Dissociating Normal Aging from Alzheimer’s Disease: A View from Cognitive Neuroscience

Toepper, Max

doi:10.3233/jad-161099

Cited by 136 publications

(101 citation statements)

References 236 publications

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“…Disentangling brain changes specific to aging versus AD has been a challenge (Fjell et al, 2014;Jagust, 2013). For example, whether AD neurodegeneration represents accelerated aging or a distinct process has not been fully resolved (Brayne and Calloway, 1988;Buckner, 2004;Ghosh et al, 2011;Toepper, 2017). We sought further insight into this topic by examining grey matter (GM) changes across the lifespan and AD conjointly.…”

Section: Introductionmentioning

confidence: 99%

Distinct changes in morphometric networks in aging versus Alzheimer’s disease dementia

Initiative

2019

Preprint

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It remains unclear which grey matter (GM) changes are associated with Alzheimer's disease (AD), and how these changes might differ from normal brain aging. Using independent component analysis of GM maps on a large, multi-cohort dataset, we derived morphometric networks and investigated GM volume in such networks in young, old adulthood, and AD. GM loss in frontal networks was more specific to aging, while it was the result of an additive effect of aging and AD in all other networks. The pattern of GM volume across all morphometric networks changed in aging and AD, but a higher degree of variability in the whole-brain pattern and GM volume characterized AD only. In cognitively normal older adults, having a preserved whole-brain pattern was related to better cognition and lower risk of developing mild cognitive impairment. These findings suggest that higher heterogeneity in GM volume and whole-brain pattern is specific to AD, as opposed to atrophy that changes additively between "normal" aging and AD.To evaluate whether the morphometric networks would be biased by patients with severe cognitive impairment, the same ICA approach was applied to lMCI and AD participants only. Qualitatively, similar morphometric networks were identified between those two groups and all participants (Figure S1). Additive effect of age and AD on GM volume was found in most morphometric networksThe GM volume across morphometric networks differed between cohorts (see repeated measures ANOVA in Figure S2), showing effects of age and disease. To diminish potential confound of site effects, we combined the six cohorts into three groups: "Young adults" (FCP-Cambridge and HCP), "Older adults" (PREVENT-AD and Controls-ADNI) and "Alzheimer's dementia" (late mild cognitive impairment [lMCI]-and AD-ADNI), and examined the general differences between these three groups.We used a ten-fold cross-validated logistic regression procedure to determine if the GM volume in each of these morphometric networks could classify Young adults vs.Older adults and Older adults vs. Alzheimer's dementia in the left out subjects. The AUCs from the ROC analyses represent the overall performance of each morphometric network to classify participants across the collated test sets (Figure 2A).Many of the AUCs showed excellent (AUCs ≥90, n=11) or good (80≤ AUCs <90, n =10) performance for classifying Young vs. Older adults (Figure 2B). Only three (www.fnih.org).

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Section: Introductionmentioning

confidence: 99%

Distinct changes in morphometric networks in aging versus Alzheimer’s disease dementia

Initiative

2019

Preprint

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“…New technologies are available such as positron-emission tomography (PET) imaging and monitoring levels of Aβ and tau in cerbrospinal fluid (Masters et al, 2015). Co-morbidities that can exist due to aging such as hippocampal sclerosis further complicate AD diagnosis(Toepper, 2017). Furthermore, questions have been raised regarding whether or not AD is simply an accelerated form of aging due to them both being associated with changes in cognition (Toepper, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Co-morbidities that can exist due to aging such as hippocampal sclerosis further complicate AD diagnosis(Toepper, 2017). Furthermore, questions have been raised regarding whether or not AD is simply an accelerated form of aging due to them both being associated with changes in cognition (Toepper, 2017). However, studies have identified clear neurocognitive differences in cognition, brain size and function in AD compared to healthy aged subjects.…”

Section: Introductionmentioning

confidence: 99%

“…However, studies have identified clear neurocognitive differences in cognition, brain size and function in AD compared to healthy aged subjects. For example, AD patients have more grey matter loss compared to white matter, impaired verbal and semantic abilities and more intense memory dysfunction compared to healthy seniors (Toepper, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Analysis of Age, Sex, and Tissue Effects on Gene Expression Variability in Alzheimer’s Disease

Brooks

Mias

2018

Preprint

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2Alzheimer's disease (AD) has been categorized by the Centers for Disease Control and 3 Prevention (CDC) as the 6 th leading cause of death in the United States. AD is a significant 4 health-care burden because of its increased occurrence (specifically in the elderly population) 5 and the lack of effective treatments and preventive methods. With an increase in life expectancy, 6 the CDC expects AD cases to rise to 15 million by 2060. Aging has been previously associated 7 with susceptibility to AD, and there are ongoing efforts to effectively differentiate between normal 8 and AD age-related brain degeneration and memory loss. AD targets neuronal function and can 9 cause neuronal loss due to the buildup of amyloid-beta plaques and intracellular neurofibrillary 10 tangles. 11Our study aims to identify temporal changes within gene expression profiles of healthy controls 12 and AD subjects. We conducted a meta-analysis using publicly available microarray expression 13 data from AD and healthy cohorts. For our meta-analysis, we selected datasets that reported 14 donor age and gender, and used Affymetrix and Illumina microarray platforms (8 datasets, 2,088 15 samples). Raw microarray expression data were re-analyzed, and normalized across arrays. We 16 then performed an analysis of variance, using a linear model that incorporated age, tissue type, 17 sex, and disease state as effects, as well as study to account for batch effects, and including 18 binary interaction between factors. Our results identified 3,735 statistically significant (Bonferroni 19 adjusted p<0.05) gene expression differences between AD and healthy controls, which we 20 filtered for biological effect (10% two-tailed quantiles of mean differences between groups) to 21 obtain 352 genes. Interesting pathways identified as enriched comprised of neurodegenerative 22 diseases pathways (including AD), and also mitochondrial translation and dysfunction, synaptic 23 vesicle cycle and GABAergic synapse, and gene ontology terms enrichment in neuronal system, 24 transmission across chemical synapses and mitochondrial translation. 25 Overall our approach allowed us to effectively combine multiple available microarray datasets 26 and identify gene expression differences between AD and healthy individuals including full age 27 and tissue type considerations. Our findings provide potential gene and pathway associations 28 that can be targeted to improve AD diagnostics and potentially treatment or prevention. (US). 29

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“…Qualitative neurocognitive differences between AD and normal aging clearly suggest that AD cannot be simply regarded as an accelerated aging process 6 . A more heuristic hypothesis, however, would embrace the hypothesis that there may be several distinct upstream pathogenetic pathways that are driven by one or more basic mechanisms of biological aging, all of which lead to a common downstream pathway of beta amyloidosis, tauopathies and inflammation 7 .…”

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confidence: 99%

"Commentary: The ageing as a therapeutic target for Alzheimer's disease"

Castro-Fuentes¹

2017

J Neurol Neuromedicine

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Dissociating Normal Aging from Alzheimer’s Disease: A View from Cognitive Neuroscience

Cited by 136 publications

References 236 publications

Distinct changes in morphometric networks in aging versus Alzheimer’s disease dementia

Distinct changes in morphometric networks in aging versus Alzheimer’s disease dementia

Data-Driven Analysis of Age, Sex, and Tissue Effects on Gene Expression Variability in Alzheimer’s Disease

"Commentary: The ageing as a therapeutic target for Alzheimer's disease"

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