Chapter #1: FIR Analysis in AFNI¶
Overview¶
In AFNI, the individual time-points you estimate are called TENTs - possibly because each basis function looks like a pup tent. The peak of this TENT function is estimated for each time-point that is specified, including the end-points of the interval; for example, if you wanted to estimate each time-point from zero (that is, when the stimulus occurs) to twelve seconds after the start of the stimulus, there would be seven estimates total: at the time-points 0, 2, 4, 6, 8, 10, and 12.
For this analysis, we will make edits to the cmd file provided on the study’s associated NITRC website (the directory can be found by clicking on the button See All Files next to the Download dropdown menu, and selecting afni_hrf_tent.tar.gz). This file uses the command afni_proc.py to create a script with all of the individual AFNI commands needed to do each step of the preprocessing and statistical analysis.
Editing the Script¶
To use the script in a for-loop to analyze all of the subjects in this dataset, we will change the set subj line to equal $1, so that our input from the for-loop will be the subject identification code. To save time, we will also insert the line -regress_run_clustsim no. The edited script will look like this:
#!/usr/bin/env tcsh
# created by uber_subject.py: version 0.37 (April 14, 2015)
# creation date: Tue Mar 22 11:56:09 2016
# set data directories
set top_dir = $PWD
# set subject and group identifiers
set subj = $1
set group_id = ToneCounting
# run afni_proc.py to create a single subject processing script
afni_proc.py -subj_id $subj \
-script proc.$subj -scr_overwrite \
-blocks tshift align tlrc volreg blur mask scale regress \
-copy_anat $top_dir/anat/${subj}_T1w.nii.gz \
-tcat_remove_first_trs 0 \
-dsets $top_dir/func/${subj}_task-tonecounting_bold.nii.gz \
-volreg_align_to third \
-volreg_align_e2a \
-volreg_tlrc_warp \
-blur_size 4.0 \
-regress_stim_times \
$top_dir/func/tone_counting_onset_times.txt \
$top_dir/func/tone_counting_probe_onsets.txt \
-regress_stim_labels \
tone_counting probe \
-regress_basis_multi \
'TENT(0,12,7)' 'TENT(0,14,8)' \
-regress_censor_motion 0.3 \
-regress_make_ideal_sum sum_ideal.1D \
-regress_run_clustsim no \
-html_review_style none
tcsh -xef proc.$subj |& tee output.proc.$subj
Either make these edits to the original script, or copy and paste the above code into a file called cmd.ap.AFNI_Tent. The main difference between this analysis and previous ones using the canonical hemodynamic response function is the line -regress_basis_multi 'TENT(0,12,7)' 'TENT(0,14,8)', which uses a TENT analysis for the onset_times condition spanning an interval from 0 to 12 seconds with 7 TENTs each, and 8 TENTs spanning the interval 0 to 14 seconds for the probe_onsets condition. Make sure the above script is in the folder that contains all of the subjects, and then copy and paste the following code into your terminal:
#!/bin/bash
if [[ ! -e subjList.txt ]]; then
ls | grep ^sub- > subjList.txt
fi
for i in `cat subjList.txt`; do
cp cmd.ap.AFNI_Tent $i;
cd $i;
tcsh cmd.ap.AFNI_Tent $i;
cd ..;
done
This will create the file subjList.txt (if it doesn’t exist already), and use a for-loop to run the above cmd script for each subject. This will take a couple of hours to run.
Examining the Results¶
Once the preprocessing and analysis is finished, navigate to the directory sub-01/sub-01.results. Similar to the results folder from the AFNI tutorials, we see output from all of the steps; however, if we look at the output from the statistical analysis by typing 3dinfo -verb stats.sub-01+tlrc, we see the following (truncated to just the tone_counting condition):
-- At sub-brick #1 'tone_counting#0_Coef' datum type is float: -50.34 to 60.68
-- At sub-brick #2 'tone_counting#0_Tstat' datum type is float: -6.03381 to 4.62057
statcode = fitt; statpar = 68
-- At sub-brick #3 'tone_counting#1_Coef' datum type is float: -64.2838 to 89.6593
-- At sub-brick #4 'tone_counting#1_Tstat' datum type is float: -5.57096 to 4.79652
statcode = fitt; statpar = 68
-- At sub-brick #5 'tone_counting#2_Coef' datum type is float: -119.9 to 102.47
-- At sub-brick #6 'tone_counting#2_Tstat' datum type is float: -5.04005 to 4.87468
statcode = fitt; statpar = 68
-- At sub-brick #7 'tone_counting#3_Coef' datum type is float: -132.215 to 136.358
-- At sub-brick #8 'tone_counting#3_Tstat' datum type is float: -5.70406 to 5.24604
statcode = fitt; statpar = 68
-- At sub-brick #9 'tone_counting#4_Coef' datum type is float: -100.065 to 99.7619
-- At sub-brick #10 'tone_counting#4_Tstat' datum type is float: -5.50184 to 5.14456
statcode = fitt; statpar = 68
-- At sub-brick #11 'tone_counting#5_Coef' datum type is float: -134.37 to 156.491
-- At sub-brick #12 'tone_counting#5_Tstat' datum type is float: -5.12016 to 6.7376
statcode = fitt; statpar = 68
-- At sub-brick #13 'tone_counting#6_Coef' datum type is float: -76.8142 to 77.716
-- At sub-brick #14 'tone_counting#6_Tstat' datum type is float: -4.45098 to 4.4271
statcode = fitt; statpar = 68
There are 14 sub-briks total for the tone_counting condition, with 7 beta weights (i.e., those labeled “Coef”) and 7 T-statistics. In other words, we have an average beta estimate for each time-point in a 12-second window after the onset of each tone_counting condition; these beta weights can then be submitted to a group-level analysis just like the beta weights from a canonical HRF analysis.