Compute estimates for the amount of blur in the data, as requested via afni_proc.py option, -regress_est_blur_errts.

• Compute estimates for the amount of blur in the data, as requested via afni_proc.py option, -regress_est_blur_errts.

• • Blur comes from the scanner and from the 3dmerge operation.
  • 3dFWHMx is used to estimate the amount of blur contained in the data.
  • Masking is essential as the data variance outside the brain will be much smaller.
  • The errts (residual) data provides the more accurate blur estimation.
  • The estimation from the scale data is similar, but generally slightly larger.
  • • 3dFWHMx -detrend -mask full_mask.$subj+orig \
    • • errts.$subj+orig"[0..63]" >> blur.errts.1D

• Average the blur values over all runs, appending the results to blur_est.$subj.1D.

• • Using “-prefix -” sends output to the terminal, which can be stored in a variable.
  • The trailing “\'” is used to transpose the 1D file upon input to 3dTstat.
  • Those variable values are then appended to the blur_est file, with descriptive text.
  • • set blurs = ( `3dTstat -mean -prefix - blur.errts.1D\'` )
    • echo "$blurs # errts blur estimates" >> blur_est.$subj.1D