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 *

97 related articles for article (PubMed ID: 28582917)

  • 1. Potential for false positive results from multi-voxel pattern analysis on functional imaging data.
    Zhang Z; Jiang Y; Sun Y; Zhang H
    Technol Health Care; 2017 Jul; 25(S1):287-294. PubMed ID: 28582917
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Voxel selection framework in multi-voxel pattern analysis of FMRI data for prediction of neural response to visual stimuli.
    Chou CA; Kampa K; Mehta SH; Tungaraza RF; Chaovalitwongse WA; Grabowski TJ
    IEEE Trans Med Imaging; 2014 Apr; 33(4):925-34. PubMed ID: 24710161
    [TBL] [Abstract][Full Text] [Related]  

  • 3. An automated ROI setting method using NEUROSTAT on cerebral blood flow SPECT images.
    Ogura T; Hida K; Masuzuka T; Saito H; Minoshima S; Nishikawa K
    Ann Nucl Med; 2009 Jan; 23(1):33-41. PubMed ID: 19205836
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Mental encoding and neural decoding of abstract cognitive categories: a commentary and simulation.
    Vindiola M; Wolmetz M
    Neuroimage; 2011 Feb; 54(4):2822-7. PubMed ID: 20974265
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Cross-validation and permutations in MVPA: Validity of permutation strategies and power of cross-validation schemes.
    Valente G; Castellanos AL; Hausfeld L; De Martino F; Formisano E
    Neuroimage; 2021 Sep; 238():118145. PubMed ID: 33961999
    [TBL] [Abstract][Full Text] [Related]  

  • 6. What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis.
    Davis T; LaRocque KF; Mumford JA; Norman KA; Wagner AD; Poldrack RA
    Neuroimage; 2014 Aug; 97():271-83. PubMed ID: 24768930
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns.
    De Martino F; Valente G; Staeren N; Ashburner J; Goebel R; Formisano E
    Neuroimage; 2008 Oct; 43(1):44-58. PubMed ID: 18672070
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting brain states associated with object categories from fMRI data.
    Behroozi M; Daliri MR
    J Integr Neurosci; 2014 Dec; 13(4):645-67. PubMed ID: 25352153
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Penalized likelihood phenotyping: unifying voxelwise analyses and multi-voxel pattern analyses in neuroimaging: penalized likelihood phenotyping.
    Adluru N; Hanlon BM; Lutz A; Lainhart JE; Alexander AL; Davidson RJ
    Neuroinformatics; 2013 Apr; 11(2):227-47. PubMed ID: 23397550
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.
    Levman JE; Gallego-Ortiz C; Warner E; Causer P; Martel AL
    J Digit Imaging; 2016 Feb; 29(1):126-33. PubMed ID: 26293705
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Analyzing the connectivity between regions of interest: an approach based on cluster Granger causality for fMRI data analysis.
    Sato JR; Fujita A; Cardoso EF; Thomaz CE; Brammer MJ; Amaro E
    Neuroimage; 2010 Oct; 52(4):1444-55. PubMed ID: 20472076
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Adaptive multi-level conditional random fields for detection and segmentation of small enhanced pathology in medical images.
    Karimaghaloo Z; Arnold DL; Arbel T
    Med Image Anal; 2016 Jan; 27():17-30. PubMed ID: 26211811
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography.
    Suzuki K; Armato SG; Li F; Sone S; Doi K
    Med Phys; 2003 Jul; 30(7):1602-17. PubMed ID: 12906178
    [TBL] [Abstract][Full Text] [Related]  

  • 14. T2 hyperintensity of medial lemniscus: higher threshold application to ROI measurements is more accurate in predicting small vessel disease.
    Hakky MM; Erbay KD; Brewer E; Midle JB; French R; Erbay SH
    J Neuroimaging; 2013 Jul; 23(3):345-51. PubMed ID: 23343196
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Exploring vision-related acupuncture point specificity with multivoxel pattern analysis.
    Li L; Qin W; Bai L; Tian J
    Magn Reson Imaging; 2010 Apr; 28(3):380-7. PubMed ID: 20071124
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The impact of study design on pattern estimation for single-trial multivariate pattern analysis.
    Mumford JA; Davis T; Poldrack RA
    Neuroimage; 2014 Dec; 103():130-138. PubMed ID: 25241907
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity.
    Coutanche MN; Thompson-Schill SL; Schultz RT
    Neuroimage; 2011 Jul; 57(1):113-123. PubMed ID: 21513803
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Reproducibility of myelin water fraction analysis: a comparison of region of interest and voxel-based analysis methods.
    Meyers SM; Laule C; Vavasour IM; Kolind SH; Mädler B; Tam R; Traboulsee AL; Lee J; Li DK; MacKay AL
    Magn Reson Imaging; 2009 Oct; 27(8):1096-103. PubMed ID: 19356875
    [TBL] [Abstract][Full Text] [Related]  

  • 19. False positive control of activated voxels in single fMRI analysis using bootstrap resampling in comparison to spatial smoothing.
    Darki F; Oghabian MA
    Magn Reson Imaging; 2013 Oct; 31(8):1331-7. PubMed ID: 23664823
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A comparison of volume-based and surface-based multi-voxel pattern analysis.
    Oosterhof NN; Wiestler T; Downing PE; Diedrichsen J
    Neuroimage; 2011 May; 56(2):593-600. PubMed ID: 20621701
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.