Lifespan Changes of the Human Brain In Alzheimer’s Disease

Coupé, Pierrick; Manjón, José V.; Lanuza, Enrique; Catheline, Gwénaëlle

doi:10.1038/s41598-019-39809-8

Cited by 145 publications

(176 citation statements)

References 88 publications

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“…As opposed to what has been observed in Alzheimer disease 22 and suggested in a small study of converters with MAPT and GRN mutations, 23 we did not observe an acceleration in cortical thinning or loss of surface area in the usual symptomatic age range.…”

Section: Discussioncontrasting

confidence: 99%

Faster Cortical Thinning and Surface Area Loss in Presymptomatic and Symptomatic C9orf72 Repeat Expansion Adult Carriers

Blanc

Pomerleau

McCarthy

et al. 2020

Annals of Neurology

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Objective C9orf72 expansion is the most common genetic cause of frontotemporal dementia (FTD). We examined aging trajectories of cortical thickness (CTh) and surface area in C9orf72 expansion adult carriers compared to healthy controls to characterize preclinical cerebral changes leading to symptoms. Methods Data were obtained from the Genetic Frontotemporal Dementia Initiative. T1‐weighted magnetic resonance imaging scans were processed with CIVET 2.1 to extract vertex‐wide CTh and cortical surface area (CSA). Symptomatic and presymptomatic subjects were compared to age‐matched controls using mixed‐effects models, controlling for demographic variables. Aging trajectories were compared between carriers and noncarriers by testing the “age by genetic status” interaction. False discovery rate corrections were applied to all vertex‐wide analyses. Results The sample included 640 scans from 386 subjects, including 54 symptomatic C9orf72 carriers (72.2% behavioral variant FTD), 83 asymptomatic carriers, and 249 controls (age range = 18–86 years). Symptomatic carriers showed fairly symmetric reduction in CTh/CSA in most of the frontal lobes, in addition to large temporoparietal areas. Presymptomatic subjects had reduced CTh/CSA in more restricted areas of the medial frontoparietal lobes, in addition to scattered lateral frontal, parietal, and temporal areas. These differences were explained by faster cortical thinning linearly throughout adulthood in a similar anatomical distribution, with differences emerging in the early 30s. CSA reduction was also faster in mutation carriers predominantly in the ventrofrontal regions. Interpretation C9orf72 mutation carriers have faster cortical thinning and surface loss throughout adulthood in regions that show atrophy in symptomatic subjects. This suggests that the pathogenic effects of the mutation lead to structural cerebral changes decades prior to symptoms. ANN NEUROL 2020 ANN NEUROL 2020;88:113–122

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Section: Discussioncontrasting

confidence: 99%

Faster Cortical Thinning and Surface Area Loss in Presymptomatic and Symptomatic C9orf72 Repeat Expansion Adult Carriers

Blanc

Pomerleau

McCarthy

et al. 2020

Annals of Neurology

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“…In the present study, we provided evidence suggesting low-normal TC (TC < 160 mg/dl) is associated with reduced GMv in medial temporal regions (including bilateral anterior HIP). Given the fact that GMv loss in medial temporal regions and HIP has been repeatedly reported in prodromal AD (Bell-McGinty et al, 2005;Whitwell et al, 2007) and hippocampal volume in midlife has been proposed as a strong risk predictor of AD (Coupé et al, 2019), the link between low-normal TC and GMv loss in these regions in the present study is worrisome, as it is possible that low-normal TC might potentially predispose individuals to AD or dementia. Indeed, a recent study has demonstrated a U-shaped association between AD and non-HDL cholesterol levels in old adults (60-79 years old) (Marcum et al, 2018).…”

Section: Discussionmentioning

confidence: 82%

Low Cholesterol Level Linked to Reduced Semantic Fluency Performance and Reduced Gray Matter Volume in the Medial Temporal Lobe

Yang

Stanford

Jiang

2020

Front. Aging Neurosci.

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Hyperlipidemia has been proposed as a risk factor of dementia and cognitive decline. However, the findings of the relationship between cholesterol level and cognitive/brain function have been inconsistent. Here, using a well-controlled sample from the Parkinson's Progression Markers Initiative (PPMI), we investigated the probable nonlinear relationship between plasma total cholesterol (TC) level, gray matter volume (GMv), and cognitive performance in 117 non-demented subjects (mean age, 61.5 ± 8.9 years), including 67 Parkinson's disease (PD) patients and 50 demographically matched controls. A quadratic relationship between semantic fluency (SF) performance and TC levels was identified. Within the subjects with a desirable TC level (TC < 200 mg/dl), low TC (lTC) levels were associated with reduced SF performance, as well as reduced GMv in three medial temporal regions [including bilateral anterior hippocampus (HIP)]. In contrast, no significant relationship between TC and cognition performance/GMv was found in individuals with a high cholesterol level (i.e., TC ≥ 200 mg/dl). Further region of interest (ROI)-based analysis showed that individuals with TC levels ranging from 100 to 160 mg/dl had the lowest GMv in the medial temporal regions. These findings suggest that low-normal TC level may be associated with reduced cognitive function and brain atrophy in regions implicated in neurodegenerative diseases, adding to a growing body of literature supporting a probable non-linear relationship between cholesterol level and brain health. However, this finding needs to be verified with other large public cohort data that do not include PD patients.

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“…Our results showed strong reliability of HIPS, volBrain and CAT. These methods have been successfully applied to brain images from those with AD [76][77][78][79] .…”

Section: Discussionmentioning

confidence: 99%

A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

Zamani

Sadr

Javadi

2020

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC). A total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer's disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain. While HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite. Our results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

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Lifespan Changes of the Human Brain In Alzheimer’s Disease

Cited by 145 publications

References 88 publications

Faster Cortical Thinning and Surface Area Loss in Presymptomatic and Symptomatic C9orf72 Repeat Expansion Adult Carriers

Faster Cortical Thinning and Surface Area Loss in Presymptomatic and Symptomatic C9orf72 Repeat Expansion Adult Carriers

Low Cholesterol Level Linked to Reduced Semantic Fluency Performance and Reduced Gray Matter Volume in the Medial Temporal Lobe

A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

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