Anders M. Dale - US grants

The goal of the proposed research is to develop and validate an integrated method for non-invasive dynamic imaging of human brain function. In the proposed method, structural MRI is used to identify possible MEG/EEG generating dipoles as lying within the cortex and oriented perpendicular to its surface. A linear approach is used to estimate the time-course of activation at every point on the cortical surface, based on observed EM field patterns. The solution is biased toward those brain areas found to be activated using fMRI acquired in the same subject in the same task. Preliminary studies suggest that an anatomical and functional data approach can provide accurate spatiotemporal maps of cerebral activity. Potential sources of error are "missing" sources and head model mis-specification, where the missing sources arise possibly due to paradigm differences or data artifacts. In Aim 1, the applicants proposed to develop acquisition techniques that permit the identical task paradigm to be used in all experiments, and proposed improvements in their fMRI measurements to reduce inherent magnetic susceptibility artifacts. Another potential source of error is caused by inaccuracies in the forward solution due to incorrect estimates of tissue conductivities. In Aim 2, the applicants proposed to develop and test a novel method for non- invasively measuring the tissue conductivity of the brain using MR diffusion imaging. Although simulation studies suggest how to best combine data, the accuracy of the spatio-temporal maps is difficult to determine in humans. In Aim 3, the applicants proposed to directly validate their non-invasive estimates via direct comparison with intracranial electrode measurements in patients.

DESCRIPTION: (Applicant's Abstract) The cerebral cortex underwent a remarkable expansion in human evolution and is, by far, the largest part of the human brain. In order to facilitate research into cortical function, we propose to create, test, streamline, and make user-friendly a new cortical mapping system designed to preserve the physical layout of the cerebral cortex, which is most accurately described as a thin, folded, two-dimensional sheet. Most previous methods have mapped the cerebral cortex and other brain structures using a volume-based system with three coordinates for each point instead of two. Volume-based systems are straightforward to apply, and they generalize to non-cortical structures. However, there are a number of fundamental advantages to using a surface-based system for mapping the cerebral cortex. Preliminary results indicate that surface-based mapping yields improved inter subject comparisons. In addition, it enables many new surface-based computations as well as providing a unified, user-comprehensible way to display the constantly growing and paradigmatically diverse corpus of brain imaging data. To make it possible to exploit the advantages of both two- and three-dimensional approaches, we show how to generate a precise mapping between these two systems. We demonstrate many of the components of the proposed surface-based analysis and visualization software to show that most of the ideas are already prototyped. A number of our recent papers using this software further illustrate its usefulness in brain-mapping. In this proposal, we first plan to automate, optimize, and validate our existing software, while expanding a surface-based brain mapping database. The database will consist of (1) structural magnetic resonance images and cortical surface reconstructions made from them, and (2) functional magnetic resonance images analyzed in a surface-based framework both taken from the same normal human subjects. This database will be used to construct an averaged "canonical" human cortical surface, with much less "blurriness" than current 3-D-averaged brains. This database will also be used to implement and validate a novel 2-D latitude and longitude coordinate system for the unfolded cortical surface. The software tools will be ported to several platforms, including SGI, Sun and PC (Linux), in order to facilitate their dissemination to the wider scientific community.

Description (provided by application): The last decade has brought revolutionary new techniques allowing visualization of the working brain in humans at the systems level. However, a large gap remains between the spatiotemporal resolution of tomographic techniques (fMRI, PET), and the circuit level where animal studies permit mechanistic neural models. It is the overall goal of this proposal to develop an integrated suite of technologies to bridge this critical gap. Two interrelated themes are found throughout this proposal: (1) to improve the spatial and temporal resolution of non-invasive technologies, which will enable direct imaging of discrete (e g column and laminar level) neural units which bridge the systems and cellular levels and (2) to clarify the mechanisms which relate the biophysics of neuronal activity "observables" in our imaging measurements. The two key technologies to be investigated are: (1) extremely high resolution MRI and fMRI, using very high strength gradients, phased-array coils, and other advances at 3T and 7T non-human primates, and 9.4T rats and (2) tomographic optical imaging, increasing the resolution and physiological range using three different optical technologies: direct reflectance imaging, optical scanning microscopy, and diffuse optical tomography. These technologies will be validated against invasive "gold standard" techniques in studies of rat whisker barrel cortex and macaque visual cortex, and further applied to animal models spreading depression in migraine and stroke. Each of these experiments is designed to allow us to serially step from more to less invasive, and move from systems where much is already known through to studies in humans that have not heretofore been explored within the spatiotemporal domains our newly developed tools will afford.

