Heterogeneity of Multi-modal Imaging Signatures of Aging, MCI, Alzheimer's disease via Pattern Analysis

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Abstract Alzheimer's Disease (AD), as well as its prodromal stages, poses a significant burden to the community and health care system. Several other pathologic processes often co-exist with AD pathology. Characterizing the multi-faceted aspects of brain changes present in aging and (prodromal) AD not only provides insights into the underlying pathophysiological processes, such as amyloid deposition, small vessel ischemic disease(SVID), and neurodegeneration or functional change, but also leads to biomarkers of these pathologic processes. In this proposal, we aim to capture the heterogeneity of various imaging patterns that reflect pathologic processes occurring with aging and prodromal AD, by applying state of the art heterogeneity analysis machine learning methods recently developed in our laboratory to multi-faceted imaging data (structural MRI, resting state functional MRI, amyloid imaging). These methods allow us to quantify the different ways/patterns in which an individual can fall off the normative brain aging trajectories. Our goal is to arrive at a new “Imaging-based coordinate SysTem for AGing and NeurodeGenerative diseases”(iSTAGING), each dimension of which will reflect a different pattern of brain alterations, hence capturing the underlying neuroanatomical, neurofunctional and neuropathological heterogeneity in quantifiable and replicable metrics. Moreover, we aim to link these neuroimaging phenotypes with neurocognitive phenotypes, as well as with progression from cognitively normal aging to MCI and to dementia. This will allow us to place each individual into a new dimensional brain coordinate system of aging and map his/her trajectory, as well to determine predictive indices emanating from multi-parametric imaging data. Prior to achieving these aims, we will strengthen and apply various imaging harmonization methods, which are critical in ensuring that data across studies and scanners can be integrated in a constructive way. In particular, Aim1 will apply inter-site imaging harmonization methods, and hence develop a platform that will allow us to integrate multi-parametric imaging datasets from more than 10,000 participants to several studies spanning ages 45 to 89; we will therefore form a large multi-modal imaging database capturing the heterogeneity of normal brain aging and prodromal AD. Aim 2 will use machine learning methods to integrate spatial patterns of brain atrophy, SVID, Aβ deposition and functional connectivity in this population, and will derive normative brain aging curves. Aim 3 will use state of the art semi-supervised learning methods to disentangle the heterogeneity of imaging patterns distinguishing resilient from advanced brain aging: this will lead to the dimensional system (iSTAGING) capturing the diverse dimensions/patterns of advanced (non-resilient) age-related brain change relative to resilient brain aging, which reflect underlying neuropathological processes. Aim 4 will relate iSTAGING coordinates and longitudinal trajectories to cognition, risk factors, and clinical progression from normal cognition to MCI to dementia, in order to further elucidate the clinical correlates of these neuroimaging dimensions.