. Three major principles are at the forefront of current understanding about the pathology and potential therapeutic approach to addressing the massive public health burden of Alzheimer?s disease (AD). First, the clinical symptoms and functional dependence resulting from this disease are known to occur after potentially decades of degenerative brain changes linked to amyloid plaque and neurofibrillary tangle cortical pathologies; second, prevalent comorbid pathologies, particularly cerebrovascular dysfunction, contribute to a hastening of disease processes and clinical decline; third, any therapeutic intervention targeting either of these pathologic domains would need to be implemented at the earliest time possible, prior to evidence of cognitive decline given that dementia is only apparent after substantial irrecoverable neurodegeneration has transpired. Functional magnetic resonance imaging (fMRI) is used to measure brain activity and previously contributed extensively to the characterization of AD progression. Individuals at genetic risk of AD show altered fMRI indicators even prior to expression of cognitive impairment, and thus, fMRI has provided critical insights into pathophysiology of preclinical AD. The fMRI signal is an indirect correlate of neural activity based on the phenomenon of ?functional hyperemia? in which metabolic activity in the brain is followed by a nutritive increase in cerebral blood flow and this hemodynamic response can be measured through the blood oxygenation level dependent (BOLD) contrast mechanism. A critical barrier in the application of fMRI to the study of AD is the intricate entanglement of neural and vascular physiology at the basis of the BOLD signal resulting in an inability to differentiate between the effects of neural dysfunction and comorbid vascular pathology. The goal of this NIH R21 research proposal is to decouple neurophysiological from vasculo-physiological components of the fMRI BOLD signal and to apply this new technology to the study of brain pathology, associated with the genetic risk of AD, before any evidence of cognitive and functional decline. To this end, we will implement a cutting-edge scanning and analysis paradigm in cognitively healthy older participants at different levels of genetic risk of AD by [1] simultaneous recording of combinations between fMRI, electro-encephalographic, and magnetoencephalographic data, [2] quantifying transient intrinsic neurophysiological states of brain networks, and [3] using these states to anchor measurement of the neurally induced hemo-dynamic response. We emphasize that the R21 mechanism is exploratory/developmental, and in this spirit, we propose to explore optimal parameters to advance this novel technology. Successful implementation of this approach would provide novel insight into how genetic vulnerabilities are linked to distinct neural and vascular dysfunctions, which have been suggested to influence the plaque and tangle pathology in AD. Targeting specific neural and vascular pathophysiology by novel, alternative therapies in preclinical AD holds promise to make prevention and early intervention, to thwart or slow down progressive neurodegeneration, possible.

Public Health Relevance

. There is a great need for functional brain mapping technologies that can differentiate abnormalities in memory-related neural networks from the impact of highly comorbid vascular pathology in the earliest stages of Alzheimer's disease. We propose here to optimize cutting-edge technology to achieve this goal through the implementation of an innovative combined functional magnetic resonance imaging, electroencephalography, and magnetoencephalography procedure to be assessed in individuals with genetic risk for Alzheimer's disease.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21AG060328-01
Application #
9587048
Study Section
Clinical Neuroscience and Neurodegeneration Study Section (CNN)
Program Officer
Hsiao, John
Project Start
2018-07-15
Project End
2020-05-31
Budget Start
2018-07-15
Budget End
2019-05-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code