4D mapping of network-specific pathological propagation in Alzheimer's disease
Author(s)Canter, Rebecca Gail
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences.
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Alzheimer's disease (AD) causes a devastating loss of memory and cognition for which there is no cure. Without effective treatments that slow or reverse the course of the disease, the rapidly aging population will require astronomical investment from society to care for the increasing numbers of AD patients. Additionally, the financial and emotional burden on families of affected individuals will be profound. Traditional approaches to the study of AD use either biochemical assays to probe cellular pathophysiology or non-invasive imaging platforms to investigate brain-wide network alterations. Though decades of research using these tools have advanced the field significantly, our increased understanding of AD has not led to successful interventions. One reason for this impediment may be that the tools used in neither approach can achieve the spatial and temporal precision necessary to study the consequences of molecular insults across the brain over time. In this thesis, I capitalize on recent advances in tissue processing technologies to gain a network-level perspective on the molecular and cellular progression of AD. First, I present optimized methods for in situ proteomic phenotyping of large-volume tissue specimens. Then, I use the techniques to map amyloid-beta (A[beta]) aggregates at the whole-brain scale across disease stages in a mouse model of AD. The spatially-unbiased, temporally-precise map demonstrates hierarchical susceptibility of increasingly large, memory-related brain networks to A[beta] deposition. Importantly, the 4D nature of the map reveals that subcortical nodes and white matter tracts of the Papez memory circuit exhibit unique, early vulnerability to A[beta] aggregates. Finally, using large-volume labeling approaches, I confirm the molecular findings by showing disease-specific A[beta] aggregation in human samples from the early hub regions. Together, this data unites desperate observations of network-level deficits and identifies critical locations of early A[beta] deposition in the brain. By linking molecular and network observations, I begin to provide biological explanations for the clinical manifestation of AD. This perspective can guide earlier patient identification and refine experimental approaches to developing cognitively efficacious treatments. These discoveries emphasize the necessity of multi-level investigations in neuroscience research and highlight the potential impacts of tools that enable researchers to bridge the gap.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 113-132).
DepartmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences.
Massachusetts Institute of Technology
Brain and Cognitive Sciences.