Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

The described protocol follows the guidelines of the human research ethics committee at the University of Salzburg, and is in accordance with the Declaration of Helsinki.

1. Prepare stimulus material

1. Download an image of the Rubin face/vase illusion¹⁴. This will be shown to half of the participants.
2. Use the Matlab command ~ to invert the original black and white binary Rubin image to create a second Rubin face/vase negative image with the black and white colors flipped with respect to the original image (white background instead of black background). This will be shown to the other half of the participants.
3. Create a mask by randomly scrambling blocks of pixels of the Rubin image. Divide the image in square blocks that are small enough to hide obvious contour features, for example between 2% and 5% of the size of the original image (5 by 5 pixels out of an image of 250 by 250), then randomly shuffle them to create the mask.
4. Create a black fixation cross on a white background, such that the fixation cross is smaller than the Rubin image (less than 5° of visual angle).

2. Set up MEG and stimulation equipment

1. Connect the stimulus presentation computer to the projector. Connect the DLP LED projector controller via a USB optoisolated extension (for data), and a digital visual interface (DVI) cable (for stimuli).
2. Connect the MEG acquisition computer to the stimulus presentation computer to let it send and receive triggers. Plug the digital input/output (DIO) system (buttons and triggers, 2x standard D24 connectors) of the integrated stimulus presentation system into the MEG connector on the optoisolated BNC breakout box.
3. Record 1 minute of empty room MEG data at 1 kHz.
4. Monitor the signals from the 102 magnetometers and 204 orthogonally placed planar gradiometers at 102 different positions by visualizing all signals in real time on the acquisition computer.

3. Prepare participant for MEG experiment

NOTE: Details of MEG data acquisition have been previously described¹⁵.

1. Make sure the participant understands the informed consent in accordance with the declaration of Helsinki and have them sign the form which also includes a statement of consent to the processing of personal data.
2. Provide the participant with non-magnetic clothes and make sure they have no metallic objects in or on their bodies. Ask participants to fill another anonymous questionnaire to ensure this, and that the participant does not have any other exclusion criteria such as neurological disorders, and to document other personal data like handedness and level of rest.
3. Seat the participant on a non-ferromagnetic (wooden) chair. Attach 5 head position indicator (HPI) coils to the head with adhesive plaster, two above one eye, one above the other eye, and one behind each ear.
4. Place the tracker sensor of the digitization system firmly on the participant's head and fix it to spectacles for maximum stability.
   NOTE: A 3D digitizer was used (Table of Materials).
5. Digitize the anatomical landmarks, the left and right pre-auricular points and the nasion, and make sure the left and right pre-auricular points are symmetrical. These fiducials define the 3D coordinate frame.
6. Digitize the 5 HPI coil positions using a 3D digitizer stylus.
7. Digitize up to 300 points along the scalp and maximize the coverage of the head shape. Cover the well-defined areas of the scalp on magnetic resonance (MR) images, above the inion on the back and the nasion on the front, as well as the nasal bridge.
   NOTE: These points will be used for the co-registration with an anatomical image for better individual source reconstruction.
8. Remove the spectacles with the tracker sensor.
9. Attach disposable electrodes above (superciliary arch) and below (medial to the zygomatic maxillary bone) the right eye to monitor vertical eye movements.
10. Attach disposable electrodes to the left of the left eye and to the right of the right eye (dorsal to the zygomatic maxillary bone) to monitor horizontal eye movements.
11. Attach disposable electrodes below the heart and below the right collarbone to monitor heart rate.
    NOTE: The eyes and the heart signals are relatively robust, so checking the impedance of disposable electrodes is not necessary.
12. Attach a disposable electrode as a ground below the neck.
13. Escort the participant to the MEG shielded room and instruct them to sit in the MEG chair.
14. Plug the HPI wiring harness and the disposable electrodes in the MEG system.
15. Raise the chair such that the participant’s head touches the top of the MEG helmet and make sure the participant is comfortable in this position.
16. Shut the door to the shielded room and communicate with the participant through the intercom system inside and outside the shielded room.
17. Instruct the participant to passively stare at an empty screen (empty except for a central fixation cross) for 5 min while recording resting-state MEG data at 1 kHz. Keep the sampling rate at 1 kHz throughout the experiment.
18. Instruct the participant of the task requirements and have them perform 20 practice trials.
    NOTE: Example instructions: "Keep your fixation at the center of the screen at all times. A cross will appear, and after the cross disappears, you will see an image followed by a scrambled image. As soon as the scrambled image disappears, click the yellow button if you had seen faces and the green button if you had seen a vase."
19. Alternate the response buttons across participants (e.g., right for faces, left for vase, or vice versa).
    NOTE: The color of response buttons does not matter.

