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MEG + Facial EMG (PF_MOTEUR)

Owned by Maximilien CHAUMON
Last updated: Oct 09, 2018

Context

This experiment examines the brain correlates of facial muscle contractions in patients with hemifacial palsy. The onset of facial movements is measured with electromyograms (EMG). Activity during motor preparation is localized at the cortical level. The topographic spatial organisation of the premotor activity is compared between patients and healthy controls.

Technology

Patients and healthy controls are equipped with facial EMG and their brain activity is recorded with MEG while they perform systematic motor tasks.

Pipeline

After noise cancellation (maxfilter) of the MEG signal, the onset of each movement is marked manually by the experimenter, the data is then processed with a focus on activity just prior to these onsets. Source localization is performed using a weighted minimum norm estimate (wMNE). Premotor activity peaks are marked manually, and their position is used as a dependent variable for statistical analysis. A mixed within- between-groups experimental design is used.

There are two processing branches at the beginning:

  • processing of the resting-state data, used to create the noise covariance matrix used for source reconstruction
  • processing of task data,

Preprocessing: resting-state data

 OperationInput formatSoftwareOutput formatComment / Link
1Maxfilter.fifmaxfilter.fif maxfilter
2Mark bad data segments.fifMuse.fifReview data with Muse
3Use ICA to remove blinks and cardiac activity.fifMatlab (FieldTrip).fif

Use ICA to subtract blink and cardiac activity.

4Data import in Brainstorm.fifBrainstormBrainstormWe use a single step of this tutorial. We import the cleaned data from previous step. Do not ignore epochs of different length.
5Compute noise covarianceBrainstormBrainstormBrainstormCompute noise covariance in Brainstorm. Note that we use a resting-state segment (and not an empty room recording). This is ok and is because we are interested in source activity deviating from rest, rather than just any brain activity.

Preprocessing: task data

 OperationInput formatSoftwareOutput formatComment / Link
1Maxfilter.fifmaxfilter.fif maxfilter
2Data import in Brainstorm.fifBrainstormBrainstorm

We use a single step of this tutorial. We import the cleaned data from previous step. Do not ignore epochs of different length.

Reading events channel, time period: 0-48000 ms

If necessary, delete the second epoch of each kind.

3Mark the onset of each movementBrainstormBrainstormBrainstorm

This part is undocumented so far. Please edit the link below.

Mark movement onsets with EMG

Source estimation

 OperationInput formatSoftwareOutput formatComment / Link
1MRI segmentatoindicomfreesurferfreesurferFreesurfer segmentation
2Import anatomyfreesurferBrainstormBrainstormBrainstorm tutorial
3Create a head modelBrainstormBrainstormBrainstormBrainstorm tutorial. Right click subject, compute head model.
4Source estimation (wMNE)BrainstormBrainstormBrainstormBrainstorm tutorial

Measures

 OperationInput formatSoftwareOutput formatComment / Link
1 Finding the premotor peaksBrainstormBrainstormBrainstormThis part is undocumented so far. Please add!

Statistics

 OperationInput formatSoftwareOutput formatComment / Link
1 ANOVA on peak positionsTable (xls)RstudioHTML pageRunning a repeated measures ANOVA on the distances between peaks. Interest in interaction effect between within & between factors. Typical script: to be linked
2     

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