Andrew J. Saykin - US grants

DESCRIPTION (provided by applicant): Systemic cancer chemotherapy has been associated with cognitive and memory deficits that may be long-lasting (Ahles, Saykin et al, 2002). There are very few prospective studies of cognitive changes and the underlying pathophysiological mechanisms have not been systematically examined. Brain imaging could help to identify these mechanisms, yet there are no prospective, well-controlled studies. We will address this knowledge gap by employing an ensemble of magnetic resonance imaging (MRI) and analysis techniques to identify specific changes in brain structure and function associated with chemotherapy. The imaging study will build on our team's experience with NCI-sponsored cognitive studies of chemotherapy (CA87845). Our specific aims are to: (i) Determine the frequency, quantity and characteristics of short and long term changes in brain structure and tissue composition in patients undergoing chemotherapy, (ii) Detect and characterize alterations in brain activation during memory processing using fMRI to assess task-relevant circuits and (iii). Systematically examine key treatment, disease and individual difference factors (e.g., chemotherapy regimen, genetic risk, age, education, baseline functioning) as predictors of cognitive deficits and recovery, to facilitate future model building. Participants will include breast cancer patients undergoing chemotherapy (n=50) or local therapy only (n=50), and 30 healthy controls. Measures will be obtained before and 1 and 12 months after chemotherapy (or equivalent intervals). An integrated MRI exam will detect and measure: focal inflammation/demyelination (FLAIR) and distributed gray and white matter changes (voxel-based morphometry), the integrity of memory critical structures (hippocampal 3D volume and shape analysis) and white matter integrity and connectivity (diffusion tensor imaging, DTI), and brain activation patterns during working and episodic memory processing (functional MRI, fMRI). Expected outcomes: The study will determine which imaging measures are most sensitive, informative and specific to chemotherapy. Identification of the mechanisms of cognitive changes and introduction of new measures will help to drive advances in assessment of neurological side effects and risk/benefit analyses, and it will provide a foundation for design and evaluation of neuroprotective agents, neuropharmacologic treatment and rehabilitative approaches.

Today's massive generation of digital data is greatly outpacing the development of computational methods and tools and presents critical challenges for achieving the full transformative potential of these data. For example, recent advances in acquiring multi-modal brain imaging and genome-wide array data provide exciting new opportunities to study the influence of genetic variation on brain structure and function. Major computational challenges are, however, bottlenecks for comprehensive joint analysis of these data due to their unprecedented scale and complexity. This project will employ the new capabilities of large-scale data mining techniques in multi-view learning, multi-task learning, and robust classification to address critical challenges in systematically analyzing massive multi-modal genetic, imaging, and other biomarker data. Specifically, this project will: (1) develop new multi-view learning methods to detect task-relevant phenotypic biomarkers from large scale heterogeneous imaging and other biomarker data, (2) implement new sparse multi-task regression models to reveal the genetic basis of phenotypic biomarkers at multiple levels (e.g., SNP, haplotype, gene and/or pathway), (3) design novel robust classification methods via structural sparsity for outcome prediction using integrated genotypic and phenotypic data, and (4) package these new methods into a data mining toolkit and release it to the public.

The intellectual merits of this project derive not only from the development of novel data mining methods, but also from their application to imaging genetic studies. These methods are designed to take into account interrelated structures among multiple data modalities and offer systematic strategies to reveal structural imaging genetic associations. The proposed methods and tools are expected to impact neurological and psychological research and enable investigators to effectively test imaging genetics hypothesis and advance biomedical science and technology. In addition, the proposed data mining framework addresses generic critical needs of large-scale data analysis and integration and, therefore, will impact a large number of research areas where high-value knowledge and complex patterns can potentially be discovered from massive high-dimensional and heterogeneous data sets. This project will facilitate the development of novel educational tools to enhance several current courses at UT Arlington and IUPUI. Both universities are minority-serving institutions, and the PIs will engage the minority students and under-served populations in research activities to give them a better exposure to cutting-edge scientific research.

