How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

The overall goal of this procedure is to detect amygdala activity during learning using MAGNETOENCEPHALOGRAPHY or MEG. This is accomplished by first designing an implicit fear learning task that activates the amygdala. The second step is to record brain activity during the task using MEG.

Next, identify the surfaces of the cortex and the amygdala using a high resolution anatomical MRI scans. The final step is to use the surfaces of the cortex and amygdala to model the neural sources of the MEJ signal. Ultimately, source imaging models can be used to show neural activation in the amygdala during learning without awareness.

So the main advantage of this technique over existing methods like FMRI is that MEG signals are recorded in real time and they can be used to study neural processes that are rapid or transient. Though this method can be used to study amygdala function, it can also be used to study the function of other subcortical regions as well. Begin by connecting the stimulus presentation computer to the MEG acquisition system using a standard DB 25 multi connector ribbon cable.

Next, connect the stimulus presentation computer to the slab standalone monitor or sam. Using the eight bit to two bit isolation adapter and the synchronization cable, the transistor to transistor logic pulses used to mark the stimulus presentations can cause artifacts in the MEG data if they are sent to the sam. To avoid these artifacts, mark the onset of the stimuli using only the bits blocked by the isolation adapter as seen here.

The shock stimulator is then connected to the sam. Next, pass a shielded extension cable through the waveguide and connected to the shock stimulator. The SAM should then be connected to a computer running the slab data acquisition software.

Connect the rotary dial to the stimulus presentation computer and the MEG acquisition system using the game port to game port BNC splitter and the game port to USB adapter. Attach electrodes and sensors to the subject using the schematic shown here. Once set up properly, digitize the position of the subject's head relative to the HPI coils using fiducial points.

Next, digitize 50 to 100 points along the subject's scalp. Escort the subject to the MEG system and connect the electrodes and sensors to the appropriate interface. Raise the chair so that the subject's head is touching the top of the MEG helmet and position the screen so that the projected image is in focus.

Next, set the shock to a level that the subject reports as painful but tolerable. Lastly, instruct the subject on the proper use of the dial using an example presentation scenario. Begin by loading the slab data acquisition software to start recording event codes and shock delivery.

The training session is programmed beforehand and consists of four blocks of differential trace fear conditioning with 15 trials per condition, stimulus or CS per block as seen here when everything is ready. Begin four training trials and record raw data at two kilohertz. During each, visually inspect the data in real time for systematic sources of noise.

After each run, ask the subject to rate the intensity of the shock to assess habituation. After completing the trials, use free surfer to create a segmented subcortical volume and surfaces of the cortex, outer skin and outer skull. Next, create and convert the amygdala and hippocampus volumes into surfaces.

Using Slicer three and ParaView, the next step is to create a new subject in the brainstorm database. Import the MRI volume into brainstorm and warp the volume into standard space by identifying the fiducial points, import the surfaces and manually align scalp surface with MRI. Once completed.

Apply this transformation to all other surfaces. Merge the peel, hippocampal, and amygdala surfaces. Lastly, create regions of interest for the amygdala and hippocampus.

Once created, import the MEG recording file for each training session. The MEG acquisition software uses signal space separation to remove artifacts caused by sources outside the magnetically shielded room. Be sure to use the clean files often found in a folder marked SSS to analyze the evoked responses.

First, use the event channel to identify epics corresponding to each of the experimental trials. Remove artifacts caused by heartbeats and eye movements using signal space projections from events identified on the electrocardiography and electro iconography channels. Next, refine the MRI registration using head points.

Compute the noise co variance from the recordings using the overlapping spheres method with cortex's input, compute the head model compute sources using the minimum norm estimate method, and continue analysis on sources. Next band pass filter sources for the individual trials. Take the absolute value of the band pass filtered sources and convert those values to Z-score based on baseline variability, spatially smooth the sources and average sources across trials.

Project these averages onto the default anatomy for the experiment. Next, compute T tests on the sources across the different conditions. Filter significant T-test results using spatial and temporal thresholds to correct for family-wise error.

Identify significantly activated regions and export the time course of activation for each subject. Finally, compute the mean and standard error of the mean across subjects at each time point. First, project the raw data from the individual trials onto the default anatomy for the experiment Compute time frequency decompositions on data from anatomical and functional regions of interest.

Convert the resulting time frequency decomposition maps to Z-score and average the resulting maps across trials for each subject. Finally, perform T-test on the maps across the different conditions shown here are behavioral results from a typical conditioning study. The graph on the left shows that subjects have similar levels of expectancy across the 60 trials, suggesting that the masking procedure blocked their ability to discriminate between the condition stimuli.

The graph on the right shows differential responses during the testing session. Notice that the unfiltered group shows larger responses to the old stimuli compared to new suggesting that the training leads to better reacquisition when compared to the filtered group. This example shows MEG results from a typical conditioning experiment.

The 3D model on the left shows the amygdala and orange and the hippocampus in green. The graph on the right represents activity from an amygdala cluster. The light colored line represents the activity evoked by unfiltered faces, while the darker line represents the activity evoked by filtered faces.

This graph represents the MEG signal recorded from the amygdala broken down by time and frequency. Warm colors represent regions in the spectro spectrograph that show significantly more power for unfiltered faces than for filtered faces. Cool colors represent the opposite regions with the striped overlay represent significant differences across the groups.

This figure shows occipital face area activation. In a typical conditioning experiment, warm colors represent larger responses to unfiltered faces than to filtered faces. Following this procedure.

Other methods like coherence analysis can be used in order to answer additional questions related to neural communication. After watching this video, you should have a good understanding of how to use source imaging to detect neural responses and subcortical brain structures.