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A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging
2016
NeuroImage
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We propose a multivariate nonlinear mixed effects model for estimating the trajectories of voxelwise neuroimaging biomarkers from longitudinal data that accounts for such differences across individuals ...
However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do not account for the fact that individuals enter a study at various disease stages and progress at different ...
Fox Foundation for Parkinson's Research, MJFF Research Grant ID: 9310. ...
doi:10.1016/j.neuroimage.2016.04.001
pmid:27095307
pmcid:PMC4912927
fatcat:xx2syc2wk5hepnb6ocidz22yqi
Brain Imaging Genomics: Integrated Analysis and Machine Learning
2019
Proceedings of the IEEE
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and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications. ...
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed ...
[118] proposed a set-based mixed effect model for gene-environment interaction (MixGE) on imaging QT. ...
doi:10.1109/jproc.2019.2947272
pmid:31902950
pmcid:PMC6941751
fatcat:rx5b44yv55d2xicdiznnwjdac4
Predicting the course of Alzheimer's progression
2019
Brain Informatics
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The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for ...
In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. ...
As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/ or provided data but did not participate in analysis or writing of this report. ...
doi:10.1186/s40708-019-0099-0
pmid:31254120
pmcid:PMC6598897
fatcat:gryiihrmyba6vc36ybu6of4pia
Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative
2018
Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
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Methods: We apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. ...
In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., r 5 0.731 for ventricles and ...
We will also leverage the model to improve prognostic prediction and to identify populations expected to experience the maximum benefit from a given intervention. ...
doi:10.1016/j.dadm.2018.07.008
pmid:30456292
pmcid:PMC6234901
fatcat:47r5h5bz7ffmbhpnj55owxsznq
Spatial patterns of correlation between cortical amyloid and cortical thickness in a tertiary clinical population with memory deficit
2020
Scientific Reports
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We therefore developed a robust MRI analysis method to identify brain regions that correlate linearly with regional amyloid burden in congruent PET images. ...
This method was designed to reduce data variance and improve the sensitivity of the detection of cortical thickness–amyloid correlation by using whole brain modeling, nonlinear image coregistration, and ...
Acknowledgements We thank Frank DiFilippo, PhD, for his review of physics aspects regarding nuclear medicine imaging, and Megan Griffiths for editorial support. ...
doi:10.1038/s41598-020-77503-2
pmid:33244036
fatcat:w27x3j6mjvf4bnzua6xmmlunci
Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers
2013
Brain Imaging and Behavior
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We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. ...
As such, many investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ...
A relevant study by the same group proposed a novel task-correlated longitudinal sparse regression model and showed its promise for relating longitudinal MRI phenotypes to AD risk genes (Wang et al. 2012c ...
doi:10.1007/s11682-013-9262-z
pmid:24092460
pmcid:PMC3976843
fatcat:a4ufw3rjcbbjzbzmeppuicws2a
Bayesian latent time joint mixed effect models for multicohort longitudinal data
2017
Statistical Methods in Medical Research
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2017 pre-print arXiv:1703.10266v1 fatcat:n6pzeaswlvgobffdu3nawm5gbmOther Versions
We propose a latent time joint mixed effects model to characterize long-term disease dynamics using this short-term data. ...
Natural history studies typically recruit multiple cohorts at different stages of disease and follow them longitudinally for a relatively short period of time. ...
Acknowledgment We are grateful to the ADNI study volunteers and their families. ...
doi:10.1177/0962280217737566
pmid:29168432
fatcat:a2thrur7everhdwi5ggedui5q4
Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease
2017
PLoS ONE
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Methods We longitudinally assessed, up to 3 years, 423 newly diagnosed patients with idiopathic PD, untreated at baseline, from 33 international movement disorder centers. ...
Objectives To assess the neurobiological substrate of initial cognitive decline in Parkinson's disease (PD) to inform patient management, clinical trial design, and development of treatments. ...
