Arthur Toga - US grants

A computerized three dimensional (3D) atlas of the rat brain will be developed to enable users to visualize the complex spatial relationships among brain structures. The systems developed will serve as a basis for quantitating morphometric measurements in three dimensions and understanding the spatial relationships between structure and function. In addition these studies will aid subsequent researchers using steriotaxic methodologies to study neural function.

This application is for a Pre-Resource to develop and provide technologies focused on neuro imaging. We will concentrate on three dimensional reconstructions and analysis of neurobiologic structure/function relationships. Our goals are to provide an imaging resource to investigators as well as to develop new ways of analyzing image data, combining the results obtained from different sources, and synthesizing new displays of brain. The research component of the proposal is organized around three major imaging programs; visualization, geometric modelling, and geometric distortion. Each of the research programs is subdivided into 4-6 specific projects. These programs address common problems among most of our collaborators and our own biologic research. Our approach is to combine the disciplines traditionally defined as computer science (image processing, computer graphics, etc.) with neuroscience. Many studies of brain generate data obtained by serial sectioning. Generally, in such studies, only a small subset of the available information is analyzed. Three dimensional neuro imaging, on the other hand, provides the means by which a more comprehensive analysis can be achieved. Techniques developed in this grant will help integrate and synthesized a variety of experimental data concerned with brain structure/function. We will quantitate the geometry, densitometry, and display of this data. Twelve different collaborative projects are also part of this interdisciplinary proposal. The investigators on these projects will be able to ask new and difficult questions of their data, and the results of these queries will provide new insight into our understanding of brain. Interactions with these collaborations also greatly influences the direction of algorithm development. This Pre-Resource will, therefore, enhance the research activities of participating neuroscientists and improve the technology that is the science of neuro imaging.

The award will support the development, testing and evaluation of algorithms to enable the functional and structural mapping of the brain between different modalities, subjects and developmental stages. The algorithms will spatially deform data from varied sources so that they become comparable. The algorithms will be applied to positioning, warping, interpolating and parameterizing of 3D brain data. The PI will model brain using volume and surface based approaches, then transform it using methods ranging from rigid repositioning to nonlinear, local deformations. Transformation algorithms will first be measured for accuracy, reliability and validity via experiments using simulations and previously acquired data. The goal is to develop algorithms that are biologically valid as well as numerically accurate. This work will complement the Human Brain Project research and will enable the integration of independent yet complementary brain information from different sources and projects.

DESCRIPTION (Adapted from Applicant's Abstract): The goal of this project is to extend neuroimaging applications beyond the visualization and measurement of individual brain data sets. To that end, the applicants proposed a series of experiments to develop and test new techniques for quantitative comparisons of anatomic and functional information from groups of subjects and across different modalities. The specific aims of this project were divided among five interrelated research programs; 1) Structure/Function Delineation and Segmentation; 2) Spatial Manipulations; 3) Acquisition; 4) Visualization; and 5) Computer Software. Algorithm would be subjected to a rigorous series of validation experiments designed to measure its accuracy, precision and reliability. Experiments would be conducted with different modalities, data types, resolutions, subjects and species. The applicants proposed to formulate rules for its application based on its validity in a subject population, the data type and computational expense. The applicants have combined efforts in algorithmic development with the collection of high resolution anatomic data, and proposed to make these results available using electronic communication. The applicant would build on their established history of research in this area and extend previous efforts focused on the reconstruction and display of brain models to include the quantitative analysis of multimodal representations of brain across populations of individuals. They would maintain the three dimensional integrity of the data and provide usable visualization, analytical and portable software to the neuroscientific community.

computer network; information dissemination; computer center; computer system design /evaluation; computer program /software; brain mapping; biomedical facility; neurosciences; telecommunications; computer data analysis; human data;

This project combines computer science and neuroscience towards die development of a computerized three dimensional (3D) atlas of the human brain. Our goals are to create a collection of human brain data sets, retaining information about morphometric variability, provide appropriate quantitative visualization and data organization tools and share this data using electronic networks. During the next five years we will generate a morphological collection of sectioned whole human brain using a novel combination of histotechnologies and advanced computer applications. We will obtain very high resolution, color image data by directly digitizing the cryoplaned blockface of human head and brain. Digital serial images will be reconstructed into 3D volumes or retained for the outlining of selected neuranatomical structures. We will refine our current histotechnology so that digital image data from retrieved histological sections may be remapped in register with the directly imaged data sets. This will ultimately provide maximal delineation of morphological subregions. The resultant digital atlas volumes will be placed in a well understood and universally accepted coordinate system to simplify application in basic and clinical neuroscience fields. These will be made freely available to the scientific, clinical and educational communities via electronic network distribution. The significance of this project is expressed in several ways.. First, die project will present very high resolution, 3D imagery of the human brain with intracranial landmarks intact; these data, presented at a resolution level never before available, will further the study of neuroanatomical structures and their spatial relationships within the brain. Secondly, data collection will generate and organize a substantial amount of information on individual brain morphometry and histology applicable to neuroscientific research. Thirdly, by placing digital 3D volume data into a standard coordinate system we provide the framework for multimodality brain mapping useful in a variety of medical imaging applications. Fourth, we will develop and implement a medical informatics approach to die distribution and sharing of a unique and important database of human brain structure. Finally, this research represents the groundwork for development of a complete, standardized 3D atlas of human brain.

We will study the spatial and temporal dynamics of functional vascular recruitment, neurovascular coupling, and modulation of this response in rodent cortex. We will do this using our established paradigm of high resolution optical intrinsic signal imaging and corroborative electrophysiology. Optical intrinsic signals (activity-related cortical reflectance changes) indicate basic in vivo physiologic processes poorly represented by other indices. A large portion of these signals is thought to arise from vascular dynamics. In support of this, we have used parallel vascular fluorescence imaging (Narayan et al., 1993) to show that optical physiology is related to changes in regional cerebral blood volume (rCBV), and indicates a similar etiology to functional magnetic resonance (fMRI: Belliveau et al., 1991). Paired with electrophysiology, intrinsic signal imaging enables the study of neurovascular coupling, a relationship which is important for understanding brain function as well as for interpreting data from other techniques which incorporate measures of vascular activity at lower resolutions. We will optically map intrinsic signals over rat somatosensory cortex to address three specific aims. First, we will examine the spatial dynamics of functional perfusion. We will determine spatial extent of responses and response location relative to vascular anatomy and electrophysiologic measurements. Second, we will characterize magnitude and timing of the perfusional response. This will include identifying perfusion response threshold as well as identifying dose-response characteristics for the full response curve. Third, we will examine vascular response robustness and modulation in response to competing stimulation. Vascular responses may differ with competing stimuli of varying intensities and temporal patterns. The significance of this proposal is due to the fact that acute activity- related perfusion can be examined with high spatial and temporal resolution. We will assess the development of perfusion redistribution in a variety of stimulation conditions. Issues addressed by this work will assume greater importance as optical methods and fMRI are increasingly applied to the study of brain function.

This is a proposal to continue a Training Program in Neuroimaging to train basic researchers and physician scientists in the scientific aspects of neuroimaging. The breadth of the program encompasses neuroimaging in all biobehavioral sciences from molecular probes to clinical psychiatry, while at the same time providing in depth focus on the theory and practice of neuroimaging strategy, physics, instrumentation and application. The specific aims of this program are: 1) to provide trainees with the skills necessary to conduct neuroimaging research studies of brain structure and function in experimental animal and human models; 2) to provide trainees with a comprehensive understanding of the use of neuroimaging techniques from wet bench procedures such as optical intrinsic signal imaging to tomographic methods like MR and PET; 3) to provide trainees with the knowledge necessary for research design, statistical analysis and interpretation of different types of imaging data; 4) to prepare trainees to establish their own laboratories and independent academic research careers in neuroimaging. This program includes didactic course work with hands- on experience in several laboratories, culminating in a focused research project. The emphasis of this training program is on the basic science of neuroimaging and the use of neuroimaging in the pursuit of basic neurobiological results. This training program in neuroimaging is needed to address the national demand for research on mapping the brain. UCLA has the range of capabilities and depth of investigator knowledge necessary to maintain a focused program devoted to neuroimaging. We have the laboratory resources, faculty interest and expertise needed to run training program of this breadth and depth.

supercomputer; computational neuroscience; biomedical equipment purchase;

Recent enhancements in the resolution of primary data and the complexity of algorithms have resulted in significant increases in the computational load for the neuroimaging, `rain mapping and biomedical imaging communities. In response to these increased demands, a group of neuro- and biomedical scientists with common interests and computational needs have come together to seek funding for a shared graphics supercomputer. This group has a critical need for potent visualization hardware, extremely fast, parallelizable CPU architectures and considerable amounts of data storage and transfer capabilities. This equipment is required to satisfy the demands generated by three dimensional volume viewing, intricate surface extraction, nonlinear warping and the volumetric blending of complex polygonal objects with both raw data and the results of statistical analysis. Housed at the Laboratory of Neuroimaging at UCLA, the device will be available on a shared basis for interactive use locally and batch processing remotely. After evaluation of several vendors of graphics platforms, the equipment selected was an Onyx graphics supercomputer manufactured by Silicon Graphics Inc. Simulations and benchmarking confirmed the ability of this instrumentation to fulfill the needs of the participants. An administrative plan was created to manage the equipment equitably and to optimize its utility for the participating research projects. The ongoing research in nine local and four remote projects will directly and immediately benefit by using existing software that is capable of taking advantage of the features of the instrument. Programmers and other experts in this architecture are already members of the staff. Technical and management personnel are also among the funded group of participants. The existing collaborations of the participants and the common algorithmic requirements will enable sharing of computer code, analytic procedures and computational strategies. The availability of suc h a machine will enhance the productivity of ongoing funded research and foster the use of leading edge technology for all participants.

Functional activation studies in health and disease often depend upon perfusion related signals to localize the source of brain activity. The goal of this proposal is to investigate perfusion related signals measured using Optical Intrinsic Signals (OIS), Near Infrared Spectroscopy (NIRS), functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG) during a variety of stimulus conditions. The application of different neuroimaging techniques in both animals and humans will provide a comprehensive view of the timing, distribution and capacities of these perfusion related signals and how perfusion related signals from different modalities relate. We will perform functional activation studies using OIS (multispectral and single wavelength), NIRS, fMRI, fluorescent dyes and electrical measures, to determine the relationship in space and time between these perfusion dependent, and evoked potential maps. The proposed experiments will provide a better understanding of the cascade of events collectively called the hemodynamic response. We will do this in different sensory systems of rodents and humans using a variety of stimulus and behavioral paradigms. In humans, perfusion related signals obtained using OIS and electrophysiologic methods intraoperatively will be compared with NIRS and fMRI in the same subjects. In both species, maps and the temporal profile of responses obtained from each modality will be compared within and across subjects following image registration. The proposed studies will begin with investigating the role of nitric oxide (NO) in mediating perfusion related responses. We will also characterize the nature of functional hemoglobin changes following functional activation using optical spectroscopy. Having characterized the normal characteristics of functional perfusion related responses, we will then determine how perfusion related signals are affected by various challenges (including seizure, cortical spreading depression, development, and experience- dependent plasticity). Finally, we will investigate and compare the relationship between perfusion related signals across multiple modalities. This proposal is a natural extension to our active grant where the focus was to characterize the basic temporal and spatial characteristics of optical signal responses to peripheral somesthetic stimulation. Since the coupling of brain function to cerebral perfusion provides the basis for a number of functional neuroimaging techniques, a precise knowledge of the specific underlying physiological mechanisms and their characteristics is essential. The experiments described here will help us achieve that goal.

