Hi,

We are trying to analyze resting state fMRI data with short RT (0.9s, multiband acquisition). We use preprocessing as follows: 3dTcat, 3dtoutcount (with 0.1 threshold), despiking, volreg, blur at 6mm) and then 3dDeconvolve with motion regressors, their derivatives, bandpass regressors (estimated with 1dBport) and with 3dTproject we add white matter and ventricule signals as regressors. The problem is that 3dDeconvolve always fail because of insufficent data to estimate too many parameters. What can we do to have less regressors? Also, we are using a 0.4 threshold for motion outliers (0.2 was far too strict).

We are aware that if subjects move too much, data should be rejected. But here, 3dDeconvolve expects 280 good parameters on 320 time points (5 minutes). It seems excessive to us.

Here is the script:

```
# create bandpass regressors
1dBport -nodata 320 0.9 -band 0.01 0.1 -invert -nozero > bandpass_rall.1D
# ------------------------------
# run the regression analysis
3dDeconvolve -input pb04.$subj.blur+tlrc.HEAD \
-censor motion_${subj}_censor.1D \
-ortvec bandpass_rall.1D bandpass \
-polort 2 -float \
-num_stimts 12 \
-stim_file 1 motion_demean.1D'[0]' -stim_base 1 -stim_label 1 roll_01 \
-stim_file 2 motion_demean.1D'[1]' -stim_base 2 -stim_label 2 pitch_01 \
-stim_file 3 motion_demean.1D'[2]' -stim_base 3 -stim_label 3 yaw_01 \
-stim_file 4 motion_demean.1D'[3]' -stim_base 4 -stim_label 4 dS_01 \
-stim_file 5 motion_demean.1D'[4]' -stim_base 5 -stim_label 5 dL_01 \
-stim_file 6 motion_demean.1D'[5]' -stim_base 6 -stim_label 6 dP_01 \
-stim_file 7 motion_deriv.1D'[0]' -stim_base 7 -stim_label 7 roll_02 \
-stim_file 8 motion_deriv.1D'[1]' -stim_base 8 -stim_label 8 pitch_02 \
-stim_file 9 motion_deriv.1D'[2]' -stim_base 9 -stim_label 9 yaw_02 \
-stim_file 10 motion_deriv.1D'[3]' -stim_base 10 -stim_label 10 dS_02 \
-stim_file 11 motion_deriv.1D'[4]' -stim_base 11 -stim_label 11 dL_02 \
-stim_file 12 motion_deriv.1D'[5]' -stim_base 12 -stim_label 12 dP_02 \
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
-x1D_uncensored X.nocensor.xmat.1D -fitts fitts.$subj \
-errts errts.${subj} -x1D_stop \
-bucket stats.$subj
```

3dTproject -polort 0 -input pb04.$subj.blur+tlrc.HEAD

-censor motion_${subj}_censor.1D -cenmode ZERO

-ort WM_timecourse_demeaned.1D

-ort LatVentricle_timecourse_demeaned.1D

-ort X.nocensor.xmat.1D -prefix errts.$subj.WM_LV_removed