Longitudinal logistic or linear mixed-effect models were used to find baseline and longitudinal predictors (treated as time-dependent predictors) of cognitive impairment over the 3-year time period. ...
doi:10.1371/journal.pone.0175674
pmid:28520803
pmcid:PMC5435130
fatcat:urvbwcsoyrhfzce2s3tzjhl6ai
Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI
2011
Human Brain Mapping
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Here, using sample size estimates, we present a comparative analysis of the overall results that come from the application of each laboratory's extensive processing stream to the ADNI database. ...
to be unrealistically low for treatments targeting amyloid-related pathology. ...
To determine 95% confidence intervals on the sample size estimates, the joint a posteriori probability density function for the mixed effects model parameters ( , , and m) was computed based on the multivariate ...
doi:10.1002/hbm.21386
pmid:21830259
pmcid:PMC3782292
fatcat:zmkzyvifrjddtap5lbyd6scyqu
Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A Systematic, Quantitative and Critical Review
[article]
2020
biorxiv/medrxiv
pre-print
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The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. ...
not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. ...
We thank the reviewers for their insightful comments that helped us to improve the manuscript, including Gaël Varoquaux who purposely disclosed his name.
620 The research leading to these results has ...
doi:10.1101/2020.09.01.20185959
fatcat:f5vrfriotrf7bn63ovcylv7rue
Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study
2018
Lancet Neurology
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We estimated rates of biomarker change as a function of estimated years from symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories ...
Longitudinal analyses can provide a more accurate and powerful way to model the temporal emergence of pathology in ADAD. ...
Acknowledgements Foremost we wish to acknowledge the dedication of the participants and their families, whom without these studies would not be possible. ...
doi:10.1016/s1474-4422(18)30028-0
pmid:29397305
pmcid:PMC5816717
fatcat:agt2ewhmkzeuxfs63ym7rhcre4
Modeling longitudinal imaging biomarkers with parametric Bayesian multi‐task learning
2019
Human Brain Mapping
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and fixed effects a priori, we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. ...
Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers ...
By far the most popular approaches are based on mixed effect modeling, which combines fixed effects, that is, pooling subjects' data to create an average trajectory for all subjects, with random effects ...
doi:10.1002/hbm.24682
pmid:31168892
pmcid:PMC6679792
fatcat:5wsi47z7mva6rozh4l3fcaqfca
The mediational effects of FDG hypometabolism on the association between cerebrospinal fluid biomarkers and neurocognitive function
2015
NeuroImage
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Results support a temporal framework model in which reduced CSF amyloid-related biomarkers occur earlier in the pathogenic pathway, ultimately leading to detrimental cognitive effects. ...
A parallelprocess latent growth curve model was used to test mediational effects of changes in regional * Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging ...
supported by a grant from the Alzheimer's Association (NIRG-12-242799, Dowling) and the National Institutes of Health (NIH-AG021155, Johnson). ...
doi:10.1016/j.neuroimage.2014.10.050
pmid:25450107
pmcid:PMC4262609
fatcat:5eklozq7dvcxjh4ehg3fgjut4q
Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning
[article]
2019
biorxiv/medrxiv
pre-print
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propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. ...
AbstractLongitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional ...
By far the most popular approaches are based on mixed effect modeling, which combines fixed effects, that is, pooling subjects' data to create an average trajectory for all subjects, with random effects ...
doi:10.1101/593459
fatcat:gzp6ozm7mbbfnc7pi5zdkqsd2i
Publishers Note: Healthy minds 0–100 years: Optimising the use of European brain imaging cohorts ("Lifebrain")
2018
European psychiatry
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Longitudinal brain imaging, genetic and health data are available for a major part, as well as cognitive and mental health measures for the broader cohorts, exceeding 27,000 examinations in total. ...
A B S T R A C T The main objective of "Lifebrain" is to identify the determinants of brain, cognitive and mental (BCM) health at different stages of life. ...
Acknowledgement This research is funded by the EU Horizon 2020 Grant: 'Healthy minds 0-100 years: Optimising the use of European brain imaging cohorts ("Lifebrain")'. Grant agreement number: 732592. ...
doi:10.1016/j.eurpsy.2017.10.005
pmid:29127911
fatcat:n6uezz6k4nbyvleblrk6zkt2l4
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