Task Background Info
During the spatial delayed response task (SDRT) (or, spatial capacity task (SCAP), subjects were shown a target array of 1, 3, 5 or 7 yellow circles positioned pseudorandomly around a central fixation cross. After a delay, subjects were shown a single green circle and were required to indicate whether that circle was in the same position as one of the target circles had been. A relatively long stimulus presentation time of two seconds was used to allow subjects to fully encode the target array, minimizing a potential encoding bias on the basis of set size interaction. Likewise, decision or selection requirements were kept constant across set sizes to reduce possible effects of set size on response processes. In addition to load, delay period was manipulated, with delays of 1.5, 3 or 4.5 seconds. Trial events included a 2-sec target-array presentation, a 1.5, 3 or 4.5 sec delay period, and a 3-sec fixed response interval. A central fixation was visible throughout each of the 48 trials (12 per memory set size, with 4 at each delay length for each memory set). Half the trials were true-positive, and half were true-negative. Before starting the in-scanner task, subjects underwent a supervised instruction and training period outside of the scanner, and once in the scanner were again reminded of the instructions. (Glahn, 2003; Cannon, 2005)
During the spatial delayed response task (SDRT), participants were shown a target array of 1, 3, 5 or 7 yellow circles positioned pseudorandomly around a central fixation. After a fixed delay, subjects were shown a single green circle and were required to indicate whether or not that circle was in the same position as one of the target circles. A relatively long stimulus presentation of two seconds was used to allow subjects to fully encode the target array, minimizing a potential encoding on the basis of set size interaction. Likewise, decision or selection requirements were kept constant across set sizes to reduce possible effects of set size on response processes. Trial events included a one second fixation to orient attention, 2-sec target-array presentation, a 3-sec delay period, and a 3-sec fixed response interval during which the subject responded via keyboard presses. A central fixation was visible throughout each of the 48 trials (12 per memory set size). Before starting the scored trials, subjects underwent a supervised instruction and training period (4 trials).
Scoring Behavioral Data
1. all scripts pull up the file ‘sublist’ to determine which subjects to run. Before you run a new batch, edit that file using emacs (emacs sublist). IDs are in the format of CNP_12345B. It is just a transient file, so you can delete what is in there. If you’d like to save the old version, just save it as sublist with the date appended. The script will only recognize the plain ‘sublist; file.
2. in matlab, run score_scap_behavioral_sublist.m
3. running this should create a file called summaryscore_all.txt in each persons behav/SCAP folder. You can check who has these files (and therefore who needs to be run) by typing
4. To create a text file summarizing all the data (which can be put into excel), run the script make_big_scap_score_log.sh which will pull from all the subjects who have ‘B’ directories and create a file called scap_summaryscore_all.txt. Since it pulls all the subjects, its ok to write over this file. To run, just go into /space/raid2/data/poldrack/CNP/scripts/behav_analyze/SCAP and type
You can now copy this into excel, although you might need to use the ‘text to columns’ tool to get each number to go into its own cell.
Creating Onset files (EVs)
1. this script also uses the sublist file- so, you can easily run the behavioral scoring and this script on the same list of new people. Update sublist as described above.
2. in matlab, run make_scap_onsets_function_sublist.m
3. running this will create a series of files in each persons own behav/SCAP. After both scripts have been run, the folder should look like this:
cond10_onsets.txt cond2_onsets.txt cond6_onsets.txt junk_onsets.txt
cond11_onsets.txt cond3_onsets.txt cond7_onsets.txt SCAP_10575.mat
cond12_onsets.txt cond4_onsets.txt cond8_onsets.txt summaryscore_all.txt
cond1_onsets.txt cond5_onsets.txt cond9_onsets.txt
4. The onset files will have contents that look something like this:
110.0196 8 1
289.0072 8 1
344.0090 8 1
373.0157 8 1
Running First Level Analyses
1. The primary script for running first levels is SCAP_firstlevel_model1.sh. This script does the first phase of SCAP fMRI processing. It checks for the relevant files, creates an individualized .fsf file for each subject, runs pre- and post stats.
It takes 4 arguments:
1 group vs subject analysis,
2. population (CONTROL, SCHZ, etc)
3. which subject to run
4. whether to run FSL or just create the fsf file (run or norun)
There are a few ways you can run it:
a. to run on one person (here, CNP_10159B) and run FSL, go to the directory,
./SCAP_firstlevel_model1.sh subject CONTROLS 10159 run
b. to run on an entire group (all controls, all patients, etc)
./SCAP_firstlevel_model1.sh group CONTROLS all run
c to run a specific group of people, you can use a second script that calls this one, run_multiple_scap.sh. for this script, you have to edit it first using emacs, and basically fill in the people you want to run in the for-loop at the top, for instance
for id in 10523 10501 10159; do
you also need to edit the other relevant options, such as population and whether to run all the way through. It’ll automatically run in single-subject mode, and just loop through these people.
This can also be submitted to the grid, after it is edited, by typing
sge qsub run_multiple_scap.sh
After first levels were run, data was checked for artifacts, motion effects, and unusual activation.
If a condition was missing that was noted in the log but the subject is still available for download.
List of Models