Multiscale Modeling of Brain Aging and Alzheimer’s Disease with MRI, Pathology and Proteomics - Project Summary Aging has a pronounced effect on the human brain, yet differences between individuals can be substantial, and are likely influenced by genetic, environmental lifestyle factors, including several modifiable risk factors that may impact brain aging and play a role in the onset of Alzheimer’s disease (AD) and AD related dementias (ADRD). However, timely recognition and understanding of accelerated brain aging and the onset of neurodegeneration underlying AD is needed in order to identify individuals at risk and take preventive measures. So far, most brain-age research uses structural neuroimaging to index brain volume, which sensitivity is inherently limited to the millimeter resolution of MRI. Instead, we will use advanced, yet clinically feasible diffusion MRI, which provides a unique window to the mesoscopic scale, intermediate between molecular and voxel dimensions, where aging and distinct pathological processes happen. We hypothesize high sensitivity of dMRI to determine brain-age and deterioration due to AD/ADRD. In addition, the role of white matter in aging and AD/ADRD is still unclear, and particularly the exact contribution of both neuro-inflammatory processes (gliosis, astrocytosis, macrophage infiltration) and neurodegenerative processes (demyelination, axonal degeneration and loss). The desired specificity is attainable using our mesoMRI modeling approach. In response of RFA-AG-24-018, we propose to develop a biologically relevant multi-scale model of brain aging using our in vivo mesoMRI approach, which holds enormous untapped potential to bridge the meso-macro gap and disentangle the extent of neuro-inflammation and neurodegeneration. We will leverage the unique presence at NYU Langone Healthy (NYULH) of a large series of mesoMRI scans of control, patient and subjects of the well-characterized cohort of the Alzheimer’s Disease Research Center (ADRC) along with a large collection of brain specimen of both controls and various dementia diagnosis. We propose in the R21-phase to create a large advanced, multi-shell dMRI dataset, unifying several large- scale dMRI datasets along with our local NYULH cohort extracted from routine clinical MRI datasets and electronic health record data. We will generate brain charts of quantitative dMRI and mesoMRI markers across the life-span, to then create a brain microstructure age framework. In the R33-phase, we evaluate the effect of race/ethnicity, sex and life style factors on aging and evaluate the sensitivity of brain microstructure to increased aging, neuro-inflammation and neuro-degeneration along the continuum of AD/ADRD, in addition to validation in postmortem human brain using histopathology and proteomics, and evaluation of longitudinal changes. Impact: Achieve a brain-age framework based on clinically feasible dMRI scans with robust sensitivity that provides meaningful insight into the microstructural processes underlying brain aging and AD/ADRD and will enable quantitative assessment of treatment response to future symptomatic and disease-modifying therapies.
