This work was supported by FWF Austrian Science Fund, Imaging the Mind: Connectivity and Higher Cognitive Function, W 1233-G17 (to E.R.) and European Research Council Grant WIN2CON, ERC StG 283404 (to N.W.). The authors would like to acknowledge the support of Nadia M&#252;ller-Voggel, Nicholas Peatfield, and Manfred Seifter for contributions to this protocol.

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
<ol>
	<li>Download an image of the Rubin face/vase illusion14. This will be shown to half of the participants.</li>
	<li>Use the Matlab command ~&#160;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.</li>
	<li>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.</li>
	<li>Create a black fixation cross on a white background, such that the fixation cross is smaller than the Rubin image (less than 5&#176; of visual angle).</li>
</ol>
2. Set up MEG and stimulation equipment
<ol>
	<li>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).</li>
	<li>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.</li>
	<li>Record 1 minute of empty room MEG data at 1 kHz.</li>
	<li>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.</li>
</ol>
3. Prepare participant for MEG experiment
NOTE: Details of MEG data acquisition have been previously described15.
<ol>
	<li>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.</li>
	<li>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.</li>
	<li>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.</li>
	<li>Place the tracker sensor of the digitization system firmly on the participant&#39;s head and fix it to spectacles for maximum stability. 
	NOTE: A 3D digitizer was used (Table of Materials).</li>
	<li>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.</li>
	<li>Digitize the 5 HPI coil positions using a 3D digitizer stylus.</li>
	<li>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.</li>
	<li>Remove the spectacles with the tracker sensor.</li>
	<li>Attach disposable electrodes above (superciliary arch) and below (medial to the zygomatic maxillary bone) the right eye to monitor vertical eye movements.</li>
	<li>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.</li>
	<li>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.</li>
	<li>Attach a disposable electrode as a ground below the neck.</li>
	<li>Escort the participant to the MEG shielded room and instruct them to sit in the MEG chair.</li>
	<li>Plug the HPI wiring harness and the disposable electrodes in the MEG system.</li>
	<li>Raise the chair such that the participant&#8217;s head touches the top of the MEG helmet and make sure the participant is comfortable in this position.</li>
	<li>Shut the door to the shielded room and communicate with the participant through the intercom system inside and outside the shielded room.</li>
	<li>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.</li>
	<li>Instruct the participant of the task requirements and have them perform 20 practice trials. 
	NOTE: Example instructions: &#34;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.&#34;</li>
	<li>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.</li>
</ol>
4. Present the experiment using Psychtoolbox16
<ol>
	<li>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.</li>
	<li>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).</li>
	<li>At the beginning of each trial, display the fixation cross for a variable time period between 1 s and 1.8 s.</li>
	<li>At the end of that time period, remove the fixation cross and display the Rubin image for 150 ms.</li>
	<li>At the end of the 150 ms, remove the Rubin image and display the mask for 200 ms.</li>
	<li>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.</li>
	<li>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.</li>
	<li>Save the timing of all 4 events as well as the response choice and its timing.</li>
	<li>Repeat the same trial structure 100 times before displaying an instruction for participants to rest briefly. This constitutes one experimental block.</li>
	<li>Repeat the block structure 4 times for a total of 400 trials.</li>
</ol>
5. Monitor MEG signal and participant during experiment
<ol>
	<li>Monitor the participant via video.</li>
	<li>At the beginning of each block, before the task starts, start measuring MEG data and record the initial position of the participants&#39;&#160;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.</li>
	<li>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.</li>
	<li>Monitor the MEG signals by visualizing them in real time on the acquisition computer.</li>