This application proposes a conference titled "Brain Mapping Addiction Neurobiology", with the goal of identifying appropriate opportunities, methodological issues and limitations of using neuroimaging technologies and approaches in the study of the neurobiology of drug addiction. The conference attendees are a collection of experts in the field of neuroimaging of drug addiction. The conference attendees are a collection of experts in the field of neuroimaging, experts in the field of drug abuse research and experts who have experience in both areas. The 1 1/2 day conference will be held at the UCLA School of Medicine, Brain Mapping Division and will be run as a workshop, permitting open dialogue between scientists and technologists, resulting in a final report identifying key areas of opportunity and specific research objectives to address these opportunities. The timeliness of this conference is excellent, as the maturity of neuroimaging technologies and the rapid advances in studies on the neurobiology of addiction provide a unique opportunity to study models in animals and humans, examining both brain structure and function in health and disease. The conference is slate for late Spring 1998.

neocortex; neuroanatomy; information systems; brain mapping; digital imaging; serotonin receptor; glutamate receptor; dopamine receptor; adrenergic receptor; mathematical model; computer graphics /printing; model design /development; muscarinic receptor; GABA receptor; light microscopy; human tissue; postmortem; reagent /indicator; magnetic resonance imaging; autoradiography; tissue /cell preparation;

DESCRIPTION (Adapted from Applicant's Abstract): The goal of this project is to extend neuroimaging applications beyond the visualization and measurement of individual brain data sets. To that end, the applicants proposed a series of experiments to develop and test new techniques for quantitative comparisons of anatomic and functional information from groups of subjects and across different modalities. The specific aims of this project were divided among five interrelated research programs; 1) Structure/Function Delineation and Segmentation; 2) Spatial Manipulations; 3) Acquisition; 4) Visualization; and 5) Computer Software. Algorithm would be subjected to a rigorous series of validation experiments designed to measure its accuracy, precision and reliability. Experiments would be conducted with different modalities, data types, resolutions, subjects and species. The applicants proposed to formulate rules for its application based on its validity in a subject population, the data type and computational expense. The applicants have combined efforts in algorithmic development with the collection of high resolution anatomic data, and proposed to make these results available using electronic communication. The applicant would build on their established history of research in this area and extend previous efforts focused on the reconstruction and display of brain models to include the quantitative analysis of multimodal representations of brain across populations of individuals. They would maintain the three dimensional integrity of the data and provide usable visualization, analytical and portable software to the neuroscientific community.

Our proposed neuroimaging Resource for multi-dimensional modeling will go beyond current atlases and maps of brain that assume a static morphology and prohibit the examination of time varying changes. We will develop the framework and tools to rigorously evaluate dynamic changes in brain structure and function focusing particularly on processes such as development, aging and the progression of specific diseases. We have identified four areas of research that are of critical importance: Surface Parameterization, Volume Parameterization, Anatomical Fundamentals and Visualization of Animation. Each of these complements the other to form an integrated and comprehensive research program. The unique focus on multi- dimensional modeling is in contrast to many other neuroimaging centers where the focus is on image acquisition. Our plans include collaborations with a diverse and well funded group of basic, applied and clinical scientists working on neuroscientific problems and health and disease. These projects were selected on the basis of their mutually beneficial, symbiotic relationship with our core research plans and the specific aims of their funded projects. Plans are provided for service to other investigators in the form of robust software access to computer hardware resources and distribution of unique data sets. We will disseminate information about our Resource by publication, electronic media and the creation of video animations of our results. Training opportunities for students and colleagues will be available on site or remotely and will include hands-on experiences, prepared material, workshops, and interactive electronic presentations. 7 An administrative structure and national recognized advisory board will be created to manage the responsibilities of the Resource. Our plans for this Resource build upon an extensive history of research in image analysis, morphometric variability, brain mapping and visualization. This, coupled with our NCRR pre-resource experience, position us perfectly to created and direct an outstanding Resource.

Visualizing biomedical data is often a prerequisite to its understanding. If we can visualize a structure, we can identify it. Visualization enables us to extract meaningful information from complex data sets. Although researching the brain in all its complexity mandates a reductionistic scientific approach. its multidimensional composition lends itself to a variety of computerized visualization techniques concerned with reconstruction. representation. manipulation and display. By employing the techniques of image processing. image synthesis and computer graphics, we combine the utility of image and number enabling us to statistically measure the visual representation. Structures that change over time can further complicate our ability to fully comprehend a particular geometry or relationships between attributes. Adding the ability to animate the visualization will help us not only to understand a multidimensional model but will facilitate its communication as well. Specific Aims. Our overall goal in this research project is to develop multidimensional imaging tools that incorporate space. time and attribute data in a statistically meaningful way. We will develop enhancements to our existing suite of software tools for the interaction. control and display of data with real time feedback in addition to improving noninteractive processing for larger data sets or more complex renderings. We will accommodate both volume and surface based methods and develop the modules for 'plug in' capabilities with other software developed in house and by other groups. We plan to borrow approaches that are successful and mature when possible, design and encourage software cooperation among other developers and focus primarily on our own niche which is visualization of multidimensional data and animation. Our specific aims are Specific Aim 1. Create an animation software system for the creation of visualizations to convey complex. dynamically changing structure and structure/function relationships. Specific Aim 2. Build a subset of the visualization/animation package that enables real-time interaction with multidimensional models for improved exploration of the data and develop an interface so that visualization/animation plug-ins can, be inserted into software programs performing the analyses and modeling described in research projects I-III. The plug-ins will enable the investigator to visualize the interim product of these calculations as the programs are running. This enable tighter control over the selection of specific parameters and the operation of compute intensive procedures. Specific Aim 3. Write, render. animate and produce short video presentations for communicating concepts in neuroscience. These will initially illustrate sensory systems such as the visual system and ultimately be used to communicate the science of selected collaborative projects. They will combine synthetically generated images with real data to best communicate the concept. Specific Aim 4. Develop mature visualization/animation software that can be implemented with VRML or Java, migrating solutions to these WEB based systems whenever possible.

The Resource activities in training are divided into five different aspects: 1) courses and workshops. both international and local: 2) a formal training program; 3) a visiting professorship series; 4) instructional web. VRML and other electronic media and 5) software manuals. Our goal in training is to provide the most comprehensive arsenal of materials to train investigators both on the theory and philosophy of image analysis and multidimensional modeling, as well as the specific applications of software developed as part of our Resource. The goal here is to provide a full set of training opportunities, depending upon the needs and interests of colleagues in neuroscience and trainees alike. Our group has had a long history of successful training efforts. First, we have developed a number of international courses and workshops in a variety of venues that have had remarkable success. Second, we have developed a series of local presentations and courses at UCLA, which can be attended by local students and trainees. Third, we developed a series of electronic training activities that have been presented at the Society for this proposal. The Resource has a responsibility to disseminate information about the product of its efforts. We believe the best way to do this is to produce high quality, peer reviewed publications, describing carefully conducted research and the results of those experiments. Nevertheless. there are many other venues that are appropriate to disseminate information about our core research and the availability for collaboration and service. The most obvious of which, and most commonly used, is the World Wide Web. We have budgeted for a web developer to help with our existing staff in continually updating, modernizing. and making more attractive our web site. The goal of which is to provide meaningful, instructional and interesting web contents that are constantly updated to reflect the current state of our research and other activities of in our proposed Resource. Given the number of web hits that currently receive (average 600/day), we believe that this will continue to perform as an efficient vehi cle for disseminating information about our activities.

Part of the brain called the prefrontal cortex plays an important role in active memory of sensory experience, but short-term memory apparently involves cortical memory networks that are more widely distributed. The general objective of this study is to clarify how the cerebral cortex retains information in the short term for goal-directed behavior. This project uses two methods of recording cortical activity in monkeys performing visual short-term memory tasks: the recording of surface field potentials and the recording of optical intrinsic signals. This combination will allow the study of neural network activity within and across cortical areas with high spatial and temporal resolution. Correlation of location and time course will be examined between electrical and optical measurements during task performance. Two specific hypotheses will be tested: (a) neuronal activation develops over prefrontal and parietal cortex during retention of spatial and nonspatial memoranda; and (b) in parietal cortex, that activation is more prominent during retention of spatial memoranda than during nonspatial memoranda. Because of the phenomenological relation between active memory and consciousness, an understanding of the dynamics of active memory may help elucidate the role of the cerebral cortex in conscious states. Analysis of electrical and optical signals should reveal underlying topographic specificity in the retention of memoranda within the cortical areas explored. In addition, by contrasting the optical data against those from an established electrical method, this study should help substantiate the biophysical basis of optical imaging and its use in cognitive neuroscience.
Results will have an impact beyond cortical physiology by leading to a better understanding of memory and consciousness, and will be important to technological developments in optical imaging.

This is a project to develop a detailed multidimensional digital atlas of the mouse nervous system. It will, for the first time, create a unified framework for representing brain maps and gene expression maps. The creation of a comprehensive framework to encompass diverse imaging and genetic information about the mouse holds tremendous promise for integrating the genotype and phenotype of this animal. This work is significant because a comprehensive description of geno- and phenotypic patterns and how they relate to the emerging morphology is crucial to our understanding of the interactions that underlie the processes of development, normal structure and function, disease and evolution. This proposal describes an ambitious project coordinated between three leading imaging centers that will combine genetic, in vivo and post mortem maps in a multidimensional digital atlas of the mouse brain. This interactive atlas will contain information from structural and diffusion-weighted microscopic magnetic resonance imaging (uMRI), metabolic studies derived using positron emission tomography (PET), high resolution cryosection imaging as well as multiple histological and in situ hybridization experiments. Comprised of many subjects collected across the development of the C57BL/6J mouse from early embryonic through adult stages and analyzed using sophisticated four- dimensional warping algorithms, this atlas will form the basis of a detailed space-time reference system. The atlas will combine genetic, anatomical and functional data for intermodality, interspecies, and cross-laboratory comparisons. In addition to the unified datasets comprising the atlas itself, this project will result in multiplatform software tools facilitating its interactive exploration and augmentation. The specific goals of this project are: 1. To develop and implement a fundamental anatomic framework to map gene expression in the brain. 2. To create a set of tools to co-localize data from different markers, animals and laboratories. 3. To disseminate the atlas and requisite interactive tools, enabling output (ability for others to use information/data) and input (ability to incorporate and correlate data from other sources). In addition to these design driven goals, we will test for hypothesis during the process of validation and evaluation of the atlas. 1. There is a relationship between gene expression and morphology. 2. Patterns of gene expression co-vary with morphological changes during development. 3. Anatomy from histological delineations accurately represents in vivo morphology. 4. At any maturational stage, within strain morphometric variability will be less than between strains. The resulting validated, multimodality, multidimensional mouse atlas will have immense value for studying normal, mutant, healthy and diseased animals in a wide range of neuroscientific investigations.

A grant has been awarded to Dr. Arthur W. Toga at the Laboratory of Neuro-Imaging (LONI) of the University of California, Los Angeles (UCLA) to enhance the graphic and compute capabilities of an existing supercomputer for distributed use in neuroinformatics. Unabated advancements in imaging technology today has provided researchers with the ability to produce very high-resolution, time-varying, multidimensional data sets of the structural and functional development of the dynamic human brain. The complexity of the new data, however, require immense computing power for effective analyzation and study.
LONI and its collaborators will upgrade the facility's SGI Onyx2 supercomputer with additional graphics pipelines, computational nodes, and the incorporation of high speed networking equipment. The graphics pipelines will allow the visual interpretation of brain data and, in addition, drive a Data Immersive Visualization Environment (DIVE). The premise behind the DIVE is to provide investigators with the unique ability to visually step inside their data and analyze it in new, unexpected ways. The additional computational nodes will accelerate the speed of both interactive manipulation of multidimensional brain data sets and complex offline computations. The incorporation of high speed networking equipment will facilitate local and offsite network access to these computer resources and improve overall communication between the local, national, and international communities of neuroscientists.
This instrumentation not only benefits ongoing research in brain development, heritability and function but because analysis and interpretation of multidimensional brain data is elevated to the next level, it will open the doors to new insights and better understanding of brain structure and function in health and disease.

