The advanced stages of Alzheimer's disease (AD) are associated with severe impairment in multiple cognitive domains, but early disease stages are heterogeneous, with a subset of patients displaying preserved memory combined with mild cognitive impairment (MCI) in non-amnestic domains. Although these non-amnestic MCI (naMCI) patients are rare, they challenge the profile of typical amnestic MCI in multiple ways. In addition to showing symptoms at a younger age, they have a higher burden of tau pathology in the neocortex and relative sparing of the medial temporal lobes (MTL). These differences call into question whether disease progression models based on amnestic MCI and AD can be applied to naMCI. Furthermore, it remains unclear whether neocortical disease observed in naMCI reflects specific vulnerability of the neocortex or protection of the MTL. The current project will investigate these questions by applying network neuroscience approaches to longitudinal imaging data from naMCI and amnestic MCI (aMCI) patients. Recent computational modeling studies in aMCI and AD have supported the trans-neuronal transmission hypothesis, which proposes that toxic proteins propagate via long-distance anatomical connections between brain areas, as well as regional changes in rates of protein aggregation and clearance. Each of these mechanisms is supported by converging evidence from experiments in rodents and cell preparations, but their contributions in naMCI have not been investigated. I will evaluate the evidence for each mechanism using network diffusion models, which represent disease spread through brain networks much as electric current flows through a circuit. I predict that disease progression in naMCI will be associated with markers of neocortical vulnerability, including increases in trans- neuronal transmission and regional production of pathology as well as decreased clearance. A complete model of disease progression in AD must be able to explain the heterogeneity that clinicians observe in patients' cognitive impairment and rates of disease progression. The current research will advance this understanding by testing whether mechanisms that are thought to influence disease progression in amnestic MCI can be generalized to naMCI. Furthermore, this work will provide me with essential training as I transition from postdoctoral research to an independent research program. I will benefit from mentorship and structured learning in network neuroscience and translational neuroimaging, positron emission tomography imaging, and the biological basis of neurodegenerative disease. By integrating the skills and domain-specific knowledge that I gain through this training, I will be able to launch a program of research focusing on predictive modeling of neurodegenerative disease progression with potential translational application. This research is particularly timely because of ongoing efforts to improve stratification and develop clinical trial end-points.

Public Health Relevance

Alzheimer's disease is typically associated with memory loss, but in some patients, early disease stages are associated with mild cognitive impairment in non-memory domains. This project uses network models of structural and molecular brain images to explain the progression of memory-related and non-memory forms of mild cognitive impairment, including differences in patterns of spreading disease as well as changes in the regional production and clearance of toxic tau proteins. The results of this research will enhance our understanding of the general mechanisms that contribute to disease progression and may ultimately help us better predict the course of disease in individual patients.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Scientist Development Award - Research & Training (K01)
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Behavior and Social Science of Aging Review Committee (NIA)
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Hsiao, John
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University of Pennsylvania
Schools of Medicine
United States
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