	<li>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.</li>
	<li>In between blocks, save the acquired MEG signals of that block.</li>
	<li>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.</li>
	<li>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.</li>
	<li>Thank the participant and provide them with monetary compensation.</li>
</ol>
6. Pre-process and segment MEG signals
<ol>
	<li>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.</li>
	<li>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 &#39;ft_&#39;&#160;are part of the Fieldtrip toolbox.</li>
	<li>Segment the MEG data by extracting the 1 second preceding the stimulus presentation on each trial.</li>
	<li>Assign these epochs a &#39;face&#39;&#160;or &#39;vase&#39;&#160;trial type label according to the participants&#39;&#160;behavioral responses on each trial.</li>
	<li>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.</li>
	<li>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.</li>
</ol>
7. Source reconstruction
<ol>
	<li>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.</li>
	<li>Band-pass filter the epoched data to the frequencies of interest, in this case between 1 and 40 Hz.</li>
	<li>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.</li>
	<li>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.</li>
	<li>Create for each participant a realistic single-shell head model using ft_prepare_headmodel.</li>
	<li>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.</li>
	<li>Align the head shape points with the scalp for a finer co-registration.</li>
	<li>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.</li>
	<li>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.</li>
	<li>Calculate the covariance matrix of each trial and average it across all trials.</li>
	<li>Compute the spatial filters using the forward model and the average covariance matrix with ft_sourceanalysis.</li>
	<li>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.</li>
</ol>
8. Analyze pre-stimulus oscillatory power in region of interest
<ol>
	<li>Define a region of interest (ROI), for example from previous literature18 (here fusiform face area [FFA]; MNI coordinates: [28 -64 -4] mm).</li>
	<li>Single out the virtual sensor that spatially corresponds to the ROI, using ft_selectdata.</li>
	<li>Split face and vase trials using ft_selectdata.</li>
	<li>Perform a frequency analysis on the ROI, separately on the data from the two trial types, using ft_freqanalysis.</li>
	<li>Set the method option to mtmfft to perform a fast Fourier transform.</li>
	<li>Set the taper option to hanning to use a Hann function taper.</li>
	<li>Define the frequencies of interest from 1 Hz to 40 Hz.</li>
	<li>Set the output option to pow to extract power values from the complex Fourier spectra.</li>
	<li>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.</li>
</ol>
9. Analyze pre-stimulus connectivity between regions of interest
<ol>
	<li>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]).</li>
	<li>Repeat steps 8.2 and 8.3.</li>
	<li>Perform time-frequency analysis on both ROIs (represented as 2 channels or &#39;virtual sensors&#39;&#160;within the same data structure), separately on the data from the two trial types, using ft_freqanalysis.</li>
	<li>Set the method to mtmconvol to implement a multitaper time-frequency transformation based on multiplication in the frequency domain.</li>
	<li>Set the taper option to dpss to use a discrete prolate spheroidal sequences function taper.</li>
	<li>Define the frequencies of interest from 8 Hz to 13 Hz.</li>
	<li>Set the width of the time window to 200 ms and the smoothing parameter to 4 Hz.</li>
	<li>Set the keeptrials option to yes to return the time-frequency estimates of the single trials.</li>
	<li>Set the output to fourier to return the complex Fourier spectra.</li>
	<li>Perform a connectivity analysis on the resulting time-frequency data using ft_connectivityanalysis.</li>
	<li>Set the method to coh and the complex field to imag to return the imaginary part of coherency19.</li>
	<li>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.</li>
</ol>
10. Statistically comparing the face and vase pre-stimulus power or coherence spectra
<ol>
	<li>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.</li>
	<li>Perform a cluster-based permutation test20 comparing the 2 resulting variables using ft_freqstatistics.</li>
	<li>Set the method option to motecarlo.</li>
	<li>Set the frequency option to [8 13] and set avgoverfreq to yes.</li>
