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Efficient modeling and inference for event-related fMRI data

Author

Listed:
  • Zhang, Chunming
  • Lu, Yuefeng
  • Johnstone, Tom
  • Oakes, Terry
  • Davidson, Richard J.

Abstract

Event-related functional magnetic resonance imaging (efMRI) has emerged as a powerful technique for detecting brains' responses to presented stimuli. A primary goal in efMRI data analysis is to estimate the Hemodynamic Response Function (HRF) and to locate activated regions in human brains when specific tasks are performed. This paper develops new methodologies that are important improvements not only to parametric but also to nonparametric estimation and hypothesis testing of the HRF. First, an effective and computationally fast scheme for estimating the error covariance matrix for efMRI is proposed. Second, methodologies for estimation and hypothesis testing of the HRF are developed. Simulations support the effectiveness of our proposed methods. When applied to an efMRI dataset from an emotional control study, our method reveals more meaningful findings than the popular methods offered by AFNI and FSL.

Suggested Citation

  • Zhang, Chunming & Lu, Yuefeng & Johnstone, Tom & Oakes, Terry & Davidson, Richard J., 2008. "Efficient modeling and inference for event-related fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4859-4871, June.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:10:p:4859-4871
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    References listed on IDEAS

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    1. Fan J. & Zhang C., 2003. "A Reexamination of Diffusion Estimators With Applications to Financial Model Validation," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 118-134, January.
    2. Zhang, Chunming, 2003. "Calibrating the Degrees of Freedom for Automatic Data Smoothing and Effective Curve Checking," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 609-628, January.
    3. M. Perone Pacifico & C. Genovese & I. Verdinelli & L. Wasserman, 2004. "False Discovery Control for Random Fields," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1002-1014, December.
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    1. Edler, Lutz & Lee, Jae Won & Mittlböck, Martina & Niland, Joyce & Victor, Norbert, 2009. "Computational statistics within clinical research," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 583-585, January.
    2. Zhang, Chunming, 2010. "Statistical inference of minimum BD estimators and classifiers for varying-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1574-1593, August.

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