Using fitts from 3dDeconvolve for MVPA

Hi experts,

Always thanks for your help!

I’m wondering if I can use the output of ‘-fitts’ from 3dDeconvolve as the input data for MVPA.
And wondering if there are any concerns if using fitts output data for MVPA.


Could you provide some context? How is the experiment structured and designed? What kind of model (e.g., regressors) are you implementing through 3dDeconvolve?

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:

-input ${inputEPI}
-mask ${Bmask}
-polort A
-jobs 2
-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
-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.

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!!