Hi Gang,
Thanks for the response.
I checked the results at the single subject level and found the same thing (a large significant chunk). The table below was extracted from the results from simple t-test (condition A vs. baseline) for one subject at p value of 0.05.
# AFNI interactive cluster table
# 3dclust -1Dformat -nosum -1dindex 10 -1tindex 11 -2thresh -1.967 1.967 -inmask -dxyz=1 1.01 20 /Volumes
#Coordinate order = RAI
#Voxels CM x CM y CM z Peak x Peak y Peak z
#------ ------ ------ ------ ------ ------ ------
6082 +29.9 +18.9 +13.6 +49.5 -19.5 -0.5 (a big chunk again)
1502 -22.0 +76.3 -10.2 -34.5 +70.5 -15.5
I don't think that the problem was specific to the certain statistical analysis because I found the same problem in single subject analysis with T-Test and in group analysis with either t-test or regression.
I provide my first level analysis for each subject below. Please let me know if there is any other information I can offer. Thanks!
afni_proc.py
-dsets XXX \
-blocks tshift align tlrc volreg blur mask regress \
-copy_anat XXX \
-anat_has_skull no \
-tcat_remove_first_trs XXX \
-volreg_align_e2a \
-volreg_tlrc_warp -tlrc_NL_warp \
-tshift_opts_ts -tpattern alt+z2 \
-blur_size 8 -volreg_align_to first -volreg_warp_dxyz 3 \
-align_opts_aea -giant_move \
-regress_stim_times XXX.txt
-regress_stim_labels Condition1 Condition2 \
-regress_est_blur_epits \
-regress_est_blur_errts \
-regress_reml_exec \
-regress_local_times \
-regress_basis 'dmBLOCK(0)' \
-regress_stim_types AM1 \
-regress_censor_outliers 0.1 \
-regress_censor_motion 0.3 \
-regress_opts_3dD \
-stim_times_subtract 12 \
-num_glt XXX \
-gltsym XXX \
-bash -execute
Veda
