Patients who develop sporadic Alzheimer's disease (AD) before age 65 (~5% of all AD patients) pose a clinical challenge and a scientific enigma. From a clinical perspective, early-onset (EO) patients often present with primary executive, language or visuospatial symptoms (with relative sparing of memory), and accurate diagnosis is challenging due to overlap with non-AD dementia and non-degenerative conditions. Emerging AD biomarkers could facilitate accurate diagnosis but have rarely been studied in this population. The performance of biomarkers in studies of typical late-onset (LO) AD cannot be generalized to EO patients because of differences in degenerative patterns and reference ranges. From a scientific perspective, EO syndromes show a striking dissociation between amyloid-beta (A?) pathology, which is diffuse and symmetric in all syndromes, and brain degeneration, which parallels symptoms and can be asymmetric or focal. This raises fundamental questions about the mechanisms that drive clinical and anatomic diversity in AD. This proposal applies detailed clinical phenotyping and multi-modal neuroimaging to optimize the diagnosis of EO syndromes, and to study mechanisms of heterogeneity in AD. Leveraging the specialization of the UCSF ADRC in early-onset dementia, the study will include 150 mildly impaired (CDR 0.5-1) EO AD patients, 50 each with a predominant executive/memory, language and visuospatial clinical phenotype, and 40 patients with LO-AD. A positive amyloid (PIB) PET scan will be required for inclusion. Patients will undergo structural MRI, functional connectivity (resting state) MRI (fcMRI), FDG-PET and CSF analysis. Comparative data from matched normal controls (NC) and non-AD dementia patients will be obtained from other ongoing studies. The central hypothesis of the proposal is that neurodegeneration in all AD variants converges in temporoparietal regions that comprise the posterior portion of the default mode network (DMN), a core, selectively vulnerable network in AD.
Aim 1 tests the diagnostic applications of this hypothesis by comparing the sensitivity and specificity of temporoparietal versus hippocampal MRI/FDG measures in discriminating EO-AD versus NC and non-AD dementia, and compares the performance of imaging biomarkers in EO vs. LO-AD.
Aim 2 applies fcMRI to test the hypothesis that the posterior DMN is affected across EO syndromes and in LO-AD, while the relative involvement of other functional networks drives the clinical phenotype in each AD variant.
Aim 3 investigates how functional connectivity in healthy adults relates to the patterns of amyloid deposition and neurodegeneration in AD variants, in order to test a model in which A? deposition is driven by nodal stress in cortical hubs, while neurodegeneration originates in syndrome-specific epicenters within the DMN that initiate the trans-neuronal spread of disease and drive the clinical phenotype. These investigations will facilitate the early and accurate diagnosis of EO AD variants, and will further our understanding of the relationships between clinical phenotype, structural and functional brain changes and molecular pathology in AD.

Public Health Relevance

Patients who develop Alzheimer's disease (AD) before age 65 often present with non-memory symptoms (difficulty with language, vision or organization/planning), leading to delayed or inaccurate diagnosis. This project applies cutting-edge brain imaging techniques to facilitate an early and accurate diagnosis of early- onset AD, and to study how the disease can cause such diverse symptoms. Findings from this study will improve the care of early-onset AD patients, and further our understanding of how symptoms relate to different elements of AD biology.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG045611-05
Application #
9412778
Study Section
Clinical Neuroscience and Neurodegeneration Study Section (CNN)
Program Officer
Hsiao, John
Project Start
2014-05-01
Project End
2019-01-31
Budget Start
2018-02-15
Budget End
2019-01-31
Support Year
5
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of California San Francisco
Department
Neurology
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94118
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