Advancing MR elastography to map mechanical signatures of key AD/ADRD processes

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1 PROJECT SUMMARY 2 Alzheimer’s disease (AD) is the leading cause of dementia in the United States and its impact is only growing with 3 shifting demographics. The development of powerful biomarkers, measuring amyloid deposition, tau accumulation, 4 and neurodegeneration, has provided important insights into the pathophysiology of AD and AD-related dementias 5 (ADRD). Nonetheless, given the large variability across individuals, our understanding of the link between pathology 6 and cognitive dysfunction remains incomplete. Structural factors contribute significantly to this pathology-cognition 7 disconnect and are termed as “brain reserve.” There is a critical need for objective measures of reserve that will 8 improve the assessment of individual prognosis and guide therapy. 9 Brain biomechanics are an understudied structural feature of the brain, due in large part to the difficulty in measuring 10 relevant biomechanical states in vivo. Magnetic resonance elastography (MRE) is currently unmatched for 11 noninvasive measurement of brain mechanical properties. We have previously demonstrated that brain stiffness is 12 reduced due to AD, and our group and others have demonstrated in multiple studies that brain stiffness is a significant 13 reporter of cognitive function. However, previous studies face important limitations, namely technologies that were 14 optimized for reliability over resolution, and incomplete pathological assessment. Therefore, we will investigate two 15 aims with the overall goals to (1) advance MRE technology in order to (2) evaluate of the role of biomechanics in 16 brain reserve. 17 In Aim 1, we will optimize our machine learning-based MRE inversion framework by incorporating new a priori 18 information into the model that is specific to the brain. These advances to the model include the incorporation of 19 partial volume effects to reduce atrophy-related bias, and mechanical anisotropy to accurately model the coherent 20 structure of white matter tracts. Each advance will be tested in simulation and phantom experiments, and finally in 21 vivo for its ability to boost sensitivity to key AD/ADRD processes. 22 In Aim 2, we will use these tools to simultaneously map the mechanical signature of 4 pathophysiological processes 23 including amyloid, tau, white matter hyperintensities, and cardiometabolic conditions. Using first a discovery data set, 24 we will extract the mechanical feature that best reports cognitive performance, both globally and in specific domains. 25 These MRE-based features will then be evaluated in an independent test set for their ability to predict concurrent and 26 future cognitive performance. Finally, we will assess the unique value of mechanical biomarkers to predict cognitive 27 performance, using a parallel analysis but controlling for existing biomarkers derived from anatomical, functional, and 28 diffusion MRI. 29 In sum, the success of this proposal will shed new light on alterations to brain biomechanics with respect to 30 AD/ADRD processes, and their role as a buffer between pathology and cognition.