Tutorial - Head Motion

Authors: Marc Lalancette

All efforts should be made to avoid any movements and in particular head movements during a MEG recording, as it can cause various issues such as blurring of signals and loss of amplitude, mis-localization of source activity, and possibly motion artefacts. Yet it is important to evaluate and account for any head motion at the time of analysis. This is possible because the positions of the head tracking coils are saved in channels along the MEG data. It is important to note however that most analysis software, including Brainstorm, assume a single fixed position for the head for most computations. This "reference" position is the one that is measured just before the recording starts and that is saved separately from the continuous head localization channels.

This tutorial will explore different options on how to deal with head motion, using the example dataset from the tutorial: MEG resting state & OMEGA database (CTF). See there for details on how to download the dataset and load it in Brainstorm. Note that importing all the subjects can take a while; for this tutorial, you can only import sub-0007.

Contents

  1. Motion visualization
  2. Adjust the reference head position
  3. Mark head motion events
  4. Split the recording
  5. Fixing swapped head coils
  6. Dealing with a missing or bad head coil [Under construction]
  7. Head motion correction with SSS [TO DO]
  8. Additional documentation

Motion visualization

First, in order to evaluate the level of motion, we will display a special montage based on the head localization coils. This calculates the distance at each point in time with respect to the initial/reference head position. That distance is based on a sphere (approximating the head) attached to these head coils, thus accounting for all types of motion of all parts of the head equally.

Adjust the reference head position

Given that the default reference position is measured at the very beginning of the recording, we can improve co-registration by replacing it with one that better represents the position throughout. For more details on how Brainstorms coregisters various files and their coordinate systems, see Coordinate Systems.

The figures don't show much difference for this recording, but we can notice a small drop looking at the LPA point and the head point near the back edge of the helmet. A message in the Matlab command window also tells us that the position changed by 3.7 mm.

Mark head motion events

For long recordings, such as is typical for epilepsy, or when a large movement is present, a single head position may not be appropriate for the entire duration. In these cases, it may be preferable to split the recording in smaller segments where the head is stable and reject segments where too much motion is present. To do this, we first need to detect and mark these segments. Note that this process does not depend in any way on the reference position, so there is no need to adjust it first as we did.

There is not much motion in this dataset, so we will use extremely conservative thresholds to illustrate this process.

In this first half of the dataset, we see one bad segment and three stable segments that correspond well with the jumps in the distance time series. We can also see these two new event groups in the Record tab. Note that it took a few tries with different thresholds and minimum durations to get the segments to fit that well.

Split the recording

We now can proceed to splitting our raw recording into segments with a stable head position, based on the head motion events.

A new condition appears in the database explorer for each extracted segment.

We now should correct the reference position for each new raw file, otherwise the splitting would not achieve much.

Note that the process will detect and replace the previous head position adjustment we made (no need to remove it first).

We now see the head distance stays below half a mm until the end of the segment where it jumps to about 1.5 mm, close to our threshold. You can check that the other segments have similarly low remaining motion.

Fixing swapped head coils

If head coils were plugged incorrectly during the recording, the MEG co-registration will be completely off. Manually editing the .hc file would prevent the other head motion tools described here from working properly.

The first step for fixing this is to identify which coils were swapped. You can determine this by opening the dataset .hc file (in any text editor) and comparing the coordinates of the standard positions with those of the measured positions "relative to dewar", or even just the signs of the x and y coordinates. For example, if the standard (x, y) signs are: nasion (+, +), left ear (-, +), right ear (+, -), and the measured signs are: nasion (+, -), left ear (+, +), right ear (-, +), then all three coils were swapped in a cycle.

If the 3 coils were wrong, there are two ways they could be ordered. The "Reverse order" check box allows specifying which one. In our example, you should not check "Reversed order". But if you try one and it doesn't fix your case, you can simply reset the channel file (Process > Import > Channel file > Adjust coordinate system), and then try the other order.

Note that this swap process also adjusts the reference head position as described above, after the swap.

Dealing with a missing or bad head coil [Under construction]

If only 3 head coils are used and one falls off or is defective, and if this issue is present both in the reference and the continuous head positions, there is no way to exactly know the head position relative to the MEG sensors for the affected recording(s), preventing precise source space analysis. However, if 2 of the 3 coils are well localized and another recording for the same participant and same session is available that has good localization of all 3 coils, we can correct the bad coil with a best guess as to its likely position, assuming the head was positionned in a similar way in the helmet. It is important to remember that this is only a best guess and that in theory, having only 2 coils leaves an undetermined head rotation.

Our fix is applied at each time sample assuming the missing coil is in the direction perpendicular to the plane spanned by the line through the 2 good coils and the head coordinate system z-axis of the provided "good head position" (typically from another recording). This gives us a position for the coil relative to the helmet.

[TO DO: add example screenshots]

Head motion correction with SSS [TO DO]

Signal Space Separation (SSS) is a data cleaning technique that uses a spherical harmonic decomposition of the MEG signal to separate it into components originating from inside or outside the sensor shell. By reconstructing the signal with only the "inside" components, environmental interference is reduced.

The same spherical harmonic expansion can be used to correct for head motion by reconstructing the signal with new sensor locations adapted to the head position at each instant. (In the head coordinate system, head motion translates to motion of the sensor array.) In other words, we correct for head motion by interpolating the field at new sensor locations, using spherical harmonics for the interpolation.

The SSS process can therefore be used both for motion correction and signal cleaning, but here we will use it only for motion correction. In that case, the process takes care of selecting the expansion orders for inside and outside harmonics, using in total as many harmonics as sensors.

Additional documentation

Articles

Tutorials/HeadMotion (last edited 2022-06-30 14:12:25 by FrancoisTadel)