DESCRIPTION (provided by applicant): Today's generation of multi-modal imaging systems produces massive high dimensional data sets, which when coupled with high throughput genotyping data such as single nucleotide polymorphisms (SNPs), provide exciting opportunities to enhance our understanding of phenotypic characteristics and the genetic architecture of human diseases. However, the unprecedented scale and complexity of these data sets have presented critical computational bottlenecks requiring new concepts and enabling tools. To address these challenges, using the study of Alzheimer's disease (AD) as a test bed, this project will develop and validate novel bioinformatics strategies for multidimensional brain imaging genetics. Aim 1 is to develop a novel bi- multivariate analysis strategy, S3K-CCA, for studying imaging genetic associations. Existing imaging genetics methods are typically designed to discover single-SNP-single-QT, single-SNP-multi-QT or multi-SNP-single- QT associations, and have limited power in revealing complex relationships between interlinked genetic markers and correlated brain phenotypes. To overcome this limitation, S3K-CCA is designed to be a sparse bi- multivariate learning model that simultaneously uses multiple response variables with multiple predictors for analyzing large-scale multi-modal neurogenomic data. Aim 2 is to develop HD-BIG, a visualization and systems biology framework for integrative analysis of High-Dimensional Brain Imaging Genetics data. Machine learning strategies to seamlessly incorporate valuable domain knowledge to produce biologically meaningful results is still an under-explored area in imaging genetics. In this aim, we will develop a user-friendly heat map interface to visualize high-dimensional results, adjust learning parameters and strategies, interact with existing bioinformatics resources and tools, and facilitate visual exploratory and systems biology analysis. A novel imaging genetic enrichment analysis (IGEA) method will be developed to identify relevant genetic pathways and associated brain circuits, and to reveal complex relationships among them. Aim 3 is to evaluate the proposed S3K-CCA and IGEA methods and the HD-BIG framework using both simulated and real imaging genetics data. This project is expected to produce novel bioinformatics algorithms and tools for comprehensive joint analysis of large scale heterogeneous imaging genetics data. The availability of these powerful methods is critical to the success of many imaging genetics initiatives. In addition, they can also help enable new computational applications in other areas of biomedical research where systematic and integrative analysis of large-scale multi-modal data is critical. Using AD as an exemplar, the proposed methods will demonstrate the potential for enhancing mechanistic understanding of complex disorders, which can benefit public health outcomes by facilitating diagnostic and therapeutic progress.

The Indiana Alzheimer Disease Center (lADC) is established on the campus of Indiana University School of Medicine. It is comprised of six cores: Administrative Core, Clinical Core, Data Management and Statistics Core, Neuropathology Core, Education and Information Transfer Core and Neuroimaging Core. The mission of the lADC is: 1) to support and carry out research on Alzheimer disease (AD) and other neurodegenerative dementias, 2) to serve as shared research resources that will facilitate research in AD and other dementias, 3)distinguish them from the processes of normal brain aging and mild cognitive impairment (MCI), 4) provide a platform for training, 5) develop novel techniques and methodologies, and 6) translate these research findings into better diagnostic, prevention and treatment strategies. The focus of the lADC is on behavioral neurology, clinico- pathological correlations, biochemistry, and genetics of AD, front temporal dementia and Parkinsonism linked to chromosome 17 (FTDP-17), Gerstmann-Straussler-Schelnker disease (GSS), Parkinson disease and other hereditary diseases associated with abnormal protein accumulation.

Project Summary ? Neuroimaging Core (NIC) The Neuroimaging Core (NIC) of the IADC brings advanced imaging tools in support of the goal of effectively preventing and treating AD by 2025. The NIC has been productive in developing and implementing leading- edge imaging protocols and imaging genetics analysis techniques, collaborating with NIA-funded initiatives (e.g., ADNI, DIAN, ADGC, etc.), and in providing training in neuroimaging and imaging genetics to scientists of all levels (undergraduate to faculty) and disciplines (neuroscience, genetics, computer science, physicians, etc.). In the next funding period the NIC will continue to pursue the following specific aims: (1) Support funded research in the IADC, IU Center for Aging Research, and related programs that currently employ or could benefit from advanced neuroimaging; (2) Provide standardized, state-of-the-art neuroimaging acquisition and analysis protocols; (3) Expand transdisciplinary regional neuroscience research using advanced neuroimaging tools to study disease mechanisms and treatments for neurodegeneration; (4) Support and collaborate with major national and international AD-related research consortia using neuroimaging and genetics methods; (5) Provide transdisciplinary educational opportunities in neuroimaging and genetics of AD and other degenerative disorders for basic and clinical scientists at all levels from undergraduates to post-doctoral fellows and faculty, as well as dissemination of neuroimaging results to the community. The NIC, working closely with the Clinical Core, will perform state-of-the-art advanced multimodal MRI ( Siemens Prisma 3T: high resolution structural and 3D pCASL perfusion, multiband resting-state and task-based fMRI, and DTI optimized for structural and functional connectome analysis) on all eligible IADC participants, as well as amyloid and/or tau PET on 225 IADC participants. Participants in preclinical or early symptomatic phases (e.g., subjective cognitive decline (SCD) or with mild cognitive impairment (MCI)), late-onset AD, and individuals from families with mutations causal for dementias will be prioritized. Imaging-pathologic correlation when available will improve the understanding of early structural, functional, and molecular changes observed in vivo and may help identify novel therapeutic targets. Cross-modality image analyses and results of imaging genetics studies with ADNI, DIAN, and other partners will contribute to advances in early detection, mechanistic understanding, and optimized use of imaging as a dynamic biomarker for the study of therapeutic effects. Acquiring standardized state-of-the-art MRI and PET data for use by many investigators will increase research productivity and facilitate the optimized quantitative analyses. Close integration with other cores will further allow the IADC to expand on these analyses by relating imaging biomarkers to neuropsychological measures, neuropathologic samples, genetic and other ?omics markers. The NIC will continue to foster and support advanced imaging research through local, regional, and national/international collaborations. The IADC NIC will help advance AD imaging research to meet the goal of effective disease prevention and treatment within the next 10 years.

