Cortical Source Analysis of High-Density EEG Recordings in Children

The overall goal of the following experiment is to estimate the neural generators of scalp measured EEG with pediatric participants. This is achieved by recording high density EEG with children and using suitable experimental procedures as a second step. A surface model is calculated based on magnetic resonance imaging data, either individual structural MRI or an age appropriate template, which leads to a boundary element model that associates different compartments with conductivity values for source reconstruction.

Next, the location of EEG channels is coregistered with the inverse model. In order to calculate the likely generators of the scalp measured voltage changes, results are obtained that showed the contribution of cortical sources based on the measured EEG data and predictions of the head model. The main advantage of this technique over existing methods like channel level analysis is that the likely contribution of several cortical areas to an event related potential component can be assessed.

We first had the idea for using this method when we wanted to assess the network structure of cortical areas using resting state EEG recordings. Visual demonstration of this method is critical as child-friendly. Recording of EEG is difficult to learn as standard recording procedures need to be adapted for children To begin this protocol.

First, ensure that the child is comfortable with the testing environment. To do this, allow younger children to sit on the lap of their caregiver or in a comfortable child's seat. Also, let the child see and feel the sensor net before applying it to the child's head.

If there is an extra net, have the parent also try one on or place one on a doll or stuffed teddy in order to keep children comfortable. During the EEG preparation, allow the child to listen to music. Watch an age appropriate cartoon or distract them using another experimenter.

Then measure the child's maximum head circumference to select the correct net size. Use a measuring tape and hold it to the nasion. Then measure the head around the maximum circumference about one centimeter above the eon.

Next, identify the vertex of the head at the intersection of the middle distance between nasion and eon, as well as the left and right per auricular point. Mark this point with a China pen to ensure that the Vertex channel is correctly positioned when applying the net. Now apply a sensor net that has been soaked, an electrolyte solution, and make sure that the key channels are aligned with the anatomical landmarks.

Then ensure that channels have good contact with the scalp. By positioning the sensors individually. Gently twist each sensor from side to side to move hair out of the way.

Next, measure channel gains and channel impedances. Click start to begin the recording in the net station, EEG recording software and start gain and impedance measurement. If measurement does not automatically start, use the calibrate amplifier and the measure net impedances button.

Check the recording software for channels with impedances higher than 50, 000 ohm, which will appear red. Apply additional electrolyte solution with a pipette to lower channel impedances. Also, check the EEG display for channels that show high frequency activity.

Despite low impedance or noticeably less activity than surrounding channels, these channels may have loose contact with the scalp and require adjustment. Once all channels are satisfactory, run the appropriate EEG experiment after the EEG experiment. First pre-process the data correct for artifacts.

Then segment A previously acquired anatomical MRI scan with free surfer software. Set up the shell environment to include free surfer for dot bash rc. Include the command seen here in the dot bash RC file.

Next, define the subject directory, which is the folder. The output will be written to using the command seen here. Then import the segments into brainstorm software and use the display tools to check for incorrect segmentation such as overlapping spheres or anatomically unlikely compartments.

Inspect the segmentation visually by right clicking and selecting display to estimate source activity first, start the brainstorm application, then create a new protocol and add a new subject to the protocol. Then import EEG data for the participant and import a channel file. Check that the boundary element models and the channels align as expected by right clicking on the channel file for the subject and navigating to MRI registration and check.

Note that if the spheres within the model are overlapping or if the channels are within the boundary element models, the source reconstruction will produce incorrect results. Adjust the alignment by using the edit option in the MRI segmentation menu. Next, calculate the noise Covance matrix from the baseline of each epic by choosing noise Covance matrix and calculate from recording.

Then calculate the source model by selecting compute source model. Calculate the inverse solution using depth weighted minimum norm estimation by selecting compute source and minimum norm estimation. Repeat these steps for all participants in the study.

Next, average source activity over trials per participants by dragging the recordings to the process menu and selecting average and by condition. Subject average. Contrast the condition by selecting processes to and dragging each condition in one window.

Then select the T-test depending on study design to perform multiple comparisons. Set the amplitude and area thresholds in the display of the resulting statistical map in the stat menu. Now calculate the event related response for a region of interest.

For parcelization based ROIs. Load the free surfer parcelization by right clicking on the cortex surface in the anatomy menu and selecting import labels. Navigate the corresponding file and loaded.

Then select a region of interest in the scout pane of the functional data menu. Obtain the region of interest event related activity by dragging files to the process one window and selecting extract scout time series. From the sources menu.

Note that several regions of interest can be selected simultaneously and the time series can be exported for further plotting and analysis. The results presented here are based on a recording with a 6-year-old boy. This figure shows channel level event related potential responses to face and scrambled face stimuli.

The response typically shows a more negative deflection between 130 and 220 milliseconds after stimulus onset on the right side for faces compared to scrambled faces. Here we see the statistical comparison of source activity projected based on a standard adult head model and an age appropriate head model. In the second and third row, the color map illustrates the effect size with red indicating higher activity in the faces condition and blue showing higher activity in the scrambled faces condition.

And finally, this figure shows source event related potential responses of the right fusiform gyrus in response to faces and scrambled faces based on source reconstruction of a recording. Using an age appropriate boundary element model with minimum norm estimation Once mastered, this technique can be done in a few hours from according to source analysis if it is performed properly Following this procedure. Other methods like time frequency, decomposition can be performed in order to answer additional questions about the contribution of neuro oscillators in different frequency bands.

After watching this video, you should have a good understanding of how to estimate the cortical generators of high density EEG recordings in pediatric participants.