Clinicians, physicists and researchers that are interested in the applications of structural and functional connectivity methods based on MRI to Alzheimer’s Disease.The attendee will understand the applications of structural MRI, functional BOLD and diffusion MRI to characterize the network-based vulnerability patterns in neurodegenerative diseases.Each neurodegenerative disease is defined by selectively vulnerable neurons, regions, networks, and functions, as well as genetic risk factors. In the past decade, new network-sensitive neuroimaging methods have made it possible to test the notion of network-based degeneration in living humans. Previous work has demonstrated that the spatial patterning of each disease relates closely to a distinct functional intrinsic connectivity network (ICN), mapped in the healthy brain with task-free or “resting-state” functional magnetic resonance imaging (fMRI) (1). Collectively, these findings raise mechanistic questions about 1) onset of each disease; 2) disease progression patterns; 3) whether and how connectivity in health/prodromal stage could predict neurodegeneration progression/severity in disease.In this talk, the basic theory/preprocessing/data analyses of structural covariance networks (MRI), functional connectivity (fMRI-BOLD) and structural connectivity (Diffusion MRI) will be introduced. We will focus on applications of these network-sensitive methods on two common causes of dementia, Alzheimer's disease (AD) and frontotemporal dementia, but uses these diseases to illustrate class-wide neurodegeneration principles whenever possible.Accumulating neuroimaging studies have shown (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state BOLD activity across several intrinsic brain circuits including default mode and attention-related networks in patients with mild cognitive impairment and mild AD compared healthy controls. Our previous work on task-free fMRI suggested that behavioral variant frontotemporal dementia and AD featured divergent functional changes within the two major networks (the default mode network and the salience network), consistent with known reciprocal network interactions and the opposite symptom-deficit profiles of the two disorders (2). One longitudinal study showed decreased intrinsic connectivity in the posterior DMN and increased connectivity in the anterior and ventral DMN subnetworks in AD compared to healthy controls at baseline. At follow-up, patients showed worsening connectivity across all default mode subsystems (3), in keeping with a network-based neurodegeneration model in which disease spreads from hot spots or “epicenters” to interconnected nodes within the target and, ultimately, off-target systems. Following this line of research, the phenotypic diversity produced by AD naturally raises the question of whether each clinical AD variant can be linked to a distinct large-scale network. A recent study tested this hypothesis by assessing intrinsic functional connectivity in healthy subjects, seeding regions commonly or specifically atrophied in early onset AD (EOAD), the logopenic variant of primary progressive aphasia (lvPPA) or posterior cortical atrophy (4). The authors found that the connectivity maps derived from commonly atrophied regions of interest resembled the default mode network, which was affected in all AD variants, whereas seeding regions specifically atrophied in each AD variant revealed distinct, syndrome-specific connectivity patterns in the healthy brain. Advanced graph theoretical methods will be briefly described to demonstrate network-based topological changes in AD and the possibility of predicting disease vulnerability. Our work on predicting neurodegeneration from the healthy brain functional connectome will be discussed (5). Four competing mechanistic hypotheses have been proposed to explain network-based disease patterning: nodal stress, transneuronal spread, trophic failure, and shared vulnerability. We used task-free fMRI and ICN methods to test model-based predictions of how intrinsic connectivity in health predicts region-by-region vulnerability to disease. For each of the five neurodegenerative diseases, specific regions emerged as critical network “epicenters” and graph theoretical analyses in healthy subjects revealed that regions with higher total connectional flow and, more consistently, shorter functional paths to the epicenters, showed greater disease-related vulnerability. These findings best fit a transneuronal spread model of network-based vulnerability.Lastly, the most important frontiers in the field of network-based neurodegeneration (e.g. searching for the onset site, monitoring the disease progression) will be reviewed. One recent multimodal longitudinal neuroimaging study in AD indicated that amyloid accumulation in remote but functionally connected brain regions may contribute to the longitudinally evolving hypometabolism in brain regions not strongly affected by local amyloid pathology, supporting the network-degeneration hypothesis (6). Further developed and tested in longitudinal multimodal dataset, brain network-based connectivity signatures from multimodal neuroimaging data may provide simple, inexpensive, and non-invasive biomarkers for differential diagnosis, disease monitoring, behavior prediction, and treatment planning in AD.