Rapid advances in the field of neuroinformatics have resulted in the generation of massive amounts of image and statistical data concerning the human brain. These advances have resulted in a need for scientific visualization of diverse kinds of dense data sets. From the widespread demand for the visualization of simple scalar fields, to the growing demand for novel techniques to render static and dynamic vector and tensor fields, the ability to answer these challenges has relied on corresponding advances in computational techniques. The Laboratory of Neuro Imaging has been a frontrunner in the adoption of cutting edge technology to understand the developmental and degenerative changes of the human brain. Our group has gained worldwide recognition for the development of high-order mathematical approaches to the investigation of brain registration, the analysis of variance between and within populations. and the visualization of these data. The techniques used, however, now must accommodate four dimensions, as research underway at LONI and elsewhere has begun to produce complex time- varying and multidimensional statistical fields. The dynamic visualization of these new data opens a new frontier in understanding the dynamic brain, enabling investigators to interact with the statistics of structural and functional change. In response to these computational challenges, a group of neuro- and biomedical scientists with common interests and computational needs have come together to seek funding to enhance the graphics and computational capabilities of a shared supercomputer. This group has a critical need for potent visualization hardware, extremely fast, parallel CPU architectures and considerable amounts of data storage and transfer capabilities to satisfy the demands placed on equipment by four dimensional volume viewing, intricate surface extraction, nonlinear warping and the volumetric blending of complex polygonal objects with both raw data and the results of statistical analyses. The requested package of graphics, computational and networking equipment would be available on a shared basis for interactive use locally and offline processing for both CPU and graphics intensive jobs remotely. The equipment package selected is compatible with and fully leverages an existing SGI Onyx2 graphics supercomputer. Simulations and benchmarking confirmed the ability of this instrumentation to fulfill the needs of the participants. An administrative plan is already in place by which the equipment can be managed equitably. Software that would greatly enhance the research projects exists to take advantage of this new equipment. Programming experts in this architecture are already members of the staff. Technical and management personnel also are part of the funded group of participants. The existing collaborations of the participants and the common algorithmic requirements will enable sharing of computer code, analytic procedures, computational strategies and infrastructural capabilities. The provision of such an instrumentation package would enhance the productivity of ongoing funded research and foster the development of leading edge technology and applications for all participants.

This competitive renewal application has an overall goal, the creation of an atlas of Alzheimer's disease. The neuroscience and informatics efforts proposed here will result in a tool set and product that is applicable not only to the basic and clinical science of Alzheimer's disease, but to the general problem of mapping the structure and function of any dynamic process in health or disease in whole populations of subjects. Leveraging the accomplishments achieved during the last period of this project and building upon our high-resolution post mortem anatomic framework, the development of atlas construction methodology and the ability to create 3D visual models of anatomy, we will construct the first multimodality probabilistic atlas of the brain representing a diseased population. Including both histologically processed post mortem tissue as well as high- resolution 3D MR images acquired from subjects in various stages of Alzheimer's disease, we will generate the average geometry and 3D variability of the anatomic structures of these populations. Further, we will describe the anatomy as cytoarchitectural features from histology and gyral sulcal features from MRI. There are 7 specific aims in this project. The first will be the collection of a cohort of post mortem specimens from an Alzheimer's disease population. Second, we will create detailed individual probabilistic maps describing the architectural boundaries in AD and matched controls. Third, we will create an MRI probabilistic atlas based upon data that has been previously acquired or will be acquired with funding from other active projects. Fourth, we will develop and refine appropriate registration deformation correction atlasing strategies to create a comprehensive multimodality atlas of Alzheimer's disease. This will enable the development of data at different spatial resolutions and representing different aspects of brain structure and function. Fifth, individualized data analysis utilizing mathematical strategies to compare individual MRI data with the probabilistic atlas will enable access by the neuroscience community to this multimodality atlas. Sixth, we will develop dynamic 4D mapping tools to express the spatial and temporal profiles of degeneration heretofore unavailable in static single time point representations of anatomy or physiology. Seventh, these will be combined into an interactive visualizable and analytic tool set made available to the neuroscientific community.

DESCRIPTION (provided by applicant): This competitive renewal application to continue a Resource for Computational Anatomy and Multidimensional Modeling will go beyond current atlases and maps of brain that assume a static morphology and prohibit the examination of time varying changes. Building upon considerable productivity and success, we will continue to develop the framework and tools to rigorously evaluate dynamic changes in brain structure and function focusing particularly on processes such as development, aging and the progression of specific diseases. There are four areas of research that are of critical importance: 1) Image Processing and Segmentation, 2) Deformation Morphometry and Registration, 3) Aliasing, and 4) Informatics. Each of these compliments the others to form an integrated and comprehensive research program. This unique focus on computational anatomy and multidimensional modeling is in contrast to many other neuroimaging centers where the focus is on image acquisition. Our efforts include numerous collaborations with a diverse and well-funded group of basic, applied and clinical scientists working on neuroscientific problems in health and disease. These active and productive collaborative projects were selected on the basis of their mutually beneficial and symbiotic relationship with our core research efforts and the specific aims of their funded projects. We will continue to provide service to other investigators in the form of robust software, access to our computer hardware resources and production of animations and image renderings. We disseminate information about our Resource by publication, electronic media, a brochure, and the creation of mini-CD's describing our Resource, its software, expertise, training and collaborative opportunities. Training opportunities for students and colleagues are available on site and remotely and include hands-on experiences, prepared material, workshops, and interactive electronic presentations. An administrative structure and nationally recognized advisory board help us manage the responsibilities of the Resource. Our plans for this Resource build upon an extensive history of research in image analysis, morphometric variability, brain mapping and visualization. This, coupled with our successes in the active period of the Resource, positions us perfectly to continue the growth of our Resource.

DESCRIPTION (provided by applicant): This is a proposal to plan and develop a Program of Excellence in computational biology from genotype to phenotype. This planning grant application includes development projects focusing on mapping brain phenotype, mapping biological structure and mapping genomic function. These will be supported by well-developed Cores: Administration, Computational Resources and Education & Training. The program is an integrated effort with multidisciplinary participants from several different departments including mathematics, computer science, neurology, genetics and biochemistry. The Institute of Pure and Applied Mathematics and the Laboratory of Neuro Imaging provide physical resources and additional infrastructure necessary to create this program. The development projects and the cores each provide a symbiotic contribution from both computation and biology. These projects were established because of their mutually beneficial relationship with one another, as well as with the core support activities and the training and educational opportunities of this campus. Plans include collaboration with other investigators and the creation and nurturing of mini-projects that will emerge and grow to full-fledged development projects. Mapping Brain Phenotype will utilize novel mathematical and computational strategies for analyzing whole brain images to understand brain variability and detecting abnormal patterns. Mapping Biological Structure will utilize partial differential equation based image processing techniques in the study of tomographic data, blood flow simulation, gene expression, micro-array segmentation and geometric computations of brain mapping. Mapping Genomic Function will develop statistical methods for measuring the strength of evidence directly from genomics raw data. The Administration Core will provide the unified coordination necessary to propel a complex program such as proposed here to the next stage as a full-fledged Center of Excellence. The Computational Resources Core will develop resources and make them available to the other entities of this program. The Education and Training Core includes plans to train undergraduate and graduate students, postdoctoral fellows, visiting scientists and faculty from other departments. An outreach component is also included. The focus of this proposal remains fixed on the computational biology of brain and yet the developments that will emerge from this Center of Excellence will have a broad and far-reaching applicability to other disciplines of science.

bioimaging /biomedical imaging

ABSTRACT NOT PROVIDED

DESCRIPTION (PROVIDED BY APPLICANT): This is a proposal to establish a new Center for Computational Biology (CCB). Our goals are to apply computational and mathematical approaches to the study of genes, cells, systems and whole brain. The major objectives of the CCB are to develop, implement and test computational biology strategies that are applicable across spatial scales and biological systems. This will help elucidate characteristics and relationships that would otherwise be impossible to detect and measure. Interactions fostered by this multidisciplinary program will result in novel strategies to fundamental problems that can be applied to genetics, biochemistry, molecular biology and brain mapping. Our view of computational biology considers the problem of constructing atlases - sets of maps on different spheres of biological information that span many scales and modalities from genotype to phenotype. In this proposal we introduce the concept of a computational atlas as a database-like infrastructure that rests on mathematical advances in modeling and optimization. We will develop the infrastructure for such a computational atlas resulting in a platform for addressing large-scale modeling problems that before now have been intractable. There are 4 Driving Biological Projects in Core 3 of this proposal: (1) Mapping Language Development Longitudinally, (2) Mapping Brain Changes in Alzheimer's & Those at Risk, (3) Mechanisms Underlying the Clinical Progression of MS and EAE, and (4) Genetic Influences on Brain Structure in Schizophrenia. Each includes essential elements of computational biology: modeling, imaging, bioinformatics, biocomputing and data management. Each was chosen specifically because of its symbiotic relationship to Cores 1 and 2 where computational science and computational tools will be developed. The entire project is integrated. The Cores not only provide support for the development of research projects but also for programmatic development. The Infrastructure and Resources Core will provide hardware, software, visualization and database contributions to facilitate sharing and cooperative development. The Education and Training Core will nourish this program by integrating training opportunities and encouraging students to enter this important area. The Dissemination Core will ensure that the products of our efforts will be communicated to the outside world efficiently and broadly. The Administrative and Management Core will provide oversight, documentation, leadership and communication.

Abstract not provided.

Abstract not provided.

bioimaging /biomedical imaging

[unreadable] DESCRIPTION (provided by applicant): Sharing data between research centers is increasingly important for contemporary brain imaging studies because they involve large numbers of subjects and complex analysis protocols that require highly specialized expertise. Our long-term objective is to facilitate brain-imaging research by enabling remote researchers to pool data between institutions and to analyze data using the appropriate algorithms executing on distributed resources. There are a number of difficult data management and technology challenges that have limited the success of data sharing environments. Rather than attempt to develop a comprehensive and general solution, we propose to develop a set of open, interoperable, and portable software tools that address critical issues currently limiting efforts to share and analyze brain-imaging data. Building upon years of providing brain-mapping expertise to collaborators, we propose to solve problems that we repeatedly encounter and that currently limit progress in brain imaging research. We propose to develop validated tools that enable collaborators to remotely access a variety of data analysis methods and databases. We will create web-based tools to perform multi-institutional studies, and provide access to complex data processing protocols executing on distributed computing resources. There are three specific aims. 1) Enable the web based acquisition and management of data utilizing an access control system that includes consideration of subject consent limits and investigator imposed conditions to facilitate data pooling for multi-institutional studies. This system will convert data files between different formats and schemas so that data can be used consistently between analysis programs and databases. It will also anonymize images and metadata according to institution-specific protocols. 2) Develop a system that is aware of data type and provenance so that it may act intelligently to arbitrate between different analysis programs. This system will capture the expertise of experienced lab personnel in the usage of various tools and assist new users in designing appropriate analytic strategies. 3) Create meta-algorithms that improve the robustness of techniques for neuroimaging analysis by intelligently combining the results from multiple algorithms. The proposed approach will provide a set of tools that address significant problems in data sharing and utilization. The resulting information technology will be scalable and applicable to other scientific data sharing problems. [unreadable] [unreadable]

The convergence of the biomedical revolution and the information technology revolution is a major event in the history of science. The emerging discipline of Computational Biology is a natural result of this convergence. The mathematical and computational sciences lie at the center of this new endeavor, providing the tools and framework for model building and quantitative analysis. The main focus of our proposed center is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging. During our planning grant, we developed a broad range of new, interdisciplinary collaborations and a strong track record of productivity across the continuum of mathematics, computer science, and neuroscience. Pressing problems in brain mapping and neuroimaging present exciting challenges and opportunities for mathematicians and computational scientists. In this Core, we describe some of the mathematical and computational techniques that we believe will be relevant. These include an extensive set of computational and biomedical advances that resulted from two years of collaborative work during our planning grant.

