• roisummary-sess

Index

Name

roisummary-sess - outputs data from func2roi-sess in ASCII format

Synopsis

roisummary-sess -sumfile <filename> -roidef <roidefname> -analysis <analysisname> -s <subjectname> or -sf <subjectfilename> -d <directory>

Arguments

Positional Arguments

Required Flagged Arguments

Optional Flagged Arguments

Description

Often, one wants to pool all the ROI averages across sessions into a single table so that they can be read into a statistics package for more elaborate analysis. The results from func2roi-sess can be collected into such a table using roisummary-sess, which has the following options:

Use -sumfile to specify the name of the file in which to store the ascii results. The ROI is specified with -roidef. Without any other options, roisummary-sess will get the ROI results from each session and store them into the summary file. Each session will have its own column, and each row represents a different measurement according to the following scheme as outlined in Outputs.

Outputs

1. session id - text identifier of the session

2. nlabel - number of functional voxels in the structural label regardless of whether they met the functional criteria.

3. nroi - number of functional voxels in the final mask (ie, ones that met the structural and functional criteria.

4. offset - mean functional baseline offset within the ROI (good for computing percent signal changes)

5. eresstd - standard deviation of the residual error. This is a measure of the variance left unexplained by the task.

6. DOF - degrees of freedom in eresstd. This can be used to asses whether there is a difference among the sessions in the DOF as could happen if some runs were excluded in some sessions and not in others.

7. TER - temporal resolution (in seconds) of the hemodynamic estimation. This is not a data value. This value is set at the time of mkanalysis-sess.new and is included here to facilitate plotting the results that follow. All sessions should have the same value.

8. tPreStim - time (in seconds) of the prestimulus baseline used in hemodynamic estimation. This is not a data value. This value is set at the time of mkanalysis-sess.new and is included here to facilitate plotting the results that follow. All sessions should have the same value.

9. nconditions - number of conditions (excluding null condition). All sessions should have the same value.

10. nestspercond - number of estimates per conditions. For block analyses, this will have a value of 1. For event-related analyses, the value will equal to the number of points in the time window.

11. There will be nconditions times nestspercond numbers starting at this row. The first set of nestspercond numbers are the values for the first condition, the second set of nestspercond numbers are the values for the second condition, etc. Percent signal change can be obtained by dividing these numbers by offset and multiplying by 100.

The format of this table can be modified slightly with the -transpose and excludesessid flags. The -transpose flag creates the same table except that the data for the session is placed on a row instead of in a column. The -excludesessid flag will eliminate the session id row from the data. The -excludesessid' is provided because some software does not allow data tables with text and numbers.

Examples

Example 1: Per-Session Label and Functional Mask

Let's say that you have two sessions, s1 and s2, for which you have already run individual event-related analyses (main2) as well as a contrast (allvfix). The analysis has 2 conditions, with TER=TR=2sec tPreStim=4sec, and Time Window=18sec. The allvfix contrast gives you an indication of which voxels are activated by the overall task. A neuroanatomist has graciously labeled the fusiform and striate regions of each subjects' brain giving you a total of 4 label files. You create a directory in s1 called labels and put the fusiform.label and striate.label for subject 1 into this directory. You create a directory in s2 called labels and put the fusiform.label and striate.label for subject 2 into this directory.

Subsection a: Fusiform with Positive Activation

func2roi-sess -roidef fusi-avf-pos-2 -analysis main2 -sesslabel fusiform -maskcontrast allvfix -maskthresh 2 -masktail pos -maskmap minsig -s s1 -s s2 -d .

This will create an ROI named fusi-avf-pos-2 which uses the fusiform.label in each subjects' labels directory along with the allvfix contrast as the functional constraint. Only voxels inside the pre-defined fusiform label that exceed a significance of 10^-2 in the minsig map and are positively correlated with the task will be selected. Once the final ROI is determined from the functional and structural constraints, func2roi-sess will average the ROI voxels from the main2 analysis together, and create a directory called fusi-avf-pos-2 under main2 (in each session).

At this point, there are four things you can do next: (1) pool the data into a text file, (2) view the hemodynamic response in the ROI for each session, (3) view the final mask, or (4) inter-subject average.

Pooling the data into a text file

Here you will run roisummary-sess to create a text file of all the ROI data for both session 1 and 2.

[roisummary-sess] -sumfile fusi-avf-pos-2.dat -roidef fusi-avf-pos-2 -analysis main2 -s s1 -s s2 -d .

This will create a file called fusi-avf-pos-2.dat in which you will find two columns. The contents will look something like the following (not including the text after the #'s):

Bugs

None

See Also

func2roi-sess

Links

FreeSurfer, FsFast

Methods Description

References

References/Lastname###

Reporting Bugs

Report bugs to <analysis-bugs@nmr.mgh.harvard.edu>

Author/s

DougGreve PhD