Project Summary ? Administrative Core (AC) The role of the Administrative Core (AC) of the Indiana Alzheimer Disease Center (IADC) is to provide strategic planning, management, resources, and oversight for the center in support of its continued mission. The significance of this role is based on the compelling national plan to prevent and effectively treat AD by 2025 through innovative research on etiology, early detection, and therapeutics. To help accomplish these goals the IADC has proposed four overall center aims for the next project period: (1) Support, enhance, and expand innovative research on AD and related dementias targeting causes, diagnosis, treatment, and prevention; (2) Provide critical research resources and infrastructure to support existing studies and enable new innovative research, including pilot studies; (3) Strongly support local, regional, and national/ international dementia research collaborations; and, (4) Provide educational and training opportunities related to dementia for learners of all levels and needs including academic programs for professionals and programs for patients, caregivers and family members, and the community at-large. The AC is responsible for overall management and strategic direction of the IADC. Leadership within the center has been extremely stable. In 2013, the center made a smooth transition of the AC to the Department of Radiology and Imaging Sciences after Dr. Andrew Saykin was appointed center director. Dr. Martin Farlow continues to serve as associate director. Brad Glazier, IADC Administrator since 1999, continues in this important role. The AC has six specific aims: (1) Overall direction and strategic planning for research on AD and related disorders. The AC will help to leverage existing strengths of faculty and programs and develop new opportunities for scientific growth and impact. It will also foster integration among the cores for thematic cohesion. The AC will coordinate the Executive Committee, including key leaders to advise and help implement the center goals. (2) Provide administrative support, resource management and oversight functions. (3) Ensure ongoing evaluation and oversight of scientific progress, research and training including coordinating the Internal and External Advisory Committees. (4) Advance the IADC goals of fostering and expanding innovative research, training and collaborative academic activities related to AD. (5) Develop and manage the IADC Pilot Project Program including solicitation of proposals, a robust and impartial review process, and tracking of outcome metrics based on external funding and publication; and (6) Serving as an effective interface to important external organizations and promoting collaboration with other national and international AD research initiatives. This includes interaction with the NIA as well as the NACC, NCRAD, ADNI, DIAN, ADCS, ADGC and other consortia. The IADC is committed to accomplishing these aims to support and accelerate progress in addressing the challenges of AD and related disorders.

? DESCRIPTION (provided by applicant): There are compelling data indicating that cancer and its therapies are associated with cognitive decline (chemo-brain) in certain survivors. Cognitive decline is an important public health issue facing our aging nation since it is a strong predictor f functional decline, hospital admission, mortality, and healthcare costs. For women 60 and older (older), who represent 71% of the three million US breast cancer survivors, cognitive decline is especially salient, since even small declines have negative effects on daily life. While there are multiple studies in younger survivors showing both persistent and late cognitive problems, there are insufficient data on long-term trajectories of and risks for cognitive decline in older survivos since past research has had small samples of older survivors; lacked pre-treatment data; was uncontrolled; and/or did not include follow-up. In recognition of this important evidence gap, the Institute of Medicine issued a recent call for collection of data about factors that influence the course of cancer survivorship in older survivors, stressing cognitive outcomes. In this revised continuation, our multi-disciplinary team will use a bio-behavioral cancer and aging framework to fill this gap by leveraging the Thinking and Living with Cancer (TLC) cohort. TLC is the only prospective cohort of older breast cancer survivors evaluated pre-treatment and 12 months later that includes matched controls. We have made excellent progress, meeting enrollment targets, publishing 19 papers, and presenting two abstracts. We also have provocative preliminary results showing divergent trajectories by treatment and genotype, with APOE ?4 positive chemotherapy-exposed survivors demonstrating dramatically steeper 12-month declines in several cognitive domains than other survivors and controls. Additionally, physical activity tended to decrease and multi-morbidity to increase the odds of 12-month decline. To understand longer-term trajectories of and risks for cognitive decline, we propose to conduct new assessments (24, 36, 48 months) of the TLC cohort, use banked DNA for COMT polymorphism testing, and add objective monitoring of physical activity and measurement of inflammatory markers. The aims are to: 1) ascertain trajectories of longitudinal cognitive function and test for differences in treatment exposure-control groups; 2) identify risk factors for cognitie decline in the 48 months post-enrollment and determine how effects are moderated by treatment exposure (chemotherapy>hormonal>control); and 3) assess how inflammatory products (CRP, IL6, and sTNFRII) co-vary with trajectories of cognition; and test if inflammation mediates effects of physical activity and multi-morbidity on cognitive decline. The results will have high impact by suggesting future mechanistic research and intervention trials, and supporting patient-physician discussions about treatment benefits and harms. Knowledge about whether trajectories of decline plateau, accelerate, or occur as late effects could improve survivorship care planning for older breast cancer survivors.

