Advanced search
1 file | 1.23 MB

Evaluating of bootstrap procedures for fMRI data

Sanne Roels (UGent) , Tom Loeys (UGent) and Beatrijs Moerkerke (UGent)
Author
Organization
Abstract
Over the last decade the bootstrap procedure is gaining popularity in the statistical analysis of neuroimaging data. This powerful procedure can be used for example in the non-parametric analysis of neuro-imaging data. As fMRI data are complexly structured with both temporal and spatial dependencies, such bootstrap procedures may indeed offer an elegant solution. However, a thorough investigation on the most appropriate bootstrapping procedure for fMRI data has to our knowledge never been performed. Friman and Westin (2005) showed that a bootstrap procedure based on pre-whitening the temporal structure of fMRI time series is superior to procedures based on wavelets or Fourier decomposition of the signal, especially in the case of blocked fMRI designs. For time-series, several bootstrap schemes can be exploited though (see e.g. Lahiri, 2003). For the re-sampling of residuals from general linear models fitted on fMRI data, we examine more specifically here the differences between 1) bootstrapping pre-whitened residuals which are based on parametric assumptions of the temporal structure, 2) a blocked bootstrapping which avoids making such assumptions (with several variants like the circular bootstrap,. . . ), and 3) a combination of both bootstrap procedures. We furthermore explore whether the bootstrap procedures is best applied before or after smoothing the volume of interest. Based on real data and simulation studies, we discuss the temporal and spatial properties of the bootstrapped volumes for all possible combinations and nd interesting differences.
Keywords
Bootstrap procedures, fMRI analysis

Downloads

  • Poster neuroimaging Roels 2011.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 1.23 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Roels, Sanne, Tom Loeys, and Beatrijs Moerkerke. 2011. “Evaluating of Bootstrap Procedures for fMRI Data.” In Berlin Workshop on Statistics and Neuroimaging 2011, Abstracts.
APA
Roels, Sanne, Loeys, T., & Moerkerke, B. (2011). Evaluating of bootstrap procedures for fMRI data. Berlin workshop on statistics and neuroimaging 2011, Abstracts. Presented at the Berlin Workshop on Statistics and Neuroimaging 2011.
Vancouver
1.
Roels S, Loeys T, Moerkerke B. Evaluating of bootstrap procedures for fMRI data. Berlin workshop on statistics and neuroimaging 2011, Abstracts. 2011.
MLA
Roels, Sanne, Tom Loeys, and Beatrijs Moerkerke. “Evaluating of Bootstrap Procedures for fMRI Data.” Berlin Workshop on Statistics and Neuroimaging 2011, Abstracts. 2011. Print.
@inproceedings{2006153,
  abstract     = {Over the last decade the bootstrap procedure is gaining popularity in the statistical analysis of neuroimaging data. This powerful procedure can be used for example in the non-parametric analysis of neuro-imaging data. As fMRI data are complexly structured with both temporal and spatial dependencies, such bootstrap procedures may indeed offer an elegant solution. However, a thorough investigation on the most appropriate bootstrapping procedure for fMRI data has to our knowledge never been performed. Friman and Westin (2005) showed that a bootstrap procedure based on pre-whitening the temporal structure of fMRI time series is superior to procedures based on wavelets or Fourier decomposition of the signal, especially in the case of blocked fMRI designs. For time-series, several bootstrap schemes can be exploited though (see e.g. Lahiri, 2003). For the re-sampling of residuals from general linear models fitted on fMRI data, we examine more specifically here the differences between 1) bootstrapping pre-whitened residuals which are based on parametric assumptions of the temporal structure, 2) a blocked bootstrapping which avoids making such assumptions (with several variants like the circular bootstrap,. . . ), and 3) a combination of both bootstrap procedures. We furthermore explore whether the bootstrap procedures is best applied before or after smoothing the volume of interest. Based on real data and simulation studies, we discuss the temporal and spatial properties of the bootstrapped volumes for all possible combinations and \unmatched{000c}nd interesting differences.},
  author       = {Roels, Sanne and Loeys, Tom and Moerkerke, Beatrijs},
  booktitle    = {Berlin workshop on statistics and neuroimaging 2011, Abstracts},
  keyword      = {Bootstrap procedures,fMRI analysis},
  language     = {eng},
  location     = {Berlin, Germany},
  title        = {Evaluating of bootstrap procedures for fMRI data},
  year         = {2011},
}