Thanks for your reply Gang!

My experiment is visual motion adaptation paradigm, so there are adaptation phase and test phase in a trial.
Specific design is that, in a trial, there are 36-sec adaptation phase, 12-sec blank phase, 4~6-sec test phase, and 2-sec blank phase, which of four phases are 8 times repeated in a run.
Here's my 3dDeconvolve script:

---

3dDeconvolve \
-input ${inputEPI} \
-mask ${Bmask} \
-polort A \
-float \
-jobs 2 \
-local_times \
-concat '1D: 0 124 248 372 496 620 744 868 992 1116 1240 1364 1488 1612 1736 1860' \
-num_stimts 26 \
-stim_times 1 ${INITvLaL} 'BLOCK5(36,1)' -stim_label 1 init_vLaL \
-stim_times 2 ${INITvLaR} 'BLOCK5(36,1)' -stim_label 2 init_vLaR \
-stim_times 3 ${INITvLaS} 'BLOCK5(36,1)' -stim_label 3 init_vLaS \
-stim_times 4 ${INITvLaN} 'BLOCK5(36,1)' -stim_label 4 init_vLaN \
-stim_times 5 ${INITvRaL} 'BLOCK5(36,1)' -stim_label 5 init_vRaL \
-stim_times 6 ${INITvRaR} 'BLOCK5(36,1)' -stim_label 6 init_vRaR \
-stim_times 7 ${INITvRaS} 'BLOCK5(36,1)' -stim_label 7 init_vRaS \
-stim_times 8 ${INITvRaN} 'BLOCK5(36,1)' -stim_label 8 init_vRaN \
-stim_times 9 ${TOPUPvLaL} 'BLOCK5(12,1)' -stim_label 9 topup_vLaL \
-stim_times 10 ${TOPUPvLaR} 'BLOCK5(12,1)' -stim_label 10 topup_vLaR \
-stim_times 11 ${TOPUPvLaS} 'BLOCK5(12,1)' -stim_label 11 topup_vLaS \
-stim_times 12 ${TOPUPvLaN} 'BLOCK5(12,1)' -stim_label 12 topup_vLaN \
-stim_times 13 ${TOPUPvRaL} 'BLOCK5(12,1)' -stim_label 13 topup_vRaL \
-stim_times 14 ${TOPUPvRaR} 'BLOCK5(12,1)' -stim_label 14 topup_vRaR \
-stim_times 15 ${TOPUPvRaS} 'BLOCK5(12,1)' -stim_label 15 topup_vRaS \
-stim_times 16 ${TOPUPvRaN} 'BLOCK5(12,1)' -stim_label 16 topup_vRaN \
-stim_times_IM 17 ${maeC} 'GAM' -stim_label 17 maeC \
-stim_times_IM 18 ${maeI} 'GAM' -stim_label 18 maeI \
-stim_times_IM 19 ${maeS} 'GAM' -stim_label 19 maeS \
-stim_times_IM 20 ${maeN} 'GAM' -stim_label 20 maeN \
-stim_file 21 ${MotionPar}'[1]' -stim_base 21 \
-stim_file 22 ${MotionPar}'[2]' -stim_base 22 \
-stim_file 23 ${MotionPar}'[3]' -stim_base 23 \
-stim_file 24 ${MotionPar}'[4]' -stim_base 24 \
-stim_file 25 ${MotionPar}'[5]' -stim_base 25 \
-stim_file 26 ${MotionPar}'[6]' -stim_base 26 \
-num_glt 15 \
-gltsym "SYM: init_vLaL +init_vLaR +init_vLaS +init_vLaN \
+init_vRaL +init_vRaR +init_vRaS +init_vRaN \
+topup_vLaL +topup_vLaR +topup_vLaS +topup_vLaN \
+topup_vRaL +topup_vRaR +topup_vRaS +topup_vRaN" -glt_label 1 "adpt:vLvR>base" \
-gltsym "SYM: init_vLaL +init_vLaR +init_vRaL +init_vRaR \
+topup_vLaL +topup_vLaR +topup_vRaL +topup_vRaR \
-init_vLaN -init_vRaN -topup_vLaN -topup_vRaN" -glt_label 2 "adpt:aLaR-aN" \
-gltsym "SYM: init_vLaL +init_vLaR +init_vRaL +init_vRaR \
+topup_vLaL +topup_vLaR +topup_vRaL +topup_vRaR \
-init_vLaN -init_vRaN -topup_vLaN -topup_vRaN \
-init_vLaS -init_vRaS -topup_vLaS -topup_vRaS" -glt_label 3 "adpt:aLaR-aSaN" \
-gltsym "SYM: init_vLaL +init_vRaR +topup_vLaL +topup_vRaR \
-init_vLaR -init_vRaL -topup_vLaR -topup_vRaL" -glt_label 4 "adpt:C-I" \
-gltsym "SYM: init_vLaL +init_vRaR +topup_vLaL +topup_vRaR \
-init_vLaS -init_vRaS -topup_vLaS -topup_vRaS" -glt_label 5 "adpt:C-S" \
-gltsym "SYM: init_vLaL +init_vRaR +topup_vLaL +topup_vRaR \
-init_vLaN -init_vRaN -topup_vLaN -topup_vRaN" -glt_label 6 "adpt:C-N" \
-gltsym "SYM: init_vLaR +init_vRaL +topup_vLaR +topup_vRaL \
-init_vLaS -init_vRaS -topup_vLaS -topup_vRaS" -glt_label 7 "adpt:I-S" \
-gltsym "SYM: init_vLaR +init_vRaL +topup_vLaR +topup_vRaL \
-init_vLaN -init_vRaN -topup_vLaN -topup_vRaN" -glt_label 8 "adpt:I-N" \
-gltsym "SYM: init_vLaS +init_vRaS +topup_vLaS +topup_vRaS \
-init_vLaN -init_vRaN -topup_vLaN -topup_vRaN" -glt_label 9 "adpt:S-N" \
-gltsym "SYM: maeC -maeI" -glt_label 10 "mae:C-I" \
-gltsym "SYM: maeC -maeS" -glt_label 11 "mae:C-S" \
-gltsym "SYM: maeC -maeN" -glt_label 12 "mae:C-N" \
-gltsym "SYM: maeS -maeI" -glt_label 13 "mae:S-I" \
-gltsym "SYM: maeN -maeI" -glt_label 14 "mae:N-I" \
-gltsym "SYM: maeS -maeN" -glt_label 15 "mae:S-N" \
-iresp 1 iresp_maeC.nii -iresp 2 iresp_maeI.nii \
-iresp 3 iresp_maeS.nii -iresp 4 iresp_maeN.nii \
-nobout \
-tout \
-x1D ${expID}.AVmae.MVPA.matrix.x1D \
-cbucket ${expID}.AVmae.MVPA.betas.nii \
-fitts ${expID}.AVmae.MVPA.fitts.nii \
-xjpeg ${expID}.AVmae.MVPA.xmat.jpg \
-bucket ${expID}.AVmae.MVPA+orig. \
-xsave

---

My interest is the test phase in which participants see motion aftereffect (stim_label 17 to 20).
The long Stim_label 1 to 16 are about adaptation phase that consisted of 8 stimuli conditions and of initial (36 s) / top-up (12 s) adaptations.
I used trial-wise beta estimates for MVPA input data so that I used 'Stim_times_IM'.
I'm doing MVPA with raw signals as a MVPA dataset, and I found '.fitts' data clearer than raw signal.
So I'm wondering if I can use '.fitts' output as a MVPA data and if I remove motion parameters in regressors if I use '.fitts'.

Thank you!!