Novel neuroscience tools and techniques are necessary to enable insight into the building blocks of neural circuits, the interactions between these circuits that underpin the functions of the human brain, and modulation of these circuits that affect our behavior. To leverage rapid technological development in sensing, imaging, and data analysis new ground breaking advances in neuroscience are necessary to facilitate knowledge discovery using data science methods. To address this societal grand challenge, the project will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral science disciplines together with partnerships in academia, industry, and government at multiple levels. The Big Data Neuroscience Spoke titled Midwest: Advanced Computational Neuroscience Network (ACNN) is strongly aligned with the national priority area of neuroscience and brings together a diverse set of committed regional partners to enable the Midwest region to realize the promise of Big Data for neuroscience. The ACNN Spoke will build broad consensus on the core requirements, infrastructure, and components needed to develop a new generation of sustainable interdisciplinary Neuroscience Big Data research. ACNN will leverage the strengths and resources in the Midwest region to increase innovation and collaboration for the understanding of the structure, physiology, and function of the human brain through partnerships and services in education, tools, and best practices.

The ACNN will design, pilot and support powerful neuroscientific computational resources for high-throughput, collaborative, and service-oriented data aggregation, processing and open-reproducible science. The ACNN Spoke framework will address three specific problems related to neuroscience Big Data: (1) data capture, organization, and management involving multiple centers and research groups, (2) quality assurance, preprocessing and analysis that incorporates contextual metadata, and (3) data communication to software and hardware computational resources that can scale with the volume, velocity, and variety of neuroscience datasets. The ACNN will build a sustainable ecosystem of neuroscience community partners in both academia and industry using existing technologies for collaboration and virtual meeting together with face-to-face group meetings. The planned activities of the ACNN Spoke will also allow the Midwest Big Data Hub to disseminate additional Big Data technologies resources to the neuroscience community, including access to supercomputing facilities, best practices, and platforms.

This award received co-funding from CISE Divisions of Advanced Cyberinfrastructure (ACI) and Information and Intelligent Systems (IIS).

Abstract The renewal of this research program addresses two critical bottlenecks in Alzheimer's disease (AD) research: earlier detection of those at risk and identification of important biological processes during preclinical and early stages of disease. These challenges are the key to development of diagnostic and therapeutic approaches, as well as prevention strategies that will need to be implemented at least 5-10 years before dementia onset. During the prior funding period, this project contributed novel information on individuals with amnestic mild cognitive impairment (MCI) and helped drive the field toward a focus on pre-MCI stages. A major innovation has been the investigation of euthymic older adults with cognitive complaints that score within the normal range on cognitive testing. In this group we have reported alterations in brain structure, functional activation, and connectivity in a network of memory-related regions that are typically intermediate between the pattern seen in cognitively normal controls (CN) and individuals with MCI. This phenotype, now referred to as subjective cognitive decline (SCD) and defined by an international consensus panel in 2013, is influencing large-scale studies including the Alzheimer's Disease Neuroimaging Initiative (ADNI), which adopted the Cognitive Change Index (CCI) developed in this project to recruit a similar group (SMC). We piloted a dual- tracer PET approach to study molecular signatures as a function of stage of disease, initially examining the relationship of amyloid burden and immune activation (microglia). With the new availability of PET tracers for in vivo measurement of tau burden, we will measure both tau and amyloid in preclinical and prodromal stages of AD, focusing on targeted regions based on neuropathological studies. The role of amyloid and tau deposition as molecular drivers of early functional disruption of brain activity and connectivity, as well as neurodegeneration, will be investigated using an integrated ensemble of advanced MRI approaches including structural MRI, memory task and resting state fMRI, arterial spin labeled perfusion (3D pCASL), and diffusion imaging (DTI and NODDI) on the Prisma 3T platform with 64 channel RF coil and new multiband acquisition sequences. Banking and analysis of fluid biomarkers (blood, CSF) and analysis of APOE and other candidate genes will provide a biological context and new opportunities to understand very early changes from a systems biology perspective. The overall objectives of this research are to validate early prognostic biomarkers and enhance the understanding of biological mechanisms in early stages through accomplishing three specific aims: (1) Determine the stage-specific profile of functional disruption of brain activity and connectivity, tau and amyloid burden, microvascular pathology, neurodegeneration, and inflammation in preclinical and prodromal AD; (2) Determine the temporal relationships and interactions among pathophysiological domains; and (3) Determine which combination of baseline markers is most predictive of clinical and MRI progression on follow- up. The ultimate impact of this research is to facilitate the development of effective precision medicine for AD.

