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 *

124 related articles for article (PubMed ID: 37036246)

  • 1. Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks.
    Lukemire J; Pagnoni G; Guo Y
    Biometrics; 2023 Dec; 79(4):3599-3611. PubMed ID: 37036246
    [TBL] [Abstract][Full Text] [Related]  

  • 2. HINT: A hierarchical independent component analysis toolbox for investigating brain functional networks using neuroimaging data.
    Lukemire J; Wang Y; Verma A; Guo Y
    J Neurosci Methods; 2020 Jul; 341():108726. PubMed ID: 32360892
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A hierarchical independent component analysis model for longitudinal neuroimaging studies.
    Wang Y; Guo Y
    Neuroimage; 2019 Apr; 189():380-400. PubMed ID: 30639837
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies.
    Guo Y; Tang L
    Biometrics; 2013 Dec; 69(4):970-81. PubMed ID: 24033125
    [TBL] [Abstract][Full Text] [Related]  

  • 5. INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.
    Shi R; Guo Y
    Ann Appl Stat; 2016 Dec; 10(4):1930-1957. PubMed ID: 28367256
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Impact of inter-individual variability on the estimation of default mode network in temporal concatenation group ICA.
    Hu Y; Yang Z
    Neuroimage; 2021 Aug; 237():118114. PubMed ID: 33933594
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.
    Xie J; Douglas PK; Wu YN; Brody AL; Anderson AE
    J Neurosci Methods; 2017 Apr; 282():81-94. PubMed ID: 28322859
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study.
    Meng X; Iraji A; Fu Z; Kochunov P; Belger A; Ford JM; McEwen S; Mathalon DH; Mueller BA; Pearlson G; Potkin SG; Preda A; Turner J; van Erp TGM; Sui J; Calhoun VD
    Neuroimage Clin; 2023; 38():103434. PubMed ID: 37209635
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A group model for stable multi-subject ICA on fMRI datasets.
    Varoquaux G; Sadaghiani S; Pinel P; Kleinschmidt A; Poline JB; Thirion B
    Neuroimage; 2010 May; 51(1):288-99. PubMed ID: 20153834
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Experimental Comparisons of Sparse Dictionary Learning and Independent Component Analysis for Brain Network Inference From fMRI Data.
    Zhang W; Lv J; Li X; Zhu D; Jiang X; Zhang S; Zhao Y; Guo L; Ye J; Hu D; Liu T
    IEEE Trans Biomed Eng; 2019 Jan; 66(1):289-299. PubMed ID: 29993466
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Artifact removal in the context of group ICA: A comparison of single-subject and group approaches.
    Du Y; Allen EA; He H; Sui J; Wu L; Calhoun VD
    Hum Brain Mapp; 2016 Mar; 37(3):1005-25. PubMed ID: 26859308
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Template independent component analysis with spatial priors for accurate subject-level brain network estimation and inference.
    Mejia AF; Bolin D; Yue YR; Wang J; Caffo BS; Nebel MB
    J Comput Graph Stat; 2023; 32(2):413-433. PubMed ID: 37377728
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data.
    Du Y; Lin D; Yu Q; Sui J; Chen J; Rachakonda S; Adali T; Calhoun VD
    Front Neurosci; 2017; 11():267. PubMed ID: 28579940
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Exploring individual and group differences in latent brain networks using cross-validated simultaneous component analysis.
    Helwig NE; Snodgress MA
    Neuroimage; 2019 Nov; 201():116019. PubMed ID: 31319181
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Towards data-driven group inferences of resting-state fMRI data in rodents: Comparison of group ICA, GIG-ICA, and IVA-GL.
    To XV; Vegh V; Nasrallah FA
    J Neurosci Methods; 2022 Jan; 366():109411. PubMed ID: 34793852
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors.
    Mejia AF; Nebel MB; Wang Y; Caffo BS; Guo Y
    J Am Stat Assoc; 2020; 115(531):1151-1177. PubMed ID: 33060872
    [TBL] [Abstract][Full Text] [Related]  

  • 19. IABC: A Toolbox for Intelligent Analysis of Brain Connectivity.
    Du Y; Kong Y; He X
    Neuroinformatics; 2023 Apr; 21(2):303-321. PubMed ID: 36609668
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Segregation of frontoparietal and cerebellar components within saccade and vergence networks using hierarchical independent component analysis of fMRI.
    Alkan Y; Biswal BB; Taylor PA; Alvarez TL
    Vis Neurosci; 2011 May; 28(3):247-61. PubMed ID: 21554775
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.