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This cj>BIRN renewal application describes a research and development effort that will result in a distributed adaptive database and multiscale, multimodality atlases of the mouse brain. Tools to incorporate and compare data from gene expression patterns to gross morphology from multiple laboratories at scales from nanometers to centimeters will be built. The goals are to create an infrastructure for relating previously disparate data collections into a single system capable of quantitative visualization and linkage with previously disconnected knowledge bases. cj>BIRN will integrate the activities of five laboratories - the Laboratory of Neuro Imaging (LONI) at UCLA, the Biological Imaging Center (BIC) at CalTech, the Center for In Vivo Microscopy (CIVM) at Duke University, the Mouse Brain Library (MBL) at UT and the National Center for Microscopy and Imaging Research (NCMIR) at UCSD. The cJsBIRN is structured as five cores titled: Imaging, Atlasing, Data Federation, Applications, and Administration. The Imaging Core will acquire data for the entire project, encompassing imaging modalities from the whole brain scale to the supramolecular. The Atlasing Core will enable the processing of imaging data, reconstruction and registration of it, using techniques to integrate the various data collected into multimodal digital atlases. The Data Federation Core will organize, manage, and archive all the data collected and develop mechanisms for interaction between databases. The Applications Core contains the neurodegenerative disease test beds, the research projects that drive the development of infrastructure. And finally, the Administration Core will manage communication between and within cores, to the BIRN Central Coordinating site (BIRN-CC), and to the scientific community at large. The infrastructure will be tested by focusing the neuroscience of this project around degenerative brain disease. We chose this for several reasons. First, there are degenerative diseases such as Alzheimer's disease (AD), multiple sclerosis (MS), and Parkinson's disease (PD) that result in characteristic morphological changes that can be detected in vivo and histologically. There are effects in gray and white matter that can be measured, catalogued, and visualized. Second, these diseases have mouse models that could greatly benefit from the integrative approach proposed here. Third, AD is the focus of MorphBIRN and a synergy afforded by comparisons between these two BIRN efforts will be amplified with a common disease test bed. The Experimental Autoimmune Encephalomyelitis (EAE) model of MS and the alpha-synuclein knock-out model of PD examined during the previous funding cycle demonstrated the feasibility and potential of this model to utilize the tools and infrastructure proposed here.

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DESCRIPTION (provided by applicant): Rapid advances in brain mapping have resulted in the generation of massive amounts of image and statistical data concerning the human brain. These advances have resulted in an acute need for high performance computing. The Laboratory of Neuro Imaging has been a frontrunner in the adoption of cutting-edge technology to understand dynamic changes such as development and degeneration of the human brain in health and disease. Our group has gained worldwide recognition for the development of computational algorithms and high-order mathematical approaches to the investigation of brain registration, the analysis of variance between and within populations, and the visualization of these data. These techniques, however, now must not only accommodate four dimensions, as ongoing projects at LONI and elsewhere generate timevarying, multidimensional statistical fields but also be able to reasonably process data that are increasingly more complex. We now routinely apply these computationally demanding methods to population studies in order to achieve sensitivity to these potentially subtle differences. In response to these computational challenges, a group of neuro-, biomedical and computer scientists with common interests and computational needs have come together to seek funding to augment a shared compute cluster currently nearing full utilization. While existing networking and storage infrastructures are able to accommodate the increasing the demands placed on these systems by four-dimensional volume computation, intricate surface extraction, nonlinear warping and the multi-modal integration of complex is surfaces with data from disparate sources, contention for available processing resources has been acute and detrimental to scientific progress. The requested computational cluster would be available on a shared basis, for interactive use locally and processing remotely and will fully leverage an existing cluster computing framework developed and optimized at LONI over the past several years. An administrative plan is already in place by which the equipment can be managed equitably. Software that would greatly enhance the research projects exists to take advantage of this new equipment. Technical and management personnel also are part of the funded group of participants. The existing collaborations of the participants and the common algorithmic requirements will enable sharing of computer code, analytic procedures, computational strategies and infrastructural capabilities. The provision of the requested instrument will alleviate our current usage contention, enhance the productivity of ongoing funded research at LONI and collaborating sites in schizophrenia, Alzheimer's disease and brain development, among others, and foster the development of leading edge technology and applications for all participants.

DESCRIPTION (provided by applicant): Innovations in imaging technologies and in the field of computational neuroscience have resulted in the generation of massive amounts of image and statistical data concerning the human brain. These advances have resulted in the need for high performance computing and novel investigative paradigms for the meaningful analysis of the available data. The Laboratory of Neuro Imaging has been a frontrunner in the adoption of cutting-edge technology to understand dynamic changes such as the development and degeneration of the human brain in health and disease. Our group has gained worldwide recognition for the development of innovative research methodologies, computational algorithms and high-order mathematical approaches to the investigation of brain registration, the analysis of variance between and within populations, and the visualization of these data. These techniques, however, now must not only accommodate four dimensions, as ongoing projects at LONI and elsewhere generate time-varying, multidimensional statistical fields but also be able to reasonably process data that are increasingly more complex. We now routinely apply these computationally demanding methods to population studies in order to achieve sensitivity to potentially subtle differences. In response to these computational challenges, a group of neuro-, biomedical and computer scientists with common interests and computational needs have come together to seek funding to modernize the network infrastructure of a shared, dedicated high performance computing cluster. The increasing computational demands placed on this system by four-dimensional volume computation, intricate surface extraction, nonlinear warping and the multi-modal integration of complex isosurfaces with data from disparate sources have clearly identified key network bottlenecks that significantly degrade the processing performance of complex analyses. The requested network upgrade would eliminate congestion and bandwidth contention and allow for the optimal utilization of the computational resource by LONI investigators and collaborators. An administrative plan is already in place by which the equipment can be managed equitably. Technical and management personnel also are part of the funded group of participants. Ongoing collaborations and the common programmatical requirements will enable sharing of computer code, analytic procedures, computational strategies and infrastructural capabilities. The provision of the requested instrument will enhance the productivity of ongoing computational neuroscience research at LONI and collaborating sites in schizophrenia, HIV/AIDS and Alzheimer's disease, among others, and foster the development of leading edge technology and applications for all participants. PUBLIC HEALTH RELEVANCE: The Laboratory of Neuro Imaging (LONI) at the University of California, Los Angeles (UCLA) seeks funding to augment the network infrastructure of a high performance computational cluster utilized by an extensive and distinguished group of local, national and international neuroscientists with a common dedication to the study of the complex, dynamic brain in health and disease. The requested instrumentation package will alleviate significant network congestion and bandwidth contention detrimental to current and future investigations requiring the utilization of this shared computational resource. If funded, ongoing neuroscience research at LONI and a compelling number of collaborative efforts involving outside institutions stand to directly benefit.

DESCRIPTION (provided by applicant): This application seeks funding for research to help understand the genetic influences on neuroanatomical shapes. Imaging genetics is a major new direction in brain mapping because it goes beyond simple descriptions to more mechanistic understandings of which genes and environmental factors affect disease expression or risk. Unique to our approach is access to an existing dataset collected on more than 1000 twins, all with GWAS and neuroimaging data. We will use this data to develop maps of: (1) the heritability of subcortical and cortical gray matter shapes, (2) the heritability of the shape of white matter tracts, (3) the influence of brain-derived neurotrophic factor (BDNF), catechol-O-methyltransferase (COMT), and fat mass and obesity-associated (FTO) polymorphisms on gray and white matter shapes, and (4) genome-wide association studies (GWAS) to identify new genes or single nucleotide polymorphisms (SNPs) that influence neuroanatomical shapes. The results generated will greatly advance our knowledge of how genes influence neuroanatomy. In addition, we will develop software tools to facilitate the community's use of neuroimaging data to characterize subtle genetic effects. Project deliverables include the dissemination of a complete, open- source multimodal imaging genetics toolkit. Through accelerating research in imaging genetics, and combining mathematical approaches from multiple fields, this project will invigorate biomedical research and expedite the merging of huge arsenals of neuroimaging data and GWAS data. The activities in the project are extremely responsive to the NIH's mission in advancing GWAS to identify common genetic factors that influence health and disease.

PROJECT SUMMARY - OVERALL The LONIR is focused on developing innovative solutions for the investigation of imaging, genetics, behavioral and clinical data. The LONIR structure is designed to facilitate studies of dynamically changing anatomic frameworks, e.g., developmental, neurodegenerative, traumatic, and metastatic, by providing methods for the comprehensive understanding of the nature and extent of these processes. Specifically, TR&D1 (Data Science) focuses on methodological developments for the management and informatics of brain and related data. This project will develop and issue new methods for robust scientific data management to create an environment where scientific analyses can be reproduced and/or enhanced, data can be easily discovered and reused, and analysis results can be visualized and made publicly searchable. TR&D2 (Diffusion MRI and Connectomics) seeks to advance the study of brain connectivity using diffusion imaging and its powerful extensions. This project will go beyond traditional tensor models of diffusion for assessing tissue and fiber microstructure and connectivity, develop tract-based statistical analysis tools using Deep Learning, introduce novel adaptive connectivity mapping approaches, using L1 fusion of multiple tractography methods, and provide mechanisms to study connectivity and diffusion imaging over 10,000 subjects. (This technology and these methods will be managed and executed by the TR&D1 framework to distributed datasets totaling over 10,000 subjects). Lastly, our TR&D3 (Intrinsic Surface Mapping) develops a general framework for surface mapping in the high dimensional Laplace-Beltrami embedding space via the mathematical optimization of their Riemannian metric. Our approach here overcomes fundamental limitations in existing methods based on spherical registration by eliminating the metric distortion during the parameterization step, thus achieving much improved accuracy in mapping brain anatomy. Coupled with a mature and efficient administrative structure and comprehensive training and dissemination, this program serves a wide and important need in the scientific community.

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Big Data; Big Data to Knowledge; Biological Process; Cells; Collection; Computational Biology; computerized data processing; Data; data acquisition; Data Collection; Data Discovery; Data Science; Data Set; Educational workshop; Elements; Generations; Genes; Individual; Knowledge; Laboratories; Methods; Mining; Organ; Protocols documentation; Research; Resolution; Science; Scientist; Source; Speed; System; technological innovation; Thirst; tool; Training; Training Programs;

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DESCRIPTION (provided by applicant): Modern biomedical data acquisition, from genes to cells to systems, is producing exponentially more data due to increases in the speed and resolution of data acquisition methods. Yet, big data is a moving target. What is considered big data today, will be relatively small data tomorrow. Moreover, singularly large data sets arise from the efforts of single laboratories or are accumulated from a collection of more modest studies across common or heterogeneous study protocols. Simply having large-scale biomedical data and making it available online, however, is not a means to an end but only the next step in turning data into actionable knowledge. Our Big Data for Discovery Science (BDDS) Center, has the following aims: 1) create a user-focused graphical system to dynamically create, modify, manage and manipulate multiple collections of big datasets, 2) enrich next generation Big Data workflow technologies coupled to modern computation and communication strategies specifically designed for large-scale biomedical datasets, 3) develop a knowledge discovery interface to enable modeling, visualizing, and the interactive exploration of Big Data. In addition to these overarching aims, the goals of this BDDS Center include training and consortium activities. Here we will create university-level degree programs in big data informatics, develop annual workshops on strategies for big data best practices, and contribute to national BD2K consortium efforts. The innovations of our BDDS Center include: 1) providing a novel data science framework for characterizing and big data as a shared resource either singularly or collectively, 2) deriving novel computer algorithms for the joint processing o multi-modal data with an emphasis on the challenges that big data present for computation, 3) designing and deploying a unique data management system focused on the user experience which is ontology agnostic, easy to use, and puts the data first, 4) providing enhanced technologies for remote data access, scientific workflow construction, and cloud-based computation on big data sets, 5) providing compelling means for big data set visualization, interaction, and hypothesis generation. Building on these technologies, we will construct and validate tools so that they may be translated to any biological system or biomedical research domain. Our team is comprised of leading neuroscience, biology, and computer science researchers, with expertise in large-scale biomedical data, experience with the present challenges and promise of big data, and a demonstrable history of delivering unique computational resources, thereby insuring big data solutions which promote a science of discovery.

