Early diagnosis of Alzheimer?s disease (AD) is still challenging, because of the subtlety of the microstructural changes it initially causes in the brain and the difficulty of identifying them with traditional neuroimaging techniques such as MRI, PET or CT scans. Recently, a novel perspective has suggested that the mechanical properties of brain tissue might serve as an important biomarker carrying information about the tissue?s physiological and pathological status. Such mechanical properties can be measured by means of magnetic resonance elastography (MRE), a technique which allows to obtain the stiffness of specific neuroanatomical regions in vivo and non-invasively with a sub-millimeter resolution. Previous MRE studies have clearly indicated that AD is characterized by brain tissue softening accompanying neurodegeneration. However, MRE at conventional field strengths (i.e., 3 Tesla, or 3T) alone is insufficient to characterize how variations in regional viscoelasticity correlate with tissue microstructure. The goal of this proposal is to bring forward a novel platform for the joint analysis of biomechanical, connectomic and pathologic markers in AD patients, thanks to ultrahigh field (7T) MR neuroimaging. The development of specialized sequences for 7T MRE and 7T diffusion MRI scans will enable the comparison of neuromechanic and microstructural data in AD patients at an unprecedented resolution; this, in turn, will provide a deeper understanding of the in vivo pathophysiology of AD and allow us to potentially identify a set of viscoelastic and tractographic markers of disease pathology. Specifically, we expect our integrated approach to help us validate ultrahigh field MRE as a unique tool to improve AD diagnosis and prognostic measurements. Our central hypothesis is that ultrahigh field MRE provides a unique and powerful measure of biomechanical changes associated with AD in the brain, and may be integrated with existing ultrahigh neuroimaging tools to achieve unprecedented visualization of the consequences of disease pathology. In short, the proposed research offers a potential shift in imaging for AD diagnosis by connecting microstructural changes and tissue-level viscoelasticity of the brain for the first time. Advanced image analysis of such data may also improve discrimination of AD from other neurodegenerative conditions displaying similar clinical manifestations. Ultimately, understanding the patterns of microstructural variations typical of AD might provide a tool for pharmacological and clinical studies aimed at developing better treatment options.

Public Health Relevance

Early diagnosis of Alzheimer?s disease (AD) is still challenging, because of the subtlety of the microstructural changes it initially causes in the brain and the difficulty of identifying them with traditional neuroimaging techniques such as MRI, PET or CT scans. However, recent evidence has revealed that the neurodegeneration characterizing AD is accompanied by effective tissue softening, which can be quantified in vivo through a technique known as magnetic resonance elastography (MRE). In this proposal, we bring forward a novel framework by combining MRE with specialized, ultrahigh resolution, MR neuroimaging at 7T, wherein this multi-modal approach will enable the joint analysis of biomechanical, connectomic and pathologic markers in AD patients and bring novel insights in our understanding of the mechanisms of AD onset and progression.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21AG071179-01
Application #
10143367
Study Section
Clinical Neuroscience and Neurodegeneration Study Section (CNN)
Program Officer
Hsiao, John
Project Start
2020-09-15
Project End
2022-08-31
Budget Start
2020-09-15
Budget End
2022-08-31
Support Year
1
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Icahn School of Medicine at Mount Sinai
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029