Difference between revisions of "SCAP"

From Pheno Wiki
Jump to: navigation, search
(Data Checking)
(Abstracts/results)
Line 86: Line 86:
 
==Abstracts/results==
 
==Abstracts/results==
  
Presented at ACNP, December 2011:
+
[[Karlsgodt_2011_ACNP | Karlsgodt et al, 2011 American College of Neuropsychopharmoacology]]
Capacity-Based Differences in Structural Connectivity and Functional Network Activation Associated With Spatial Working Memory
+
Katherine H. Karlsgodt, Eliza Congdon, Russell A. Poldrack, Angelica A. Bato, Fred W. Sabb, Edythe London, Robert Bilder, Tyrone D. Cannon
+
 
+
Background: Working memory is a core cognitive function that is thought to play a role in a number of more complex, higher-level processes. However, working memory capacity varies substantially even across healthy individuals. While there are indications that white matter structure, grey-matter integrity, neural signaling changes, and other factors may contribute to this variation, the roots of these individual differences are still under investigation. It is of particular interest to probe what neural signatures differentiate high-performing individuals, as this information may help us understand how to improve functioning in individuals who have lower performance either due to natural variation or to effects of neurocognitive disorders. Here we sought to assess differences in  functional activation in a large sample of healthy individuals with a wide range of behavioral performance using functional magnetic resonance imaging (fMRI) during a spatial working memory task.
+
 
+
Methods:  As a part of the Consortium for Neuropsychiatric Phenomics project at UCLA, we assessed 117 healthy community participants aged 21-50 years. We administered a Sternberg-style spatial working memory task with 4 levels of difficulty during fMRI. To quantify performance differences, we calculated each subject’s working memory capacity using Cowan’s formula. We then performed a voxel-wise analysis, corrected for age and sex, to determine which activation patterns were correlated and anti-correlated with individual working memory capacity.
+
 
+
Results: Across the entire group, the task elicited activation in regions previously associated with working memory, namely the superior frontal lobes, superior parietal lobes, anterior cingulate, and striatum. In addition, there was significantly decreased activation in regions associated with the default mode network, including medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and superior temporal lobes. Notably, voxel-wise regression of working memory capacity predicting functional activation across the whole task revealed that the primary difference in activation associated with higher capacity was a more pronounced decrease in mPFC activation during task performance.
+
 
+
Discussion: Individuals with higher working memory capacity were characterized by more successful disengagement of areas associated with the default mode network during task performance. This effect suggests that the hallmark of high performance is dexterous coordination of interactive neural networks rather than simply increased or decreased activation in isolated task-related nodes. The finding has implications for our understanding of why certain healthy individuals have higher and lower working memory abilities. It also can inform our conceptualization of working memory deficits in patient populations, particularly those associated with neural connectivity deficits, such as schizophrenia.
+
  
 
==Group level fsfs==
 
==Group level fsfs==

Revision as of 10:43, 14 June 2012

SCAP

Task Background Info

Sample Text

During the SDRT (or, 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 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. (Glahn, 2003; Cannon, 2005)

Scoring Behavioral Data

/space/raid2/data/poldrack/CNP/scripts/behav_analyze/SCAP

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
ls /space/raid2/data/poldrack/CNP/CONTROLS/*B/behav/SCAP/*

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
./make_big_scap_score_log.sh

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)

/space/raid2/data/poldrack/CNP/scripts/behav_analyze/SCAP

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

/space/raid2/data/poldrack/CNP/scripts/run_level1_scripts/SCAP

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

Data Checking

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

SCAP_model1

Model description and contrasts

SCAP model1 detail

Behavioral variables

SCAP model1 behavioral variables

Completed analyses

Abstracts/results

Karlsgodt et al, 2011 American College of Neuropsychopharmoacology

Group level fsfs