Core C. Neuro-lmaging Core The Neuro-lmaging Core (Core C) will obtain structural and diffusion tensor Magnetic Resonance Imaging (MRI) scans of the subjects in this program project, and provide quantitative measures of specific anatomical structures for testing the hypotheses delineated in the scientific projects. Typically Developing children as well as in the clinical populations under study in this proposal (Specific Language Impairment, Focal Brain Lesions, High Functioning Autism, and Williams Syndrome) will be scanned shortly after enrolment in this study. MRI provides high-resolution images of the human brain with minimal risk, and thus is appropriate for children. From their differential tissue properties and relative anatomical locations, it is possible to delineate and quantify different subcortical structures and cortical areas. Similarly, white matter tracts can be localized from diffusion tensor MRI. Recently, reliable and validated tools have been developed to automatically and exhaustively label cortical and subcortical structures in adults. Labelling of white matter tracts is less validated but under rapid development. We propose in this Core to modify these tools so that they operate reliably in the pediatric population, specifically quantify anatomical entities relevant to the hypotheses advanced in the individual projects, and validate their performance in target and control populations. Our overall goal is to provide quantitative measures of human brain anatomy that can be correlated with the neuropsychological and psychophysiological measures, as well as the clinical status, of the pediatric populations included in this Program Project.

DESCRIPTION (provided by applicant): The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a large multi-site study in which serial clinical, biological, neuropsychological and neuroimaging data are being collected from 200 healthy controls, 400 individuals with mild cognitive impairment (MCI) and 200 patients with mild Alzheimer's disease (AD). This proposed ancillary study aims to analyze all ADNI structural and metabolic neuroimaging data to characterize morphometric and metabolic changes in early AD, with the goal of determining optimal predictor variables for identifying individuals at risk for developing progressive AD-related neurodegeneration. The variables thus identified could serve as surrogate endpoints in Phase 2 and 3 clinical treatment trials. To achieve these goals, MRI, PET, and cognitive data will be downloaded from the publicly accessible ADNI database. Methods based on FreeSurfer software will be used to perform automated volumetric segmentation, cortical surface reconstruction and cerebral parcellation on all baseline structural MRIs to obtain measures of regional cortical thickness and subcortical volumes. Automated, longitudinal, within-subject change analyses will be performed on serial MRIs to determine region-specific structural change trajectories related to disease progression. Semi-automated procedures for quantifying metabolic activity within MRI-derived anatomically- defined regions of interest will be applied to PET data from the baseline session to determine effect size of disease-related differences in metabolic activity in all cortical and subcortical structures, before and after correcting for morphometric differences. Within-subject change in metabolic activity over time, before and after correcting for morphometric changes, will be computed to determine region-specific trajectories of disease-related metabolic changes. Multivariate classification analyses will be applied to MRI measures to determine sensitivity and specificity for discriminating controls from subjects with MCI, and for discriminating MCI subjects who convert to AD from those who remain stable in diagnosis. PET and cognitive measures will be added to determine whether they improve classification accuracy. Multivariate analyses will be performed on structural measures obtained from control and MCI subjects during the test sessions of the first study year to determine the optimal set of measures for predicting risk of conversion to AD. Metabolic and cognitive measures will be assessed to determine whether they improve predictive ability. All derived data values and processed image volumes from this study will be made publicly available through the Biomedical Informatics Research Network. This study will significantly enhance understanding of brain changes that occur in early AD;identify candidate neuroimaging biomarkers for use in clinical trials;facilitate the research of other AD investigators;and provide normative data for use in investigation of other aging-related disorders. PUBLIC HEALTH RELEVANCE: The results of this project will provide important new information about the changes in brain structure and metabolism that occur in the earliest stages of Alzheimer's Disease (AD), and relate these measures to change in cognitive performance. This knowledge may improve our ability to predict who is most likely to develop AD and will provide researchers with objective measures that can be used to assess the ability of new treatments to prevent or delay the neurodegeneration associated with AD. Additionally, the high-throughput neuroimaging analysis methods developed here could be used in future studies for detecting and monitoring brain changes that occur in other neurological disorders.