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ABSTRACT: DATA MANAGEMENT AND BIOSTATISTICAL CORE The overall objective of the Data Management and Statistical Core is to provide high quality services and resources to Clinical, Neuropathology, Imaging and Education Cores, and ADRC investigators (including ADRC projects, pilots, and ADRC-affiliated investigators). Over the past funding period, the Data Core has continued to contribute significantly to the ongoing data management related to subject recruitment, tracking and data collection and sharing. We have further made major biostatistical contributions to dementia-related research at USC, contributing to clinical trials designs and analyses related to metabolic and vascular contributions to cognition/dementia. In this renewal period, the Data Core resources will provide an informatics and statistics environment to contribute to cutting-edge dementia research both locally and nationally. Existing strengths of our Core include data management and biostatistical resources embedded within the Division of Biostatistics in the Department of Preventive Medicine, with biostatistical expertise in clinical trials and statistical genetics; Dr. Wendy Mack has extensive statistical experience in dementia-related research and clinical trials. In this competitive renewal, the Data Core has been considerably strengthened by the addition of Arthur Toga as Core Leader, bringing the unique resources of the USC Institute for Neuroimaging and Informatics (INI). The INI will support large scale data management and dissemination of imaging, genetics and other data consistent with systems supporting Alzheimer Disease Neuroimaging Initiative (ADNI), CHDI, Parkinson Progression Marker Initiative (PPMI) and others. All activities of this Core will support the mission of the ADRC as well as the broader national Alzheimer's Disease research community. The multimodality nature of data collected in the investigation of the role of vascular factors in AD requires robust informatics, data management and statistics. ?

DESCRIPTION (provided by applicant): Age related diseases causing dementia are an increasing global, social and economic catastrophe that mandates broad and aggressive research. Alzheimer's disease (AD) is the most common cause of cognitive impairment in older adults and affects over 5 million people in the US alone. Vascular contributions to dementia and AD are increasingly recognized. However, the role of the cerebrovascular system in the pathogenesis of dementia and AD, and the underlying neurovascular mechanisms remain, to date, largely unknown and under researched, representing a critical barrier in the field. The overall goals of this program are to advance current knowledge on the vascular contributions to dementia and AD, and establish whether the neurovasculature plays a major role in cognitive decline, and therefore is a key new therapeutic target to treat dementia and AD. This is a program project application with multiple projects, cores, institutions and investigators. It represents an integrated whole far greater than the sum of its parts. Each project and core complements the others so that a synergistic relationship among them is achieved with a common focus on goals of the program, namely to test the neurovascular hypothesis of AD. This hypothesis holds that cerebrovascular dysfunction and disruption in the neurovascular integrity underlies and contributes to the onset and progression of cognitive decline. We have enlisted the established clinical and translational research groups that collectively bring significant expertise in all aspects of the research plan and each have contributed productively over many years to the study of dementia and AD. To test the 'neurovascular hypothesis', the participating investigators will apply cutting-edge molecular and imaging methods. We will perform parallel studies with analogous measures in humans and rats in two AD genetic risk groups with the major genetic risk factors for late-onset AD, i.e., apolipoprotein E-?4 (APOE4) gene and early-onset autosomal dominant AD (ADAD), i.e., presenilin 1 (PSEN1) mutations that both develop early vascular dysfunction and significant cerebrovascular pathology, and in the rat model of AD (line TgF344-AD) that faithfully recapitulates the rich clinico-pathological spectrum of human AD including the presence of early vascular dysfunction and cerebrovascular pathology. Central to our approach is our commitment to take a new research direction with the overarching goal to provide an answer to the broader question; 'what is the role of the vascular system in the pathogenesis of dementia and AD', and 'what is the prognostic and diagnostic value of neurovascular molecular and imaging biomarkers in predicting cognitive decline'. The relationship between neurovascular integrity, brain connectivity and cognitive function has not been explored. The collective expertise of the investigators, overall environment, preliminary results, and experimental design for each of the projects and supporting cores hold tremendous promise for the success of this program. We are confident that the proposed studies will have a significant impact on our understanding of pathogenesis, treatment, and early prevention of dementia and AD.

OVERALL ? PROJECT SUMMARY/ABSTRACT Age related diseases causing dementia are an increasing global, social and economic catastrophe that mandates broad and aggressive research. Alzheimer?s disease (AD) is the most common cause of cognitive impairment in older adults and affects over 5 million people in the US alone. Vascular contributions to dementia and AD are increasingly recognized. However, the role of the cerebrovascular system in the pathogenesis of dementia and AD, and the underlying neurovascular mechanisms remain, to date, largely unknown and under- researched, representing a critical barrier in the field. The overall goals of this program are to advance current knowledge on the vascular contributions to dementia and AD, and establish whether the neurovasculature plays a major role in cognitive decline, and therefore is a key new therapeutic target to treat dementia and AD. This is a program project application with multiple projects, cores, institutions and investigators. It represents an integrated whole far greater than the sum of its parts. Each project and core complements the others so that a synergistic relationship among them is achieved with a common focus on goals of the program, namely to test the neurovascular hypothesis of AD. This hypothesis holds that cerebrovascular dysfunction and disruption in the neurovascular integrity underlies and contributes to the onset and progression of cognitive decline. We have enlisted established clinical and translational research groups that collectively bring significant expertise in all aspects of the research plan and each have contributed productively over many years to the study of dementia and AD. To test the ?neurovascular hypothesis?, the participating investigators will apply cutting-edge molecular and imaging methods. We will perform parallel studies with analogous measures in humans and rats in two AD genetic risk groups with the major genetic risk factors for late-onset AD, i.e., apolipoprotein E-?4 (APOE4) gene and early-onset autosomal dominant AD (ADAD), i.e., presenilin 1 (PSEN1) mutations that both develop early vascular dysfunction and significant cerebrovascular pathology, and in the rat model of AD (line TgF344-AD) that faithfully recapitulates the rich clinico-pathological spectrum of human AD including the presence of early vascular dysfunction and cerebrovascular pathology. Central to our approach is our commitment to take a new research direction with the overarching goal to provide an answer to the broader question; ?what is the role of the vascular system in the pathogenesis of dementia and AD?, and ?what is the prognostic and diagnostic value of neurovascular molecular and imaging biomarkers in predicting cognitive decline?. The relationship between neurovascular integrity, brain connectivity and cognitive function has not been explored. The collective expertise of the investigators, overall environment, preliminary results, and experimental design for each of the projects and supporting cores hold tremendous promise for the success of this program. We are confident that the proposed studies will have a significant impact on our understanding of pathogenesis, treatment, and early prevention of dementia and AD.

There is now a recognized need to include sophisticated informatics components to biomedical research. This is critical to the success of any multisite or coordinated effort and enables subsequent re-use of data and findings in unanticipated ways and greatly enhances the overall value of the project. Core C of the proposed P01 ?Vascular Contributions To Dementia And Genetic Risk Factors For Alzheimer?s Disease? will provide high quality computational, harmonization, and imaging resources for Projects 1, 2, and 3, Core B, and all P01 investigators, staff, and authorized scientists. The activities of Core C will support the scientific goals of the P01 as well as the broader national Alzheimer?s disease and dementia research community. Given the multimodality nature of data collected as a part of the P01, robust informatics, data management, and harmonization are critical. Core C will 1) provide an informatics data management framework for harmonized clinical and neuropsychological data for Projects 1 and 2, and harmonized imaging data for all P01 Projects. This centralized web-based data system will be used for registration and documentation of participants, including archival and tracking of raw and pre-processed data and open data dissemination; 2) ensure high quality control of all neuroimaging data across P01 Projects using a state-of-the-art workflow for the review and assessment of diffusion tensor imaging (DTI), functional MRI (fMRI), structural magnetic resonance imaging (MRI), dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL) MRI, and dynamic susceptibility contrast (DSC) MRI data; 3) deliver a harmonized analysis workflow for each imaging modality for each Project that can accommodate any computational environment via a user-friendly, uniform data analysis pipeline that is accessible to all investigators; and 4) coordinate biostatistical support for on analytic approach, data harmonization, and modeling of longitudinal datasets.

Vascular contributions to dementia and AD are increasingly recognized. Thus, the focus of this entire program and Project 1 is on the ?neurovascular hypothesis?, which holds that dysfunction in the cerebral vascular system contributes to cognitive decline, dementia and AD. Major genetic risk factors for late-onset AD, i.e., apolipoprotein E-?4 (APOE4) gene, and early-onset autosomal dominant AD, i.e., presenilin 1 (PSEN1) mutations, exert direct toxic effects on the cerebrovascular system and neurons, and influence amyloid-? (A?) metabolism and clearance, and tau pathology. However, the role of cerebrovascular changes in disease pathogenesis, and in predicting neuronal injury, neurodegeneration and cognitive decline in individuals with genetic risk for AD remains elusive. Based on published data and our preliminary findings, we hypothesize that neurovascular dysfunction and breakdown in the blood-brain barrier (BBB) are detectable by molecular and imaging biomarkers early in the disease process in APOE4 and PSEN1 mutation carriers relative to non-carriers, and predict neuronal injury, disrupted brain connectivity and cognitive decline. To test our hypothesis we will evaluate 294 APOE4 carriers and 340 non-carriers ages 45-90, and 44 PSEN1 mutation carriers and 44 non-carriers ages 18 and older, at early stages with no or mild cognitive impairment that will be followed longitudinally over 4-5 years. We will use: 1) a novel molecular biomarker assessment of the neurovascular unit (NVU) in biofluids (CSF and plasma) to determine how vascular/BBB injury relates to neuronal injury and responses of non-neuronal neighboring cells (e.g., astrocytes, microglia, inflammatory response), A? and tau biomarkers; 2) advanced neuroimaging assessment of neurovascular function using a novel dynamic contrast enhanced magnetic resonance imaging protocol (DCE-MRI) to examine regional BBB permeability in relation to cerebral blood flow (CBF; arterial spin labeling, ASL-MRI), white matter lesion (WML) pathology, and structural/functional connectivity (Project 2 collaboration); 3) cognitive assessment by Uniform Data Set and other neuropsychological measures of memory. Four aims will test our hypothesis in APOE4 and PSEN1 mutation carriers and non-carriers to 1) Evaluate NVU molecular biomarkers in biofluids in relation to cognitive function (AIM 1); 2) Determine regional BBB permeability (DCE-MRI) in relation to NVU molecular biomarkers and cognitive decline (AIM 2); 3) Examine the temporal relationship between BBB permeability (DCE-MRI), CBF (ASL-MRI) and WML (AIM 3); and 4) Evaluate NVU molecular biomarkers in biofluids in relation to structural/functional connectivity (AIM 4; Project 2 collaboration). The relationship between neurovascular integrity, brain connectivity and cognitive function has not been explored. This project will apply a hypothesis-driven approach to understand how neurovascular integrity changes in individuals with genetic risk for AD, and whether it can predict cognitive decline, thus allowing the discovery of new diagnostic tools and potential new therapeutic targets and treatment opportunities within the vascular system to prevent dementia and AD.

