A Bayesian group lasso classification for ADNI volumetrics data

2021 ◽  
Vol 30 (10) ◽  
pp. 2207-2220
Author(s):  
Atreyee Majumder ◽  
Tapabrata Maiti ◽  
Subha Datta
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Group Lasso ◽  
Primary Objective ◽  
Elderly Subjects ◽  
Optimal Model ◽  
Lasso Penalty ◽  
Low Dimensional ◽  
Lasso Method

The primary objective of this paper is to develop a statistically valid classification procedure for analyzing brain image volumetrics data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) in elderly subjects with cognitive impairments. The Bayesian group lasso method thereby proposed for logistic regression efficiently selects an optimal model with the use of a spike and slab type prior. This method selects groups of attributes of a brain subregion encouraged by the group lasso penalty. We conduct simulation studies for high- and low-dimensional scenarios where our method is always able to select the true parameters that are truly predictive among a large number of parameters. The method is then applied on dichotomous response ADNI data which selects predictive atrophied brain regions and classifies Alzheimer’s disease patients from healthy controls. Our analysis is able to give an accuracy rate of 80% for classifying Alzheimer’s disease. The suggested method selects 29 brain subregions. The medical literature indicates that all these regions are associated with Alzheimer’s patients. The Bayesian method of model selection further helps selecting only the subregions that are statistically significant, thus obtaining an optimal model.

scholarly journals Electroencephalographic Rhythms in Alzheimer’s Disease

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Roberta Lizio ◽  
Fabrizio Vecchio ◽  
Giovanni B. Frisoni ◽  
Raffaele Ferri ◽  
Guido Rodriguez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Aging ◽  
Event Related Potentials ◽  
Brain Regions ◽  
Elderly Subjects ◽  
Single Individual ◽  
Healthy Elderly ◽  
Age Related ◽  
Related Potentials

Physiological brain aging is characterized by synapses loss and neurodegeneration that slowly lead to an age-related decline of cognition. Neural/synaptic redundancy and plastic remodelling of brain networking, also due to mental and physical training, promotes maintenance of brain activity in healthy elderly subjects for everyday life and good social behaviour and intellectual capabilities. However, age is the major risk factor for most common neurodegenerative disorders that impact on cognition, like Alzheimer's disease (AD). Brain electromagnetic activity is a feature of neuronal network function in various brain regions. Modern neurophysiological techniques, such as electroencephalography (EEG) and event-related potentials (ERPs), are useful tools in the investigation of brain cognitive function in normal and pathological aging with an excellent time resolution. These techniques can index normal and abnormal brain aging analysis of corticocortical connectivity and neuronal synchronization of rhythmic oscillations at various frequencies. The present review suggests that discrimination between physiological and pathological brain aging clearly emerges at the group level, with suggested applications also at the level of single individual. The possibility of combining the use of EEG together with biological/neuropsychological markers and structural/functional imaging is promising for a low-cost, non-invasive, and widely available assessment of groups of individuals at-risk.

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

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

2018 ◽  
Vol 15 (5) ◽  
pp. 429-442 ◽  
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.

Reproducibility of Brain Volume Changes in Longitudinal Voxel-Based Morphometry Between Non-Accelerated and Accelerated Magnetic Resonance Imaging

2021 ◽  
pp. 1-10
Author(s):  
Hidemasa Takao ◽  
Shiori Amemiya ◽  
Osamu Abe ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Elderly Subjects ◽  
Voxel Based Morphometry ◽  
Volume Changes ◽  
Matter Volume ◽  
Healthy Elderly

Background: Scan acceleration techniques, such as parallel imaging, can reduce scan times, but reliability is essential to implement these techniques in neuroimaging. Objective: To evaluate the reproducibility of the longitudinal changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) between non-accelerated and accelerated magnetic resonance images (MRI) in normal aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database, comprising subjects who underwent non-accelerated and accelerated structural T1-weighted MRI at screening and at a 2-year follow-up on 3.0 T Philips scanners, we examined the reproducibility of longitudinal gray matter volume changes determined by longitudinal VBM processing between non-accelerated and accelerated imaging in 50 healthy elderly subjects, 54 MCI patients, and eight AD patients. Results: The intraclass correlation coefficient (ICC) maps differed among the three groups. The mean ICC was 0.72 overall (healthy elderly, 0.63; MCI, 0.75; AD, 0.63), and the ICC was good to excellent (0.6–1.0) for 81.4%of voxels (healthy elderly, 64.8%; MCI, 85.0%; AD, 65.0%). The differences in image quality (head motion) were not significant (Kruskal–Wallis test, p = 0.18) and the within-subject standard deviations of longitudinal gray matter volume changes were similar among the groups. Conclusion: The results indicate that the reproducibility of longitudinal gray matter volume changes determined by VBM between non-accelerated and accelerated MRI is good to excellent for many regions but may vary between diseases and regions.

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

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.

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

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.

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

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 216 ◽  
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.

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

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.

Learning Deficits Accompanied by Microglial Proliferation After the Long-Term Post-Injection of Alzheimer’s Disease Brain Extract in Mouse Brains

2021 ◽  
Vol 79 (4) ◽  
pp. 1701-1711
Author(s):  
Tetsuo Hayashi ◽  
Shotaro Shimonaka ◽  
Montasir Elahi ◽  
Shin-Ei Matsumoto ◽  
Koichi Ishiguro ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Brain ◽  
Functional Decline ◽  
Brain Regions ◽  
Insoluble Fraction ◽  
Brain Extract ◽  
Post Injection ◽  
Learning Deficits

Background: Human tauopathy brain injections into the mouse brain induce the development of tau aggregates, which spread to functionally connected brain regions; however, the features of this neurotoxicity remain unclear. One reason may be short observational periods because previous studies mostly used mutated-tau transgenic mice and needed to complete the study before these mice developed neurofibrillary tangles. Objective: To examine whether long-term incubation of Alzheimer’s disease (AD) brain in the mouse brain cause functional decline. Methods: We herein used Tg601 mice, which overexpress wild-type human tau, and non-transgenic littermates (NTg) and injected an insoluble fraction of the AD brain into the unilateral hippocampus. Results: After a long-term (17–19 months) post-injection, mice exhibited learning deficits detected by the Barnes maze test. Aggregated tau pathology in the bilateral hippocampus was more prominent in Tg601 mice than in NTg mice. No significant changes were observed in the number of Neu-N positive cells or astrocytes in the hippocampus, whereas that of Iba-I-positive microglia increased after the AD brain injection. Conclusion: These results potentially implicate tau propagation in functional decline and indicate that long-term changes in non-mutated tau mice may reflect human pathological conditions.

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