DESCRIPTION (provided by applicant): This application is submitted by a group of investigators at 9 sites distributed throughout the U.S. where there are active developmental research programs involving substantial numbers of typically developing children, and neuroimaging investigators with experience in multi-site imaging initiatives. We propose to join forces to leverage these ongoing pediatric studies to assemble, over a period of 2 years, a large, cross- sectional imaging-genomics dataset to be used as a shared resource. The major aim of our proposal is to create a database that will include genome-wide association results for a large number of neural architectural phenotypes obtained using multimodal structural imaging. We propose to offer this database - essentially a map depicting the genomic landscape of the developing human brain - as a resource to the scientific community. Across the sites, investigators will administer the brief NIH Neuroscience Blueprint Toolbox Cognitive assessment;acquire standardized structural and diffusion images, and collect DNA samples in 1575 children and adolescents who are participants in their ongoing studies. The DNA samples will be shipped to a central Genetics Core for analysis, the imaging data will be uploaded for quality control and computational morphometry by the Imaging Core, and other data will be uploaded to the Coordinating Core, where an aggregate database of demographic, behavioral, imaging, and genomics deliverables will be compiled from 1400 individuals and maintained for shared access. A large, cross-sectional pediatric dataset would fill a significant gap that currently prevents description of gene and gene-by-age effects on neural architecture in children (i.e., main effects of genetic variation and gene effects on developmental trajectories) that are likely to be relevant to variability in behavioral and neuropsychiatric outcomes. Preliminary results of analyses of large adult cohorts suggest that common genetic variation accounts for substantial variability in brain morphology. The age-span of participants in these studies has made it possible to detect gene-by-age interactions relevant to variability in brain aging. Unfortunately because there are no well-powered studies with data from individuals spanning the childhood and adolescent age range, it is not known whether these neural phenotypes are present in children;and if they are, whether they can be observed early in development or evolve as ongoing remodeling of neural structures proceeds during childhood. This project would address the discrepancy between currently available imaging-genetics data in children of different ages and those available in adults. In addition to providing an informative data resource, the project would create a collaborative hub of investigators prepared to participate in an imaging- genomics adjunct study of the National Children's Study, which is in the early planning stages at this time. PUBLIC HEALTH RELEVANCE: The aim of this project is to assemble, over a period of 2 years, a large, cross-sectional imaging-genomics dataset to be used as a shared resource for investigations of genetic bases of neural phenotypes and age-by- genotype interactions that may represent genetically-mediated differences in developmental trajectories.

DESCRIPTION (provided by applicant): This application requests funds for purchase of a two-photon microscopy system with imaging and uncaging capabilities that will constitute a shared facility in the Departments of Neurosciences and Radiology at the University of California, San Diego. Two-photon microscopy is becoming an important tool for biomedical research, providing the ability to image intact brain tissue to depths of up to >500 microns, with sub-micron resolution. We have assembled an outstanding broadly based group of Users from multiple departments at UCSD with a major focus on pre-clinical studies in animal models of neurological conditions and cancer. We expect the User base to grow, as new projects get funded, and as we attract additional investigators. The proposed research involves neurovascular coupling, stroke, Parkinson's and Alzheimer's diseases, nerve regeneration, injury repair, cortical communication and developmental neuroscience. These research projects require deep penetration of light in tissue, minimization of photodamage, ability to image and co-localize multiple fluorescent indicators, sub-cellular photoactivation and capability of simultaneous electrophysiological recordings. All these requirements are addressed by the proposed state-of-the-art two-photon microscopy multi-user facility. The requested system was chosen because of its high image quality, high acquisition speed, easy calibration of the two-photon imaging and uncaging optical pathways and compatibility with electrophysiology components. The system allows versatile imaging of the structure and function under in vivo, in vitro and ex vivo conditions, supports two-photon photoactivation of caged compounds and Fluorescent Lifetime Imaging Microscopy (FLIM). Administratively, the proposed instrument will be part of The Center for fMRI at UCSD. The Center provides comprehensive imaging facilities for users in the greater San Diego, with established infrastructure for resource administration, recharge mechanisms, and user training. The proposed two-photon instrument is complementary to the existing neuroimaging modalities at the Center such as MEG, PET and fMRI, and will provide an essential and central component of the UCSD Multimodality Imaging effort. Specifically, combining fMRI, PET and in vivo two-photon microscopy will facilitate bridging the gap between non-invasive human and single-cell animal characterization of disease-related changes. Studying of animal models of human diseases is a major ongoing effort at UCSD School of Medicine. In summary, the proposed cutting-edge two-photon facility will provide NIH-funded investigators at UCSD with an essential tool to study the intricate pathways involved in brain activation, features of disease progression, disease diagnosis and treatment. These studies will impact our understanding of normal and pathological brain function, opening new avenues for treatment and prevention of human disease. The proposed facility is also expected to play a significant role in the training and teaching efforts at UCSD.

