Compute the Source Estimates (mne_make_movie)

Applying the Inverse Operator

This script applies the inverse operator to MEG data and outputs the current estimates. The current estimates are morphed to the [#averagebrain average brain] (see below), for [#grandaverage grand-averaging] (see further below). The results (*.stc-files) can be viewed in mne_analyze, and read into Matlab using mne_read_stc_file. You can get infos on your stc-files (e.g. maximum value, relevant for scaling your display) using mne_process_stc.

The main ingredients are

* the inverse operator

* the averaged MEG data (fiff-files)

* the average cortical surface (see below)

The parameters below are reasonable choices for standard analyses. However, these Wiki pages are not supposed to substitute the [http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.6.pdf MNE manual], [http://imaging.mrc-cbu.cam.ac.uk/meg/MEGpapers reading papers], and [http://imaging.mrc-cbu.cam.ac.uk/imaging/ImagersInterestGroup discussions] with more experienced researchers.

#

## Your variables

datapath='<myMEGdatapath>'    # root directory for your MEG data

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

outpath='/myoutpath'  # path for output files

#condition names as used in file names to which inverse operator shall be applied
conds=('cond1' 'cond2' 'cond3')


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

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

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

## Processing:

nsubjects=${#subjects[*]}
lastsubj=`expr $nsubjects - 1`

nconds=${#conds[*]}
lastcond=`expr $nconds - 1`


for m in `seq 0 ${lastsubj}`
do
  echo " "
  echo " Computing movies  for SUBJECT  ${subjects[m]}"
  echo " "
 
        for c in `seq 0 ${lastcond}`
        do
        
                # Current Estimates
                mne_make_movie \
                --subject {subjects[m]} \
                --inv ${datapath}/${subj_pre[m]}/${subj_dir[m]}/YourName_1L-MEG-loose0.2-inv.fif \
                --meas ${datapath}/${subj_pre[m]}/${subj_dir[m]}/${conds[c]}.fif \
                --morph average \
                --smooth 5 \
                --bmin -100 \
                --bmax 0 \
                --stc ${outpath}/${subj_pre[m]}_${conds[c]}
                        
        done # conditions

done # subjects

The --morph option produces source estimates morphed to the average brain (necessary for grand-averaging). If you don't use it, source estimates will be computed on individual cortical surfaces.

Some degree of smoothing (--smooth) is necessary for display.

The baseline definition (--bmin/bmax) can be omitted if input data are already appropriately baseline-corrected. Note that this option will not baseline-correct the source estimates.

Anchor(averagebrain)

Computing the Average Cortical Surface

#

make_average_subject --subjects   Subject1 Subject2 Subject3

"Subject1, Subject2..." etc. are the MRI sub-directories created by Freesurfer. The paths to these subdirectories have to be specified in the environment variables, e.g. "setenv SUBJECTS_DIR /MyMriDirectory/".

Anchor(grandaverage)

Grand-averaging STC-files

For grand-averaging, STC-files should have been created using the --morph option in mne_make_movie (see above). You can then average them using the command

mne_average_estimates --desc <descriptionfile.txt>

where descriptionfile.txt is of the form

stc /yourpath/filetoaverage1.stc
stc /yourpath/filetoaverage2.stc
stc /yourpath/filetoaverage3.stc

deststc <youroutputfile>

You can create description files for every average you want to compute, and execute them in one script

#

mne_average_estimates --desc <descriptionfile1.txt>
mne_average_estimates --desc <descriptionfile2.txt>
mne_average_estimates --desc <descriptionfile3.txt>