4. Present the experiment using Psychtoolbox¹⁶

1. Display instructions to the participants, telling them which button to press when they see faces and which button to press when they see a vase.
2. Create a single trial with 4 events which will apply to all trials in this order: fixation cross, Rubin image, mask, and response prompt (Figure 1).
3. At the beginning of each trial, display the fixation cross for a variable time period between 1 s and 1.8 s.
4. At the end of that time period, remove the fixation cross and display the Rubin image for 150 ms.
5. At the end of the 150 ms, remove the Rubin image and display the mask for 200 ms.
6. At the end of the 200 ms, remove the mask and display a question to prompt participants to respond with a maximum response deadline of 2 s.
7. Program the response period such that if participants respond within 2 s, the next trial (starting with a fixation cross) begins when they do so. Otherwise, start the next trial after 2 s.
8. Save the timing of all 4 events as well as the response choice and its timing.
9. Repeat the same trial structure 100 times before displaying an instruction for participants to rest briefly. This constitutes one experimental block.
10. Repeat the block structure 4 times for a total of 400 trials.

5. Monitor MEG signal and participant during experiment

1. Monitor the participant via video.
2. At the beginning of each block, before the task starts, start measuring MEG data and record the initial position of the participants' head position with respect to the MEG. In the MEG system used, click GO to start. When a dialog asks if the HPI data is to be omitted or added to the recording, inspect the HPI coils signal, and click Accept to record that initial head position. After that, click Record raw to start recording MEG data.
3. If at any point throughout the experiment the participant wishes to stop the experiment, terminate the experiment and go inside the shielded room to unplug all sensors from the MEG system and release the participant from the chair.
4. Monitor the MEG signals by visualizing them in real time on the acquisition computer.
5. In between blocks, communicate with the participant through the speaker system to make sure they are well and ready to continue, and instruct them to move their limbs if they wish, but not their head.
6. In between blocks, save the acquired MEG signals of that block.
7. After the end of the experiment, go inside the shielded room, unplug all sensors from the MEG system, and release the participant from the chair.
8. Escort the participant out of the shielded room and offer them the choice to either detach all sensors from their face and body themselves, or detach the sensors for them.
9. Thank the participant and provide them with monetary compensation.

6. Pre-process and segment MEG signals

1. Use the signal space separation algorithm implemented in the Maxfilter program (provided by the MEG manufacturer) with default parameter values to remove external noise from the continuous MEG signals.
2. Apply a 0.1 Hz high-pass filter to the continuous data using the Fieldtrip toolbox¹⁷ function ft_preprocessing.
   NOTE: all subsequently reported functions prefixed with 'ft_' are part of the Fieldtrip toolbox.
3. Segment the MEG data by extracting the 1 second preceding the stimulus presentation on each trial.
4. Assign these epochs a 'face' or 'vase' trial type label according to the participants' behavioral responses on each trial.
5. Visually inspect trials and channels to identify and remove those showing exceeding noise or artifacts, regardless of the nature of the artefacts, using ft_rejectvisual.
6. Reject trials and channels with z-scores above 3 by clicking zscore and selecting trials and channels exceeding the value of 3 or trials with excess variance by removing outliers showing after clicking var. Inspect the MEG signal for all the trials before or after this procedure.