DESCRIPTION (provided by applicant): The overall goal of the previous cycle of this BRP project has been to improve the spatial resolution of functional Magnetic Resonance Imaging (fMRI) and optical technology in order to bridge the gap between macroscopic fMRI and the underlying microscopic electrical activity of single neurons and dilation and constriction of single blood vessels. To achieve this goal, we have developed and experimentally validated an integrated suite of technologies providing improved sensitivity to the observable physiological and biophysical parameters, with high spatial and temporal resolution, to measure direct and indirect consequences of brain neuronal activity at multiple scales. We successfully applied the new tools to study the relationship between imaging signals and the underlying neuronal activity in healthy brain and under clinical conditions such as cortical spreading depression and stroke. Of particular note, data acquired during the previous funding cycle, along with data from others, increasingly suggest that differential vascular control originates from activation of distinct neuronal sub-populations through release of specific vasoactive agents. Thus, the main goal of this renewal is to combine the BRP technological developments with recent revolutionary methods in genetic labeling and remote control of neuronal activity allowing targeted activation of identified neuronal cells and cellular populations to identify macro- and microscopic hemodynamic signatures of activation in populations of neurons with known phenotype and neurotransmitter content. Specifically, we will directly control firing patterns in labeled single cells and cellular populations, while measuring the resultant vascular, metabolic and neuronal response in normal and genetically-modified rats and mice across spatial scales. The proposed experiments will provide important insights into pathways of neurovascular communication and will identify vasoactive messengers, release of which drives stimulus- evoked hemodynamic signals. While targeted activation of identified neuronal phenotypes was not available when our BRP was originally funded, today we are in a unique position to combine these novel genetic tools with a suite of imaging technologies that have been developed during the previous funding cycle. The experimental measurements will be integrated within a comprehensive modeling framework that relates physiological parameters and imaging observables at the microscopic and macroscopic scales. This combines 1) bottom-up models that enable prediction of macroscopic imaging signals from microscopic measures of vessel diameters, flow, and the intravascular and extravascular oxygenation state; and 2) top-down models that enable estimation of physiological variables of interest (primarily cerebral blood flow and O2 metabolism) from non-invasive Blood Oxygenation Level Dependent (BOLD) and Arterial spin Labeling (ASL) fMRI experiments. This work will greatly extend the utility of fMRI as a quantitative probe of physiology for both basic and clinical basic neuroscience applications. PUBLIC HEALTH RELEVANCE: The central challenge limiting quantitative applications of Blood Oxygenation Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) is that the physiological coupling between neuronal activity, cerebral blood flow and O2 metabolism are still relatively poorly understood. The proposed project combines advanced imaging technologies developed during the previous funding cycle and recent revolutionary genetic methods for targeted neuronal activation with a multi-scale modeling framework, which links microscopic measurements in animal models to macroscopic measurements appropriate for human studies. This work will deliver a mechanistic understanding of local regulation of cerebral blood flow and lay a solid foundation for the quantitative estimation of physiological parameters of interest, specifically blood flow and O2 metabolism, from human fMRI data.

