Selxavg3-sess: Difference between revisions

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This error happens when I try to run selxavg3
This error happens when I try to run selxavg3
<code>ERROR: fast_selxavg3() failed\n</code>
<code>ERROR: fast_selxavg3() failed\n</code>
Solution pending...
Solution pending...
*It isn't that matlab isn't in our path
*It isn't that matlab isn't in our path

Revision as of 11:22, 16 March 2017

Before you begin

Ensure that your BOLD data has the correct timing info. See Freesurfer BOLD files for information on changing the TR for your data. If your BOLD data files are not named 'f.nii', you likely have missed a step.

Running selxavg3-sess

This command runs a GLM regression on the BOLD time series for each run separately. This creates a 3D file that stores a statistic for each voxel for each experimental run that indicates how well the activity for that voxel conformed with a predicted time series associated with each of your experimental conditions. Assuming you have run all the previous steps in the FS-FAST overview, there will be one, two or three analysis directories in your $SUBJECTS_DIR, generated by mkanalysis-sess. These directories will contain all the information to perform surface-space analyses in your left/right or mni305 (voxel-space, for subcortical regions which are not included in the surface models) models. If you have configured contrasts using mkcontrast-sess, then selxavg3-sess will perform the statistical condition contrasts you had specified in that step.

Batch mode

If you want to run the same analysis on multiple participants in batch mode, you must first create a plain text file containing all the participant IDs (i.e., the folder names) for those you wish to analyze. If you ran preproc-sess in batch mode, then you can simply reuse the sessid file you created for that purpose. See the preproc-sess instructions for creating that file. Batch mode uses the -sf switch, which is the same as the switch used for running preproc-sess in batch mode.

selxavg3-sess -sf <sessid_file> -analysis <analysis_dir>

Individual mode

Alternatively, you may run the analysis on one participant at the time using the -s switch, and providing a single participant ID name.

selxavg3-sess -s <sessid> -analysis <analysis_dir>

Example

selxavg3-sess -s FS_T1_501 -analysis booth500.sm6.lh

Troubleshooting

ERROR: fast_selxavg3()

This error happens when I try to run selxavg3

ERROR: fast_selxavg3() failed\n

Solution pending...

  • It isn't that matlab isn't in our path
  • Maybe pre-proc -sess? No.

Check your results

You can look at the contrast results in the surface model in tksurfer. First, load the surface you want to overlay your results on. All of our examples have been done using the self surface, but it is possible to instead map data to the fsaverage template, which facilitates comparisons between individuals.

tksurfer FS_T1_501 lh pial

Next, you will load the relevant statistical overlay. This file will be found in the subject folder in the bold subdirectory, which has an analysis folder for each analysis that you ran with selxavg3-sess. In this example, there are two folders:

  • $SUBJECTS_DIR
    • FS_T1_501
      • bold
        • booth500.sm6.lh
        • booth500.sm6.rh

In each of these analysis folders are several compressed .nii.gz files. The statistical overlay you will want to overlay will depend on your interests, but most likely you will want to open up a diagnostic (i.e., easily interpretable) contrast map, which you will find in subdirectories within each of the analysis directories. In my case, I had run a word_vs_fix contrast which generated a t-statistic and f-statistic map. I chose to load the t-map for my left-hemisphere surface. To do so, I did the following:

  1. File>Load Overlay...
  2. Browse to $SUBJECTS_DIR/FS_T1_501/bold/booth500.sm6.lh/word_vs_fix/t.nii.gz
  3. Additional Registration information may be required to map each of the voxels on to your surface space. When mapping voxels onto a self surface, it's safe to select the Find registration in data directory
  4. Click the OK button. In a moment you should see hot- and cold- colored blobs overlaid on the surface model. Depending on the nature of the contrast and your experience, you might be able to use this overlay to assess whether the data look reasonable. For example a contrast between button-press trials and rest should probably show a robust activation in left motor cortex (assuming right-handed participants).