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Resource:FMRISTAT - A general statistical analysis for fMRI data

Name: | Resource:FMRISTAT - A general statistical analysis for fMRI data |

Description: | A Matlab toolbox for the statistical analysis of fMRI data.
The fMRI data was first converted to percentage of whole volume. The statistical analysis of the percentages was based on a linear model with correlated errors. The design matrix of the linear model was first convolved with a hemodynamic response function modelled as a difference of two gamma functions timed to coincide with the acquisition of each slice. Temporal drift was removed by adding a cubic spline in the frame times to the design matrix (one covariate per 2 minutes of scan time), and spatial drift was removed by adding a covariate in the whole volume average. The correlation structure was modelled as an autoregressive process of degree 1. At each voxel, the autocorrelation parameter was estimated from the least squares residuals using the Yule-Walker equations, after a bias correction for correlations induced by the linear model. The autocorrelation parameter was first regularized by spatial smoothing, then used to `whiten' the data and the design matrix. The linear model was then re-estimated using least squares on the whitened data to produce estimates of effects and their standard errors. In a second step, runs, sessions and subjects were combined using a mixed effects linear model for the effects (as data) with fixed effects standard deviations taken from the previous analysis. This was fitted using ReML implemented by the EM algorithm. A random effects analysis was performed by first estimating the the ratio of the random effects variance to the fixed effects variance, then regularizing this ratio by spatial smoothing with a Gaussian filter. The variance of the effect was then estimated by the smoothed ratio multiplied by the fixed effects variance. The amount of smoothing was chosen to achieve 100 effective degrees of freedom. The resulting T statistic images were thresholded using the minimum given by a Bonferroni correction and random field theory, taking into account the non-isotropic spatial correlation of the errors. |

Parent Organization: | McGill University; Montreal; Canada |

Related to: | Resource:NITRC |

Resource Type(s): | Image analysis software |

Keywords: | fMRI, PET, statistical analysis, AFNI BRIK, ANALYZE, Console (Text Based), Linear, MATLAB, MINC, Modeling, Magnetic resonance, NIfTI, OS Independent, Principal Component Analysis, Regression, Statistical Operation |

Abbreviation: | FMRISTAT |

Resource: | Resource |

URL: | http://www.math.mcgill.ca/keith/fmristat/ |

Availability: | Freeware |

Id: | nif-0000-00303 |

Link to OWL / RDF: | Download this content as OWL/RDF |

## Curation status: Curated

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## Notes

This page uses this default form:Resource

Old URL: http://www.bic.mni.mcgill.ca/users/keith/

Alt. NITRC URL: http://www.nitrc.org/projects/fmristat

Abbrev | FMRISTAT + |

Availability | Freeware + |

CurationStatus | curated + |

DefiningCitation | http://www.math.mcgill.ca/keith/fmristat/ + |

Definition | A Matlab toolbox for the statistical analy … A Matlab toolbox for the statistical analysis of fMRI data.
The fMRI data was first converted to percentage of whole volume. The statistical analysis of the percentages was based on a linear model with correlated errors. The design matrix of the linear model was first convolved with a hemodynamic response function modelled as a difference of two gamma functions timed to coincide with the acquisition of each slice. Temporal drift was removed by adding a cubic spline in the frame times to the design matrix (one covariate per 2 minutes of scan time), and spatial drift was removed by adding a covariate in the whole volume average. The correlation structure was modelled as an autoregressive process of degree 1. At each voxel, the autocorrelation parameter was estimated from the least squares residuals using the Yule-Walker equations, after a bias correction for correlations induced by the linear model. The autocorrelation parameter was first regularized by spatial smoothing, then used to `whiten' the data and the design matrix. The linear model was then re-estimated using least squares on the whitened data to produce estimates of effects and their standard errors. In a second step, runs, sessions and subjects were combined using a mixed effects linear model for the effects (as data) with fixed effects standard deviations taken from the previous analysis. This was fitted using ReML implemented by the EM algorithm. A random effects analysis was performed by first estimating the the ratio of the random effects variance to the fixed effects variance, then regularizing this ratio by spatial smoothing with a Gaussian filter. The variance of the effect was then estimated by the smoothed ratio multiplied by the fixed effects variance. The amount of smoothing was chosen to achieve 100 effective degrees of freedom. The resulting T statistic images were thresholded using the minimum given by a Bonferroni correction and random field theory, taking into account the non-isotropic spatial correlation of the errors. otropic spatial correlation of the errors. |

ExampleImage | + |

Has default formThis property is a special property in this wiki. | Resource + |

Has role | Image analysis software + |

Id | nif-0000-00303 + |

Is part of | McGill University; Montreal; Canada + |

Keywords | Functional MRI assay +, PET imaging protocol +, Statistical analysis +, AFNI BRIK +, ANALYZE +, Console (Text Based) +, Linear +, MATLAB +, MINC +, Modeling +, Magnetic resonance +, NIfTI +, OS Independent +, Principal Component Analysis +, Regression +, and Statistical Operation + |

Label | Resource:FMRISTAT - A general statistical analysis for fMRI data + |

ModifiedDate | 5 November 2013 + |

Page has default formThis property is a special property in this wiki. | Resource + |

RelatedTo | Resource:NITRC + |

SuperCategory | Resource + |