Produces a threshold value based on cluster or voxel level statistics

Produces a threshold value based on cluster or voxel level statistics

statFieldThresh(
  x,
  pval,
  nvox,
  n,
  fwhm,
  resels,
  df,
  fieldType,
  threshType,
  pp = 0.001,
  verbose = FALSE
)

Arguments

x

statistical map of class antsImage

pval

p-value for determining threshold

nvox

minimum desired cluster size (in voxels)

n

number of images in conjunction

fwhm

full width at half maxima

resels

resolution elements in search region

df

degrees of freedom expressed as df = c(degrees of interest, degrees of error)

fieldType

  • T: T-field

  • F: F-field

  • X: Chi-square field'

  • Z: Gaussian field

threshType

  • cRFT: computes a threshold per expected cluster level probability

  • pRFT: uses the mask and pval calculates the minimum statistical threshold

  • cFDR: uses an uncorrected threshold at the alpha level and then computes and FDR threshold based on cluster maxima

  • pFDR: computes the fdr threshold for the entire field of voxels

pp

primary (initial) p-value threshold used in FDR methods

verbose

enables verbose output

Value

Outputs a statistical value to be used for threshold a SPM

Details

A statistical threshold level is determined using the estimated p-value pval given the provided parameters. "cRFT" and "pRFT" specify the method of estimation should use RFT cluster and peak statistic estimates respectively.

In addition to RFT based thresholds the user may choose to utilize false-discovery rate (FDR) based thresholds. These use a primary p-value threshold (default pp = .001) to create suprathreshold clusters which in turn are used to determine a final threshold (utilizing pval at this point). If the estimated peak-FDR statistic is used ("pFDR") then all suprathreshold voxels are FDR corrected to determine the threshold. If the estimated cluster-FDR statistic is used then the cluster maxima are FDR corrected in order to determine the threshold.

It is important that the user recognize that when statistical analyses are computed using RFT that the threshold level plays a role in predicting the p-value. This is because the probability of obtaining results is weighted against the probability of any occurence given the threshold. For example, if two clusters where exactly the same but had one was obtained using a lower threshold it would have a lower p-value. This has been shown with a power analysis using similar parameters to those presented herein with the additional variable of signal characteristics to demonstrate the effect of image modality on analysis (Friston et al., 1996). Therefore, parameters should be chosen according to the type of analysis being performed (fMRI, PET, or VBM) and the hypothesis being tested. Due to widely varying oppinions on appropriate thresholding procedures no specific recommendations are made here. This function simply facilitates the use of several approaches that users may utilize after consulting available literature.

References

Chumbley J., (2010) Topological FDR for neuroimaging

Friston K.J., (1994) Assessing the Significance of Focal Activations Using Their Spatial Extent

Friston K.J., (1996) Detecting Activations in PET and fMRI: Levels of Inference and Power

Author

Zachary P. Christensen

Examples

if (FALSE) { # \dontrun{
# threshold according to estimated peak height
thresh_image <- statFieldThresh(timg, .05, 10, 1, mysmooth$fwhm,
  c(1, fit$df.residual),
  fieldType = "T",
  threshType = pRFT
)
} # }