SCH: Novel and Interpretable Statistical Learning for Brain Images in AD/ADRDs - Biomedical imaging technology has undergone rapid advancements over the last several decades, producing large volumes of multimodal imaging data that hold great promise as biomarkers for agingrelated diseases such as Alzheimer’s. Current imaging biomarkers are primarily based on specific extracted one-dimensional measures that may not fully capture the richness of imaging data. Utilizing three-dimensional (3D) or higher imaging information directly may facilitate the identification of more effective disease biomarkers to inform diagnosis, prognosis, and treatment. However, this also brings significant challenges, such as analyzing ir-regularly shaped 3D objects, managing high-dimensional and high-resolution data, addressing noisiness and complexity, quantifying uncertainty, and ensuring the interpretability of the results. Our multi-institutional, inter-disciplinary team of investigators will develop efficient statistical learning approaches and scalable computing tools to extract and assess biomarkers from large-scale brain imaging studies. We will also incorporate genetic and clinical information in constructing the biomarkers. Specifically, our proposal comprises five interrelated research aims carried out by investigators with complementary expertise from three institutions. Aim 1 focuses on developing an interpretable model for genome-wide association studies (GWAS) with brain imaging pheno-types and non-visual contextual information. Aim 2 targets to develop novel nonparametric distributed learning methods for analyzing 3D brain imaging data using an innovative domain decomposition strategy to improve computing performance. Aim 3 quantifies the bias effect in image processing and develops inference methods to reveal the underlying signal from brain imaging data and identify significant brain regions among different diagnosis groups. Aims 4-5 aim to develop statistical methods for obtaining and evaluating imaging-adjusted biomarkers for disease diagnosis and prognosis and assess the incremental value of imaging information over genetic biomarkers on diagnosis and prediction accuracy. The efficacy of the methods developed in this pro-posal will be tested by data collected from studies in Alzheimer’s disease and brain sciences. The proposed research will address critical gaps in current biomarker development and analysis by utilizing advanced sta-tistical learning approaches and computing tools to directly utilize the 3D or higher imaging information. This innovative approach holds the potential to provide more effective disease biomarkers, leading to improved accuracy in diagnosis, prognosis, and treatment for Alzheimer’s disease and related dementias.
