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Journal Abstract Search

156 related items for PubMed ID: 28777721

  • 1. Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data.
    Røge RE, Madsen KH, Schmidt MN, Mørup M.
    Neural Comput; 2017 Oct; 29(10):2712-2741. PubMed ID: 28777721
    [Abstract] [Full Text] [Related]

  • 2. A hierarchical model for integrating unsupervised generative embedding and empirical Bayes.
    Raman S, Deserno L, Schlagenhauf F, Stephan KE.
    J Neurosci Methods; 2016 Aug 30; 269():6-20. PubMed ID: 27141854
    [Abstract] [Full Text] [Related]

  • 3. Fast Bayesian whole-brain fMRI analysis with spatial 3D priors.
    Sidén P, Eklund A, Bolin D, Villani M.
    Neuroimage; 2017 Feb 01; 146():211-225. PubMed ID: 27876654
    [Abstract] [Full Text] [Related]

  • 4. Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms I: Revisiting Cluster-Based Inferences.
    Gopinath K, Krishnamurthy V, Sathian K.
    Brain Connect; 2018 Feb 01; 8(1):1-9. PubMed ID: 28927289
    [Abstract] [Full Text] [Related]

  • 5. A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI.
    Ryali S, Chen T, Supekar K, Menon V.
    Neuroimage; 2013 Jan 15; 65():83-96. PubMed ID: 23041530
    [Abstract] [Full Text] [Related]

  • 6. Inversion of hierarchical Bayesian models using Gaussian processes.
    Lomakina EI, Paliwal S, Diaconescu AO, Brodersen KH, Aponte EA, Buhmann JM, Stephan KE.
    Neuroimage; 2015 Sep 15; 118():133-45. PubMed ID: 26048619
    [Abstract] [Full Text] [Related]

  • 7. A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data.
    Chen S, Bowman FD, Mayberg HS.
    Biometrics; 2016 Jun 15; 72(2):596-605. PubMed ID: 26501687
    [Abstract] [Full Text] [Related]

  • 8. Time series analysis of fMRI data: Spatial modelling and Bayesian computation.
    Teng M, Johnson TD, Nathoo FS.
    Stat Med; 2018 Aug 15; 37(18):2753-2770. PubMed ID: 29717508
    [Abstract] [Full Text] [Related]

  • 9. Grouped Spherical Data Modeling Through Hierarchical Nonparametric Bayesian Models and Its Application to fMRI Data Analysis.
    Fan W, Yang L, Bouguila N.
    IEEE Trans Neural Netw Learn Syst; 2024 Apr 15; 35(4):5566-5576. PubMed ID: 36173782
    [Abstract] [Full Text] [Related]

  • 10. Bayesian mixture models of variable dimension for image segmentation.
    Ferreira da Silva AR.
    Comput Methods Programs Biomed; 2009 Apr 15; 94(1):1-14. PubMed ID: 19036468
    [Abstract] [Full Text] [Related]

  • 11. Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.
    Gopinath K, Krishnamurthy V, Lacey S, Sathian K.
    Brain Connect; 2018 Feb 15; 8(1):10-21. PubMed ID: 29161884
    [Abstract] [Full Text] [Related]

  • 12. Bayesian hierarchical multi-subject multiscale analysis of functional MRI data.
    Sanyal N, Ferreira MA.
    Neuroimage; 2012 Nov 15; 63(3):1519-31. PubMed ID: 22951257
    [Abstract] [Full Text] [Related]

  • 13. Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping.
    Faisan S, Thoraval L, Armspach JP, Foucher JR, Metz-Lutz MN, Heitz F.
    Acad Radiol; 2005 Jan 15; 12(1):25-36. PubMed ID: 15691723
    [Abstract] [Full Text] [Related]

  • 14. Let's Not Waste Time: Using Temporal Information in Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR) for Parcellating FMRI Data.
    Janssen RJ, Jylänki P, van Gerven MA.
    PLoS One; 2016 Jan 15; 11(12):e0164703. PubMed ID: 27935937
    [Abstract] [Full Text] [Related]

  • 15. Bayesian spatiotemporal modeling on complex-valued fMRI signals via kernel convolutions.
    Yu CH, Prado R, Ombao H, Rowe D.
    Biometrics; 2023 Jun 15; 79(2):616-628. PubMed ID: 35143043
    [Abstract] [Full Text] [Related]

  • 16. Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data.
    Dang S, Chaudhury S, Lall B, Roy PK.
    J Neurosci Methods; 2017 Feb 15; 278():87-100. PubMed ID: 28065836
    [Abstract] [Full Text] [Related]

  • 17. How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection.
    Soch J, Haynes JD, Allefeld C.
    Neuroimage; 2016 Nov 01; 141():469-489. PubMed ID: 27477536
    [Abstract] [Full Text] [Related]

  • 18. Statistical power and prediction accuracy in multisite resting-state fMRI connectivity.
    Dansereau C, Benhajali Y, Risterucci C, Pich EM, Orban P, Arnold D, Bellec P.
    Neuroimage; 2017 Apr 01; 149():220-232. PubMed ID: 28161310
    [Abstract] [Full Text] [Related]

  • 19. Gaussian process based independent analysis for temporal source separation in fMRI.
    Hald DH, Henao R, Winther O.
    Neuroimage; 2017 May 15; 152():563-574. PubMed ID: 28249758
    [Abstract] [Full Text] [Related]

  • 20. Predicting individual brain functional connectivity using a Bayesian hierarchical model.
    Dai T, Guo Y, Alzheimer's Disease Neuroimaging InitiativeDepartment of Biostatistics and Bioinformatics, The Rollins School of Public Health, Emory University, Atlanta, GA, United States..
    Neuroimage; 2017 Feb 15; 147():772-787. PubMed ID: 27915121
    [Abstract] [Full Text] [Related]

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