PROJECT SUMMARY The goal of the proposed project is to understand the role of personal social network dynamics in the etiology and clinical progression of mild cognitive impairment (MCI) and Alzheimer disease (AD). We propose to characterize social-behavioral and biological mechanisms underlying relationships between social networks and aging-related neuropathology. AD and dementia takes a devastating toll on individuals, families, and the health care system. A critical point of intervention in AD is the social environment, which has the potential to moderate underlying neuropathology, altering the typical cognitive course of dementia. Positive social interaction ? including number of confidants, frequency of social contact, support, and social engagement ? is associated with reduced risk for dementia and a slower trajectory of cognitive decline among diagnosed individuals. However, the existing literature relies on limited and unidimensional measures of social interaction, and has yet to consider the role of underlying biological neurodegeneration, which manifests long before observable clinical cognitive symptoms of dementia. The proposed project addresses these gaps via three specific aims: Aim 1 is to identify baseline associations between social network characteristics and neurodegeneration (QNPs). Aim 2 is to examine longitudinal relationships between personal social network dynamics and neurodegenerative changes. Aim 3 is to evaluate alternative models of the coevolution of personal social networks and neurodegenerative changes in trajectories of clinical cognitive decline. The proposed study is interdisciplinary, combining leading-edge methods from the social and biomedical sciences, and leveraging the resources of funded centers for AD, neuroimaging, and network science. By increasing our understanding of the links between biological and social processes, this project may help identify novel targets for intervention to reduce the burden of AD on individuals, families, and the health care system.

Abstract This administrative supplemental application is focused on seeking additional support to the Indiana Alzheimer?s Disease Center (IADC) in order to (1) provide phenotypic data from the Indianapolis-Ibadan Dementia Project to the Alzheimer?s Disease Sequencing Project (ADSP); and (2) further develop the IADC data hub and provide dashboard of real-time data reporting and data visualization. The Indianapolis-Ibadan Dementia Project is a unique, population-based cohort study that was the largest and longest NIH funded study involving populations of African descent. The project enrolled over 8000 community-dwelling elderly (age > 65 years) African Americans living in Indianapolis and Yoruba living in Ibadan, Nigeria. A unique strength of the IIDP was that the study employed identical study design, research methods and timelines for the African American cohort and the Nigerian cohort. Detailed longitudinal assessment of cognitive, psychosocial and functional data were collected over 20 years. The project also included the collection of blood samples and DNA. Genome-wide association study analyses (GWAS) data from the study are available in NIAGADS for African Americans and dbGAP for Yoruba. Whole genome sequencing is now being conducted on both African American and Yoruba samples by Alzheimer?s Disease Sequencing Project (ADSP). Since the original IIDP databases were not set up for data sharing, we seek funding support to provide detailed phenotypic data to ADSP. The development of the IADC data hub provides programming tools that can be adopted by other ADCs for real-time data access, data reporting and visualization leading to better and more efficient use of research data for Alzheimer?s disease.

Abstract The renewal of this research program addresses two critical bottlenecks in Alzheimer's disease (AD) research: earlier detection of those at risk and identification of important biological processes during preclinical and early stages of disease. These challenges are the key to development of diagnostic and therapeutic approaches, as well as prevention strategies that will need to be implemented at least 5-10 years before dementia onset. During the prior funding period, this project contributed novel information on individuals with amnestic mild cognitive impairment (MCI) and helped drive the field toward a focus on pre-MCI stages. A major innovation has been the investigation of euthymic older adults with cognitive complaints that score within the normal range on cognitive testing. In this group we have reported alterations in brain structure, functional activation, and connectivity in a network of memory-related regions that are typically intermediate between the pattern seen in cognitively normal controls (CN) and individuals with MCI. This phenotype, now referred to as subjective cognitive decline (SCD) and defined by an international consensus panel in 2013, is influencing large-scale studies including the Alzheimer's Disease Neuroimaging Initiative (ADNI), which adopted the Cognitive Change Index (CCI) developed in this project to recruit a similar group (SMC). We piloted a dual- tracer PET approach to study molecular signatures as a function of stage of disease, initially examining the relationship of amyloid burden and immune activation (microglia). With the new availability of PET tracers for in vivo measurement of tau burden, we will measure both tau and amyloid in preclinical and prodromal stages of AD, focusing on targeted regions based on neuropathological studies. The role of amyloid and tau deposition as molecular drivers of early functional disruption of brain activity and connectivity, as well as neurodegeneration, will be investigated using an integrated ensemble of advanced MRI approaches including structural MRI, memory task and resting state fMRI, arterial spin labeled perfusion (3D pCASL), and diffusion imaging (DTI and NODDI) on the Prisma 3T platform with 64 channel RF coil and new multiband acquisition sequences. Banking and analysis of fluid biomarkers (blood, CSF) and analysis of APOE and other candidate genes will provide a biological context and new opportunities to understand very early changes from a systems biology perspective. The overall objectives of this research are to validate early prognostic biomarkers and enhance the understanding of biological mechanisms in early stages through accomplishing three specific aims: (1) Determine the stage-specific profile of functional disruption of brain activity and connectivity, tau and amyloid burden, microvascular pathology, neurodegeneration, and inflammation in preclinical and prodromal AD; (2) Determine the temporal relationships and interactions among pathophysiological domains; and (3) Determine which combination of baseline markers is most predictive of clinical and MRI progression on follow- up. The ultimate impact of this research is to facilitate the development of effective precision medicine for AD.

