Hello,
I am trying to use the TENT function to run an FIR analysis. I did this using afni_proc:
afni_proc.py -subj_id s$sub \
-script proc.s$sub.3conds_norming_tent \
-out_dir s$sub.3conds_norming_tent.results \
-dsets func/sub-s${sub}_ses-01_task-combo_run-0*_bold.nii.gz \
-copy_anat anat/anatSS.s${sub}.nii \
-anat_has_skull no \
-blocks tshift align volreg mask blur scale regress \
-tshift_opts_ts -tpattern alt+z \
-volreg_align_to third \
-volreg_zpad 4 \
-volreg_interp -heptic \
-volreg_align_e2a \
-mask_apply anat \
-blur_to_fwhm -blur_size 6.0 \
-regress_bandpass .008 99999 \
-regress_stim_times \
timingFiles/sub${sub}_imagine_amb_norming_tent.txt \
timingFiles/sub${sub}_imagine_att_norming_tent.txt \
timingFiles/sub${sub}_imagine_rel_norming_tent.txt \
-regress_stim_labels \
amb att rel \
-regress_stim_types times times times \
-regress_basis_multi 'TENT(0,18,10)' 'TENT(0,18,10)' 'TENT(0,18,10)' \
-regress_opts_3dD \
-gltsym 'SYM: +.5*att +.5*rel -amb' \
-gltsym 'SYM: +att -rel' \
-glt_label 1 UNAMBvsAMB \
-glt_label 2 ATTvsREL \
-jobs 24 \
-regress_est_blur_epits \
When I went to analyze the betas output in the stats, I found that for all subjects, ROIs, and conditions, my betas at time 0 were extreme relative to the other time points. For example:
File Sub-brick Mean_1
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 0[amb#0_Coe] -112.195664
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 1[amb#1_Coe] 0.551796
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 2[amb#2_Coe] 0.063248
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 3[amb#3_Coe] 0.018582
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 4[amb#4_Coe] 0.149465
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 5[amb#5_Coe] 0.360442
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 6[amb#6_Coe] 0.363047
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 7[amb#7_Coe] 0.374584
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 8[amb#8_Coe] 0.316820
stats.s104+orig[1,3,5,7,9,11,13,15,17,19] 9[amb#9_Coe] 0.255430
File Sub-brick Mean_1
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 0[amb#0_Coe] 86.534339
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 1[amb#1_Coe] -0.072039
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 2[amb#2_Coe] 0.605378
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 3[amb#3_Coe] 0.593267
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 4[amb#4_Coe] 0.472463
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 5[amb#5_Coe] 0.326305
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 6[amb#6_Coe] 0.071705
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 7[amb#7_Coe] 0.268122
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 8[amb#8_Coe] 0.072327
stats.s105+orig[1,3,5,7,9,11,13,15,17,19] 9[amb#9_Coe] -0.000413
File Sub-brick Mean_1
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 0[amb#0_Coe] -18.552854
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 1[amb#1_Coe] -0.021274
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 2[amb#2_Coe] -0.044923
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 3[amb#3_Coe] -0.024933
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 4[amb#4_Coe] -0.114865
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 5[amb#5_Coe] -0.240969
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 6[amb#6_Coe] -0.208894
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 7[amb#7_Coe] -0.188989
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 8[amb#8_Coe] -0.184979
stats.s106+orig[1,3,5,7,9,11,13,15,17,19] 9[amb#9_Coe] -0.209463
File Sub-brick Mean_1
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 0[amb#0_Coe] 206.078016
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 1[amb#1_Coe] -0.542887
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 2[amb#2_Coe] 0.361305
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 3[amb#3_Coe] 0.384412
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 4[amb#4_Coe] 0.272852
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 5[amb#5_Coe] 0.322065
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 6[amb#6_Coe] 0.221116
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 7[amb#7_Coe] 0.054475
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 8[amb#8_Coe] 0.052189
stats.s108+orig[1,3,5,7,9,11,13,15,17,19] 9[amb#9_Coe] -0.064882
Am I right in thinking this is unusual? And, if so, is there any explanation as to why this might have happened or suggestions for ameliorating the situation?
Thanks!
Heather