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 *

568 related articles for article (PubMed ID: 26774612)

  • 1. State-space model with deep learning for functional dynamics estimation in resting-state fMRI.
    Suk HI; Wee CY; Lee SW; Shen D
    Neuroimage; 2016 Apr; 129():292-307. PubMed ID: 26774612
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.
    Suk HI; Lee SW; Shen D
    Med Image Comput Comput Assist Interv; 2015 Oct; 9349():573-580. PubMed ID: 27054199
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.
    Khazaee A; Ebrahimzadeh A; Babajani-Feremi A
    Brain Imaging Behav; 2016 Sep; 10(3):799-817. PubMed ID: 26363784
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.
    Kam TE; Zhang H; Jiao Z; Shen D
    IEEE Trans Med Imaging; 2020 Feb; 39(2):478-487. PubMed ID: 31329111
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel method for sparse dynamic functional connectivity analysis from resting-state fMRI.
    Wang H; Chen J; Yuan Z; Huang Y; Lin F
    J Neurosci Methods; 2024 Nov; 411():110275. PubMed ID: 39241968
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model.
    Surampudi SG; Misra J; Deco G; Bapi RS; Sharma A; Roy D
    Neuroimage; 2019 Jan; 184():609-620. PubMed ID: 30267857
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Unsupervised learning of functional network dynamics in resting state fMRI.
    Eavani H; Satterthwaite TD; Gur RE; Gur RC; Davatzikos C
    Inf Process Med Imaging; 2013; 23():426-37. PubMed ID: 24683988
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A unified framework for personalized regions selection and functional relation modeling for early MCI identification.
    Lee J; Ko W; Kang E; Suk HI;
    Neuroimage; 2021 Aug; 236():118048. PubMed ID: 33878379
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI.
    Khazaee A; Ebrahimzadeh A; Babajani-Feremi A;
    Behav Brain Res; 2017 Mar; 322(Pt B):339-350. PubMed ID: 27345822
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis.
    Kam TE; Zhang H; Shen D
    Med Image Comput Comput Assist Interv; 2018 Sep; 11072():293-301. PubMed ID: 31106304
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI.
    Hojjati SH; Ebrahimzadeh A; Khazaee A; Babajani-Feremi A;
    Comput Biol Med; 2018 Nov; 102():30-39. PubMed ID: 30245275
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dynamic effective connectivity in resting state fMRI.
    Park HJ; Friston KJ; Pae C; Park B; Razi A
    Neuroimage; 2018 Oct; 180(Pt B):594-608. PubMed ID: 29158202
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models.
    Shappell H; Caffo BS; Pekar JJ; Lindquist MA
    Neuroimage; 2019 May; 191():243-257. PubMed ID: 30753927
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Estimating functional brain networks by incorporating a modularity prior.
    Qiao L; Zhang H; Kim M; Teng S; Zhang L; Shen D
    Neuroimage; 2016 Nov; 141():399-407. PubMed ID: 27485752
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Interpreting temporal fluctuations in resting-state functional connectivity MRI.
    LiƩgeois R; Laumann TO; Snyder AZ; Zhou J; Yeo BTT
    Neuroimage; 2017 Dec; 163():437-455. PubMed ID: 28916180
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Construct validation of a DCM for resting state fMRI.
    Razi A; Kahan J; Rees G; Friston KJ
    Neuroimage; 2015 Feb; 106():1-14. PubMed ID: 25463471
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.
    Taghia J; Ryali S; Chen T; Supekar K; Cai W; Menon V
    Neuroimage; 2017 Jul; 155():271-290. PubMed ID: 28267626
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Aberrant dynamic properties of whole-brain functional connectivity in acute mild traumatic brain injury revealed by hidden Markov models.
    Lu L; Li F; Li H; Zhou L; Wu X; Yuan F
    CNS Neurosci Ther; 2024 Mar; 30(3):e14660. PubMed ID: 38439697
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 29.