The overall goal of the proposed research is to define the specific brain networks that are vulnerable or resilient in aging and Alzheimer’s disease (AD), and subsequently derive new accurate, precise, and robust connectomic imaging biomarkers for (especially preclinical) AD, which could improve diagnosis, disease staging, prediction, assessment of progression, and therapeutic efficacy. Information flows in the human brain through a complex set of structural and functional networks. The complete connectivity map among brain areas, i.e. the connectome, can help to better understand the vulnerability and resilience of the brain architecture and function to aging effects and debilitating neurodegenerative diseases, such as AD, and to discover diagnostically and therapeutically important biomarkers. Focusing on brain regions, but not interregional connectivity, may have hindered progress in understanding and treating disorders characterized as “disconnection syndromes”. Diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI) are used to noninvasively quantify structural and functional brain networks, respectively. Network-based analysis of the brain has proved promising in revealing the basis of cognitive dysfunction in mild cognitive impairment (MCI) and AD, demonstrating changes distinct from those with healthy aging. Development of treatments to prevent or delay the onset of AD would be greatly facilitated by a noninvasive, sensitive, and specific diagnostic biomarker able to discriminate cognitively normal people and MCI patients who will progress to AD from those who will age healthily. Structural connectivity between two brain regions is often defined based on the dMRI tractography-derived streamlines between them. The direct fiber bundle connecting two brain areas is expected to be the major signal carrier between them; however, multi-synaptic neural pathways (those mediated through other regions) also provide connectivity. The investigators prop