Filters








264 Hits in 4.9 sec

A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging

Murat Bilgel, Jerry L. Prince, Dean F. Wong, Susan M. Resnick, Bruno M. Jedynak
2016 NeuroImage  
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

Li Shen, Paul M. Thompson
2019 Proceedings of the IEEE  
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

Samuel Iddi, for the Alzheimer's Disease Neuroimaging Initiative, Dan Li, Paul S. Aisen, Michael S. Rafii, Wesley K. Thompson, Michael C. Donohue
2019 Brain Informatics  
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

Dan Li, Samuel Iddi, Wesley K. Thompson, Michael S. Rafii, Paul S. Aisen, Michael C. Donohue
2018 Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring  
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

Jagan A. Pillai, Mykol Larvie, Jacqueline Chen, Anna Crawford, Jeffery L. Cummings, Stephen E. Jones
2020 Scientific Reports  
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

Li Shen, Paul M. Thompson, Steven G. Potkin, Lars Bertram, Lindsay A. Farrer, Tatiana M. Foroud, Robert C. Green, Xiaolan Hu, Matthew J. Huentelman, Sungeun Kim, John S. K. Kauwe, Qingqin Li (+12 others)
2013 Brain Imaging and Behavior  
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

Dan Li, Samuel Iddi, Wesley K Thompson, Michael C Donohue
2017 Statistical Methods in Medical Research  
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

Chelsea Caspell-Garcia, Tanya Simuni, Duygu Tosun-Turgut, I-Wei Wu, Yu Zhang, Mike Nalls, Andrew Singleton, Leslie A. Shaw, Ju-Hee Kang, John Q. Trojanowski, Andrew Siderowf, Christopher Coffey (+11 others)
2017 PLoS ONE  
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

Dominic Holland, Linda K. McEvoy, Anders M. Dale
2011 Human Brain Mapping  
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]

Manon Ansart, Stephane Epelbaum, Giulia Bassignana, Alexandre Bone, Simona Bottani, Tiziana Cattai, Raphael Couronne, Johann Faouzi, Igor Koval, Maxime Louis, Elina Thibeau-Sutre, Junhao Wen (+5 others)
2020 biorxiv/medrxiv   pre-print
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

Brian A Gordon, Tyler M Blazey, Yi Su, Amrita Hari-Raj, Aylin Dincer, Shaney Flores, Jon Christensen, Eric McDade, Guoqiao Wang, Chengjie Xiong, Nigel J Cairns, Jason Hassenstab (+28 others)
2018 Lancet Neurology  
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

Leon M. Aksman, Marzia A. Scelsi, Andre F. Marquand, Daniel C. Alexander, Sebastien Ourselin, Andre Altmann
2019 Human Brain Mapping  
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

N. Maritza Dowling, Sterling C. Johnson, Carey E. Gleason, William J. Jagust
2015 NeuroImage  
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]

Leon M. Aksman, Marzia A. Scelsi, Andre F. Marquand, Daniel C. Alexander, Sebastien Ourselin, Andre Altmann
2019 biorxiv/medrxiv   pre-print
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")

K.B. Walhovd, A.M. Fjell, R. Westerhausen, L. Nyberg, K.P. Ebmeier, U. Lindenberger, D. Bartre s-Faz, W.F.C. Baare, H.R. Siebner, R. Henson, C.A. Drevon, G.P. Knudsen (+7 others)
2018 European psychiatry  
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
« Previous Showing results 1 — 15 out of 264 results