AnalyzingData/MNE_Labels - Meg Wiki

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WHat wOrd is made by the captiaL lettErs?

Revision 10 as of 2010-07-16 13:16:32

location: AnalyzingData / MNE_Labels

Processing ROIs

ROIs in MNE are defined as "label" files. Freesurfer already creates a parcellation of the cortical surface in various regions of interest (see subdirectory "label" in your subjects' MRI directory). These can be converted to label files for MNE. You can then use mne_make_movie in order to extract amplitudes for these ROIs, e.g. for statistical analysis.

The parameters below are reasonable choices for standard analyses. However, these Wiki pages are not supposed to substitute the [ MNE manual], [ reading papers], and [ discussions] with more experienced researchers.

If you've morphed all your individual STC-files to an average brain (called "average" in the example), you can create MNE-labels from these:

mne_annot2labels --subject average --parc aparc

This will result in files such as "fusiform-lh.label" in the MRI label subdirectory. These are text files that you can read into a text editor etc.

Then you can extract ROI information using mne_make_movie. The following script does it for multiple subjects, conditions, labels and both hemispheres. The resulting *.amp-files are text files that you can read into Excel, Matlab etc.


## Your variables:

datapath='/myMEGdatapath/'    # root directory for your MEG data

MRIpath='/myMRIdirectory/'    # where your MRI subdirectories are

STCpath='/mySTCpath/'         # where your STC files are

outpath='/myoutputdirectory'  # where the results should go

#condition names as used in file names
conds=('cond1' 'cond2' 'cond3')

# subjects names used for MRI data
        'Subject1' \
        'Subject2' \
        'Subject3' \

# MEG IDs (your directory structure may differ)
        'meg10_0001' \
        'meg10_0002' \
        'meg10_0003' \

# MEG subdirectories (your directory structure may differ)      
         '100001' \
         '100002' \
         '100003' \

# labels to be processed        
        'lh' \
        'rh' \

# Processing:

lastsubj=`expr $nsubjects - 1`

lastcond=`expr $nconds - 1`

lastlabel=`expr $nlabels - 1`

lasthemisphere=`expr $nhemispheres - 1`

# Processing:

for ss in `seq 0 ${lastsubj}`
  echo " "
  echo " Computing movies  for SUBJECT  ${subjects[m]}"
  echo " "
        for cc in `seq 0 ${lastcond}`
          echo " "
          echo " Computing movies for condition  ${conds[c]}"
          echo " "
                        for bb in `seq 0 ${lastlabel}`
                          echo " "
                          echo " Computing movies  for label  ${labels[b]}"
                          echo " "                      
                                for hh in `seq 0 ${lasthemisphere}`
                                echo " "
                                echo " Computing movies  for hemisphere  ${hemispheres[h]}"
                                echo " "        
                                        mne_make_movie \
                                          --subject ${subjects[ss]} \
                                          --stcin ${STCpath}/${subj_pre[m]}_0${subjects[m]}_${conds[c]}_-${hemispheres[h]}.stc \
                                          --label ${MRIpath}/average/label/${labels[b]}-${hemispheres[h]}.label \
                                          --labelverts \
                                          --labeltag -${subjects[m]}_${conds[c]}.amp \
                                          --labeloutdir ${outpath}

                                done # hemispheres
                        done # labels                   
        done # conditions

done # subjects

Create your own labels

1) Display a cortical surface (and e.g. an STC file) in mne_analyze.

2) Define the borders of your ROI by defining points using your right mouse button.

3) Hold the "CTRL" key and click into the centre of your points with your right mouse button.

4) Save the label.

5) Use mne_make_movie to extract information for this label.

Note that you can use mne_morph_labels to morph labels between subjects, for example to the average brain.