Neurodegenerative diseases like Alzheimer’s Disease (AD) and related dementias represent some of the most complex and multifaceted challenges in modern medicine. Their etiology involves a combination of genetic, epigenetic, environmental, and neurophysiological factors, making their study particularly challenging. Despite significant advancements in understanding key pathological features such as amyloid-β plaques and tau tangles, crucial gaps remain in our ability to link molecular biomarkers to observable neuroimaging phenotypes. Addressing these gaps is critical for early detection, effective diagnosis, and personalized therapeutic interventions. This proposal aims to bridge these gaps by developing advanced computational and mathematical methods to create synthetic cohorts and analyze multi-dimensional data from diverse modalities. Synthetic cohorts will replicate original datasets across modalities such as clinical assessments, cognitive evaluations, neuroimaging, and omics data (genomics, transcriptomics, and proteomics). These synthetic datasets will overcome barriers associated with patient recruitment, ethical constraints, and legal challenges, enabling cross-institutional research and significantly accelerating scientific progress in AD studies. Our approach begins with comprehensive data integration and preprocessing. Leveraging observational datasets like the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), we will unify diverse datasets comprising antemortem imaging and postmortem molecular data. Rigorous quality control, normalization, and integration will ensure a unified analytical framework, allowing for seamless analysis of high-dimensional data. Advanced computational resources will enable efficient processing of these datasets, ensuring readiness for downstream tasks. The second focus is on discovering cross-modality associations. Machine learning models will identify patterns linking brain imaging features, such as structural and functional connectivity, to molecular biomarkers. These data-driven approaches will uncover novel biomarkers and elucidate molecular pathways involved in neurodegeneration. The integration of imaging phenotypes with molecular data promises to yield a multidimensional understanding of AD, improving predictive diagnostics and opening avenues for targeted interventions. Finally, the project will develop synthetic cohorts using advanced generative models, including Generative Adversarial Networks (GANs), diffusion models, and autoencoders. These models will produce data that closely replicates original datasets, incorporating multimodal features such as neuroimaging, longitudinal metadata, and cognitive assessments. Synthetic cohorts will be evaluated for their fidelity to real datasets, their usability in machine learning tasks, and their potential to uncover new associations in neurodegenerative diseases. Crucially, these models will ensure privacy-preservation, preventing the generation of data that is too similar to original patient data. This proposal represents a comprehensive approach to accelerating AD research through multi-modal data integration and synthetic data generation. By combining innovative computational techniques with robust datasets, it will address key challenges in understanding and diagnosing neurodegenerative diseases. The insights gained will not only contribute to the scientific community but also pave the way for transformative changes in clinical practice, including early diagnosis and personalized therapeutic interventions.