Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer’s Disease

AbstractMuch is still unknown about the neurobiology of Alzheimer’s disease (AD). To better understand AD, we generated 636 ATAC-seq libraries from cases and controls to construct detailed genomewide chromatin accessibility maps of neurons and non-neurons from two AD-affected brain regions, the entorhinal cortex and superior temporal gyrus. By analyzing a total of 19.6 billion read pairs, we expanded the known repertoire of regulatory sequences in the human brain. Multi-omic data integration associated global patterns of chromatin accessibility with gene expression and identified cell-specific enhancer-promoter interactions. Using inter-individual variation in chromatin accessibility, we define cis-regulatory domains capturing the 3D structure of the genome. Multifaceted analyses uncovered disease associated perturbations impacting chromatin accessibility, transcription factor regulatory networks and the 3D genome, and implicated transcriptional dysregulation in AD. Overall, we applied a systematic approach to understand the role of the 3D genome in AD and to illuminate novel disease biology that can advance diagnosis and therapy.

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The Integrated Voxel Analysis Method (IVAM) to Diagnose Onset of Alzheimer’s Disease and Identify Brain Regions through Structural MRI Images

10.1101/19009597 ◽

2019 ◽

Author(s):

Matthew Hur ◽

Armen Aghajanyan

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Nearest Neighbor ◽

Feature Vector ◽

Three Dimensional ◽

Structural Mri ◽

Brain Regions ◽

Brain Atlas ◽

Analysis Method ◽

K Nearest Neighbor

AbstractMagnetic Resonance Imaging (MRI) provides three-dimensional anatomical and physiological details of the human brain. We describe the Integrated Voxel Analysis Method (IVAM) which, through machine learning, classifies MRI images of brains afflicted with early Alzheimer’s Disease (AD). This fully automatic method uses an extra trees regressor model in which the feature vector input contains the intensities of voxels, whereby the effect of AD on a single voxel can be predicted. The resulting tree predicts based on the following two steps: a K-nearest neighbor (KNN) algorithm based on Euclidean distance with the feature vector to classify whole images based on their distribution of affected voxels and a voxel-by-voxel classification by the tree of every voxel in the image. An Ising model filter follows voxel-by-voxel tree-classification to remove artifacts and to facilitate clustering of classification results which identify significant voxel clusters affected by AD. We apply this method to T1-weighted MRI images obtained from the Open Access Series of Imaging Studies (OASIS) using images belonging to normal and early AD-afflicted individuals associated with a Client Dementia Rating (CDR) which we use as the target in the supervised learning. Furthermore, statistical analysis using a pre-labeled brain atlas automatically identifies significantly affected brain regions. While achieving 90% AD classification accuracy on 198 images in the OASIS dataset, the method reveals morphological differences caused by the onset of AD.

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Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100129930 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1060-1061

Author(s):

Mark Ellisman ◽

Maryann Martone ◽

Gabriel Soto ◽

Eleizer Masliah ◽

David Hessler ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Three Dimensional ◽

Neuritic Plaque ◽

Dimensional Structure ◽

Data Sets ◽

Molecular Physiology ◽

Research Activities ◽

Computer Aided ◽

Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

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Three-Dimensional Chromatin Architecture Landscape of Aging and Alzheimer's Disease

SSRN Electronic Journal ◽

10.2139/ssrn.3733075 ◽

2020 ◽

Author(s):

Guofeng Meng ◽

Jialan Huang ◽

Dong Lu ◽

Zhenzhen Zhao ◽

Feng Yu ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Three Dimensional ◽

Chromatin Architecture

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A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

Current Alzheimer Research ◽

10.2174/1567205014666171106150309 ◽

2018 ◽

Vol 15 (5) ◽

pp. 429-442 ◽

Cited By ~ 3

Author(s):

Nishant Verma ◽

S. Natasha Beretvas ◽

Belen Pascual ◽

Joseph C. Masdeu ◽

Mia K. Markey ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Cognitive Impairment ◽

Latent Variable ◽

Brain Regions ◽

Disease Assessment ◽

Latent Variable Analysis ◽

The Relationship ◽

Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

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A Super-Resolved View of the Alzheimer’s Disease-Related Amyloidogenic Pathway in Hippocampal Neurons

Journal of Alzheimer s Disease ◽

10.3233/jad-215008 ◽

2021 ◽

pp. 1-20

Author(s):

