The NPAIRS computational statistics framework for data analysis in Neuroimaging

Proceedings of COMPSTAT 2010 - 19th International Conference on Computational Statistics, Keynote, Invited and Contributed Papers

Volumn , Issue , 2010, Pages 111-120

  Strother, Stephen ^a,b   Oder, Anita ^a   Spring, Robyn ^a,b   Grady, Cheryl ^a  

^a ROTMAN RESEARCH INSTITUTE   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

fMRI; Penalized discriminant analysis; Prediction; Reproducibility

Indexed keywords

DISCRIMINANT ANALYSIS; FORECASTING; NEUROIMAGING;

BRAIN ACTIVATION; COMPUTATIONAL STATISTICS; DISCRIMINANT MODELS; FMRI; FUNCTIONAL MAGNETIC RESONANCE IMAGING; PRINCIPAL COMPONENTS; REPRODUCIBILITIES; TRANSITION REGIONS;

MAGNETIC RESONANCE IMAGING;

EID: 84904137130     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-7908-2604-3_10     Document Type: Conference Paper

References (17)

1. AFSHINPOUR B, HAMID S-Z, GHOLAM- ALI H-Z, GRADY C, and STROTHER SC. (In press). Mutual Information Based Metrics for Evaluation of fMRI Data Processing Approaches. Hum Brain Mapp.
2. EFRON B, and TIBSHIRANI R. (1993). An Introduction to the Bootstrap. London, U.K.: Chapman & Hall.
3. EFRON B, and TIBSHIRANI R. (1997). Improvements on cross-validation: The .632+ bootstrap method. J. Amer. Statist. Assoc. 92, 548-560.
4. EVANS JW, TODD RM, TAYLOR MJ, and STROTHER SC. (2010). Group specific optimisation of fMRI processing steps for child and adult data. NeuroImage, 50, 479-490.
5. GRADY C, SPRINGER M, HONGWANISHKUL D, MCINTOSH A, and WINOCUR G. (2006). Age-Related Changes in Brain Activity across the Adult Lifespan: A Failure of Inhibition? J Cogn Neurosci, 18, 227-241.
6. JACOBSEN D.J., HANSEN L.K. and MADSEN K.H. (2008): Bayesian model comparison in nonlinear BOLD fMRI hemodynamics. Neural Comput, 20, 738- 55.
7. KJEMS U., HANSEN L.K., ANDERSON J., FRUTIGER S., MULEY S., SIDTIS J., ROTTENBERG D. and STROTHER S.C. (2002): The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves. NeuroImage, 15, 772-86.
8. KUSTRA R. and STROTHER S.C. (2001). Penalized discriminant analysis of [15O]-water PET brain images with prediction error selection of smoothness and regularization hyperparameters. IEEE Trans Med Imaging, 20, 376-87.
9. LACONTE S., ANDERSON J., MULEY S., ASHE J., FRUTIGER S., REHM K., HANSEN L.K., YACOUB E., HU X., ROTTENBERG D. and STROTHER S. (2003): The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics. NeuroImage, 18, 10-27.
10. MARDIA K.V., KENT J.T. and BIBBY J.M. (1979): Multivariate Analysis. San Diego: Academic Press.
11. PEREIRA F., MITCHELL T., and BOTVINICK M. (2009): Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45, 199-209.
12. SCHMAH T., HINTON G., ZEMEL R.S., SMALL S.L., and STROTHER S.C. (2008): Generative versus discriminative training of RBMs for classification of fMRI images. Neural Information Processing Systems. Vancouver, Canada. p 1409-1416.
13. STROTHER S.C. (2006): Evaluating fMRI preprocessing pipelines. IEEE Eng Med Biol Mag, 25, 27-41.
14. STROTHER S.C., ANDERSON J., HANSEN L.K., KJEMS U., KUSTRA R., SIDTIS J., FRUTIGER S., MULEY S., LACONTE S. and ROTTENBERG D. (2002): The quantitative evaluation of functional neuroimaging experiments: The NPAIRS data analysis framework. NeuroImage, 15, 747-71.
15. STROTHER S.C., LACONTE S., KAI HANSEN L., ANDERSON J., ZHANG J., PULAPURA S. and ROTTENBERG D. (2004): Optimizing the fMRI dataprocessing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysis. NeuroImage, 23, 196-207.
16. YOURGANOV G., XU C., LUKIC A., GRADY C., SMALL S., WERNICK M. and STROTHER S.C. (In press): Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data. NeuroImage.
17. ZHANG J., ANDERSON J.R., LIANG L., PULAPURA S.K., GATEWOOD L., ROTTENBERG D.A. and STROTHER S.C. (2009): Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA. Magn Reson Imaging, 27, 264-78.