BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

183 related articles for article (PubMed ID: 34269612)

  • 1. Robust Correlation for Link Definition in Resting-State fMRI Brain Networks Can Reduce Motion-Related Artifacts.
    Burkhardt M; Thiel CM; Gießing C
    Brain Connect; 2022 Feb; 12(1):18-25. PubMed ID: 34269612
    [No Abstract]   [Full Text] [Related]  

  • 2. 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]  

  • 3. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI.
    Parkes L; Fulcher B; Yücel M; Fornito A
    Neuroimage; 2018 May; 171():415-436. PubMed ID: 29278773
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. Combining Prospective Acquisition CorrEction (PACE) with retrospective correction to reduce motion artifacts in resting state fMRI data.
    Lanka P; Deshpande G
    Brain Behav; 2019 Aug; 9(8):e01341. PubMed ID: 31297966
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines.
    Kopal J; Pidnebesna A; Tomeček D; Tintěra J; Hlinka J
    Hum Brain Mapp; 2020 Dec; 41(18):5325-5340. PubMed ID: 32881215
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics.
    Yan CG; Cheung B; Kelly C; Colcombe S; Craddock RC; Di Martino A; Li Q; Zuo XN; Castellanos FX; Milham MP
    Neuroimage; 2013 Aug; 76():183-201. PubMed ID: 23499792
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity.
    Hallquist MN; Hwang K; Luna B
    Neuroimage; 2013 Nov; 82():208-25. PubMed ID: 23747457
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing.
    Phạm DĐ; McDonald DJ; Ding L; Nebel MB; Mejia AF
    Neuroimage; 2023 Apr; 270():119972. PubMed ID: 36842522
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Modular preprocessing pipelines can reintroduce artifacts into fMRI data.
    Lindquist MA; Geuter S; Wager TD; Caffo BS
    Hum Brain Mapp; 2019 Jun; 40(8):2358-2376. PubMed ID: 30666750
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Reduction of motion-related artifacts in resting state fMRI using aCompCor.
    Muschelli J; Nebel MB; Caffo BS; Barber AD; Pekar JJ; Mostofsky SH
    Neuroimage; 2014 Aug; 96():22-35. PubMed ID: 24657780
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparing resting state fMRI de-noising approaches using multi- and single-echo acquisitions.
    Dipasquale O; Sethi A; Laganà MM; Baglio F; Baselli G; Kundu P; Harrison NA; Cercignani M
    PLoS One; 2017; 12(3):e0173289. PubMed ID: 28323821
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project.
    Burgess GC; Kandala S; Nolan D; Laumann TO; Power JD; Adeyemo B; Harms MP; Petersen SE; Barch DM
    Brain Connect; 2016 Nov; 6(9):669-680. PubMed ID: 27571276
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals.
    Vakamudi K; Trapp C; Talaat K; Gao K; Sa De La Rocque Guimaraes B; Posse S
    Brain Connect; 2020 Oct; 10(8):448-463. PubMed ID: 32892629
    [No Abstract]   [Full Text] [Related]  

  • 19. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.
    Griffanti L; Salimi-Khorshidi G; Beckmann CF; Auerbach EJ; Douaud G; Sexton CE; Zsoldos E; Ebmeier KP; Filippini N; Mackay CE; Moeller S; Xu J; Yacoub E; Baselli G; Ugurbil K; Miller KL; Smith SM
    Neuroimage; 2014 Jul; 95():232-47. PubMed ID: 24657355
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity.
    Kassinopoulos M; Mitsis GD
    Magn Reson Imaging; 2022 Jan; 85():228-250. PubMed ID: 34715292
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.