547 research outputs found
Alzheimer's Disease Prediction Using Longitudinal and Heterogeneous Magnetic Resonance Imaging
Recent evidence has shown that structural magnetic resonance imaging (MRI) is
an effective tool for Alzheimer's disease (AD) prediction and diagnosis. While
traditional MRI-based diagnosis uses images acquired at a single time point, a
longitudinal study is more sensitive and accurate in detecting early
pathological changes of the AD. Two main difficulties arise in longitudinal
MRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects
(i.e., different scanning time and different total number of scans); (2) the
heterogeneous progressions of high-dimensional regions of interest (ROIs) in
MRI. In this work, we propose a novel feature selection and estimation method
which can be applied to extract features from the heterogeneous longitudinal
MRI. A key ingredient of our method is the combination of smoothing splines and
the -penalty. We perform experiments on the Alzheimer's Disease
Neuroimaging Initiative (ADNI) database. The results corroborate the advantages
of the proposed method for AD prediction in longitudinal studies
Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps.
Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small
Association of common genetic variants with brain microbleeds : A genome-wide association study
This study was not industry sponsored. M.J. Knol, D. Lu, and M. Traylor report no disclosures relevant to the manuscript. H.H.H. Adams is supported by ZonMW grant 916.19.151. J.R.J. Romero, A.V. Smith, M. Fornage, E. Hofer, and J. Liu report no disclosures relevant to the manuscript. I.C. Hostettler received funding from the Alzheimer Research UK and Dunhill Medical Trust Foundation. M. Luciano, S. Trompet, A.-K. Giese, S. Hilal, E.B. van den Akker, D. Vojinovic, S. Li, S. Sigurdsson, S.J. van der Lee, and C.R. Jack, Jr. report no disclosures relevant to the manuscript. D. Wilson received funding from the Stroke Foundation/British Heart Foundation. P. Yilmaz, C.L. Satizabal, D.C.M. Liewald, J. van der Grond, C. Chen, Y. Saba, A. van der Lugt, M.E. Bastin, B.G. Windham, C.Y. Cheng, L. Pirpamer, K. Kantarci, J.J. Himali, Q. Yang, Z. Morris, A.S. Beiser, D.J. Tozer, M.W. Vernooij, N. Amin, M. Beekman, J.Y. Koh, and D.J. Stott report no disclosures relevant to the manuscript. H. Houlden received funding from the Alzheimer Research UK and Dunhill Medical Trust Foundation. R. Schmidt, R.F. Gottesman, and A.D. MacKinnon report no disclosures relevant to the manuscript. C. DeCarli is supported by the Alzheimer's Disease Center (P30 AG 010129) and serves as a consultant of Novartis Pharmaceuticals. V. Gudnason, I.J. Deary, C.M. van Duijn, P.E. Slagboom, T.Y. Wong, and N.S. Rost report no disclosures relevant to the manuscript. J.W. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). T.H. Mosley reports no disclosures relevant to the manuscript. D.J. Werring received funding from the Stroke Foundation/British Heart Foundation. H. Schmidt, J.M. Wardlaw, M.A. Ikram, S. Seshadri, L.J. Launer, and H.S. Markus report no disclosures relevant to the manuscript. Go to Neurology.org/N for full disclosures. Publisher Copyright: Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.OBJECTIVE: To identify common genetic variants associated with the presence of brain microbleeds (BMBs). METHODS: We performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs. RESULTS: BMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR]any BMB [95% confidence interval (CI)] 1.33 [1.21-1.45]; p = 2.5 × 10-10). APOE ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; p = 1.0 × 10-6) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; p = 0.68). APOE ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB. CONCLUSIONS: Genetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.Peer reviewe
Bayesian inference and role of astrocytes in amyloid-beta dynamics with modelling of Alzheimer's disease using clinical data
Alzheimer's disease (AD) is a prominent, worldwide, age-related
neurodegenerative disease that currently has no systemic treatment. Strong
evidence suggests that permeable amyloid-beta peptide (Abeta) oligomers,
astrogliosis and reactive astrocytosis cause neuronal damage in AD. A large
amount of Abeta is secreted by astrocytes, which contributes to the total Abeta
deposition in the brain. This suggests that astrocytes may also play a role in
AD, leading to increased attention to their dynamics and associated mechanisms.
Therefore, in the present study, we developed and evaluated novel stochastic
models for Abeta growth using ADNI data to predict the effect of astrocytes on
AD progression in a clinical trial. In the AD case, accurate prediction is
required for a successful clinical treatment plan. Given that AD studies are
observational in nature and involve routine patient visits, stochastic models
provide a suitable framework for modelling AD. Using the approximate Bayesian
computation (ABC) approach, the AD etiology may be modelled as a multi-state
disease process. As a result, we use this approach to examine the weak and
strong influence of astrocytes at multiple disease progression stages using
ADNI data from the baseline to 2-year visits for AD patients whose ages ranged
from 50 to 90 years. Based on ADNI data, we discovered that the strong
astrocyte effect (i.e., a higher concentration of astrocytes as compared to
Abeta) could help to lower or clear the growth of Abeta, which is a key to
slowing down AD progression.Comment: 10, figures and 30 page
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Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer's Disease
As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets
Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression
We present a new method for the detection of gene pathways associated with a
multivariate quantitative trait, and use it to identify causal pathways
associated with an imaging endophenotype characteristic of longitudinal
structural change in the brains of patients with Alzheimer's disease (AD). Our
method, known as pathways sparse reduced-rank regression (PsRRR), uses group
lasso penalised regression to jointly model the effects of genome-wide single
nucleotide polymorphisms (SNPs), grouped into functional pathways using prior
knowledge of gene-gene interactions. Pathways are ranked in order of importance
using a resampling strategy that exploits finite sample variability. Our
application study uses whole genome scans and MR images from 464 subjects in
the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs
are mapped to 185 gene pathways from the KEGG pathways database. Voxel-wise
imaging signatures characteristic of AD are obtained by analysing 3D patterns
of structural change at 6, 12 and 24 months relative to baseline. High-ranking,
AD endophenotype-associated pathways in our study include those describing
chemokine, Jak-stat and insulin signalling pathways, and tight junction
interactions. All of these have been previously implicated in AD biology. In a
secondary analysis, we investigate SNPs and genes that may be driving pathway
selection, and identify a number of previously validated AD genes including
CR1, APOE and TOMM40
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