Project Summary Brain imaging genetics studies the relationship between genetic variations and brain imaging quantitative traits (QTs) and offers enormous potential to reveal the genetic underpinning of the neurobiological system that can impact the development of diagnostic, therapeutic and preventative approaches for complex brain disorders. Two critical gaps limiting the progress of brain imaging genetics include (1) the unprecedented scale and complexity of the imaging genetic data sets, and (2) lack of intermediate-level omics data to capture the molecular effects linking genetics to brain QTs. Our prior studies have contributed substantially to addressing the first gap. The proposed project will develop new informatics strategies to bridge the second gap, where valuable existing data in the omics domain will be leveraged to link brain imaging and genetics. In this project, we will focus on transcriptomics, and will make use of major transcriptomics data repositories including Genotype-Tissue Expression (GTEx) Project, UK Brain Expression Consortium (UKBEC), and Allen Human Brain Atlas (AHBA). Our overarching goal is to identify brain imaging genetic associations with evidence manifested in the human brain transcriptome. Our hypothesis is that, with additional source of evidence at the transcriptomic level, the identified brain imaging genetic associations are biologically more meaningful and less likely to be false positives. To achieve our goal, we propose four aims. Aim 1 is to develop novel bi-multivariate models incorporating regional tissue-specific expression quantitative trait locus (eQTL) knowledge for mining brain imaging genetic associations. Given that eQTL is a source of tissue-specific evidence to link genotype, gene expression, and brain QTs, we will develop novel eQTL-guided bi-multivariate models to identify imaging genetic associations potentially evidenced by regional tissue-specific eQTL knowledge. Aim 2 is to develop novel bi-multivariate models incorporating brain-wide genome-wide (BWGW) cross-domain co-expression patterns for mining brain imaging genetics associations. AHBA, a BWGW gene expression database, is a natural connection between genome and brain. We propose to develop novel biclustering and bi-multivariate methods to identify meaningful AHBA modules with cross-domain co-expression patterns, and use these patterns to guide the search for co-expression-aware associations between genetic variations and multimodal brain imaging measures. Aim 3 is to develop open source software tools for structure-aware mining of brain imaging genetic associations. Aim 4 is to perform evaluation and validation on both simulated data and real imaging genetics cohorts. Successful completion of the above aims will produce innovative informatics methods and tools for integrative analysis of imaging, genetics and transcriptomics data to address a critical barrier in brain imaging genetics. Using ADNI and related cohorts as test beds, these methods and tools will be shown to have considerable potential for understanding the molecular mechanism of Alzheimer?s disease, and be expected to impact neurological and psychiatric research in general and benefit public health outcomes.

ABSTRACT As the US population ages, it is increasingly important to understand heterogeneity in cognitive aging including pathologic conditions like Alzheimer?s disease (AD) and cancer-related cognitive decline (CRCD). There is data to suggest that CRCD and AD share important cognitive aging features. The objective of this secondary data analysis project is to test if older breast cancer survivors with CRCD have clinical-pathological features of AD, including AD-pathology biomarker abnormalities, cognitive changes, brain imaging alterations, and similar risk factor profiles. To accomplish this goal, we will use existing de-identified data and banked specimens from the Thinking and Living with Cancer (TLC) study cohort. TLC includes female breast cancer survivors ages 60- 98 years old assessed pre-treatment and annually for up to 60 months and an equal number of contemporaneously assessed non-cancer controls (n=700/group). Consent included future use of data and specimens for new research purposes. Studying older breast cancer survivors is logical since they are already facing cognitive aging, CRCD has been described most often in breast cancer, the survivors are in the age range where non-cancer populations with APOE-?4 develop AD, AD rates are higher in females vs. males, and 35% of TLC survivors already have global cognitive decline based on significantly greater change than the non-cancer controls. Longitudinal TLC data include scores on neuropsychological tests of memory, executive functioning, language, and visuospatial abilities; demographics; AD risk factors; and inflammation markers (IL- 6, TNF-a, IL-8, IL-10, IFNg, CRP). We add to these data by using banked specimens to test plasma AD- pathology biomarkers (A?1-42, tau, p-tau, and neurofilament light chain [NFL]) and danger-associated molecular patterns (DAMPs: A?, S100 proteins, and HMBG1). A sub-set of TLC survivors at Indiana University has baseline and 12-month MRI data using the NIA-funded Indiana Alzheimer?s Disease Center (IADC) protocol. We will complete 24-month imaging of these survivors (n=75) to assess post-acute effects. We will compare TLC survivor results to TLC non-cancer controls and published AD data, including those specific to women. The aims are to test hypotheses about associations between: 1) CRCD and clinical-pathological features of AD, 2) CRCD and established AD-risk factors, and 3) AD-related inflammatory markers and AD clinical- pathological features in CRCD and explore if inflammation mediates CRCD risk. This research is significant because we are looking at biological mechanisms for two important cognitive aging processes- CRCD and AD. We will advance NIA research goals by elucidating the impact of genetics and inflammatory processes on cognitive aging. This research is significant because cognitive aging has clinically important effects on daily life. We are not aware of any studies comparing CRCD and AD, and none that include an established collaboration of cancer, Alzheimer?s, and geriatrics investigators working together across silos. Overall, this study will move the field forward by determining potential bidirectional mechanisms between CRCD and AD.

