Hi Rick,

These are the instances when the warning message is coming:

set minindex = `3dTstat -argmin -prefix - outcount_rall.1D\'`
3dTstat -argmin -prefix - outcount_rall.1D'
++ 3dTstat: AFNI version=AFNI_17.2.02 (Jul 10 2017) [64-bit]
++ Authored by: KR Hammett & RW Cox
*+ WARNING: Input dataset is not 3D+time; assuming TR=1.0
set ovals = ( `1d_tool.py -set_run_lengths $tr_counts
-index_to_run_tr $minindex` )

3dmaskave: AFNI version=AFNI_17.2.02 (Jul 10 2017) [64-bit]
+++ 49188 voxels survive the mask
3dTstat -sos -prefix - gmean.errts.unit.1D'
++ 3dTstat: AFNI version=AFNI_17.2.02 (Jul 10 2017) [64-bit]
++ Authored by: KR Hammett & RW Cox
*+ WARNING: Input dataset is not 3D+time; assuming TR=1.0


1d_tool.py -infile X.nocensor.xmat.1D -show_indices_interest
3dTstat -sum -prefix sum_ideal.1D X.nocensor.xmat.1D[0..$]
++ 3dTstat: AFNI version=AFNI_17.2.02 (Jul 10 2017) [64-bit]
++ Authored by: KR Hammett & RW Cox
*+ WARNING: Input dataset is not 3D+time; assuming TR=1.0
++ Output dataset ./sum_ideal.1D
1dcat X.nocensor.xmat.1D[0..$]

My TR is 2s, I am starting with 147 volumes. The regressor file for bandpass filtering has 90 different regressors (90 regressors). In the log, it says it attempts to estimate in total 108 parameters.

So it does seem that the main issue is with bandpass filtering rather than motion for a lot of the subjects.

I did try to run the regression analysis without actually censoring volumes but I included a regression file with 1's for volumes that stay and 0's for ones that can be ignored. The analysis finishes, even for subjects where a lot of the volumes would have been deleted. Just wondering what do you think about this approach. The volumes can be deleted after the regression/band pass filtering step, and I believe regression with the censoring parameters would not smear motion artifacts through out the timecourse when bandpass filtering is performed.

Thanks a lot for your help on this.

Regards,
George