Cortical microstructural correlates of astrocytosis in autosomal-dominant Alzheimer disease

ObjectiveTo study the macrostructural and microstructural MRI correlates of brain astrocytosis, measured with 11C-deuterium-L-deprenyl (11C-DED)–PET, in familial autosomal-dominant Alzheimer disease (ADAD).MethodsThe total sample (n = 31) comprised ADAD mutation carriers (n = 10 presymptomatic, 39.2 ± 10.6 years old; n = 3 symptomatic, 55.5 ± 2.0 years old) and noncarriers (n = 18, 44.0 ± 13.7 years old) belonging to families with mutations in either the presenilin-1 or amyloid precursor protein genes. All participants underwent structural and diffusion MRI and neuropsychological assessment, and 20 participants (6 presymptomatic and 3 symptomatic mutation carriers and 11 noncarriers) also underwent 11C-DED-PET.ResultsVertex-wise interaction analyses revealed a differential relationship between carriers and noncarriers in the association between 11C-DED binding and estimated years to onset (EYO) and between cortical mean diffusivity (MD) and EYO. These differences were due to higher 11C-DED binding in presymptomatic carriers, with lower binding in symptomatic carriers compared to noncarriers, and to lower cortical MD in presymptomatic carriers, with higher MD in symptomatic carriers compared to noncarriers. Using a vertex-wise local correlation approach, 11C-DED binding was negatively correlated with cortical MD and positively correlated with cortical thickness.ConclusionsOur proof-of-concept study is the first to show that microstructural and macrostructural changes can reflect underlying neuroinflammatory mechanisms in early stages of Alzheimer disease (AD). The findings support a role for neuroinflammation in AD pathogenesis, with potential implications for the correct interpretation of neuroimaging biomarkers as surrogate endpoints in clinical trials.

Download Full-text

Disease duration in autosomal dominant familial Alzheimer disease

Neurology Genetics ◽

10.1212/nxg.0000000000000507 ◽

2020 ◽

Vol 6 (5) ◽

pp. e507

Author(s):

Ivanna M. Pavisic ◽

Jennifer M. Nicholas ◽

Antoinette O'Connor ◽

Helen Rice ◽

Kirsty Lu ◽

...

Keyword(s):

Alzheimer Disease ◽

Survival Time ◽

Disease Duration ◽

Autosomal Dominant ◽

Age At Onset ◽

Survival Models ◽

Research Centre ◽

Mutation Carriers ◽

Familial Alzheimer Disease ◽

Atypical Presentations

ObjectiveTo use survival modeling to estimate disease duration in autosomal dominant familial Alzheimer disease (ADAD) and ascertain whether factors influencing age at onset also affect survival.MethodsSymptomatic mutation carriers (201 presenilin 1 [PSEN1] and 55 amyloid precursor protein [APP]) from ADAD families referred to the Dementia Research Centre, between 1987 and 2019, were included. Survival was assessed with respect to age at onset, year of birth, APOE ε4 status, cognitive presentation, and sex using multilevel mixed-effects Weibull survival models. The contribution of mutation and family to variance in age at onset and duration was also assessed.ResultsEstimated mean survival was 11.6 (10.4–12.9) years and was similar for APP and PSEN1 mutations. Sixty-seven percent of the variance in age at onset was explained by mutation and 72% by mutation and family together. In contrast, only 6% of the variance in disease duration was explained by mutation specificity and 18% by family membership. Irrespective of gene, survival appeared longer for successive generations and in individuals with atypical presentations. Older age at onset was associated with longer duration within PSEN1 and shorter duration within APP mutation carriers. No differences in survival time were found between sexes or between mutations located before or beyond codon 200 within PSEN1.ConclusionsSurvival is influenced by mutation to a much lesser extent than age at onset. Survival time has increased over time and is longer in atypical presentations. These insights may inform the interpretation of disease-modifying therapy trials in ADAD.

Download Full-text

Application of machine learning to predict amyloid, metabolic, and structural neuroimaging biomarkers in the progression of autosomal dominant Alzheimer disease

Alzheimer s & Dementia ◽

10.1002/alz.040452 ◽

2020 ◽

Vol 16 (S2) ◽

Author(s):

Patrick H. Luckett ◽

Austin A. McCullough ◽

Eric McDade ◽

Tammie L.S. Benzinger ◽

John C. Morris ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer Disease ◽

Autosomal Dominant ◽

Structural Neuroimaging ◽

Neuroimaging Biomarkers

Download Full-text

Bundle-specific associations between white matter microstructure and Aβ and tau pathology in preclinical Alzheimer's disease

eLife ◽

10.7554/elife.62929 ◽

2021 ◽

Vol 10 ◽

Author(s):

Alexa Pichet Binette ◽

Guillaume Theaud ◽

François Rheault ◽

Maggie Roy ◽

D Louis Collins ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Autosomal Dominant ◽

Mean Diffusivity ◽

Free Water ◽

Tau Proteins ◽

Uncinate Fasciculus ◽

Mutation Carriers ◽

White Matter Microstructure

Beta-amyloid (Aβ) and tau proteins, the pathological hallmarks of Alzheimer's disease (AD), are believed to spread through connected regions of the brain. Combining diffusion imaging and positron emission tomography, we investigated associations between white matter microstructure specifically in bundles connecting regions where Aβ or tau accumulates and pathology. We focussed on free-water corrected diffusion measures in the anterior cingulum, posterior cingulum, and uncinate fasciculus in cognitively normal older adults at risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD. In Aβ-positive or tau-positive groups, lower tissue fractional anisotropy and higher mean diffusivity related to greater Aβ and tau burden in both cohorts. Associations were found in the posterior cingulum and uncinate fasciculus in preclinical sporadic AD, and in the anterior and posterior cingulum in presymptomatic mutation carriers. These results suggest that microstructural alterations accompany pathological accumulation as early as the preclinical stage of both sporadic and autosomal dominant AD.