PROJECT SUMMARY The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a large multi-site study in which serial clinical, biological, neuropsychological and neuroimaging data are being collected from 200 healthy controls, 400 individuals with mild cognitive impairment (MCI) and 200 patients with mild Alzheimer's disease (AD). This proposed ancillary study aims to analyze all ADNI structural and metabolic neuroimaging data to characterize morphometric and metabolic changes in early AD, with the goal of determining optimal predictor variables for identifying individuals at risk for developing progressive AD-related neurodegeneration. The variables thus identified could serve as surrogate endpoints in Phase 2 and 3 clinical treatment trials. To achieve these goals, MRI, PET, and cognitive data will be downloaded from the publicly accessible ADNI database. Methods based on FreeSurfer software will be used to perform automated volumetric segmentation, cortical surface reconstruction and cerebral parcellation on all baseline structural MRIs to obtain measures of regional cortical thickness and subcortical volumes. Automated, longitudinal, within-subject change analyses will be performed on serial MRIs to determine region-specific structural change trajectories related to disease progression. Semi-automated procedures for quantifying metabolic activity within MRI-derived anatomically- defined regions of interest will be applied to PET data from the baseline session to determine effect size of disease-related differences in metabolic activity in all cortical and subcortical structures, before and after correcting for morphometric differences. Within-subject change in metabolic activity over time, before and after correcting for morphometric changes, will be computed to determine region-specific trajectories of disease-related metabolic changes. Multivariate classification analyses will be applied to MRI measures to determine sensitivity and specificity for discriminating controls from subjects with MCI, and for discriminating MCI subjects who convert to AD from those who remain stable in diagnosis. PET and cognitive measures will be added to determine whether they improve classification accuracy. Multivariate analyses will be performed on structural measures obtained from control and MCI subjects during the test sessions of the first study year to determine the optimal set of measures for predicting risk of conversion to AD. Metabolic and cognitive measures will be assessed to determine whether they improve predictive ability. All derived data values and processed image volumes from this study will be made publicly available through the Biomedical Informatics Research Network. This study will significantly enhance understanding of brain changes that occur in early AD; identify candidate neuroimaging biomarkers for use in clinical trials; facilitate the research of other AD investigators; and provide normative data for use in investigation of other aging-related disorders. RELEVANCE The results of this project will provide important new information about the changes in brain structure and metabolism that occur in the earliest stages of Alzheimer's Disease (AD), and relate these measures to change in cognitive performance. This knowledge may improve our ability to predict who is most likely to develop AD and will provide researchers with objective measures that can be used to assess the ability of new treatments to prevent or delay the neurodegeneration associated with AD. Additionally, the high-throughput neuroimaging analysis methods developed here could be used in future studies for detecting and monitoring brain changes that occur in other neurological disorders.

Knowledge of a specific neural network supporting memory function in the human brain stems from the case of patient H.M. who, in 1953 underwent an experimental medial temporal lobectomy in the hope of reducing the frequency and severity of his epileptic seizures. The operation was successful in that respect, but it unexpectedly left him incapable of creating new memories. For more than five decades, H.M. participated in hundreds of experiments and his case was discussed in thousands of scientific publications. His brain contained the clues to understand how memory works; however, determining with precision which structures were damaged was not possible because even the latest neuroimaging could not clearly resolve the anatomy of the temporal lobes. With support from the National Science Foundation, Dr. Jacopo Annese will complete an 'open source', web-based microscopic atlas of H.M.'s brain which was donated to science post-mortem. The tools-embedded atlas will support the creation of teaching curricula that will expose students to raw neuroimaging data from multiple modalities, cutting edge brain mapping algorithms, web-based exploration tools, all within the clinical and biographical context of H.M. as an individual. The cyber infrastructure created through this project is expected to enable discovery neuroscience by participants world wide.

Specifically, Dr. Annese and his research team will (1) provide a dedicated support infrastructure to maintain and manage the web atlas for H.M.'s brain; (2) significantly increase the accuracy of the atlas by increasing the number of digitized histological slices to achieve 1 mm per slice interval (from 3mm interval); (3) acquire and deliver image stacks to enable remote quantitative studies; (4) implement new web tools to enable the handling of remote request and curation of results from different laboratories; (5) convert the images into formats that can be 3-D printed using consumer products. Such cyber infrastructure will make the valuable H.M. data available for new retrospective studies that may further change our current view of how memory is established in the human brain and enable quantitative analyses at the cellular level using a 'virtual microscope'. The resulting atlas will be used by researchers worldwide to re-interpret, based on clear anatomical evidence, the results from hundreds of neuropsychological exams conducted when H.M. was alive.

