ALZHEIMERS RESEARCH PROJECT: Neuroimaging Predictors of Alzheimer's Disease and Cognitive Decline

National Institute On Aging

Investigators

Linked publications & trials

Abstract

Through our research program on early markers of Alzheimers disease (AD), we continue to perform serial magnetic resonance imaging (MRI), including measures of vascular changes, positron emission tomography (PET), and neuropsychological assessments in participants from the Baltimore Longitudinal Study of Aging (BLSA) to investigate the neurobiologic basis of individual differences in memory change over time, as well as predictors of cognitive impairment and resilience. These evaluations allow us to examine changes in brain structure and function which may be early preclinical predictors of cognitive change, impairment, including AD, and resilience or maintenance of cognitive health. We also investigate modifiers of cognitive and brain changes, including sex differences in cognitive and brain aging, genetic, metabolic, and inflammatory risk factors. Longitudinal neuroimaging evaluations of BLSA participants began in 1994 with approximately 160 individuals aged 55-85 at enrollment. In 2009, we expanded MRI assessments to more than 1000 individuals (with greater than 60% receiving 2 or more scans) and cognitive assessments. Our progress over the last year has focused on continued acquisition of MRI scans in BLSA participants, analyses of existing data, and methods development of new approaches to analysis of longitudinal neuroimaging data. In addition, we took advantage of newly available Simoa assays to investigate AD biomarkers and modifiers. We expanded assays using the Quanterix Neuro4plex plasma assays for ABeta40, ABeta42, Neurofilament Light, GFAP and ptau181 for participants receiving PET and MRI studies. In a key paper published in final form in October 2023 (Bilgel et al 2023), we related cross-sectional and longitudinal plasma biomarkers of AD neuropathology, astrocytic activation, and neuronal injury to brain amyloid burden measured by PET-PiB. As expected, AB42/AB40 ratio was lower and ptau values were higher in amyloid positive versus negative individuals. Interestingly we found significant declines in the AB42/AB40 ratio in amyloid negative but not positive individuals, suggesting the change in plasma ABeta had already occurred before PET amyloid positivity. Conversely, ptau181 and ptau231 showed significant longitudinal increases in amyloid positive but not negative individuals. Using these data, Dark et al (2024) demonstrated associations between specific biomarkers and longitudinal changes in brain atrophy and domain-specific cognitive changes. We have expanded the investigation of biomarkers of amyloid and ptau to blood samples from visits prior to the PET amyloid studies in collaboration with the laboratory of Dr. Randy Bateman at Washington University using more sensitive mass spec-based assays. These assays will allow exploration of the temporal course of AD biomarkers in relation to timing of brain amyloid accumulation. They have recently been completed, and data are being prepared for analysis. We continue to work closely with Dr. Christos Davatzikos and his group at University of Pennsylvania through the iSTAGING consortium and with Dr. Bennett Landman and Derek Archer at Vanderbilt University. Several key publications over the last year use deep learning/AI approaches to characterize the heterogeneity of brain atrophy patterns in aging and disease in a large sample of MRI scans. In one recent paper (Yang et al. 2024) five dominant dimensions of brain atrophy were identified that showed unique patterns of associations with clinical, genetic, and lifestyle characteristics. In another large consortium study (Archer et al, 2024), we examined white matter microstructural decline in normal and abnormal aging using diffusion MRI. The data generated under this project also continue to be used by many intramural and extramural collaborators, involving analysis of skeletal mitochondrial function (Tian et al., 2023), multi-sensory loss (Tian et al., 2024), walking energetics (Dougherty et al., 2023), sleep patterns (Spira et al., 2024), and as outcomes in proteomic studies (Duggan et al., 2024). They also are actively used through our participation in the Preclinical AD Consortium, led by Dr. Marilyn Albert of Johns Hopkins.