Yang Yu ◽

Yang Gao ◽

Bengt Winblad ◽

Lars Tjernberg ◽

Sophia Schedin Weiss

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Hippocampal Neurons ◽

Three Dimensional ◽

Amyloid Β ◽

Stimulated Emission ◽

Amyloid Β Peptide ◽

Primary Neurons ◽

Amyloid Β Protein ◽

Early Endosomes

Background: Processing of the amyloid-β protein precursor (AβPP) is neurophysiologically important due to the resulting fragments that regulate synapse biology, as well as potentially harmful due to generation of the 42 amino acid long amyloid β-peptide (Aβ 42), which is a key player in Alzheimer’s disease. Objective: Our aim was to clarify the subcellular locations of the amyloidogenic AβPP processing in primary neurons, including the intracellular pools of the immediate substrate, AβPP C-terminal fragment (APP-CTF) and the product (Aβ 42). To overcome the difficulties of resolving these compartments due to their small size, we used super-resolution microscopy. Methods: Mouse primary hippocampal neurons were immunolabelled and imaged by stimulated emission depletion (STED) microscopy, including three-dimensional, three-channel imaging and image analyses. Results: The first (β-secretase) and second (γ-secretase) cleavages of AβPP were localized to functionally and distally distinct compartments. The β-secretase cleavage was observed in early endosomes, where we were able to show that the liberated N- and C-terminal fragments were sorted into distinct vesicles budding from the early endosomes in soma. Lack of colocalization of Aβ 42 and APP-CTF in soma suggested that γ-secretase cleavage occurs in neurites. Indeed, APP-CTF was, in line with Aβ 42 in our previous study, enriched in the presynapse but absent from the postsynapse. In contrast, full-length AβPP was not detected in either the pre- or the postsynaptic side of the synapse. Furthermore, we observed that endogenously produced and endocytosed Aβ 42 were localized in different compartments. Conclusion: These findings provide critical super-resolved insight into amyloidogenic AβPP processing in primary neurons.

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Genome-wide analysis identifies a novel LINC-PINT splice variant associated with vascular amyloid pathology in Alzheimer’s disease

Acta Neuropathologica Communications ◽

10.1186/s40478-021-01199-2 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Joseph S. Reddy ◽

Mariet Allen ◽

Charlotte C. G. Ho ◽

Stephanie R. Oatman ◽

Özkan İş ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genome Wide Association Study ◽

Transcriptome Profiling ◽

Brain Regions ◽

Common Finding ◽

Amyloid Angiopathy ◽

Genome Wide ◽

A Genome ◽

Male Sex

AbstractCerebral amyloid angiopathy (CAA) contributes to accelerated cognitive decline in Alzheimer’s disease (AD) dementia and is a common finding at autopsy. The APOEε4 allele and male sex have previously been reported to associate with increased CAA in AD. To inform biomarker and therapeutic target discovery, we aimed to identify additional genetic risk factors and biological pathways involved in this vascular component of AD etiology. We present a genome-wide association study of CAA pathology in AD cases and report sex- and APOE-stratified assessment of this phenotype. Genome-wide genotypes were collected from 853 neuropathology-confirmed AD cases scored for CAA across five brain regions, and imputed to the Haplotype Reference Consortium panel. Key variables and genome-wide genotypes were tested for association with CAA in all individuals and in sex and APOEε4 stratified subsets. Pathway enrichment was run for each of the genetic analyses. Implicated loci were further investigated for functional consequences using brain transcriptome data from 1,186 samples representing seven brain regions profiled as part of the AMP-AD consortium. We confirmed association of male sex, AD neuropathology and APOEε4 with increased CAA, and identified a novel locus, LINC-PINT, associated with lower CAA amongst APOEε4-negative individuals (rs10234094-C, beta = −3.70 [95% CI −0.49—−0.24]; p = 1.63E-08). Transcriptome profiling revealed higher LINC-PINT expression levels in AD cases, and association of rs10234094-C with altered LINC-PINT splicing. Pathway analysis indicates variation in genes involved in neuronal health and function are linked to CAA in AD patients. Further studies in additional and diverse cohorts are needed to assess broader translation of our findings.

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Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications ◽

10.1007/s00521-021-06105-4 ◽

2021 ◽

Author(s):

Antonio Giovannetti ◽

Gianluca Susi ◽

Paola Casti ◽

Arianna Mencattini ◽

Sandra Pusil ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Brain Regions ◽

Ensemble Classification ◽

The Novel ◽

Ensemble Classifiers ◽

Deep Convolutional Neural Networks ◽

Early Signs ◽

Data Representations ◽

The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

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Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Entropy ◽

10.3390/e23020216 ◽

2021 ◽

Vol 23 (2) ◽

pp. 216 ◽

Cited By ~ 1

Author(s):

Jianjia Wang ◽

Xichen Wu ◽

Mingrui Li ◽

Hui Wu ◽

Edwin Hancock

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Ensemble Methods ◽

Brain Regions ◽

Fmri Data ◽

Thermal Systems ◽

Network Construction ◽

Network Characteristics ◽

Network Entropy ◽

Canonical Ensembles

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

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The extracellular chaperone Clusterin enhances Tau aggregate seeding in a cellular model

Nature Communications ◽

10.1038/s41467-021-25060-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Patricia Yuste-Checa ◽

Victoria A. Trinkaus ◽

Irene Riera-Tur ◽

Rahmi Imamoglu ◽

Theresa F. Schaller ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Progression ◽

Frontotemporal Dementia ◽

Brain Regions ◽

Model System ◽

Cellular Model ◽

Tau Pathology ◽

Extracellular Chaperone ◽

Tau Aggregate

AbstractSpreading of aggregate pathology across brain regions acts as a driver of disease progression in Tau-related neurodegeneration, including Alzheimer’s disease (AD) and frontotemporal dementia. Aggregate seeds released from affected cells are internalized by naïve cells and induce the prion-like templating of soluble Tau into neurotoxic aggregates. Here we show in a cellular model system and in neurons that Clusterin, an abundant extracellular chaperone, strongly enhances Tau aggregate seeding. Upon interaction with Tau aggregates, Clusterin stabilizes highly potent, soluble seed species. Tau/Clusterin complexes enter recipient cells via endocytosis and compromise the endolysosomal compartment, allowing transfer to the cytosol where they propagate aggregation of endogenous Tau. Thus, upregulation of Clusterin, as observed in AD patients, may enhance Tau seeding and possibly accelerate the spreading of Tau pathology.

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