Alzheimer?s disease (AD) is the most common cause of cognitive impairment in older adults and affects over 5 million people in the US alone. Individuals who carry the apolipoprotein E-?4 (APOE4) gene are at heightened risk for developing late onset AD while individuals with the presenilin 1 (PSEN1) gene mutation will develop autosomal-dominant AD. Project 2 of the proposed P01 ?Vascular Contributions to Dementia and Genetic Risk Factors for Alzheimer?s Disease? will provide critical advances towards discovering how changes in brain connectivity, structure and function relate to neurovascular integrity and ultimately confer cognitive impairment in AD genetic risk groups. Across 5 sites, Project 2 will recruit 722 participants, including 294 APOE4 carriers and 340 non-carriers, and 44 PSEN1 mutation carriers and 44 non-carriers, followed longitudinally to evaluate changes in brain structure and function. Participants with NCI or early MCI will receive our imaging protocol every 2 years: 1) multi-shell DTI for white matter network connectivity; 2) resting fMRI for functional network connectivity; 3) structural MRI for gray matter shape, volume; and 4) DCE-MRI, ASL perfusion (from Project 1); in addition to Uniform Data Sets (UDS) cognitive tests. 150 participants (50 APOE4 carriers, 100 non-carriers) will also complete an amyloid PET scan to examine the effect of amyloid deposition on brain function and structure. We will address aims directed at assessing differences in structural and functional connectivity, examining the temporal association between brain connectivity changes over time, understanding how brain connectivity predicts future cognitive decline, and evaluating how blood-brain barrier integrity impacts brain connectivity. The relationship between structural connectivity, functional connectivity, and neurovascular integrity has not been explored. Using advanced neuroimaging methodology, this project will apply a hypothesis-driven approach to understand how brain structure and function change in individuals with high genetic risk for AD, and the impact of neurovascular integrity on these changes.

Neurovascular dysfunction has been linked to AD evolution in experimental, imaging, pathological, and epidemiological studies. These key findings have led to an emerging ?neurovascular hypothesis? of AD, which holds that cerebrovascular dysfunction contributes to the onset and progression of cognitive decline. Project 3 will test this hypothesis using a novel rat model of AD (line TgF344-AD) harboring two transgenes that are independently causative of early autosomal AD: ?E9 presenilin 1 (PSEN1) mutation and human ?Swedish? amyloid precursor protein. TgF334-AD rats faithfully recapitulate the rich clinico-pathological spectrum of human AD including cognitive/behavioral deficits, amyloid-? (A?) deposition, tau pathology, and neuronal loss, and develop an early neurovascular dysfunction characterized by blood-brain barrier (BBB) breakdown and injury to BBB-associated cells, pericytes, prior to A? deposition, tau pathology, neuroinflammation and neuronal loss, as we show preliminarily. Based on our preliminary findings we hypothesize that vascular/BBB dysfunction is an early pathogenic event that precedes and predicts onset and progression of behavioral and neurodegenerative changes in TgF3444-AD rats, and that manipulation of BBB integrity impacts the disease course. To test our hypothesis we will use cutting-edge approaches: 1) a novel molecular biomarker assessment of the neurovascular unit (NVU) in biofluids (cerebrospinal fluid, CSF; plasma) to determine how vascular/BBB injury relates to neuronal injury and responses of non-neuronal neighboring cells (e.g., astrocytes, microglia, inflammatory response), A? and tau; 2) advanced neuroimaging assessment of neurovascular function using a dynamic contrast-enhanced magnetic resonance imaging (DCEMRI) of BBB permeability, dynamic susceptibility contrast imaging (DSC-MRI) of cerebral blood flow (CBF) and diffusion tensor imaging (DTI-MRI) tractography; 3) behavioral assessment; and 4) brain tissue analysis. We will evaluate NVU biomarkers in CSF, plasma and tissue by age and in relation to cognitive function, behavior and AD-like neuropathology in Tg344-AD rats compared to control rats (AIM 1); examine longitudinally regional BBB permeability (DCE-MRI) and CBF (DSC-MRI) (AIM 2), and structural connectivity (DTI-MRI) (AIM 3) in relation to cognitive and behavioral function and AD-like neuropathology in Tg344-AD rats compared to control rats; and, determine whether therapeutic manipulation of the BBB to prevent early BBB breakdown (AIM 4) or mechanical manipulation to advance the degree of a spontaneous early BBB breakdown (AIM 5) in TgF344- AD rats can impact the disease course and neuropathology. Understanding how neurovascular integrity relates to brain connectivity and AD neuropathology will be a critical advance towards discovering how these factors influence cognitive impairment. Project 3 is essential for this P01 because it provides the only way to experimentally manipulate the vascular system to test the overarching hypothesis of the program.

This is a program project application with multiple projects, cores, institutions and investigators. It represents an integrated whole far greater than the sum of its parts. Each project and core complements the others so that a synergistic relationship among them is achieved with a common focus on goals of the program, namely to test the neurovascular hypothesis of AD. The Administrative Core is structured to insure that this program functions as efficiently, productively and transparently as possible. The program includes participation by accomplished investigators in all aspects of this research plan from the Zilkha Neurogenetic Institute, Stevens Neuroimaging and Informatics Institute, Alzheimer?s Disease Research Center (ADRC) and Dornsife College of Letters, Arts and Sciences Department of Psychology, University of Southern California (USC), Los Angeles, CA; Washington University Knight ADRC, St. Louis, MO; Huntington Medical Research Institutes, Pasadena, CA; Banner Alzheimer?s Institute and Mayo Clinic, Arizona; Dominantly Inherited Alzheimer?s Network (DIAN) including sites at the Washington University, St. Louis and USC, Los Angeles; and the Beckman Institute, California Institute of Technology, Pasadena, CA. The Aims of Core A are designed to provide effective scientific and administrative leadership, promote transparent communication between all clinical sites, laboratories, all P01 investigators and the Scientific Advisory Board (SAB) members, ensure open data sharing between all P01 investigators and the broader national AD and dementia research community, establish milestones and metric measures to monitor progress and internal quality of research, contribute to education, and develop business models for the program. The Aims of this Core reflect the commitment of the head PIs, Drs. Zlokovic and Toga, to provide the scientific and administrative leadership for the program consisting of 3 research projects and 3 cores, which are all integrated and use complementary approaches, technologies and analyses focused on the program project?s wide aims as described in the Overview. The Aims of Core A are: 1. To provide scientific and administrative leadership to ensure effective and transparent communication across projects and cores and cross-project fertilization and collaboration to achieve common objectives for the program project as a whole. 2. To establish milestones and metric measures for the program as a whole and for all projects and cores, provide a plan for internal quality control of research and hold monthly meetings of all project and core leaders and co-investigators and an annual evaluation meeting with our SAB to effectively monitor development of the program and maintain imaginative and high-quality scientific progress. 3. To contribute to education of the program?s members and the broader national AD and dementia research community at large via the seminar series ?Vascular Contributions to Dementia and AD? with 3-4 speakers annually. 4. To develop business models to efficiently manage the program?s business activities including day-to- day activities, contractual agreements, allocation of funds, and regular income/expense reports.

PROJECT SUMMARY - ADMINISTRATION CORE The LONI Resource (LONIR) has grown and matured since 1998, when it was initially funded. Administrative functions include measuring, tracking and reporting scientific publications, collaborations, community services, educational activities and web resource activity. To manage the existing LONIR infrastructure and support the proposed developments, we have established an efficient and comprehensive administrative structure. Specifically, the main responsibilities of the Administration Core are; allocation of resources, budgetary control, tracking and monitoring, policy, operations and implementing SAB recommendations. A pivotal LONIR guiding principle is the resource's collaborative and open nature. Appropriate rules of governance are important components to its success. The structure of our Biomedical Technology Resource Center (BTRC) is comprised of three Technology Research and Development (TR&D) projects, coupled to a national distribution of innovative and meritorious collaborative projects (CPs) that both push and pull the TR&D technologies. The LONIR supports numerous, service projects (SPs) that access the advanced technologies developed in the TR&Ds and trains users and disseminates technologies in an open and accessible way. LONIR is administered by competent and experienced investigators and staff. Finally, a fully engaged and expert panel of scientists ? our SAB ? provides advice and counsel on all aspects of this BTRC.

The overarching goal of this proposal is to aggregate, harmonize and secure the rich collection of data acquire across all waves of the Seattle Longitudinal Study (SLS) from 1956 to 2012, creating a valuable and accessible resource for epidemiological research on Alzheimer?s Disease (AD), as well as normal aging. Begun in 1956 and now active for 60 years, the SLS is one of the oldest and longest running studies of longitudinal change in cognition and the protective and risk factors associated with normal and pathological aging, involving over 5,000 participants. It represents a population-based, randomized sample recruited from a large health maintenance organization in western Washington. The study is unique in its cohort sequential design that examines cognitive and psychosocial change in birth cohorts from 1889 through 1976, thus providing opportunity to examine cognitive change in multiple birth cohorts over the same chronological age range. The study involves not only extensive cognitive longitudinal data sets but also multiple biomarkers of significance in AD, including blood samples with APOE genotyping, brain tissue at autopsy, and longitudinal multi-occasion neuroimaging data sets. Unlike most prospective AD studies beginning in old age, the SLS (age range: 22-100+ yrs) permits examination of cognitive change from young adulthood to very old age. The Laboratory of Neuro Imaging (LONI) will provide a secure data repository infrastructure for the SLS from which all data and supporting documents will be broadly accessible to the research community worldwide for the first time. Use of innovative approaches will provide an interactive and visual view of the entire SLS data set, harmonizing the multiple waves of SLS data into a uniform data model. A search interface will be provided to permit download of SLS data without extensive preprocessing before incorporating it into subsequent analyses. NCRAD will serve as a repository for blood and brain autopsy biomarkers with linkages to data within the LONI repository. Creation of rich archives can have a positive impact on scientists, research funders and potential for effective reuse of data extending beyond the original intent for which it was collected.

ABSTRACT ? INFORMATICS AND ANALYTICS CORE The Informatics and Analytics Core (IAC) will centralize an enduring data archive and analytic tools that will allow the broader epilepsy research community to identify and validate biomarkers of epileptogenesis in images, electrophysiology, and molecular/serological/tissue studies. Beyond creating a centralized data repository, the IAC will pioneer innovative standardization/co-registration methods, fully supported by novel image and electrophysiology processing methods to extract candidate biomarkers from the diverse data. Not only will a well-curated and standardized multi-modal data set facilitate the development of models of epileptogenesis, it will also ensure that such models are statistically significant and can be validated. Based on our previous experience with similar multicenter projects, we are confident that our infrastructure will lead to success in this project. The amount of data to be collected in these studies is unprecedented: video-EEG from animals after TBI recorded continuously for 6 months, in addition to prolonged continuous ICU EEG recordings from humans and intermittent sampling of brain images, blood, and tissue data. To analyze these data properly, it requires a diverse, accomplished group of investigators spanning neurology, neuroscience, imaging, mathematics, engineering, and computer science, as well as collecting comprehensive data in parallel from humans and animal models after TBI. The IAC will be seamlessly integrated with Projects 1-3, assisting in collecting data and providing analytic tools that will lead to biomarkers of epileptogenesis. By combining new data capabilities and our powerful, best-in-class, interdisciplinary team, quantitative models of epileptogenesis may be possible. These types of models will enrich preclinical trial populations, expedite interventions to prevent epilepsy after brain insults, and document epilepsy before late seizures occur. Based on previous studies, it is likely that there are reproducible changes in biomarkers, which identify the presence of epilepsy before its overt clinical expression61,71,72. The IAC will bring big data techniques and rigorous analysis to longitudinal data collected from humans and animal models of TBI, epilepsy, and their interaction. It will develop and implement new approaches, including novel graphical methods to visualize multivariable interactions, to quantify phenotype and molecular profiles in these disorders. A first-rate bioinformatics platform, LONI, will focus on TBI and epileptogenesis research. The tools, pipelines, and protocols developed for this proposal will be made available to the epilepsy research community, with the potential to change, long-term, the way that images, video, electrophysiology, proteomics, and metadata are analyzed in these fields. Quantitative and data mining methods will enable investigators to record and analyze gold-standard data and create a shared bioinformatics resource for epilepsy research that will live on long after the end of this project. Perhaps most importantly, the IAC will provide the technical sophistication to tease out the interaction between the complex processes studied in Projects 1-3, integrating multi-modal data in a way that has been beyond the capability of a single laboratory or center. The IAC will provide a lasting and open platform for standardized biomarker research in both TBI and epilepsy as well as engage with and guide the projects that, together, will lead to future clinical trial development. !

