurobiological mechanisms involved in substance use (SU) and addiction, progress remains modest toward integrative knowledge on how psychosocial, neurocognitive, and neurobiological risk factors jointly influence SU initiation, escalation, and addiction, and how they are affected in return. The complexity of SU behaviors, their emergence during critical periods of neurodevelopment, and their strong linkages with physical and mental health, demands a comprehensive large- scale, prospective longitudinal study that begins with youth prior to initiation of SU and that incorporates genetic, psychosocial, cultural, neuropsychological, and neuroimaging measures. The aims of this study align with those of the Adolescent Brain Cognitive Development (ABCD) Study Consortium as set forth in RFA-DA-15-015. These are to: (1) Establish how diverse patterns of SU use impact the structure and function of the developing brain; (2) Identify the impact of SU use on health, psychosocial development, neurocognition, academic achievement, motivation, and emotional regulation; (3) Understand how SU and addiction affect the onset, course, and severity of psychopathology, and vice versa; (4) Identify factors that influence trajectories of SU and its consequences; and (5) Establish how use of one substance contributes to use of other substances. As the largest ethnic minority group in the US, Latinos merit a significant position in the enrollment plan for th ABCD study. The Florida International University (FIU) ABCD site will uniquely contribute to achieving these aims and enhance their impact and significance through enrollment of 900 multi-ethnic Latino youth from South Florida who will be 9 to 10 years old at baseline and substance naïve. The vast majority of our sample will be normally developing, but 30% will have a diagnosis of a disruptive behavior disorder (DBD; i.e., ADHD, Conduct Disorder, or Oppositional Defiant Disorder) to increase likelihood of observing initiation and escalation of SU in the sample and to better understand mechanisms accounting for the strong linkages between DBDs and SU trajectories. Furthermore, multidimensional assessment of cultural factors at the individual, intra-familial, and community level in this unique sample, will allow for characterization of how dynamic relationships between cultural factors (e.g., acculturation and biculturalism) influence SU initiation, escalation, and addiction, as well as underlying mechanisms. Participants will complete six assessment waves during the first 5 years of the study, which includes detailed assessments of SU and various psychosocial, cultural, neuropsychological, and neuroimaging measures. In conjunction with the ABCD Coordinating Center, Data Center, and selected sites, this study will reveal how psychosocial (including cultural), neurocognitive, and neurobiological factors dynamically interact to influence SU trajectories during development from childhood through adolescence and into young adulthood. The findings of the ABCD Study will further NIDA's mission to apply cutting-edge science to issues of SU and addiction in order to inform policy and improve prevention and treatment.

? DESCRIPTION (provided by applicant): Despite significant recent breakthroughs in our understanding of the neurobiological mechanisms involved in substance use (SU) and addiction, progress remains modest toward integrative knowledge on how psychosocial, neurocognitive, and neurobiological risk factors jointly influence SU initiation, escalation, and addiction, and how they are affected in return. The complexity of SU behaviors, their emergence during critical periods of neurodevelopment, and their strong linkages with physical and mental health, demands a comprehensive large- scale, prospective longitudinal study that begins with youth prior to initiation of SU and that incorporates genetic, psychosocial, cultural, neuropsychological, and neuroimaging measures. The aims of this study align with those of the Adolescent Brain Cognitive Development (ABCD) Study Consortium as set forth in RFA-DA-15-015. These are to: (1) Establish how diverse patterns of SU use impact the structure and function of the developing brain; (2) Identify the impact of SU use on health, psychosocial development, neurocognition, academic achievement, motivation, and emotional regulation; (3) Understand how SU and addiction affect the onset, course, and severity of psychopathology, and vice versa; (4) Identify factors that influence trajectories of SU and its consequences; and (5) Establish how use of one substance contributes to use of other substances. As the largest ethnic minority group in the US, Latinos merit a significant position in the enrollment plan for th ABCD study. The Florida International University (FIU) ABCD site will uniquely contribute to achieving these aims and enhance their impact and significance through enrollment of 900 multi-ethnic Latino youth from South Florida who will be 9 to 10 years old at baseline and substance naïve. The vast majority of our sample will be normally developing, but 30% will have a diagnosis of a disruptive behavior disorder (DBD; i.e., ADHD, Conduct Disorder, or Oppositional Defiant Disorder) to increase likelihood of observing initiation and escalation of SU in the sample and to better understand mechanisms accounting for the strong linkages between DBDs and SU trajectories. Furthermore, multidimensional assessment of cultural factors at the individual, intra-familial, and community level in this unique sample, will allow for characterization of how dynamic relationships between cultural factors (e.g., acculturation and biculturalism) influence SU initiation, escalation, and addiction, as well as underlying mechanisms. Participants will complete six assessment waves during the first 5 years of the study, which includes detailed assessments of SU and various psychosocial, cultural, neuropsychological, and neuroimaging measures. In conjunction with the ABCD Coordinating Center, Data Center, and selected sites, this study will reveal how psychosocial (including cultural), neurocognitive, and neurobiological factors dynamically interact to influence SU trajectories during development from childhood through adolescence and into young adulthood. The findings of the ABCD Study will further NIDA's mission to apply cutting-edge science to issues of SU and addiction in order to inform policy and improve prevention and treatment.

