The brain is a complex network of anatomically interconnected regions, supporting a wide range of cognitive functions that deteriorate with increasing age. Understanding the neurological basis of this decline is crucial for developing early therapeutic interventions. The aim of this project is to use multimodal neuroimaging data, including structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), and positron emission tomography (PET) in order to investigate the association between the cognitive decline and various risk factors, including lifestyle, genetic or demographics variables. Each modality provides unique insights into the brain's structure, function, and microstructural properties, enabling a comprehensive understanding of the brain organization throughout aging. Efficient and high-quality preprocessing of such large brain imaging datasets is essential for the successful application of subsequent methods, such as deep learning algorithms. By integrating these multi-modal neuroimaging biomarkers, this study aims to identify the vulnerable connections within the aging brain and provide novel insights into the neurobiological mechanisms underlying the age-related cognitive decline. Our findings hold the potential to contribute to the development of targeted interventions to preserve the cognitive health in the aging population.