Simple taskFMRI function.

Input 4D time series matrix. (Perform slice timing correction externally). Estimate hemodynamicRF from block design. Compute brain mask on average bold image. Get nuisance variables : motion , compcor , globalsignal. High-frequency filter the time series ( externally ). Correct for autocorrelation using bullmore 1996 MRM and AR(2) model with parameters derived from global residual signal. Estimate final glm.

taskFMRI(
  mat,
  hrf,
  myvars,
  correctautocorr = FALSE,
  residualizedesignmatrix = FALSE,
  myformula = NA
)

Arguments

mat	input matrix
hrf	input hrf
myvars	output of getfMRInuisanceVariables
correctautocorr	correction auto correlation boolean
residualizedesignmatrix	boolean
myformula	statistical equation to be assessed at each voxel

Value

list of betas and other names entries is output

Author

Avants BB

Examples

if (FALSE) {
# read the fmri image in and maybe do slice timing correction
  fmri<-getANTsRData("pcasl")
  fmri<-antsImageRead( fmri )
#  fmri<-iMath(fmri,"SliceTimingCorrection","bspline") # optional
  myvars<-getfMRInuisanceVariables( fmri, moreaccurate = 0, maskThresh=100 )
  mat <- myvars$matrixTimeSeries
  mat<-frequencyFilterfMRI(mat, 2.5, freqLo=0.01, freqHi=0.1, opt="butt")
  blockfing = c(0, 36, 72 )
  hrf <- hemodynamicRF( scans=dim(fmri)[4] , onsets=blockfing ,
    durations=rep(  12,  length( blockfing ) ) ,  rt=2.5 )
  activationBeta<-taskFMRI(  mat , hrf , myvars )
}