PROJECT SUMMARY - EPIBIOS4RX ADMINSTRATIVE CORE The Administrative Core for EpiBioS4Rx will serve as the central nexus for all operational aspects of the Center without Walls (CWOW). This Administrative Core brings together the strongest teams, ensuring success of the Cores and Research Projects to achieve the goals and objectives of EpiBioS4Rx. Scientific Premise: Epileptogenesis after TBI can be prevented with specific treatments; the identification of relevant biomarkers and performance or rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies. The CWOW has special administrative requirements in order to be successful; these include the management of each of the participating sites to ensure their efforts are complimentary, coordinated and maximally efficient. EpiBioS4Rx will include the Administrative Core, an Informatics Analytics Core (IAC), a Public Engagement Core (PEC), and three Research Projects focused on identification of biomarkers of epileptogenesis, targets for novel approaches to antiepileptogenesis, and preclinical trials of potential preventive therapies. We have now completed a three-year planning grant designed to identify available animal and patient data that might contain biomarkers of epileptogenesis and collected and analyzed these data, leveraging two existing bioinformatics platforms, in collaboration with other, more specialized systems. There are protocols in place for the transfer and storage of data that will be used for the new, easily accessible common interface. The EpiBioS4Rx Administrative Core will oversee, coordinate, and provide logistical support for the activities of the Cores and Research Projects, and oversee the management of the Shared Resource Facility, to provide critical infrastructure for CWOW investigators, and to make data, reagents, methods, and other resources available to the broader epilepsy research community. Leadership of EpiBioS4Rx will consist of a Director and six other co-PIs, as well as Management, Executive, and Steering Committees. A Charter for the CWOW, universal protocols, common data elements, regulatory provisions, plans for data sharing, and an Authorship Agreement has been created. The timeline for building the new infrastructure as well as a plan for communications and cooperation among CWOW investigators and collaborators outside the CWOW are contained within the Charter.

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), a CWOW proposal in response to RFA-NS-16-012, is designed to facilitate the development of antiepileptogenic therapies by removing barriers and promoting large-scale collaborative research efforts by multidisciplinary teams of basic and clinical neuroscientists with access to extensive patient populations, well-defined and rigidly standardized animal models, and cutting-edge analytic methodology. We focus our proposal on antiepileptogenesis in post- traumatic epilepsy (PTE) following traumatic brain injury (TBI), as this condition offers the best opportunity to determine the time of onset of the epileptogenic process in patients. The EpiBioS4Rx Scientific Premise is: Epileptogenesis after TBI can be prevented with specific treatments; the identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies. Based on the work from a P20 planning grant, our program will consist of the following: (1) identify biomarkers of epileptogenesis in our animal model and in patients, (2) Develop and utilize a standardized platform for preclinical trials of potential antiepileptogenic (AEG) drugs, (3) Identify 1 or more lead antiepileptogenic drugs for a future interventional clinical trial, (4) Establish a network of advanced TBI centers capable of carrying out future clinical trials featuring our lead antiepileptogenic drugs used in the context of a personalized, medicine-based approach utilizing our panel of biomarkers, and (5) Develop and incorporate a public engagement program involving the mutual education and collaboration of consumers, consumer organizations and professionals to design and execute future large-scale interventional clinical trials of antiepileptogenic therapies.

PROJECT SUMMARY - TR&D1: DATA SCIENCE Large-scale data aggregation has generated considerable interest within the neuroscience community, both for its potential to increase the statistical significance of research results as well as for the reuse of data that has already been collected. New federated approaches are needed to bring together research studies that operate independently from one another and to manage the complex needs of data access, aggregation, harmonization, and analysis. In Aim 1 we build upon our extensive experience in developing federated database systems and propose a one-time application process that simplifies data access by consolidating disparate applications across multiple research institutions. We also propose a single secure and unified pathway for downloading binary and tabular files from different research studies that would significantly reduce the effort required to retrieve these files. In Aim 2 we introduce a new approach for harmonizing data collected by different research studies that incrementally applies transformations and provides immediate visual feedback with tabular updates and interactive summaries. In Aim 3 we propose to integrate recent Docker technologies into our framework and establish an archive for analyses that can be transferred to and executed on any Linux computer. Input and output data will be linked to their respective analyses and used as query criteria when searching the archive. In Aim 4 we propose a new mediator that acts as a bridge that connects all the components of our framework. This Analysis Assembler utilizes the unified pathway of Aim 1 and automatically downloads all files needed for an analysis. After retrieving the analysis itself from the archive in Aim 3, the Assembler proceeds to execute the analysis on the data files. After the analysis has completed, the Assembler records the provenance of all output data, which will be made accessible in visual queries of our federated search system. In Aim 5 we propose to extend our quality control system to use machine learning to automatically assign ?poor? and ?good? quality ratings to neuroimaging MRI data. With the goal of locating hard-to-see artifacts, we also propose to implement interactive 3D visualizations to more accurately assess image quality. All five of our aims provide a framework upon which neuroscience can be conducted, shared, and replicated ? comprising a foundation for reproducible science.

ABSTRACT The overarching goal of this project is to secure, link, and disseminate BRAIN Initiative data, including electrophysiology, imaging, behavioral, and clinical data with all pertinent recording and imaging parameters, coming from participating sites. Our plan for a Data Archive for the Brain Initiative (DABI) is in response to RFA-MH-17-255. The Laboratory of Neuro Imaging (LONI) at USC has established itself as a hub for delivering effective informatics and analytics solutions in the context of big data for major projects in the study of a range of neurological diseases. LONI has demonstrated and proven experience with variations in data descriptions, data incompleteness, and data harmonization; we have built data portals and query engines for efficient search and utilization, and we have, most importantly, enabled broad data (re-)use toward accelerated discovery. Just as the ADNI (http://adni.loni.usc.edu/) project has been a powerful catalyst for success in biomarker research in Alzheimer's disease, this project has the power to foster a similar and potentially greater level of success for human neurophysiological data. Furthermore, we understand the needs of investigators who have collected these data and their concerns associated with data sharing, in addition to the privacy of the subjects from whom the data are collected. We have the extensive infrastructure in place to handle such large-scale data as well as LONI's Pipeline Processing Environment for streamlined integration of analytic tools. This platform will be designed with the flexibility to integrate input from those awarded grants to address standardization of data (RFA-MH-17-256) as well as those who design tools to interface with archives (RFA-MH-17-257). We will receive and de-identify data of various modalities from the participating sites, incorporating analysis tools previously developed at LONI and elsewhere, managing the data access systems, providing user interfaces to explore, visualize, interpret, and download the data, and provide comprehensive information about the projects and corresponding data through the public website that will be developed specifically for DABI. We aim to provide a tool for investigators that will decrease the burden of archiving the data once it has been generated. We have already created a true community by having established good communication among our group of collaborators from funded BRAIN Initiative programs. Furthermore, we will be working with industry partners that are anticipated major sources of data to specifically streamline methods for data upload from those sources.

PROJECT SUMMARY/ABSTRACT Recognizing the critical need for a broad range of expertise in biomarker analysis at several levels of analysis, we propose a unique postdoctoral training program titled ?Training for the Multiscale and Multimodal Analysis of Biomarkers in Alzheimer's Disease? (AD). USC is home to leading experts who utilize biomarkers in their work investigating AD through a myriad of methods and analyses. Drawing from USC's eminent resources and expertise, this proposal will focus on preparing investigators for independent research careers in the multiscale and multimodal analysis of biomarkers in AD, by integrating molecular and cellular methods, imaging tools and informatics, quantitative methods for clinical trials research, and large scale population analyses. Our proposed training program draws preceptors and faculty from six schools, departments, institutes, and centers at USC, fulfilling these methodological areas of expertise: Mark and Mary Stevens Neuroimaging and Informatics Institute (INI) in the Keck School of Medicine; Department of Biomedical Engineering (BME) in the Viterbi School of Engineering; Zilkha Neurogenetic Institute (ZNI) in the Keck School of Medicine; Department of Psychology in the Dana and David Dornsife College of Letters, Arts and Sciences; Leonard D. Schaeffer Center for Health Policy and Economics, a unique collaboration between the USC Sol Price School of Public Policy and School of Pharmacy; and Leonard Davis School of Gerontology. Trainees will complete coursework, lab rotations, and career development activities, such as seminar series, instruction for problem solving, communication, time management, and leadership skills, and instruction and training in grant writing. Trainees will become conversant in all thematic areas and will be able to effectively collaborate outside of their own particular area of expertise. Specifically, the USC Training for the Multiscale and Multimodal Analysis of Biomarkers in AD will aim to: equip trainees with a combination of skills to conduct multiscale and multimodal analyses of biomarkers in AD by providing tailored, didactic research education opportunities to further the research potential of trainees; facilitate eminent research in the multiscale and multimodal analysis of biomarkers in AD by trainees, including the development of research ideas, execution of research projects, and dissemination of research findings; establish among trainees a collaborative and multidisciplinary team approach to advance research in the multiscale and multimodal analysis of biomarkers in AD; and successfully transition trainees to independent research careers, including securing their own research funding.

NiAD Summary/Abstract: Individuals with Down syndrome (DS) have been largely neglected in therapeutic and biomarker studies of Alzheimer's disease (AD). Adults with DS are uniformly affected by AD pathology by their 30's and have a 70-80% chance of clinical dementia by their 60's. In 95% of cases, DS is associated with three copies of chromosome 21, each containing of copy of the Amyloid-beta (A?) Precursor Protein gene (leading to a 1.5-fold increase in A? protein). Yet, nowhere is it clearer than in DS that A? deposition is not sufficient to produce dementia, as individuals harbor this pathology for over a decade before cognitive decline is apparent. DS can be seen as a setting of amplified sensitivity to risk and protective factors that moderate the relationship between A?, neurodegeneration and clinical dementia. Understanding the factors that moderate this relationship in DS and biomarkers for those factors is critically important in the design of therapeutic trials for AD in DS and in general. Thus, this longitudinal study of Neurodegeneration in Aging DS (NiAD) and its relationship to cognition has the potential to: 1) identify critical factors that link A? deposition to neurodegeneration and, ultimately, dementia; 2) define biomarkers for these factors; and, most importantly, 3) set a foundation for an efficient transition from this biomarker study to a therapeutic trial to combat AD in DS augmented by biomarker outcomes. For the past 5 years, the three independent research groups included in this application have been studying the course of A? deposition and other imaging biomarkers and their impact on cognitive/functional measures in adults with DS [(a) the combined Pittsburgh/Madison study; (b) the Banner Alzheimer's Institute study; and (c) the Cambridge study]. In their ongoing work, 140 adults with DS (including 23 with DS/AD-dementia) have undergone magnetic resonance imaging (MRI) and amyloid-positron emission tomography (PET) scans and neuropsychological/ functional assessments. These three research groups now propose to combine resources and harmonize all protocols in response to the request from NIA/NICHD to develop a large AD biomarker study in DS. This study will be further strengthened by aligning NiAD with the three largest ongoing longitudinal studies of AD biomarkers in the general population: the Alzheimer Disease Neuroimaging Initiative (ADNI), the Dominantly Inherited Alzheimer Network (DIAN) and the Alzheimer Prevention Initiative (API). All data will be made available in an open-access format using a model similar to ADNI. The established DS cohort is a significant advantage that will shorten the recruitment phase, maximize longitudinal data that can be acquired and allow for addition of new biomarkers to be compared to longitudinal clinical and imaging measures. The proposed 5-year longitudinal study will examine progression of AD related biomarkers (A?-, tau- and fluorodeoxyglucose-PET, structural and functional MRI, cerebrospinal fluid A? and tau, plasma A? and proteomics, genetics, neuropathology) and cognitive/functional measures in 180 adults with DS (>25 yrs. of age) and 40 biomarker-controls. Subjects will be re-evaluated every 15 months to assess changes in cognition/adaptive functioning and every 30 months to detect biomarker changes.