DESCRIPTION (provided by applicant): The ABCD-USA Consortium proposes a study designed to permit the scientific community to answer important questions about the effects of substance use (SU) patterns on behavioral and brain development of adolescents. We have assembled a team of investigators with unparalleled research experience with children and adolescents, and specific expertise in adolescent SU, child and adolescent development, developmental psychopathology, longitudinal multi-site imaging, developmental neuroimaging, developmental cognitive neuroscience, genetics and imaging genetics, bioassays, epidemiology, survey research, bioinformatics, and mobile assessment technologies. We propose a comprehensive, nationwide study to be conducted at 21 sites organized into 11 hubs (over 89 million Americans, 29% of the US population, live within 50 miles of our geographically spread sites), that, uniquely, can provide a nationally representative sample and a large twin sample that together can help distinguish environmental, sociocultural, and genetic factors relevant to SU. We ensure cohesion and standardization by employing a recruitment strategy designed by a professional survey company (experience with Monitoring the Future); standardized environmental, neurocognitive and mental health assessments, MRI assessments with all scanners using harmonized Human Connectome Project procedures, and computerized data collection with real-time quality control. Developmentally tailored assessments will have stable sensitivity and construct validity across the childhood and adolescent developmental period. They minimize participant burden, yet capture even subtle changes over time in substance use, mental health, neurocognition, development, and environment, and we employ novel state-of-the-art bioassays and passive data collection from mobile devices. A detailed retention plan builds on the experience and success of our investigators. This application describes the ABCD-USA Data Analysis and Informatics Center (DAIC), which will: establish a harmonized MRI acquisition protocol, compatible with all major scanner platforms, taking advantage of recent technological advances in structural and functional MRI; establish rigorous quality control and quantitative calibration procedures to ensure accuracy and comparability of derived imaging measures across scanners and across time; implement advanced computational analysis workflows for all imaging data; implement reliable data entry, quality control, and monitoring tools for the substance use questionnaire, neurocognitive assessments, bioassay-derived measures, and mobile technologies assessment data; implement the state- of-the-art statistical analysis tools and procedures needed to integrate information across measures and modalities; and implement infrastructure and procedures for public sharing of raw- and derived data and associated tools and computational workflows, and enable interactive data exploration and analytics through a web-based Portal.

The computational properties of the human brain arise from an intricate interplay between billions of neurons connected in complex networks. However, our ability to study these networks in healthy human brain is limited by the necessity to use noninvasive technologies. This is in contrast to animal models where a rich, detailed view on the cellular level brain function has become available due to recent advances in microscopic optical imaging and genetics. Thus, a central challenge facing neuroscience today is leveraging these mechanistic insights from animal studies to accurately draw physiological inferences from human noninvasive signals. In the proposed project, we focus on the ?Calibrated? Blood Oxygenation Level Dependent (BOLD) fMRI technology asking the questions: ?Which aspects of the underlying neuronal activity can be reliably inferred from noninvasive cerebral blood flow (CBF) and Cerebral Metabolic Rate of O2 (CMRO2) observables?? and ?What further information can be obtained from combining Calibrated BOLD with Magnetoencephalography (MEG)?? Our central hypothesis is that specific neuronal cell types have identifiable ?signatures? in the way they drive changes in energy metabolism (CMRO2), blood flow (CBF) and contribute to macroscopic electrical signals (MEG current dipole dynamics). Because other factors may affect baseline flow and metabolism, our focus is on the evoked absolute CMRO2 and CBF changes associated with increased or decreased neuronal activity. We will perform parallel experiments in mice and humans to empirically connect the dots between the microscopic properties of brain's functional organization and their manifestation on the macroscopic level of noninvasive observables. Based on the experimental results, we will then develop a computational framework that will establish connections between scales and measurement modalities enabling robust estimation of the critical aspects of neuronal circuit activity from noninvasive measurements in humans. The proposed project will deliver a quantitative probe for neuronal activity of known cell types in human brain enabling a paradigm shift in human fMRI studies: from a simple mapping of fMRI signal change to the explicit estimation of the respective activity levels of specific neuronal cell types without confounding effects of the baseline state of flow and metabolism.  

