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 *

272 related articles for article (PubMed ID: 32771617)

  • 1. Modelling subject variability in the spatial and temporal characteristics of functional modes.
    Harrison SJ; Bijsterbosch JD; Segerdahl AR; Fitzgibbon SP; Farahibozorg SR; Duff EP; Smith SM; Woolrich MW
    Neuroimage; 2020 Nov; 222():117226. PubMed ID: 32771617
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Large-scale probabilistic functional modes from resting state fMRI.
    Harrison SJ; Woolrich MW; Robinson EC; Glasser MF; Beckmann CF; Jenkinson M; Smith SM
    Neuroimage; 2015 Apr; 109():217-31. PubMed ID: 25598050
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evaluation of spatio-temporal decomposition techniques for group analysis of fMRI resting state data sets.
    Afshin-Pour B; Grady C; Strother S
    Neuroimage; 2014 Feb; 87():363-82. PubMed ID: 24201012
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Group-representative functional network estimation from multi-subject fMRI data via MRF-based image segmentation.
    Tang B; Iyer A; Rao V; Kong N
    Comput Methods Programs Biomed; 2019 Oct; 179():104976. PubMed ID: 31443856
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Functional density and edge maps: Characterizing functional architecture in individuals and improving cross-subject registration.
    Tong T; Aganj I; Ge T; Polimeni JR; Fischl B
    Neuroimage; 2017 Sep; 158():346-355. PubMed ID: 28716714
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.
    Glasser MF; Coalson TS; Bijsterbosch JD; Harrison SJ; Harms MP; Anticevic A; Van Essen DC; Smith SM
    Neuroimage; 2018 Nov; 181():692-717. PubMed ID: 29753843
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.
    Jie B; Liu M; Shen D
    Med Image Anal; 2018 Jul; 47():81-94. PubMed ID: 29702414
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Quantifying functional connectivity in multi-subject fMRI data using component models.
    Madsen KH; Churchill NW; Mørup M
    Hum Brain Mapp; 2017 Feb; 38(2):882-899. PubMed ID: 27739635
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Groupwise spatial normalization of fMRI data based on multi-range functional connectivity patterns.
    Jiang D; Du Y; Cheng H; Jiang T; Fan Y
    Neuroimage; 2013 Nov; 82():355-72. PubMed ID: 23727315
    [TBL] [Abstract][Full Text] [Related]  

  • 10. LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data.
    Liu C; JaJa J; Pessoa L
    Neuroimage; 2018 Apr; 169():363-373. PubMed ID: 29246846
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs.
    Yuan J; Li X; Zhang J; Luo L; Dong Q; Lv J; Zhao Y; Jiang X; Zhang S; Zhang W; Liu T
    Neuroimage; 2018 Oct; 180(Pt B):350-369. PubMed ID: 29102809
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Dynamic mode decomposition of resting-state and task fMRI.
    Casorso J; Kong X; Chi W; Van De Ville D; Yeo BTT; Liégeois R
    Neuroimage; 2019 Jul; 194():42-54. PubMed ID: 30904469
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data.
    James GA; Hazaroglu O; Bush KA
    Magn Reson Imaging; 2016 Feb; 34(2):209-18. PubMed ID: 26523655
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Analysis of fMRI data by blind separation into independent spatial components.
    McKeown MJ; Makeig S; Brown GG; Jung TP; Kindermann SS; Bell AJ; Sejnowski TJ
    Hum Brain Mapp; 1998; 6(3):160-88. PubMed ID: 9673671
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration.
    Kassinopoulos M; Mitsis GD
    Neuroimage; 2019 Nov; 202():116150. PubMed ID: 31487547
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Hierarchical modelling of functional brain networks in population and individuals from big fMRI data.
    Farahibozorg SR; Bijsterbosch JD; Gong W; Jbabdi S; Smith SM; Harrison SJ; Woolrich MW
    Neuroimage; 2021 Nov; 243():118513. PubMed ID: 34450262
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification.
    Wee CY; Yang S; Yap PT; Shen D;
    Brain Imaging Behav; 2016 Jun; 10(2):342-56. PubMed ID: 26123390
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.
    Colclough GL; Woolrich MW; Harrison SJ; Rojas López PA; Valdes-Sosa PA; Smith SM
    Neuroimage; 2018 Sep; 178():370-384. PubMed ID: 29746906
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Comparison of multi-subject ICA methods for analysis of fMRI data.
    Erhardt EB; Rachakonda S; Bedrick EJ; Allen EA; Adali T; Calhoun VD
    Hum Brain Mapp; 2011 Dec; 32(12):2075-95. PubMed ID: 21162045
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 14.