Tetrahedral Spectral Feature-Based Bayesian Manifold Learning for Grey Matter Morphometry: Findings from the Alzheimer’s Disease Neuroimaging Initiative

The objective of this study was to investigate the cross-sectional associations of atrial fibrillation with neuroimaging measures of cerebrovascular disease and Alzheimer’s disease-related pathology, and their interaction with cognitive impairment. MRI scans of non-demented individuals (n=1044) from the population-based Mayo Clinic Study of Aging were analyzed for infarctions, total grey matter, hippocampal and white matter hyperintensity volumes. A subset of 496 individuals underwent FDG and C-11 Pittsburgh compound B (PiB) PET scans. We assessed the associations of atrial fibrillation with i) categorical MRI measures (cortical and subcortical infarctions) using multivariable logistic regression models, and with ii) continuous MRI measures ( hippocampal, total grey matter, and white matter hyperintensity volumes) and FDG-PET and PiB-PET measures using multivariable linear regression models, and adjusting for confounders. Among participants who underwent MRI (median age, 77.8, 51.6% male), 13.5% had atrial fibrillation. Presence of atrial fibrillation was associated with subcortical infarctions (odds ratio [OR], 1.83; p=0.002), cortical infarctions (OR, 1.91; p=0.03), total grey matter volume (Beta [β], -.025, p<.0001) after controlling for age, education, gender, APOE e4 carrier status, coronary artery disease, diabetes, history of clinical stroke, and hypertension. However, atrial fibrillation was not associated with white matter hyperintensity volume, hippocampal volume, Alzheimer’s pattern of FDG hypometabolism or PiB uptake. There was a significant interaction of cortical infarction (p for interaction=0.004) and subcortical infarction (p for interaction =0.015) with atrial fibrillation with regards to odds of mild cognitive impairment (MCI). Using subjects with no atrial fibrillation and no infarction as the reference, the OR (95% confidence intervals [CI]) for MCI was 2.98 (1.66, 5.35;p = 0.0002) among participants with atrial fibrillation and any infarction, 0.69 (0.36, 1.33;p= 0.27) for atrial fibrillation and no infarction, and 1.50 (0.96, 2.32;p = 0.07) for no atrial fibrillation and any infarction. These data highlight that atrial fibrillation is associated with MCI in the presence of infarctions.

Download Full-text

Nonlinear Classification of EEG recordings from patients with Alzheimer’s Disease using Gaussian Process Latent Variable Model

10.1101/2020.05.07.20093922 ◽

2020 ◽

Cited By ~ 2

Author(s):

S. Rajintha. A. S. Gunawardena ◽

Fei He ◽

Ptolemaios Sarrigiannis ◽

Daniel J. Blackburn

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Gaussian Process ◽

Manifold Learning ◽

Latent Variable ◽

Time Window ◽

Support Vector ◽

Nonlinear Dimensionality Reduction ◽

Variable Model

AbstractIn this work, nonlinear temporal features from multi-channel EEGs are used for the classification of Alzheimer’s disease patients from healthy individuals. This was achieved by temporal manifold learning using Gaussian Process Latent Variable Models (GPLVM) as a nonlinear dimensionality reduction technique. Classification of the extracted features was undertaken using a nonlinear Support Vector Machine. Comparisons were made against the linear counterpart, Principle Component Analysis while exploring the effect of the time window or EEG epoch length used. It was demonstrated that temporal manifold learning using GPLVM is better in extracting features that attain high separability and prediction accuracy. This work aims to set the significance of using GPLVM temporal manifold learning for EEG feature extraction in the classification of Alzheimer’s disease.

Download Full-text

Longitudinal progression of grey matter atrophy in non-amnestic Alzheimer’s disease

Brain ◽

10.1093/brain/awz091 ◽

2019 ◽

Vol 142 (6) ◽

pp. 1701-1722 ◽

Cited By ~ 10

Author(s):

Jeffrey S Phillips ◽

Fulvio Da Re ◽

David J Irwin ◽

Corey T McMillan ◽

Sanjeev N Vaishnavi ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Grey Matter ◽

Grey Matter Atrophy

Download Full-text

P3-403: LOSS OF GREY MATTER CONNECTIVITY IN THE PRECUNEUS IS ASSOCIATED WITH FASTER ATROPHY RATES IN PRECLINICAL ALZHEIMER'S DISEASE

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.1766 ◽

2006 ◽

Vol 14 (7S_Part_23) ◽

pp. P1257-P1257

Author(s):

Betty M. Tijms ◽

Ellen Dicks ◽

Philip Scheltens ◽

Frederik Barkhof ◽

Wiesje M. Van der Flier

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Grey Matter ◽

Preclinical Alzheimer’S Disease

Download Full-text

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer’s disease

Brain Communications ◽

10.1093/braincomms/fcaa102 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Lisa Vermunt ◽

Ellen Dicks ◽

Guoqiao Wang ◽

Aylin Dincer ◽

Shaney Flores ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Symptom Onset ◽

Grey Matter ◽

Autosomal Dominant ◽

Single Subject ◽

Mutation Carriers ◽

Autosomal Dominant Alzheimer’S Disease ◽

Network Properties ◽

Over Time

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

Download Full-text

Structural grey matter changes in the substantia innominata in Alzheimer's disease and dementia with Lewy bodies: a DARTEL-VBM study

International Journal of Geriatric Psychiatry ◽

10.1002/gps.4500 ◽

2016 ◽

Vol 32 (6) ◽

pp. 615-623 ◽

Cited By ~ 14

Author(s):

Sean J. Colloby ◽

Greg J. Elder ◽

Riham Rabee ◽

John T. O'Brien ◽

John-Paul Taylor

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Dementia With Lewy Bodies ◽

Grey Matter ◽

Lewy Bodies ◽

Substantia Innominata

Download Full-text

Longitudinal neuroimaging biomarkers differ across Alzheimer’s disease phenotypes

Brain ◽

10.1093/brain/awaa155 ◽

2020 ◽

Vol 143 (7) ◽

pp. 2281-2294 ◽

Cited By ~ 2

Author(s):

Irene Sintini ◽

Jonathan Graff-Radford ◽

Matthew L Senjem ◽

Christopher G Schwarz ◽

Mary M Machulda ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Grey Matter ◽

Clinical Phenotype ◽

Principal Component ◽

Cortical Atrophy ◽

Posterior Cortical Atrophy ◽

Longitudinal Patterns ◽

Progressive Aphasia ◽

Neuroimaging Biomarkers

Abstract Alzheimer’s disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer’s disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer’s disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer’s disease (18 with typical amnestic Alzheimer’s disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer’s disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer’s disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer’s disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer’s disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer’s disease.

Download Full-text