Subcortical amyloid relates to cortical morphology in cognitively normal individuals

Purpose Amyloid (Aβ) brain deposition can occur in cognitively normal individuals and is associated with cortical volume abnormalities. Aβ-related volume changes are inconsistent across studies. Since volume is composed of surface area and thickness, the relative contribution of Aβ deposition on each of these metrics remains to be understood in cognitively normal individuals. Methods A group of 104 cognitively normal individuals underwent neuropsychological assessment, PiB-PET scan, and MRI acquisition. Surface-based cortical analyses were performed to investigate the effects of cortical and subcortical Aβ burden on cortical volume, thickness, and surface area. Mediation analyses were used to study the effect of thickness and surface area on Aβ-associated volume changes. We also investigated the relationships between structural metrics in clusters with abnormal morphology and regions underlying resting-state functional networks and cognitive performance. Results Cortical Aβ was not associated with cortical morphology. Subcortical Aβ burden was associated with changes in cortical volume, thickness, and surface area. Aβ-associated volume changes were driven by cortical surface area with or without thickness but never by thickness alone. Aβ-associated changes overlapped greatly with regions from the default mode network and were associated with lower performance in visuospatial abilities, episodic memory, and working memory. Conclusions In cognitively normal individuals, subcortical Aβ is associated with cortical volume, and this effect was driven by surface area with or without thickness. Aβ-associated cortical changes were found in the default mode network and affected cognitive performance. Our findings demonstrate the importance of studying subcortical Aβ and cortical surface area in normal agein

Amyloid burden and white matter hyperintensities mediate age-related cognitive differences

This study examined the additive versus synergistic contribution of beta-amyloid (Aβ) and white matter hyperintensities (WMHs) across 7 cognitive domains in 104 cognitively normal older adults. It also measured the extent to which age-related differences in cognition are driven by measurable brain pathology. All participants underwent neuropsychological assessment along with magnetic resonance imaging and Pittsburg compound B-positron emission tomography imaging for Aβ quantification. WMH severity was quantified using the age-related white matter changes scale. Stepwise regressions, moderation, and mediation modeling were performed. Our findings show that Aβ deposition single-handedly predicts poorer episodic memory performance and that Aβ and WMHs contribute additively to poorer performance in working memory and language while carrying synergistic associations with executive functions and attention. Through mediation modeling, we demonstrated that the influence of age over episodic memory, working memory, executive functions, and language is fully mediated by brain pathology. This study permits to conclude that, in healthy older adults, (1) Aβ burden and WMHs have synergistic associations with some cognitive domains and (2) age-related differences in most cognitive domains are driven by brain pathology associated with dementia

Education as a moderator of the relationship between episodic memory and amyloid load in normal aging

The current study explored whether education, a proxy of cognitive reserve, modifies the association between episodic memory (EM) performance and βeta-amyloid load (Aβ), a biomarker of Alzheimer’s disease, in a cohort of cognitively normal older adults. One hundred and four participants (mean age 73.3 years) evenly spread out in three bands of education were recruited. Participants underwent neuropsychological assessment, structural MRI as well as PET imaging to quantify Aβ load. Moderation analyses and the Johnson–Neyman technique were carried out to examine the interaction of education with Aβ load to predict EM performance. Linear regressions were then performed within each group of education to better illustrate the interaction effect (all analyses were controlled for age and sex). The interaction between education and Aβ load was significant (p < .05) for years of education, reaching a cutoff point of 13.5 years, above which the relationship between Aβ load and EM was no longer significant. Similarly, significant associations were found between Aβ and EM among participants with secondary (p < .01) and preuniversity education (p < .01), but not with a university degree (p = .253). EM performance is associated with Aβ load in cognitively normal older individuals, and this relationship is moderated by educational attainment

The use of random forests to classify amyloid brain PET

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.Purpose: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification. Methods: The data set included 57 baseline 18F-florbetapir (Amyvid; Lilly, Indianapolis, IN) brain PET scans in participants with severe white matter disease, presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a multicenter prospective observational trial. Scans were processed using the MINC toolkit to generate SUV ratios, normalized to cerebellar gray matter, and clinically read by 2 nuclear medicine physicians with interpretation based on consensus (35 negative, 22 positive). SUV ratio data and clinical reads were used for super- vised training of an RF classifier programmed in MATLAB. Results: A 10,000-tree RF, each tree using 15 randomly selected cases and 20 randomly selected features (SUV ratio per region of interest), with 37 cases for training and 20 cases for testing, had sensitivity = 86% (95% confidence in- terval [CI], 42%–100%), specificity = 92% (CI, 64%–100%), and classification accuracy = 90% (CI, 68%–99%). The most common features at the root node (key regions for stratification) were (1) left posterior cingulate (1039 trees), (2) left middle frontal gyrus (1038 trees), (3) left precuneus (857 trees), (4) right an- terior cingulate gyrus (655 trees), and (5) right posterior cingulate (588 trees). Conclusions: Random forests can classify brain PET as positive or negative for amyloid deposition and suggest key clinically relevant, regional features for classification.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals

The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score

Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.Purpose: The aim of this study was to evaluate random forests (RFs) to identify ROIs on 18F-florbetapir and 18F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score. Materials and Methods: Fifty-seven subjects with significant white matter disease presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a mul- ticenter prospective observational trial, had MoCA and 18F-florbetapir PET; 55 had 18F-FDG PET. Scans were processed using the MINC toolkit to gen- erate SUV ratios, normalized to cerebellar gray matter (18F-florbetapir PET), or pons (18F-FDG PET). SUV ratio data and MoCA score were used for su- pervised training of RFs programmed in MATLAB. Results: 18F-Florbetapir PETs were randomly divided into 40 training and 17 testing scans; 100 RFs of 1000 trees, constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: right posterior cingulate gyrus, right anterior cingulate gyrus, left precuneus, left posterior cingulate gyrus, and right precuneus. Amyloid in- creased with decreasing MoCA score. 18F-FDG PETs were randomly di- vided into 40 training and 15 testing scans; 100 RFs of 1000 trees, each tree constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: left fusiform gyrus, left precuneus, left posterior cingulate gyrus, right precuneus, and left middle orbitofrontal gyrus. 18F-FDG decreased with decreasing MoCA score. Conclusions: Random forests help pinpoint clinically relevant ROIs associ- ated with MoCA score; amyloid increased and 18F-FDG decreased with de- creasing MoCA score, most significantly in the posterior cingulate gyrus.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals

Beta-amyloid burden and white matter hyperintensities as mediators of the relationship between age and cognition in normal aging

International audienc

Poorer cognitive performance in healthy older individuals is associated with the presence of beta-amyloid burden and white matter hyperintensities

International audienc

The relation between episodic memory and amyloid burden in healthy elderly adults is moderated by educational level

International audienc