Hi All,

My understanding is that during 3ddeconvolve the GLM automatically creates a baseline regression that models baseline shifts between and within runs (via the polort function). This is all incorporated to the output file: stats.subj+tlrc. After 3ddeconvolve has run my script concatenates all of the runs in my task (named: all_runs+subj+tlrc). I have four runs that were all collected during one fMRI session. I am assuming that the concatenated file (all_runs) is just that and do not include any adjustments for scanner drifts or jumps in values between scans. Is this correct?

If so, is there a way to concatenate the time series across all of the runs and adjust for scanner drift and shifts in values between runs?

If my logic is incorrect, thanks for clarifying the steps. Below is my 3ddeconvolve script for reference:

# run the regression analysis

3dDeconvolve -input pb04.{$subj}.r*.scale+tlrc.HEAD

-censor motion_{$subj}_censor.1D

-polort 3

-num_stimts 17

-num_glt 12

-stim_times 1 {$subj}_FMN1_FA.txt ‘BLOCK(6,1)’

-stim_label 1 FA

-stim_times 2 {$subj}_FMN2_MA.txt ‘BLOCK(6,1)’

-stim_label 2 MA

-stim_times 3 {$subj}_FMN3_NA.txt ‘BLOCK(6,1)’

-stim_label 3 NA

-stim_times 4 {$subj}_FMN4_FW.txt ‘GAM’

-stim_label 4 FW

-stim_times 5 {$subj}_FMN5_MW.txt ‘GAM’

-stim_label 5 MW

-stim_times 6 {$subj}_FMN6_NW.txt ‘GAM’

-stim_label 6 NW

-stim_times 7 {$subj}_FMN7_FNW.txt ‘GAM’

-stim_label 7 FNW

-stim_times 8 {$subj}_FMN8_MNW.txt ‘GAM’

-stim_label 8 MNW

-stim_times 9 {$subj}_FMN9_NNW.txt ‘GAM’

-stim_label 9 NNW

-stim_times 10 {$subj}_FMN10_F.txt ‘BLOCK(3,1)’

-stim_label 10 F

-stim_times 11 {$subj}_FMN11_G.txt ‘BLOCK(4,1)’

-stim_label 11 G

-stim_file 12 motion_demean.1D’[0]’ -stim_base 12 -stim_label 12 roll

-stim_file 13 motion_demean.1D’[1]’ -stim_base 13 -stim_label 13 pitch

-stim_file 14 motion_demean.1D’[2]’ -stim_base 14 -stim_label 14 yaw

-stim_file 15 motion_demean.1D’[3]’ -stim_base 15 -stim_label 15 dS

-stim_file 16 motion_demean.1D’[4]’ -stim_base 16 -stim_label 16 dL

-stim_file 17 motion_demean.1D’[5]’ -stim_base 17 -stim_label 17 dP

-gltsym ‘SYM: FA -MA’

-glt_label 1 FA-MA

-gltsym ‘SYM: FA -NA’

-glt_label 2 FA-NA

-gltsym ‘SYM: MA -NA’

-glt_label 3 MA-NA

-gltsym ‘SYM: FW -FNW’

-glt_label 4 FW-FNW

-gltsym ‘SYM: MW -MNW’

-glt_label 5 MW-MNW

-gltsym ‘SYM: FW -MW’

-glt_label 6 FW-MW

-gltsym ‘SYM: FNW -MNW’

-glt_label 7 FNW-MNW

-gltsym ‘SYM: FW -NW’

-glt_label 8 FW-NW

-gltsym ‘SYM: FNW -NNW’

-glt_label 9 FNW-NNW

-gltsym ‘SYM: MW -NW’

-glt_label 10 MW-NW

-gltsym ‘SYM: MNW -NNW’

-glt_label 11 MNW-NNW

-gltsym ‘SYM: NW -NNW’

-glt_label 12 NW-NNW

-fout -tout -x1D X.xmat.1D -xjpeg X.jpg

-x1D_uncensored X.nocensor.xmat.1D

-fitts fitts.{$subj}

-errts errts.{$subj}

-bucket stats.{$subj}

# if 3dDeconvolve fails, terminate the script

if ( $status != 0 ) then

echo ‘---------------------------------------’

echo ‘** 3dDeconvolve error, failing…’

echo ’ (consider the file 3dDeconvolve.err)’

exit

endif

# display any large pairwise correlations from the X-matrix

1d_tool.py -show_cormat_warnings -infile X.xmat.1D |& tee out.cormat_warn.txt

# create an all_runs dataset to match the fitts, errts, etc.

3dTcat -prefix all_runs.{$subj} pb04.{$subj}.r*.scale+tlrc.HEAD