These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

414 related articles for article (PubMed ID: 20381530)

  • 1. Visual inspection of independent components: defining a procedure for artifact removal from fMRI data.
    Kelly RE; Alexopoulos GS; Wang Z; Gunning FM; Murphy CF; Morimoto SS; Kanellopoulos D; Jia Z; Lim KO; Hoptman MJ
    J Neurosci Methods; 2010 Jun; 189(2):233-45. PubMed ID: 20381530
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Automatic independent component labeling for artifact removal in fMRI.
    Tohka J; Foerde K; Aron AR; Tom SM; Toga AW; Poldrack RA
    Neuroimage; 2008 Feb; 39(3):1227-45. PubMed ID: 18042495
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion.
    Xu Y; Tong Y; Liu S; Chow HM; AbdulSabur NY; Mattay GS; Braun AR
    Neuroimage; 2014 Dec; 103():33-47. PubMed ID: 25225001
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers.
    Salimi-Khorshidi G; Douaud G; Beckmann CF; Glasser MF; Griffanti L; Smith SM
    Neuroimage; 2014 Apr; 90():449-68. PubMed ID: 24389422
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings.
    Ryali S; Glover GH; Chang C; Menon V
    Neuroimage; 2009 Nov; 48(2):348-61. PubMed ID: 19580873
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI.
    Steel A; Garcia BD; Silson EH; Robertson CE
    Neuroimage; 2022 Dec; 264():119723. PubMed ID: 36328274
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR).
    Wong CK; Zotev V; Misaki M; Phillips R; Luo Q; Bodurka J
    Neuroimage; 2016 Apr; 129():133-147. PubMed ID: 26826516
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series.
    Patel AX; Kundu P; Rubinov M; Jones PS; Vértes PE; Ersche KD; Suckling J; Bullmore ET
    Neuroimage; 2014 Jul; 95(100):287-304. PubMed ID: 24657353
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A correlation-based method for extracting subject-specific components and artifacts from group-fMRI data.
    Pamilo S; Malinen S; Hotta J; Seppä M
    Eur J Neurosci; 2015 Nov; 42(9):2726-41. PubMed ID: 26226919
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power.
    Glasser MF; Coalson TS; Bijsterbosch JD; Harrison SJ; Harms MP; Anticevic A; Van Essen DC; Smith SM
    Neuroimage; 2019 Aug; 197():435-438. PubMed ID: 31026516
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Denoising task-related fMRI: Balancing noise reduction against signal loss.
    Hoeppli ME; Garenfeld MA; Mortensen CK; Nahman-Averbuch H; King CD; Coghill RC
    Hum Brain Mapp; 2023 Dec; 44(17):5523-5546. PubMed ID: 37753711
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Optimizing preprocessing and analysis pipelines for single-subject fMRI: 2. Interactions with ICA, PCA, task contrast and inter-subject heterogeneity.
    Churchill NW; Yourganov G; Oder A; Tam F; Graham SJ; Strother SC
    PLoS One; 2012; 7(2):e31147. PubMed ID: 22383999
    [TBL] [Abstract][Full Text] [Related]  

  • 13. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data.
    Pruim RHR; Mennes M; van Rooij D; Llera A; Buitelaar JK; Beckmann CF
    Neuroimage; 2015 May; 112():267-277. PubMed ID: 25770991
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising.
    Heo KS; Shin DH; Hung SC; Lin W; Zhang H; Shen D; Kam TE
    Neuroimage; 2022 Jul; 254():119127. PubMed ID: 35337965
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Intersubject MVPD: Empirical comparison of fMRI denoising methods for connectivity analysis.
    Li Y; Saxe R; Anzellotti S
    PLoS One; 2019; 14(9):e0222914. PubMed ID: 31550276
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion.
    Maziero D; Rondinoni C; Marins T; Stenger VA; Ernst T
    Neuroimage; 2020 May; 212():116594. PubMed ID: 32044436
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI.
    Abreu R; Leite M; Jorge J; Grouiller F; van der Zwaag W; Leal A; Figueiredo P
    Neuroimage; 2016 Jul; 135():45-63. PubMed ID: 27012501
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI.
    Pruim RHR; Mennes M; Buitelaar JK; Beckmann CF
    Neuroimage; 2015 May; 112():278-287. PubMed ID: 25770990
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Akaike causality in state space. Instantaneous causality between visual cortex in fMRI time series.
    Wong KF; Ozaki T
    Biol Cybern; 2007 Aug; 97(2):151-7. PubMed ID: 17579884
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Spatially regularized machine learning for task and resting-state fMRI.
    Song X; Panych LP; Chen NK
    J Neurosci Methods; 2016 Jan; 257():214-28. PubMed ID: 26470627
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.