Project Summary/Abstract The Korean Brain Aging Study for the Early Diagnosis and Prediction of AD (KBASE) is a comprehensive prospective cohort study launched at Seoul National University (SNU) in 2014 using a similar design and methods as the North American Alzheimer?s Disease Neuroimaging Initiative (ADNI). The KBASE cohort consists of well-characterized participants including cognitively normal (CN) controls with a wide age range (20 to 90 years), mild cognitive impairment (MCI) and AD dementia (AD). A unique aspect of KBASE is the systematic longitudinal collection of comprehensive clinical, cognitive and lifestyle data, multimodal neuroimaging (MRI/MRA, DTI, rsfMRI, amyloid, tau and FDG PET), and bio-specimens in Korea for the first five years (?KBASE1?). Some KBASE data have been analyzed and reported but much of the extensive KBASE data set and samples await comprehensive analysis. The proposed project (?KBASE2?) represents a collaboration between the NIA Alzheimer?s Disease Sequencing Project (ADSP) and partners, Indiana University (ADNI Genetics Core, Indiana NIA-designated ADRC, and IU Network Science Institute), the KBASE team at SNU in Korea, University of Southern California (USC), and the University of Pennsylvania. Over 1000 whole genome sequences (WGS) of Korean participants will be contributed to the ADSP, and the extensive ADSP multi-ethnic data set will be analyzed. WGS data will be harmonized by the NIA Genetics and Genomics Center for AD (GCAD) and shared via the NIA Genetics of AD Data Storage Site (NIAGADS). The Laboratory of Neuroimaging (LONI) at USC will support sharing of MRI and PET and related endophenotypes, as it does for ADNI. KBASE2 will continue longitudinal data and sample collection and provide high throughput WGS and RNA-Seq as well as data harmonization and sharing (Aim 1), perform intensive brain network-based analyses of longitudinal amyloid, tau, neurodegeneration and vascular (A/T/N/V) imaging biomarkers in relation to clinical data (Aim 2), employ integrative systems biology and functional genomics methods to analyze multi-omics data for association with A/T/N/V biomarkers for AD, and provide new insight into AD biomarker-related dysregulated gene modules and pathways (Aim 3). The overarching concepts driving this multidisciplinary international collaborative project are that 1) development of precision medicine for AD and related disorders (ADRD) requires systematic multi-modal biomarker collection in diverse cohorts during early at-risk stages of disease to identify robust diagnostic, prognostic and therapeutic targets and 2) sophisticated analytic strategies that address the complexity of multi- layer multimodal data and heterogeneous and diverse participant cohorts are essential. We hypothesize that integrative longitudinal analysis of genetic and -omics networks with structural and functional brain networks will yield new diagnostic and treatment-relevant insights related to A/T/N/V and other aging related pathways. Results of this collaboration and data sharing will facilitate translation of ADSP findings for therapeutic development in support of the National Alzheimer's Project Act goal of prevention and treatment of AD by 2025.

Project Summary ? IADRC Overall The Indiana Alzheimer?s Disease Research Center (IADRC) was established in 1991 to bring investigators and institutional resources at the Indiana University School of Medicine (IUSM) together to address the fundamental causes and treatment of Alzheimer?s disease (AD) and related dementias (ADRD). Despite many important gains, the need for targeted research is greater than ever, with an estimated 5.8 million people in the U.S. suffering from AD/ADRD. Unfortunately, we do not yet know how to prevent AD or have an approved disease modifying intervention. Both are critical to stem the growth in dementia prevalence. The overarching goal of the IADRC going forward is to support the goal of the NAPA to prevent and effectively treat AD by 2025, through innovative research on etiology, early detection, and therapeutics. Biomarker studies indicate that processes leading to AD begin at least 20 years prior to dementia, suggesting that successful interventions must be implemented early. This presents a potential opportunity for early intervention, but the field is challenged by critical barriers decreasing the prospects of timely success. The IADRC has identified the barriers as: a) The current understanding of etiology and pathophysiology is fragmented and incomplete; b) Sensitive, specific, and cost-effective methods for early detection are not available; c) Therapeutic development is hampered by the heterogeneity and complexity of ADRD; d) Shortage of data and translational scientists; and, e) Inadequate diversity at all levels. The IADRC specific aims entail innovation to overcome these barriers and accelerate research toward prevention and effective treatment: 1) Support, enhance, and expand innovative research on ADRD targeting causes, diagnosis, treatment, and prevention; 2) Provide critical research resources and infrastructure to support existing studies and enable new innovative research, utilizing a well-characterized longitudinal clinical cohort, with prioritization of diverse populations including underrepresented groups (URG) and those in preclinical and early symptomatic phases, including subjective cognitive decline and mild cognitive impairment, which will help to advance the identification of easily accessible biomarkers for early detection; 3) Identify and prioritize novel therapeutic targets from high-throughput approaches with rapid translation to proof- of-concept studies using genetic and other enrichment strategies for better biological targeting and reduction of phenotypic and biological heterogeneity for more efficient and cost-effective clinical trials; 4) Increase the number of investigators with deep expertise in advanced data sciences to bridge cellular/molecular processes of neurodegeneration and clinical phenotypes, as well as clinical and translational researchers who can move therapeutic approaches from model systems to clinical trials; 5) Provide educational and training opportunities related to dementia for a broad array of learners, with special emphasis on increasing participation from URG in ADRD related research and healthcare specialties. The IADRC is well-positioned to help achieve the NIA/NAPA goals through sustained and impactful contributions towards prevention and treatment of AD/ADRD.

