BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

274 related articles for article (PubMed ID: 26221668)

  • 21. Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators.
    Mejia AF; Nebel MB; Shou H; Crainiceanu CM; Pekar JJ; Mostofsky S; Caffo B; Lindquist MA
    Neuroimage; 2015 May; 112():14-29. PubMed ID: 25731998
    [TBL] [Abstract][Full Text] [Related]  

  • 22. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data.
    Wu GR; Liao W; Stramaglia S; Ding JR; Chen H; Marinazzo D
    Med Image Anal; 2013 Apr; 17(3):365-74. PubMed ID: 23422254
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Agreement between functional connectivity and cortical thickness-driven correlation maps of the medial frontal cortex.
    Park H; Park YH; Cha J; Seo SW; Na DL; Lee JM
    PLoS One; 2017; 12(3):e0171803. PubMed ID: 28328993
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Single-subject structural networks with closed-form rotation invariant matching mprove power in developmental studies of the cortex.
    Kandel BM; Wang DJ; Gee JC; Avants BB
    Med Image Comput Comput Assist Interv; 2014; 17(Pt 3):137-44. PubMed ID: 25320792
    [TBL] [Abstract][Full Text] [Related]  

  • 25. The Simpson's paradox and fMRI: Similarities and differences between functional connectivity measures derived from within-subject and across-subject correlations.
    Roberts RP; Hach S; Tippett LJ; Addis DR
    Neuroimage; 2016 Jul; 135():1-15. PubMed ID: 27101735
    [TBL] [Abstract][Full Text] [Related]  

  • 26. A SVM-based quantitative fMRI method for resting-state functional network detection.
    Song X; Chen NK
    Magn Reson Imaging; 2014 Sep; 32(7):819-31. PubMed ID: 24928301
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex.
    Arslan S; Ktena SI; Makropoulos A; Robinson EC; Rueckert D; Parisot S
    Neuroimage; 2018 Apr; 170():5-30. PubMed ID: 28412442
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI.
    Schaefer A; Kong R; Gordon EM; Laumann TO; Zuo XN; Holmes AJ; Eickhoff SB; Yeo BTT
    Cereb Cortex; 2018 Sep; 28(9):3095-3114. PubMed ID: 28981612
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics.
    Fan L; Zhong Q; Qin J; Li N; Su J; Zeng LL; Hu D; Shen H
    Hum Brain Mapp; 2021 Apr; 42(5):1416-1433. PubMed ID: 33283954
    [TBL] [Abstract][Full Text] [Related]  

  • 30. A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs.
    Kang J; Bowman FD; Mayberg H; Liu H
    Neuroimage; 2016 Nov; 141():431-441. PubMed ID: 27474522
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Brain connectivity hyper-network for MCI classification.
    Jie B; Shen D; Zhang D
    Med Image Comput Comput Assist Interv; 2014; 17(Pt 2):724-32. PubMed ID: 25485444
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Group-wise consistent parcellation of gyri via adaptive multi-view spectral clustering of fiber shapes.
    Chen H; Cai X; Zhu D; Nie F; Liu T; Huang H
    Med Image Comput Comput Assist Interv; 2012; 15(Pt 2):271-9. PubMed ID: 23286058
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation.
    Geerligs L; Cam-Can ; Henson RN
    Neuroimage; 2016 Jul; 135():16-31. PubMed ID: 27114055
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior.
    Kong R; Yang Q; Gordon E; Xue A; Yan X; Orban C; Zuo XN; Spreng N; Ge T; Holmes A; Eickhoff S; Yeo BTT
    Cereb Cortex; 2021 Aug; 31(10):4477-4500. PubMed ID: 33942058
    [TBL] [Abstract][Full Text] [Related]  

  • 35. The effect of scan length on the reliability of resting-state fMRI connectivity estimates.
    Birn RM; Molloy EK; Patriat R; Parker T; Meier TB; Kirk GR; Nair VA; Meyerand ME; Prabhakaran V
    Neuroimage; 2013 Dec; 83():550-8. PubMed ID: 23747458
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Combining spatial independent component analysis with regression to identify the subcortical components of resting-state FMRI functional networks.
    Malherbe C; Messé A; Bardinet E; Pélégrini-Issac M; Perlbarg V; Marrelec G; Worbe Y; Yelnik J; Lehéricy S; Benali H
    Brain Connect; 2014 Apr; 4(3):181-92. PubMed ID: 24575752
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Integration of resting-state FMRI and diffusion-weighted MRI connectivity analyses of the human brain: limitations and improvement.
    Zhu DC; Majumdar S
    J Neuroimaging; 2014; 24(2):176-86. PubMed ID: 23279672
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering.
    Kurmukov A; Mussabaeva A; Denisova Y; Moyer D; Jahanshad N; Thompson PM; Gutman BA
    Brain Connect; 2020 May; 10(4):183-194. PubMed ID: 32264696
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.
    Khazaee A; Ebrahimzadeh A; Babajani-Feremi A
    Clin Neurophysiol; 2015 Nov; 126(11):2132-41. PubMed ID: 25907414
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Individual parcellation of resting fMRI with a group functional connectivity prior.
    Chong M; Bhushan C; Joshi AA; Choi S; Haldar JP; Shattuck DW; Spreng RN; Leahy RM
    Neuroimage; 2017 Aug; 156():87-100. PubMed ID: 28478226
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 14.