Abstract: Data Core The Data Management and Statistical Core will provide informatics and statistics support for ADRC related dementia related research. The multimodal nature of data collected to investigate the etiology, pathogenesis, diagnosis, treatment, and prevention of AD requires robust informatics, data management and statistical approaches. Specifically, we will provide for: standardized collection of local assessments, scales and enrollment status, de-identifying, storing and tracking neuroimaging data, web-based systems for the databasing, protection, tracking and access of all data to qualified and authenticated scientists. We will ensure timely and accurate data entry to NACC related UDS and neuropathology databases and provide biostatistical consulting to ADRC investigators in the design, coordination and analysis of ADRC-related projects, including development projects. We will establish connections and coordinate with both local and national efforts to share and exchange well curated and comprehensively described data. Finally, we will provide education and training in design, statistical models and informatics in coordination with the ORE core. Existing strengths of our Core include data management and biostatistical resources embedded within the USC Mark and Mary Stevens Neuroimaging and Informatics Institute (INI) and the Department of Preventative Medicine. We have strong leadership by investigators involved in many of the national and global AD research related efforts and possess the resources necessary to insure success in our core.

Abstract The 3 overarching goals of the USC Alzheimer Disease Research Center (ADRC) are to: 1) Elucidate vascular contributions to Alzheimer disease (AD), 2) catalyze basic, clinical, and translational research in AD at USC, and 3) contribute expertise in vascular disease, biomarkers, and imaging to national collaborative initiatives. The ADRC is led by 3 multiple PD/PIs: Chui, Zlokovic, and Toga and comprised of 6 required cores, the required Research Education Component (REC), and 1 optional imaging core. The Administration Core (Chui, Zlokovic, Toga) provides administrative and scientific oversight across USC ADRC, including fostering development projects and supporting ADRC-affiliated studies. The Clinical Core (Schneider, Ringman, Chui) performs standardized evaluations and diagnoses using the NACC Uniform Data Set (UDS), enrolls and follows participants in our 2 primary ADRC cohorts: Vascular Cohort Study (VCS) and Brain Research Study (BRS). The Data Management and Statistical Core (Toga and Chen) oversees the NACC UDS database, provides study- and core-specific databases and curates our large imaging data sets as a local and national resource. The Neuropathology Core (Miller and Hawes) performs standardized neuropathological examinations, stores and distributes biological tissues to research investigators. The Outreach, Recruitment, and Engagement Core (Aranda) works closely with the Clinical Core to recruit and retain the primary ADRC cohort, focusing on under- represented minority groups (especially Latinx), and the development of a participant-caregiver dyad resource database. The Biomarker Core (Zlokovic) uses state of the art methods to determine cell-and system-specific biomarkers related to the neurovascular unit, as well as to measure standard AD biomarkers. The Imaging Core (Toga and Pa) provides high field (3T and 7T) MR imaging, as well as amyloid/tau PET scans. The Research Education Component (Yassine) is dedicated to mentoring post-doctoral students committed to the study of minority issues in Alzheimer disease and related disorders. The USC Health Science Campus is located near high Latinx catchment areas. Treatable vascular-metabolic risk factors (VMRF) are particularly prevalent among the Latinx population and dovetail with the research focus of the USC ADRC.

PROJECT ABSTRACT As a part of the USC Alzheimer Disease Research Center competitive renewal, the Imaging Core will continue to serve the overall mission to provide high-quality MR and PET imaging resources to the local and broader AD research community. The ADRC Imaging Core will provide core resources, including MR physics and PET expertise, multi-scanner sequence and protocol development, image data quality control and management, research collaboration, and a global framework for conducting AD neuroimaging research. The Imaging Core is led by a well-integrated and collaborative team, including Arthur Toga (Core Leader), Judy Pa (Core Co-Leader), Meredith Braskie, Danny JJ Wang, Nasim Sheikh-Bahaei, and Paul Thompson, who together provide expertise in vascular, functional, and structural imaging and its analyses for 3T and 7T MRI, and PET. We propose 5 specific aims that provide an imaging framework for all ADRC projects; perform high quality control of all imaging data; develop technical and scientific approaches for ultra- high field 7T MRI; assist in PET protocol development and quality control; and conduct education and training in modern imaging techniques. Taken together, these goals will serve the USC ADRC?s unifying scientific theme of vascular contributions to Alzheimer disease: risk factors, neurovascular and metabolic mechanisms, and risk reduction.

PROJECT SUMMARY - OVERALL The LONIR is focused on developing innovative solutions for the investigation of imaging, genetics, behavioral and clinical data. The LONIR structure is designed to facilitate studies of dynamically changing anatomic frameworks, e.g., developmental, neurodegenerative, traumatic, and metastatic, by providing methods for the comprehensive understanding of the nature and extent of these processes. Specifically, TR&D1 (Data Science) focuses on methodological developments for the management and informatics of brain and related data. This project will develop and issue new methods for robust scientific data management to create an environment where scientific analyses can be reproduced and/or enhanced, data can be easily discovered and reused, and analysis results can be visualized and made publicly searchable. TR&D2 (Diffusion MRI and Connectomics) seeks to advance the study of brain connectivity using diffusion imaging and its powerful extensions. This project will go beyond traditional tensor models of diffusion for assessing tissue and fiber microstructure and connectivity, develop tract-based statistical analysis tools using Deep Learning, introduce novel adaptive connectivity mapping approaches, using L1 fusion of multiple tractography methods, and provide mechanisms to study connectivity and diffusion imaging over 10,000 subjects. (This technology and these methods will be managed and executed by the TR&D1 framework to distributed datasets totaling over 10,000 subjects). Lastly, our TR&D3 (Intrinsic Surface Mapping) develops a general framework for surface mapping in the high dimensional Laplace-Beltrami embedding space via the mathematical optimization of their Riemannian metric. Our approach here overcomes fundamental limitations in existing methods based on spherical registration by eliminating the metric distortion during the parameterization step, thus achieving much improved accuracy in mapping brain anatomy. Coupled with a mature and efficient administrative structure and comprehensive training and dissemination, this program serves a wide and important need in the scientific community.

PROJECT SUMMARY/ABSTRACT To prepare ourselves for the aging of our increasingly diverse population, we must increase minority representation in the sciences, and train young researchers to understand neurocognitive aging and Alzheimer's disease and related dementias (ADRD), and associated health disparities. CSUF and USC propose an innovative and transformative program entitled, ?Neurocognitive Aging & Analytics Research Education (NAARE)? that builds upon our current biomedical research education program on data science and brain health. In the coming decades, the United States and the world will face a rapidly growing aging population, with an insufficient number of experts trained in ADRD research to accommodate this increase. Even though ethnic and racial minorities, including African Americans, Latinos and Native Americans/Alaskans comprise over a third of the U.S. population, they are under-represented in aging studies, leaving us with insufficient knowledge of the genetic and environmental risk factors that contribute to cognitive decline in these populations. The representation of minorities in science and biomedical fields also remains comparatively low. Increasing the number of trained researchers from these underrepresented groups may provide insights into social contextual issues and other factors related to underrepresentation, which may subsequently promote access to these important populations in order to subsequently improve health disparities related to neurocognitive aging in these vulnerable populations. Therefore increasing the number of underrepresented aging researchers continues to be a high national priority. The NAARE program addresses this gap as follows: Aim 1: Provide NAARE scholars hands-on research experiences: Engage three consecutive cohorts (n = 10 per year) of predominantly underrepresented minority undergraduates (50% female) in a 1.5 - year faculty mentored, student-driven research focusing on neurocognitive aging, ADRD, and related health disparities and modifiable risk factors. Aim 2: Develop educational curricula: Traditional and multimedia curricula will be developed for NAARE scholars and also targeted towards a larger diverse student audience (n = 4,500) on the following: basic science behind normal brain aging and ADRD; health disparities, modifiable risk factors and neurocognitive aging; neuroimaging and analytics; and research methods in neurocognitive aging. Aim 3: NAARE Scholar Graduate/Career Preparation: To ensure sustainability and student success, NAARE students will engage in ongoing faculty advising, receive exhaustive graduate school application support via hands-on guidance, and explore first-hand, research intensive universities. In-depth mentored, yet student- owned research experiences that integrate intensive neurocognitive aging and ADRD training, and belonging in the broader scientific community will result in successful future scientists. This program will lead to a greater number of students from underrepresented backgrounds choosing careers in neurocognitive aging and succeeding at them. Our students will become diverse role models for future students for generations to come.

Abstract The 3 overarching goals of the USC Alzheimer Disease Research Center (ADRC) are to: 1) Elucidate vascular contributions to Alzheimer disease (AD), 2) catalyze basic, clinical, and translational research in AD at USC, and 3) contribute expertise in vascular disease, biomarkers, and imaging to national collaborative initiatives. The ADRC is led by 3 multiple PD/PIs: Chui, Zlokovic, and Toga and comprised of 6 required cores, the required Research Education Component (REC), and 1 optional imaging core. The Administration Core (Chui, Zlokovic, Toga) provides administrative and scientific oversight across USC ADRC, including fostering development projects and supporting ADRC-affiliated studies. The Clinical Core (Schneider, Ringman, Chui) performs standardized evaluations and diagnoses using the NACC Uniform Data Set (UDS), enrolls and follows participants in our 2 primary ADRC cohorts: Vascular Cohort Study (VCS) and Brain Research Study (BRS). The Data Management and Statistical Core (Toga and Chen) oversees the NACC UDS database, provides study- and core-specific databases and curates our large imaging data sets as a local and national resource. The Neuropathology Core (Miller and Hawes) performs standardized neuropathological examinations, stores and distributes biological tissues to research investigators. The Outreach, Recruitment, and Engagement Core (Aranda) works closely with the Clinical Core to recruit and retain the primary ADRC cohort, focusing on under- represented minority groups (especially Latinx), and the development of a participant-caregiver dyad resource database. The Biomarker Core (Zlokovic) uses state of the art methods to determine cell-and system-specific biomarkers related to the neurovascular unit, as well as to measure standard AD biomarkers. The Imaging Core (Toga and Pa) provides high field (3T and 7T) MR imaging, as well as amyloid/tau PET scans. The Research Education Component (Yassine) is dedicated to mentoring post-doctoral students committed to the study of minority issues in Alzheimer disease and related disorders. The USC Health Science Campus is located near high Latinx catchment areas. Treatable vascular-metabolic risk factors (VMRF) are particularly prevalent among the Latinx population and dovetail with the research focus of the USC ADRC.