Download Full-text

Association of education with Aβ burden in preclinical familial and sporadic Alzheimer disease

Neurology ◽

10.1212/wnl.0000000000010314 ◽

2020 ◽

Vol 95 (11) ◽

pp. e1554-e1564

Author(s):

Julie Gonneaud ◽

Christophe Bedetti ◽

Alexa Pichet Binette ◽

Tammie L.S. Benzinger ◽

John C. Morris ◽

...

Keyword(s):

Alzheimer Disease ◽

Autosomal Dominant ◽

Structural Mri ◽

Cross Sectional ◽

Parental History ◽

Level Of Education ◽

Mutation Carriers ◽

History Of ◽

Asymptomatic Individuals ◽

Sporadic Alzheimer Disease

ObjectiveTo determine whether years of education and the ε4 risk allele at APOE influence β-amyloid (Aβ) pathology similarly in asymptomatic individuals with a family history of sporadic Alzheimer disease (AD) and presymptomatic autosomal dominant AD mutation carriers.MethodsWe analyzed cross-sectional data from 106 asymptomatic individuals with a parental history of sporadic AD (PREVENT-AD cohort; age 67.28 ± 4.72 years) and 117 presymptomatic autosomal dominant AD mutation carriers (DIAN cohort; age 35.04 ± 9.43 years). All participants underwent structural MRI and Aβ-PET imaging. In each cohort we investigated the influence of years of education, APOE ε4 status, and their interaction on Aβ-PET.ResultsAsymptomatic individuals with a parental history of sporadic AD showed increased Aβ burden associated with APOE ε4 carriage and lower level of education, but no interaction between these. Presymptomatic mutation carriers of autosomal dominant AD showed no relation between APOE ε4 and Aβ burden, but increasing level of education was associated with reduced Aβ burden. The association between educational attainment and Aβ burden was similar in the 2 cohorts.ConclusionsWhile the APOE ε4 allele confers increased tendency toward Aβ accumulation in sporadic AD only, protective environmental factors, like increased education, may promote brain resistance against Aβ pathology in both sporadic and autosomal dominant AD.

Download Full-text

Estimating diagnostic accuracy for clustered ordinal diagnostic groups in the three-class case—Application to the early diagnosis of Alzheimer disease

Statistical Methods in Medical Research ◽

10.1177/0962280217742539 ◽

2017 ◽

Vol 27 (3) ◽

pp. 701-714

Author(s):

Chengjie Xiong ◽

Jingqin Luo ◽

Ling Chen ◽

Feng Gao ◽

Jingxia Liu ◽

...

Keyword(s):

Alzheimer Disease ◽

Diagnostic Accuracy ◽

Mixed Model ◽

Linear Mixed Model ◽

Diagnostic Marker ◽

Likelihood Estimation ◽

Statistical Inferences ◽

Mutation Carriers ◽

Medical Diagnostic ◽

Neuroimaging Biomarkers

Many medical diagnostic studies involve three ordinal diagnostic populations in which the diagnostic accuracy can be summarized by the volume or partial volume under the receiver operating characteristic surface for a diagnostic marker. When the diagnostic populations are clustered, e.g. by families, we propose to model the diagnostic marker by a general linear mixed model that takes into account of the correlation on the diagnostic marker from members of the same clusters. This model then facilitates the maximum likelihood estimation and statistical inferences of the diagnostic accuracy for the diagnostic marker. This approach naturally allows the incorporation of covariates as well as missing data when some clusters do not have subjects on all diagnostic groups in the estimation of, and the subsequent inferences on the diagnostic accuracy. We further study the performance of the proposed methods in a large simulation study with clustered data. Finally, we apply the proposed methodology to the data of several biomarkers collected by the Dominantly Inherited Alzheimer Network, an international family-clustered registry to study autosomal dominant Alzheimer disease which is a rare form of Alzheimer disease caused by mutations in any of the three genes including the amyloid precursor protein, presenilin 1 and presenilin 2. We estimate the accuracy of several cerebrospinal fluid and neuroimaging biomarkers in differentiating three diagnostic and genetic populations: normal non-mutation carriers, asymptomatic mutation carriers, and symptomatic mutation carriers.

Download Full-text

Multimodal IVA fusion for detection of linked neuroimaging biomarkers

10.1101/2021.12.13.472507 ◽

2021 ◽

Author(s):

Rogers F Silva ◽

Eswar Damaraju ◽

Xinhui Li ◽

Peter Kochonov ◽

Aysenil Belger ◽

...

Keyword(s):

Diffusion Mri ◽

Large Scale ◽

Analysis Model ◽

Subspace Analysis ◽

Independent Vector Analysis ◽

Multiple Datasets ◽

Fusion Methods ◽

Neuroimaging Biomarkers ◽

Magnetic Resonance Imaging Mri ◽

And Diffusion

With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal components in multiple datasets. In this work we utilized the multimodal independent vector analysis model in MISA to directly identify meaningful linked features across three neuroimaging modalities --- structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI --- in two large independent datasets, one comprising of healthy subjects and the other including patients with schizophrenia. Results show several linked subject profiles (the sources/components) that capture age-associated reductions, schizophrenia-related biomarkers, sex effects, and cognitive performance.

Download Full-text