Abstract. A plethora of neuroscience and neuroimaging studies have shown that Alzheimer’s disease (AD) differentially affects certain regions of the brain and specific cell types. Since AD-related pathological events often propagate trans-neuronally, the selective vulnerability to neuron loss and structure damage also manifest in the topological patterns of network alteration. Along with many other studies, the research team has found the strong evidence that (1) AD preferentially affects hub nodes in the network that are densely connected in the network, and (2) the propagation of neuropathological burdens such as amyloid plaques and neurofibrillary tangles exhibit unique topological patterns that are governed by the self-organized harmonic bases. However, the factors underlying this network vulnerability and the molecular mechanism regulating the selectivity in AD remain unclear. In this regard, we aim to continue the development of cutting-edge network analysis tools with a greater methodological understanding of how neuropathological events selectively affect certain harmonic bases (harmonic-selective network vulnerability) and how brain networks counteract AD pathology (network resilience). In this context, the backbone of this project is a harmonic factor analysis model that can be used as a neurobiological basis to accurately characterize the whole-brain mapping of neurodegeneration at a system level, where each harmonic factor explains how the ubiquitous propagation (wave) pattern of neuropathological event emerges from the particular structural connectome pathway. In Aim 1, we will leverage the well-studied biophysics concept of power and energy to identify a set of harmonic-selective vulnerable patterns that account for network vulnerability between normal aging and AD. Also, we will associatethe identified network vulnerability with couple factors from diverse research fields which include stochastics of selectivity (statistics), system criticality (physics), network organization (network neuroscience), and cognitive domains (clinic). After that, we will seek for the putative harmonic-genetics biomarker based on the discovered association between network vulnerability and genetics factor in Aim 2 and develop a harmonic-genetic approach to capture network resilience in Aim 3. In Aim 4, we will apply the computational approaches developed in Aim 1-3 to establish (1) a fine- grain understanding of network vulnerability and resilience across A (amyloid-PET), T (Tau-PET), and N (FDG- PET and cortical thickness) biomarkers, and (2) a longitudinal underpinning of the dynamics of network vulnerability by investigating the longitudinal change of AT[N] biomarkers. The diagnostic power of our novel harmonic-genetics biomarker and resilience will be evaluated in our current AD diagnostic engines. We will release the software (both binary program and source code), to facilitate the other AD biomarker projects and the neuroimaging studies of other n...