The overall goal of the program is to provide multidisciplinary training in Alzheimer?s disease and AD-related dementias (AD/ADRD) to predoctoral and postdoctoral trainees, producing a critically-needed translational workforce for developing effective treatments. The Indiana University School of Medicine (IUSM) is nationally recognized for AD/ADRD research. IUSM-associated centers include the Indiana Alzheimer?s Disease Research Center, the National Cell Repository for Alzheimer?s Disease, the Indiana Center for Neuroimaging, the Regenstrief Institute and the Center for Aging Research. Importantly, the NIA recently funded the MODEL-AD Center, the Alzheimer?s Disease Drug Discovery Center, and the IUSM-directed Longitudinal Early-Onset Alzheimer?s Disease Study. IUSM has invested significantly in state-of-the-art facilities, instrumentation, and the hiring of 10 new AD/ADRD faculty over the past 4 years. This T32 application has enlisted 25 participating faculty with a broad range of expertise including data science, non-invasive imaging, the development and study of animal models, genomics, epidemiology, drug development and discovery, and clinical studies and trials. These faculty are exceptional mentors and are funded at $42M total ($1.69M/faculty annually). This application requests funding for 4 pre- and 4 post-doctoral trainees. We will recruit trainees with diverse backgrounds and train them in AD/ADRD research with contemporary approaches and methodologies using state-of-the-art instrumentation. The predoctoral trainees are graduate students in the Medical Neuroscience Graduate Program, recruited from the IUSM Indiana Biomedical Gateway graduate program and the MD/PhD program. Targeted recruiting of postdoctoral trainees with diverse backgrounds is achieved informally, through our website, and at national and regional meetings. Enrollment in the MedNeuro Graduate Program has doubled since 2017 and the number of postdoctoral fellows with participating faculty has increased from 4 to 27. The predoctoral training program consists of foundational course work in neuroscience, neurodegenerative diseases, and data science along with elective studies. Postdoctoral trainees are offered didactic training in data science. The NIA-sponsored centers and resources at IUSM allow trainees to work on new disease models, advanced drug-discovery technologies, and in clinical settings. This positions trainees to conduct innovative basic, translational, and clinical AD/ADRD research.

ABSTRACT In response to PAR-19-269 ?Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data (U01 Clinical Trial Not Allowed)?, our project unites experts in AD genomics, machine learning and AI (including deep learning), large-scale data integration, and international data harmonization to work in a carefully-designed Consortium Structure in close partnership with the NIH, ADSP, and NIAGADS. We will develop a suite of complementary big data analytic approaches for ultra-scale analysis of Alzheimer?s Disease (AD) genomic and phenotypic data. The vast data volumes now generated by the Alzheimer?s Disease Sequencing Project (ADSP), National Alzheimer?s Coordinating Center (NACC), Alzheimer?s Disease Neuroimaging Initiative (ADNI), Accelerating Medications Partnership AD (AMP-AD), and UK Biobank (UKBB), far exceed the capacity of all current analytic methods, which have not kept pace with the scale and speed of data collection. This vast amount of genetic and phenotypic data mandates new and more powerful algorithms to: (1) store, manage, and manipulate whole-genome sequences and associated data on an ever-growing scale; (2) discover novel AD risk and protective loci by merging informatics and AD genomics databases; (3) relate whole-genome changes to the ATN(v) biomarkers that now define biological AD. Our Ultrascale Machine Learning Initiative, or ?ULTRA? - will offer new AI and deep learning tools to discover features in massive scale genomics data - relating whole genome data to biomarker features by merging all relevant data sources. Our team of experienced PIs will coordinate efforts across the U.S. to create these large-scale data analytic tools. Our MPI team and 6 Core Leads have decades of experience working together and with the AD community in pioneering machine learning methods for AD genetics and neuroimaging, including leadership of international neuroimaging consortia across the world. Dedicated Cores focus on Genomic, Imaging, and Cognitive Data Harmonization. Curated data will then be efficiently imported into AI approaches and informatics pipelines that will allow the AD research community to leverage ultra-scale, multidimensional genomic and phenotypic data from the ADSP, NACC, ADNI, AMP-AD, and others. Our work is organized by a carefully-designed and coordinated Consortium guided by all stake-holders, clinical leaders, and pioneering analysts in AD genomics and neuroimaging. Our ultrascale AI tools will advance AD genomics research and will include efforts in training, and a dedicated Drug Repurposing Core. This team effort will accelerate understanding of the genetic, molecular and neurobiological mechanisms of AD, yielding significant translational impact on disease and drug development.