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MEG + Facial EMG (PF_MOTEUR)
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
| Operation | Input format | Software | Output format | Comment / Link | |
|---|---|---|---|---|---|
| 1 | Maxfilter | .fif | maxfilter | .fif | maxfilter |
| 2 | Mark bad data segments | .fif | Muse | .fif | Review data with Muse |
| 3 | Use ICA to remove blinks and cardiac activity | .fif | Matlab (FieldTrip) | .fif | |
| 4 | Data import in Brainstorm | .fif | Brainstorm | Brainstorm | We use a single step of this tutorial. We import the cleaned data from previous step. Do not ignore epochs of different length. |
| 5 | Compute noise covariance | Brainstorm | Brainstorm | Brainstorm | Compute 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
| Operation | Input format | Software | Output format | Comment / Link | |
|---|---|---|---|---|---|
| 1 | Maxfilter | .fif | maxfilter | .fif | maxfilter |
| 2 | Data import in Brainstorm | .fif | Brainstorm | Brainstorm | 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. |
| 3 | Mark the onset of each movement | Brainstorm | Brainstorm | Brainstorm | This part is undocumented so far. Please edit the link below. |
Source estimation
| Operation | Input format | Software | Output format | Comment / Link | |
|---|---|---|---|---|---|
| 1 | MRI segmentatoin | dicom | freesurfer | freesurfer | Freesurfer segmentation |
| 2 | Import anatomy | freesurfer | Brainstorm | Brainstorm | Brainstorm tutorial |
| 3 | Create a head model | Brainstorm | Brainstorm | Brainstorm | Brainstorm tutorial. Right click subject, compute head model. |
| 4 | Source estimation (wMNE) | Brainstorm | Brainstorm | Brainstorm | Brainstorm tutorial |
Measures
| Operation | Input format | Software | Output format | Comment / Link | |
|---|---|---|---|---|---|
| 1 | Finding the premotor peaks | Brainstorm | Brainstorm | Brainstorm | This part is undocumented so far. Please add! |
Statistics
| Operation | Input format | Software | Output format | Comment / Link | |
|---|---|---|---|---|---|
| 1 | ANOVA on peak positions | Table (xls) | Rstudio | HTML page | Running 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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