Project Summary/Abstract In its initial funding period, the ABCD consortium used a rigorous epidemiological approach to recruit a diverse sample of 11,878 9-10-year-olds through our 21 research sites, of which 2136 are twins or triplets. ABCD has created a comprehensive yet efficient protocol for participating youth and parents, using standardized and state-of-the-art methods for neuroimaging, biospecimens, and assessments of substance use, mental and physical health, neurocognition, culture, and environment, using a fully digital platform and making use of mobile technologies. We ensure cohesion and standardization by having employed a recruitment strategy designed by a professional survey company (experience with Monitoring the Future); standardized environmental, neurocognitive and mental health assessments, MRI assessments with all scanners using harmonized Human Connectome Project procedures, and computerized data collection with real-time quality control. Developmentally tailored assessments will have stable sensitivity and construct validity across the childhood and adolescent developmental period. They minimize participant burden, yet capture even subtle changes over time in substance use, mental health, neurocognition, development, and environment, and we employ novel state-of-the-art bioassays and passive data collection from mobile devices. A detailed retention plan builds on the experience and success of our investigators. This application describes the ABCD Data Analysis, Informatics and Resource Center (DAIRC), which will: maintain and update the harmonized MRI acquisition protocol, compatible with all major scanner platforms, taking advantage of recent technological advances in structural and functional MRI; perform rigorous quality control and quantitative calibration procedures to ensure accuracy and comparability of derived imaging measures across scanners and across time; implement advanced computational analysis workflows for all imaging data; implement reliable data entry, quality control, and monitoring tools for the substance use questionnaire, neurocognitive assessments, bioassay- derived measures, and mobile technologies assessment data; implement the state-of-the-art statistical analysis tools and procedures needed to integrate information across measures and modalities; continue development of infrastructure and procedures for public sharing of raw and derived data and associated tools and computational workflows; and enable interactive data exploration and analytics through a web-based Portal.

PROJECT SUMMARY The landmark Healthy Brain and Child Development (HBCD) study will provide a representative reference data resource to the scientific community enabling unprecedented investigation of neurodevelopment and the impact of environmental, genetic, and biological factors on brain and behavioral health and developmental trajectories from infancy through childhood. Through this study, the Healthy Brain and Child Development National Consortium (HBCD-NC) will recruit and retain a sociodemographically diverse cohort of 7,500 pregnant women from 24 sites across the U.S. and follow these families and their children through the first decade of life. Children will undergo rigorous data collection across modalities including neuroimaging, neurophysiology, behavioral and cognitive assessments, and collection of biospecimens via a balanced protocol developed by field-leading experts. Building upon the substantive complementary experience and expertise of its multidisciplinary team and leveraging multiple population-specific technical innovations, the Healthy Brain and Child Development National Consortium Data Coordinating Center (HDCC) will provide the leadership, management, and oversight of data collection, quality control, curation, processing, management, sharing, and analytics to facilitate and support the activities of the HBCD-NC and ensure its success. Included is development and implementation of an optimized, state-of-the-art MRI protocol harmonized for the first time in infants/toddlers across all three major vendors which leverages the latest innovations in scanner technology with age-specific structural, microstructural, quantitative, functional, and spectroscopy sequences. Also detailed is a targeted EEG protocol linked with a field-leading automated processing pipeline for developmental EEG which provides innovative derivative measures. Data and project management will occur through a centralized tracking and distribution platform linked to a high-throughput compute backbone which overcomes limits of commercially-available systems for management and integrated processing of multimodality data from large, multi-site studies. High performance computing will be supported through unique access to a combination of field-leading resources. Detailed procedures are outlined for secure collection, management, and analysis of personally identifiable information (PII) data, including flexible methods designed to accommodate heterogeneity in electronic health record systems across sites. Finally, substantive HBCD-specific enhancements to the Data Exploration and Analysis Portal (DEAP 2.0) will produce a crucial tool for data access to authenticated users while promoting best practices in reproducible statistical analysis and providing flexible computation without the need to download restricted-access data. The result of this field- leading combination of HDCC resources will be a state-of-the-art, longitudinal data set of unparalleled scale which provides deep understanding of the biological and environmental factors that affect a child?s health, brain, and behavioral development and shapes research, clinical care, and public policy for decades to come.