GLM with TENT parameter

AFNI Message Board
1

My data is an event-related design, and with one trial, there is a planning, an execution, and a return session (Execution is what we are interested in). In GLM analysis with SPMG2 parameter, I will put different time files for planning, execution and return sessions, respectively.
Now I am trying to use GLM analysis with TENT, and I wonder if I should just include the time files with planning and set a long duration including the planning, execution and return and the baseline following them.
Besides, planning lasts for 6TR, execution 7TR and return for 2TR in one trial (TR = 0.782 s).

afni_proc.py -subj_id C04 -script GLM_SPMG2  -scr_overwrite                      \
    -blocks despike tshift align tlrc volreg blur mask scale regress                      \
    -copy_anat                                                                            \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatSS.C04.nii          \
    -anat_has_skull no -anat_follower anat_w_skull anat                                   \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatU.C04.nii           \
    -dsets                                                                                \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/EPI_prep/C04_USINGFOOT1_run1.3D.nii.gz    \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/EPI_prep/C04_USINGFOOT2_run1.3D.nii.gz    \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/EPI_prep/C04_USINGHAND1_run1.3D.nii.gz    \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/EPI_prep/C04_USINGHAND2_run1.3D.nii.gz    \
    -radial_correlate_blocks tcat volreg regress -tcat_remove_first_trs 0                 \
    -tshift_opts_ts -tpattern alt+z2 -align_unifize_epi local                             \
    -align_opts_aea -giant_move -cost lpc+ZZ -check_flip -tlrc_base                       \
    /home/zhiqing/abin/MNI152_2009_template_SSW.nii.gz -tlrc_NL_warp                      \
    -tlrc_NL_warped_dsets                                                                 \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatQQ.C04.nii          \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatQQ.C04.aff12.1D     \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatQQ.C04_WARP.nii     \
    -volreg_align_to MIN_OUTLIER -volreg_align_e2a -volreg_tlrc_warp                      \
    -volreg_warp_dxyz 2.0 -volreg_compute_tsnr yes -mask_epi_anat yes                     \
    -blur_size 4.0 -blur_in_mask yes -regress_stim_times                                  \
    /mnt/mydisk/ACTIONDATA/protocol-all/C04/final_U_H_grasp_execution.1D                  \
    /mnt/mydisk/ACTIONDATA/protocol-all/C04/final_U_H_draw_execution.1D                   \
    /mnt/mydisk/ACTIONDATA/protocol-all/C04/final_U_H_grasp_plan.1D                       \
    /mnt/mydisk/ACTIONDATA/protocol-all/C04/final_U_H_draw_plan.1D                        \
    /mnt/mydisk/ACTIONDATA/protocol-all/C04/final_return.1D                               \
    -regress_stim_labels UHgrasp_e UHdraw_e           \
    UHgrasp_p UHdraw_p   return          \
    -regress_basis_multi 'SPMG2(5.474)'   'SPMG2(5.474)'  'SPMG2(4.692)'  'SPMG2(4.692)'  'SPMG2(1.564,1)' -regress_opts_3dD                  \
    -ortvec                                                                               \
    /mnt/mydisk/ACTIONDATA/AFNI_pre_Results/CSF/C04/CSF_timeseries.1D CSF                 \
    -mask mask_anat.C04+tlrc -regress_apply_mot_types demean deriv                        \
    -regress_anaticor -regress_motion_per_run -regress_censor_motion 1.0                  \
    -regress_censor_prev no -regress_censor_outliers 0.05                                 \
    -regress_compute_fitts -regress_fout yes -regress_3dD_stop                            \
    -regress_reml_exec -regress_make_ideal_sum sum_ideal.1D                               \
    -regress_est_blur_errts -regress_run_clustsim no -html_review_style                   \
    pythonic

Should I change it into when I would like to do a GLM with TENT:

afni_proc.py -subj_id C04 -script GLM_TENT -scr_overwrite                      \
    -blocks despike tshift align tlrc volreg blur mask scale regress                      \
    -copy_anat                                                                            \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatSS.C04.nii          \
    -anat_has_skull no -anat_follower anat_w_skull anat                                   \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatU.C04.nii           \
    -dsets                                                                                \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/EPI_prep/C04_USINGFOOT1_run1.3D.nii.gz    \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/EPI_prep/C04_USINGFOOT2_run1.3D.nii.gz    \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/EPI_prep/C04_USINGHAND1_run1.3D.nii.gz    \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/EPI_prep/C04_USINGHAND2_run1.3D.nii.gz    \
    -radial_correlate_blocks tcat volreg regress -tcat_remove_first_trs 0                 \
    -tshift_opts_ts -tpattern alt+z2 -align_unifize_epi local                             \
    -align_opts_aea -giant_move -cost lpc+ZZ -check_flip -tlrc_base                       \
    /home/zhiqing/abin/MNI152_2009_template_SSW.nii.gz -tlrc_NL_warp                      \
    -tlrc_NL_warped_dsets                                                                 \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatQQ.C04.nii          \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatQQ.C04.aff12.1D     \
    /mnt/mydisk/ACTIONDATA/Reorganized_data/C04/to3dfile/SSwarper/anatQQ.C04_WARP.nii     \
    -volreg_align_to MIN_OUTLIER -volreg_align_e2a -volreg_tlrc_warp                      \
    -volreg_warp_dxyz 2.0 -volreg_compute_tsnr yes -mask_epi_anat yes                     \
    -blur_size 4.0 -blur_in_mask yes -regress_stim_times                                  \
    /mnt/mydisk/ACTIONDATA/protocol-all/C04/final_U_H_grasp_plan.1D                       \
    /mnt/mydisk/ACTIONDATA/protocol-all/C04/final_U_H_draw_plan.1D                        \
    -regress_stim_labels           \
    UHgrasp_p UHdraw_p           \
    -regress_basis_multi 'TENT(0,20.332,27)'   'TENT(0,20.332,27)'  -regress_opts_3dD                  \
    -ortvec                                                                               \
    /mnt/mydisk/ACTIONDATA/AFNI_pre_Results/CSF/C04/CSF_timeseries.1D CSF                 \
    -mask mask_anat.C04+tlrc -regress_apply_mot_types demean deriv                        \
    -regress_anaticor -regress_motion_per_run -regress_censor_motion 1.0                  \
    -regress_censor_prev no -regress_censor_outliers 0.05                                 \
    -regress_compute_fitts -regress_fout yes -regress_3dD_stop                            \
    -regress_reml_exec -regress_make_ideal_sum sum_ideal.1D                               \
    -regress_est_blur_errts -regress_run_clustsim no -html_review_style                   \
    pythonic
2

with one trial, there is a planning, an execution, and a return session

What are the intervals between the three phases within each trial -- planning, execution, and return? Are these intervals fixed or variable? Additionally, how many trials are included in the entire experiment?

Gang Chen

3

Thank you so much for your reply!
There are no intervals between planning, execution, and return because they are in one trial, but the interval between two trials is 11 TRs. They are both fixed.
The entire experiment included 12 trials in the grasp condition and 16 trials in the draw condition.

4

The fixed interval of 11 TRs without jittering could pose challenges for modeling. Are the sequences randomized between the grasp and draw conditions?

In addition, without any gaps between the three phrases, distinguishing their respective responses might be difficult, even with a predefined HRF (e.g., SPMG2). Do the results using 'TENT(0,20.332,27)' appear reasonable?

Gang Chen

5

The conditions are actually more than two conditions, and they are all randomized. For simplify the script, I just showed two conditions. I am still runing the GLM using TENT. Hope the results seem reasonable. Thank you!