BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

386 related articles for article (PubMed ID: 25791013)

  • 1. WASICA: An effective wavelet-shrinkage based ICA model for brain fMRI data analysis.
    Wang N; Zeng W; Shi Y; Ren T; Jing Y; Yin J; Yang J
    J Neurosci Methods; 2015 May; 246():75-96. PubMed ID: 25791013
    [TBL] [Abstract][Full Text] [Related]  

  • 2. SACICA: a sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis.
    Wang N; Zeng W; Chen L
    J Neurosci Methods; 2013 May; 216(1):49-61. PubMed ID: 23563324
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources.
    Ge R; Wang Y; Zhang J; Yao L; Zhang H; Long Z
    J Neurosci Methods; 2016 Apr; 263():103-14. PubMed ID: 26880161
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A two-step super-Gaussian independent component analysis approach for fMRI data.
    Ge R; Yao L; Zhang H; Long Z
    Neuroimage; 2015 Sep; 118():344-58. PubMed ID: 26057592
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data.
    Kim YH; Kim J; Lee JH
    Neuroimage; 2012 Dec; 63(4):1864-89. PubMed ID: 22939873
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Multivariate analysis of neuronal interactions in the generalized partial least squares framework: simulations and empirical studies.
    Lin FH; McIntosh AR; Agnew JA; Eden GF; Zeffiro TA; Belliveau JW
    Neuroimage; 2003 Oct; 20(2):625-42. PubMed ID: 14568440
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering.
    Ren T; Zeng W; Wang N; Chen L; Wang C
    Magn Reson Imaging; 2014 Jul; 32(6):736-46. PubMed ID: 24721006
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Novel Sparse Dictionary Learning Separation (SDLS) Model With Adaptive Dictionary Mutual Incoherence Constraint for fMRI Data Analysis.
    Wang N; Zeng W; Chen D
    IEEE Trans Biomed Eng; 2016 Nov; 63(11):2376-2389. PubMed ID: 26929024
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A comparative evaluation of wavelet-based methods for hypothesis testing of brain activation maps.
    Fadili MJ; Bullmore ET
    Neuroimage; 2004 Nov; 23(3):1112-28. PubMed ID: 15528111
    [TBL] [Abstract][Full Text] [Related]  

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

  • 11. Bayesian reconstruction of multiscale local contrast images from brain activity.
    Song S; Ma X; Zhan Y; Zhan Z; Yao L; Zhang J
    J Neurosci Methods; 2013 Oct; 220(1):39-45. PubMed ID: 23999175
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The impact of denoising on independent component analysis of functional magnetic resonance imaging data.
    Pignat JM; Koval O; Van De Ville D; Voloshynovskiy S; Michel C; Pun T
    J Neurosci Methods; 2013 Feb; 213(1):105-22. PubMed ID: 23261654
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process.
    Yao S; Zeng W; Wang N; Chen L
    Magn Reson Imaging; 2013 Jul; 31(6):970-5. PubMed ID: 23587929
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics.
    Khullar S; Michael A; Correa N; Adali T; Baum SA; Calhoun VD
    Neuroimage; 2011 Feb; 54(4):2867-84. PubMed ID: 21034833
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing.
    Wink AM; Roerdink JB
    IEEE Trans Med Imaging; 2004 Mar; 23(3):374-87. PubMed ID: 15027530
    [TBL] [Abstract][Full Text] [Related]  

  • 16. SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis.
    Shi Y; Zeng W
    Comput Biol Med; 2018 Nov; 102():75-85. PubMed ID: 30248514
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data.
    Zhang C; Song S; Wen X; Yao L; Long Z
    J Neurosci Methods; 2015 Apr; 245():15-24. PubMed ID: 25681758
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.
    Cong F; Puoliväli T; Alluri V; Sipola T; Burunat I; Toiviainen P; Nandi AK; Brattico E; Ristaniemi T
    J Neurosci Methods; 2014 Feb; 223():74-84. PubMed ID: 24333752
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Blind source separation of fMRI data by means of factor analytic transformations.
    Langers DR
    Neuroimage; 2009 Aug; 47(1):77-87. PubMed ID: 19362596
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A semi-blind online dictionary learning approach for fMRI data.
    Long Z; Liu L; Gao Z; Chen M; Yao L
    J Neurosci Methods; 2019 Jul; 323():1-12. PubMed ID: 31085215
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.