PROJECT SUMMARY Alzheimer’s Disease and related dementia are a growing public health crisis affecting 5.8 million Americans, yet there are only four FDA-approved medications for Alzheimer’s Disease, none of which are disease-modifying. Hence, early detection and diagnosis are key to successful patient management and biomarkers are needed for evaluating new therapies in clinical trials. White matter changes are increasingly implicated in early Alzheimer’s Disease progression, and diffusion weighted magnetic resonance imaging (DW-MRI) has been included in many national-scale studies. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variation in acquisition protocols, sites, and scanners. DW-MRI enables quantification of brain microstructure and facilitates structural connectivity mapping. Substantial recent progress has been made with calibration and harmonization to reduce inter-subject variance and improve interpretability of computed measures. Yet, the fundamental challenge remains that clinical application of DW-MRI (as currently implemented) is confounded by inter-scanner and inter-site effects. To improve understanding of structural changes in Alzheimer’s Disease, we will construct and evaluate three separate analysis strategies to characterize, calibrate, and optimize DW-MRI for single-subject biomarker development for Alzheimer’s Disease. We will integrate and optimize our strategies using large retrospective multi-site studies and validate the approaches on two distinct prospective cohorts. Specifically, we aim to: Aim 1: Optimize data-driven techniques for stability across sessions, scanners/sites, and field strengths Impact: Harmonized DW-MRI methods will increase sensitivity to Alzheimer’s Disease and its prodromal stages. Aim 2: Translate innovations in microstructural harmonization to structural connectivity (tractography) Impact: Harmonizing structural connectivity will improve understanding of white matter in Alzheimer’s Disease. Aim 3: Advance statistical tools for single-subject inference through normative database construction Impact: Data-driven resources for uncertainty estimation will enable robust single-single subject inference. Relevance and Impact on Healthcare: The proposed research will advance understanding of Alzheimer’s Disease through (1) quantitative harmonization of DW-MRI biomarkers, (2) protocols for harmonization of retrospective and prospective DW-MRI studies, and (3) new tools for single subject inference targeting older cohorts. We will organize workshops/challenges to maximize the translational impact on clinical science. The long-term goal of our research is to (1) provide a well-validated strategy to quantitatively evaluate DW-MRI data across sites, (2) enhance DW-MRI biomarkers for Alzheimer’s Disease, and (3) advance patient care. Our research strategy will transform the manner in which DW-MRI data are interpreted and enable single-subject machine learning to interpret brain properties. The resources, software, and visualization tools will be made freely available in open source through DIPY to facilitate continued innovation.

PROJECT NARRATIVE Alzheimer’s Disease is a significant public health concern, and identifying opportunities for early intervention is critical to developing effective interventional strategies. Diffusion weighted magnetic resonance imaging (DW- MRI) has shown that subtle changes in cerebral white matter are prognostic for disease progression, but quantitative interpretation of these measures in existing large studies is confounded by protocol, scanner, and site effects. This study will translate recent advances in harmonization of DW-MRI to improve specificity of white matter microarchitecture metrics (aim 1), translate connectivity analyses (aim 2) for existing large Alzheimer’s Disease neuroimaging studies, and enable robust single subject inference for abnormality detection and prognosis (aim 3).