PROJECT SUMMARY/ABSTRACT Despite strong theoretical and clinical interest, characterization of the common and distinct neurobiological alterations across drug and behavioral addictions cannot be feasibly addressed within a single neuroimaging study. This research project will fill this knowledge gap by integratively using neuroimaging meta-analytic tools and a large amalgamated resting state fMRI (rs-fMRI) data set to rigorously characterize common (addiction- general) and distinct (drug/condition-specific) network-level brain alterations across addictive disorders. Available neuroimaging meta-analytic tools allow for synthesis of the extant literature and can be exploited to inform common and distinct neurobiological alterations across addiction. In addition, assessment of large-scale brain networks through (meta-analytic and rs-fMRI approaches) provides a more complete and coherent framework to appreciate such addiction-related alterations. As such, the innovative combination of such data streams offers the ability to inform heuristic frameworks guiding future research, fractionation of the addiction phenotype, and identification of neurobiological intervention targets. The overall objective of this project is to quantitatively synthesize the addiction-related neuroimaging literature (Aim 1), that then inform mega-analysis of a large amalgamated rs-fMRI data set (Aim 2), the behavioral interpretation of which will be facilitated by emerging meta-analytic techniques (Aim 3), thereby enabling cross-drug comparisons of network-level brain alterations. The feasibility of this overall analytic framework is evidenced by significant preliminary work in nicotine addiction. Specifically, this project will comprehensively synthesize the addiction-related neuroimaging literature to identify disrupted addiction-general and drug/condition-specific regional nodes across drug and behavioral addictions (e.g., alcohol, nicotine, marijuana, stimulants, opiates) and behavioral addictions (e.g., gambling, internet gaming) as well as obesity (Aim 1). Harnessing the accumulated volume of published neuroimaging results will allow for direct comparison of conditions that were never compared with each other in the primary studies. Meta-analytically informed hypotheses will be applied to an amalgamated rs-fMRI data set for targeted testing of altered functional connectivity across large-scale brain networks (Aim 2). To more fully contextualize the behavioral consequences of such alterations, we will employ network-level meta-analytic techniques to quantitatively delineate behavioral phenomena linked with regional and network-level alterations impacted by addiction (Aim 3). Efforts to archive, mine, and synthesize the accumulated knowledge of addictions impact on the brain are critical to inform analysis of large neuroimaging data sets generated through amalgamated sources or new data collection efforts.

Abstract Adolescent Brain Cognitive Development (ABCD) is the largest long-term study of brain development and child health in the United States. The ABCD Research Consortium consists of 21 research sites across the country, a Coordinating Center, and a Data Analysis and Informatics Resource Center. In its first five years, under RFA-DA-15-015, ABCD enrolled a diverse sample of 11,878 9-10 year olds from across the consortium, and will track their biological and behavioral development through adolescence into young adulthood. All participants received a comprehensive baseline assessment, including state-of-the-art brain imaging, neuropsychological testing, bioassays, careful assessment of substance use, mental health, physical health, and culture and environment. A similar detailed assessment recurs every 2 years. Interim in-person annual interviews and mid-year telephone or mobile app assessments provide refined temporal resolution of developmental changes and life events that occur over time with minimal burden to participating youth and parents. Intensive efforts are made to keep the vast majority of participants involved with the study through adolescence and beyond, and retention rates thus far are very high. Neuroimaging has expanded our understanding of brain development from childhood into adulthood. Using this and other cutting-edge technologies, ABCD can determine how different kinds of youth experiences (such as sports, school involvement, extracurricular activities, videogames, social media, unhealthy sleep patterns, and vaping) interact with each other and with a child?s changing biology to affect brain development and social, behavioral, academic, health, and other outcomes. Data, securely and privately shared with the scientific community, will enable investigators to: (1) describe individual developmental pathways in terms of neural, cognitive, emotional, and academic functioning, and influencing factors; (2) develop national standards of healthy brain development; (3) investigate the roles and interaction of genes and the environment on development; (4) examine how physical activity, sleep, screen time, sports injuries (including traumatic brain injuries), and other experiences influence brain development; (5) determine and replicate factors that influence mental health from childhood to young adulthood; (6) characterize relationships between mental health and substance use; and (7) specify how use of substances such as cannabis, alcohol, tobacco, and caffeine affects developmental outcomes, and how neural, cognitive, emotional, and environmental factors influence the risk for adolescent substance use.

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.