Description Usage Arguments Value Author(s) Examples
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.
1 2 3 4 5 6 7 8 |
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 |
list of betas and other names entries is output
Avants BB
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## Not run:
# 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 )
## End(Not run)
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