Abstract
BackgroundPathological processes contributing to Alzheimer’s disease begin decades prior to the onset of clinical symptoms. There is significant variation in cognitive changes in the presence of pathology, functional connectivity may be a marker of compensation to amyloid; however, this is not well understood.MethodsWe recruited 64 cognitively normal older adults who underwent neuropsychological testing and biannual magnetic resonance imaging (MRI), amyloid imaging with Pittsburgh compound B (PiB)-PET, and glucose metabolism (FDG)-PET imaging for up to 6 years. Resting-state MRI was used to estimate connectivity of seven canonical neural networks using template-based rotation. Using voxel-wise paired t-tests, we identified neural networks that displayed significant changes in connectivity across time. We investigated associations among amyloid and longitudinal changes in connectivity and cognitive function by domains.ResultsLeft middle frontal gyrus connectivity within the memory encoding network increased over time, but the rate of change was lower with greater amyloid. This was no longer significant in an analysis where we limited the sample to only those with two time points. We found limited decline in cognitive domains overall. Greater functional connectivity was associated with better attention/processing speed and executive function (independent of time) in those with lower amyloid but was associated with worse function with greater amyloid.ConclusionsIncreased functional connectivity serves to preserve cognitive function in normal aging and may fail in the presence of pathology consistent with compensatory models.
Highlights
Alzheimer’s disease (AD) is an age-related neurodegenerative disease affecting approximately 5.5 million people and is the sixth leading cause of death in the USA
We investigated the longitudinal effect of amyloid deposition on resting-state functional connectivity in cognitively normal older adults
We investigated the association between connectivity and the following predictors: time, FDG Standardized uptake value ratios (SUVR), total hippocampal volume, normalized White matter hyperintensity (WMH) volume, and Pittsburgh compound B (PiB) SUVR—each predictor’s interaction with time was modeled only if it was significant to avoid over-fitting
Summary
Alzheimer’s disease (AD) is an age-related neurodegenerative disease affecting approximately 5.5 million people and is the sixth leading cause of death in the USA. The pathophysiological processes contributing to AD begin decades prior to the onset of clinical symptoms [2] This period is referred to as preclinical AD where an individual is cognitively normal but demonstrates in vivo amyloid burden. The prevailing model of AD progression hypothesizes that amyloid-beta (Aβ) deposition is the first detectable biomarker indicating an (2020) 12:7 individual’s risk for developing AD, which occurs in this preclinical stage [3]. In this preclinical stage and prior to cognitive impairment, previous studies have shown that greater amyloid load is associated with differences in resting-state functional connectivity [4,5,6,7,8]. There is significant variation in cognitive changes in the presence of pathology, functional connectivity may be a marker of compensation to amyloid; this is not well understood
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