Hi Experts,

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I would like to inquire about the basic concept of the command 3dTfitter.
According to the help information of the command, 3dTfitter could be used in PPI analysis in the step of estimating the neuronal responses with the BOLD signal of the brain region. The basic algorithm is a de-convolution process. To get a better understanding on the command's function, I did some test analysis, but I got confused on one result.
I did two streams of test processing on my data.
1) I ran a 3dDeconvolve step, in which I included some task-related regressors and the head motion regressors. The task related regressors included non-interested factors and the interested factor. Specifically, the interested factor is the starting time for each trial in my task, and I input this factor in the 3dDeconvolve model with -stim_times_IM. By this setting I got the estimated beta values for each trial in my task, and the beta values will be used in later MVPA analysis. The beta values could be interpreted as the HRF response amplitudes of each trial, or the neuronal activation of each trial.
The process command was as below:
3dDeconvolve -input func_tc+orig -mask mask+orig \
-num_stimts 11 \
-stim_file 1 vol_reg[1] -stim_label 1 roll -stim_base 1 -stim_maxlag 1 1 \
-stim_file 2 vol_reg[2] -stim_label 2 pitch -stim_base 2 -stim_maxlag 2 1 \
-stim_file 3 vol_reg[3] -stim_label 3 yaw -stim_base 3 -stim_maxlag 3 1 \
-stim_file 4 vol_reg[4] -stim_label 4 I_S -stim_base 4 -stim_maxlag 4 1 \
-stim_file 5 vol_reg[5] -stim_label 5 R_L -stim_base 5 -stim_maxlag 5 1 \
-stim_file 6 vol_reg[6] -stim_label 6 A_P -stim_base 6 -stim_maxlag 6 1 \
-local_times -stim_times_IM 7 IM_all_local.1D 'BLOCK(1,1)' \
-local_times -stim_times_AM1 8 psy_ANSWER1_dmRT 'dmBLOCK' -stim_label 8 ANSEWR1_dmRT \
-local_times -stim_times_AM1 9 psy_ANSWER2_dmRT 'dmBLOCK' -stim_label 9 ANSEWR2_dmRT \
-local_times -stim_times_AM1 10 psy_ANSWER3_dmRT 'dmBLOCK' -stim_label 10 ANSEWR3_dmRT \
-local_times -stim_times_AM1 11 psy_ANSWER4_dmRT 'dmBLOCK' -stim_label 11 ANSEWR4_dmRT \
-tout -bucket func_decon
2) I firstly ran a 3dDeconvolve step. In this stream, the model of the 3dDeconvolve is the same as the model in 1), except that I removed the "interested factor", and got the residual data of the regression analysis. Then I ran 3dTfitter on the residual data, and got the estimated neuronal responses in each time point of the fMRI scan. Last step, I selected the estimated neuronal responses of the time points according to the starting time of each trial in my task.
The process commands were as below:
3dDeconvolve -input func_tc+orig -mask mask+orig \
-num_stimts 10 \
-stim_file 1 vol_reg[1] -stim_label 1 roll -stim_base 1 -stim_maxlag 1 1 \
-stim_file 2 vol_reg[2] -stim_label 2 pitch -stim_base 2 -stim_maxlag 2 1 \
-stim_file 3 vol_reg[3] -stim_label 3 yaw -stim_base 3 -stim_maxlag 3 1 \
-stim_file 4 vol_reg[4] -stim_label 4 I_S -stim_base 4 -stim_maxlag 4 1 \
-stim_file 5 vol_reg[5] -stim_label 5 R_L -stim_base 5 -stim_maxlag 5 1 \
-stim_file 6 vol_reg[6] -stim_label 6 A_P -stim_base 6 -stim_maxlag 6 1 \
-local_times -stim_times_AM1 7 psy_ANSWER1_dmRT 'dmBLOCK' -stim_label 7 ANSEWR1_dmRT \
-local_times -stim_times_AM1 8 psy_ANSWER2_dmRT 'dmBLOCK' -stim_label 8 ANSEWR2_dmRT \
-local_times -stim_times_AM1 9 psy_ANSWER3_dmRT 'dmBLOCK' -stim_label 9 ANSEWR3_dmRT \
-local_times -stim_times_AM1 10 psy_ANSWER4_dmRT 'dmBLOCK' -stim_label 10 ANSEWR4_dmRT \
-nobucket -errts func_decon_errts
3dTfitter -mask mask+orig -RHS func_decon_errts+orig -FALTUNG GammaHR.1D func_decon_neuro.nii 012 0
3dcalc -prefix func_decon_3dTfitter -a func_decon_neuro.nii[trial_starting_index] -expr "a"
If my understanding on the 3dDeconvolve and 3dTfitter is correct, the resultant data of the above two streams, i.e., func_decon+orig in 1), and func_decon_3dTfitter+orig in 2), should be similar as each other, or at least highly correlated with each other at the temporal dimension. But actually I found the correlation coefficients of the two data sets were very low (about ~0.2).
Thus, here is my question: A) is there any mistake or misuse of the commands in my processing above? B) are there any factors that may cause a low correlation between the resultant data of two methods?
Thank you very much for your help!!
Best regards,
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Nan