Neuroimaging Predictors of Alzheimer's Disease and Cognitive Decline

National Institute On Aging

Investigators

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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. Neuroimaging studies are also useful in investigating modifiers of cognitive and brain changes, including sex differences in cognitive and brain aging, genetic, metabolic, and inflammatory risk factors. Early detection of accelerated brain changes during the preclinical or asymptomatic stages of disease will be critical in identifying individuals likely to benefit from interventions if a successful treatment for prevention or delaying onset of AD is available. 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 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. In collaboration with Drs. Moghekar and Walker, we have completed Quanterix Neuro4plex plasma assays for ABeta40, ABeta42, Neurofilament Light and GFAP for all participants receiving PET scans, as well as selected participants in a case-control design, and analyses are underway. Selected publications over the last year are highlighted (studies including PET Tau are in a separate report): Accelerated white matter microstructural change in subsequently impaired individuals (Shafer, Williams, et al. 2022). An advantage of the long-standing BLSA neuroimaging study is the ability to evaluate brain changes in individuals who are initially cognitively normal but later development cognitive impairment. Comparing these individuals to those who remain cognitively normal provides the opportunity to detect the earliest changes associated with subsequent impairment in individuals who would be classified as cognitively normal in cross-sectional studies of brain and cognitive aging. In this study, we used longitudinal measures of cognitive function and white matter microstructure from diffusion tensor imaging to compare 50 older adults who subsequently developed mild cognitive impairment or dementia (subsequently impaired) and 200 cognitively normal controls. Rates of white matter microstructural decline were compared between groups using voxel-wise linear mixed-effects models, and associations between change in white matter microstructure and cognition also were examined. Subsequently impaired individuals had a faster decline in fractional anisotropy in the right inferior fronto-occipital fasciculus and bilateral splenium of the corpus callosum. A decline in right inferior fronto-occipital fasciculus fractional anisotropy was related to a decline in scores of verbal memory, visuospatial ability, processing speed and mini-mental state examination. A decline in bilateral splenium fractional anisotropy was related to declines in verbal fluency, processing speed and mini-mental state examination. Our findings showed that accelerated regional white matter microstructural decline is evident during the preclinical phase of mild cognitive impairment/dementia and is related to domain-specific cognitive decline. Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts (Betthauser et al., 2022). In previous work, we demonstrated that longitudinal PET measures of amyloid burden could be used to estimate age at amyloid onset on an individual basis and that amyloid onset age was earlier in APOE e4 carriers compared with those who do not carry the risk allele. In a recently published paper, we collaborated with Drs. Tobey Betthauser and Sterling Johnson of the Wisconsin Registry for Alzheimers Prevention (WRAP) study to evaluate three different approaches to estimating amyloid onset, using data from WRAP, BLSA and ADNI. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured rates of amyloid accumulation. We confirmed the earlier onset of amyloid accumulation in APOE e4 carriers compared with noncarriers but found no sex difference in timing of onset. In a subset of 595 ADNI participants not impaired before amyloid onset, e4 carriage, being female and having a later amyloid onset age were associated with a shorter time from amyloid onset to impairment onset. The ability to estimate timing of amyloid onset provides a way to investigate factors that may modify the timing of clinical impairment in Alzheimer's disease. We continue to work closely with Dr. Christos Davatzikos and his group on the iSTAGING consortium. Publications over the last year have included characterization of the heterogeneity of late life depression using neuroimaging, cognitive, clinical, and genetic information (Wen et al., 2022), disentangling normal aging from AD neurodegeneration from typical aging using machine learning (Hwang et al., 2021) and using deep learning for a dimensional approach to brain structure in AD (Zhang et al., 2022). Data generated by this project are also used by many intramural and extramural collaborators for methods development, including DTI metrics (Rheault et al., 2021; Cai et al., 2021; Wang et al., 2022), and image harmonization (Chen et al., 2022; Bashyam et al., 2022) and in studies of the neural underpinnings of motor function (Tian et al., 2022) and walking energetics with brain atrophy (Dougherty et al., 2021) and amyloid burden (Dougherty et al., 2021). They also are actively used through our participation in the Preclinical AD Consortium, led by Dr. Marilyn Albert of Johns Hopkins.