7. Source reconstruction

1. Include both trial types to perform the source localization to obtain common linearly constrained minimum variance¹² spatial filters in the beamforming procedure implemented in Fieldtrip.
2. Band-pass filter the epoched data to the frequencies of interest, in this case between 1 and 40 Hz.
3. Select the time of interest to calculate the covariance matrix, in this case the 1 second prestimulus period.
   NOTE: The resulting data segments (selected between -1 to 0 s and 1 to 40 Hz) are used in all the following steps that require data input.
4. Segment the brain and the scalp out of individual structural MR images with ft_volumesegment. If not available, use a standard T1 (from the statistical parametric mapping [SPM] toolbox) Montreal Institute of Neurology (MNI, Montreal, Quebec, Canada) brain scan instead.
5. Create for each participant a realistic single-shell head model using ft_prepare_headmodel.
6. On individual MR images, locate the fiducial landmarks by clicking on their location on the image to initiate a coarse co-registration with ft_volumerealign.
7. Align the head shape points with the scalp for a finer co-registration.
8. Prepare an individual 3D grid at 1.5 cm resolution based on the MNI template brain morphed into the brain volume of each participant with ft_prepare_sourcemodel.
9. Compute the forward model for the MEG channels and the grid locations with ft_prepare_leadfield. Use the configuration fixedori to compute the leadfield for only one optimal dipole orientation.
10. Calculate the covariance matrix of each trial and average it across all trials.
11. Compute the spatial filters using the forward model and the average covariance matrix with ft_sourceanalysis.
12. Multiply the sensor-level signal to the LCMV filters to obtain the time series for each source location in the grid and for each trial.

8. Analyze pre-stimulus oscillatory power in region of interest

1. Define a region of interest (ROI), for example from previous literature¹⁸ (here fusiform face area [FFA]; MNI coordinates: [28 -64 -4] mm).
2. Single out the virtual sensor that spatially corresponds to the ROI, using ft_selectdata.
3. Split face and vase trials using ft_selectdata.
4. Perform a frequency analysis on the ROI, separately on the data from the two trial types, using ft_freqanalysis.
5. Set the method option to mtmfft to perform a fast Fourier transform.
6. Set the taper option to hanning to use a Hann function taper.
7. Define the frequencies of interest from 1 Hz to 40 Hz.
8. Set the output option to pow to extract power values from the complex Fourier spectra.
9. Repeat the procedure for each participant before averaging the spectra across participants and plotting the resulting grand-averaged power values as a function of the frequencies of interest.

9. Analyze pre-stimulus connectivity between regions of interest

1. Define one (or more) ROI with which the previously selected ROI is hypothesized to be connected, for example from previous literature¹⁸ (here V1; MNI coordinates: [12 -88 0]).
2. Repeat steps 8.2 and 8.3.
3. Perform time-frequency analysis on both ROIs (represented as 2 channels or 'virtual sensors' within the same data structure), separately on the data from the two trial types, using ft_freqanalysis.
4. Set the method to mtmconvol to implement a multitaper time-frequency transformation based on multiplication in the frequency domain.
5. Set the taper option to dpss to use a discrete prolate spheroidal sequences function taper.
6. Define the frequencies of interest from 8 Hz to 13 Hz.
7. Set the width of the time window to 200 ms and the smoothing parameter to 4 Hz.
8. Set the keeptrials option to yes to return the time-frequency estimates of the single trials.
9. Set the output to fourier to return the complex Fourier spectra.
10. Perform a connectivity analysis on the resulting time-frequency data using ft_connectivityanalysis.
11. Set the method to coh and the complex field to imag to return the imaginary part of coherency¹⁹.
12. Repeat the procedure for each participant before averaging the coherence spectra across frequencies and participants and plotting the resulting grand-averaged imaginary coherency values as a function of time.

10. Statistically comparing the face and vase pre-stimulus power or coherence spectra

1. Combine the pre-stimulus power or coherence data from each subject, within each of the 2 conditions, into one Matlab variable using ft_freqgrandaverage with the option keepindividual set to yes.
2. Perform a cluster-based permutation test²⁰ comparing the 2 resulting variables using ft_freqstatistics.
3. Set the method option to motecarlo.
4. Set the frequency option to [8 13] and set avgoverfreq to yes.
5. Set clusteralpha to 0.05 and set correcttail to alpha.
6. Set the statistic option to ft_statfun_depsamplesT.
7. Create a design matrix with a first row of 20 ones followed by 20 twos, and a second row of consecutive numbers from 1 to 20 repeated twice. Pass this design matrix to the design option.
   NOTE: The design matrix is divided in blocks of 20 because the data were collected from 20 participants.
8. Set the ivar option to 1 and the uvar option to 2.