	<li>Set clusteralpha to 0.05 and set correcttail to alpha.</li>
	<li>Set the statistic option to ft_statfun_depsamplesT.</li>
	<li>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.</li>
	<li>Set the ivar option to 1 and the uvar option to 2.</li>
</ol>

<ol>
	<li>Arieli, A., Sterkin, A., Grinvald, A., Aertsen, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamics+of+ongoing+activity:+explanation+of+the+large+variability+in+evoked+cortical+responses.">Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses.</a> Science. 273 (5283), 1868-1871 (1996).</li><li>Boly, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Baseline+brain+activity+fluctuations+predict+somatosensory+perception+in+humans.">Baseline brain activity fluctuations predict somatosensory perception in humans.</a> Proceedings of the National Academy of Sciences of the United States of America. 104 (29), 12187-12192 (2007).</li><li>Rahn, E., Ba&#351;ar, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prestimulus+EEG-activity+strongly+influences+the+auditory+evoked+vertex+response:+a+new+method+for+selective+averaging.">Prestimulus EEG-activity strongly influences the auditory evoked vertex response: a new method for selective averaging.</a> The International Journal of Neuroscience. 69 (1-4), 207-220 (1993).</li><li>Sup&#232;r, H., van der Togt, C., Spekreijse, H., Lamme, V. A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Internal+state+of+monkey+primary+visual+cortex+(V1)+predicts+figure-ground+perception.">Internal state of monkey primary visual cortex (V1) predicts figure-ground perception.</a> The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 23 (8), 3407-3414 (2003).</li><li>Frey, J. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Tactile+Window+to+Consciousness+is+Characterized+by+Frequency-Specific+Integration+and+Segregation+of+the+Primary+Somatosensory+Cortex.">The Tactile Window to Consciousness is Characterized by Frequency-Specific Integration and Segregation of the Primary Somatosensory Cortex.</a> Scientific Reports. 6, 20805 (2016).</li><li>Leonardelli, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prestimulus+oscillatory+alpha+power+and+connectivity+patterns+predispose+perceptual+integration+of+an+audio+and+a+tactile+stimulus.">Prestimulus oscillatory alpha power and connectivity patterns predispose perceptual integration of an audio and a tactile stimulus.</a> Human Brain Mapping. 36 (9), 3486-3498 (2015).</li><li>Leske, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prestimulus+Network+Integration+of+Auditory+Cortex+Predisposes+Near-Threshold+Perception+Independently+of+Local+Excitability.">Prestimulus Network Integration of Auditory Cortex Predisposes Near-Threshold Perception Independently of Local Excitability.</a> Cerebral Cortex. 25 (12), 4898-4907 (2015).</li><li>Weisz, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prestimulus+oscillatory+power+and+connectivity+patterns+predispose+conscious+somatosensory+perception.">Prestimulus oscillatory power and connectivity patterns predispose conscious somatosensory perception.</a> Proceedings of the National Academy of Sciences of the United States of America. 111 (4), 417-425 (2014).</li><li>Busch, N. A., Dubois, J., VanRullen, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+phase+of+ongoing+EEG+oscillations+predicts+visual+perception.">The phase of ongoing EEG oscillations predicts visual perception.</a> The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 29 (24), 7869-7876 (2009).</li><li>Mathewson, K. E., Gratton, G., Fabiani, M., Beck, D. M., Ro, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=To+see+or+not+to+see:+prestimulus+alpha+phase+predicts+visual+awareness.">To see or not to see: prestimulus alpha phase predicts visual awareness.</a> The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 29 (9), 2725-2732 (2009).</li><li>Blake, R., Logothetis, N. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+competition.">Visual competition.</a> Nature Reviews Neuroscience. 3 (1), 13-21 (2002).</li><li>Van Veen, B. D., van Drongelen, W., Yuchtman, M., Suzuki, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Localization+of+brain+electrical+activity+via+linearly+constrained+minimum+variance+spatial+filtering.">Localization of brain electrical activity via linearly constrained minimum variance spatial filtering.</a> IEEE Transactions on Biomedical Engineering. 44 (9), 867-880 (1997).</li><li>Bastos, A. M., Schoffelen, J. -. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Tutorial+Review+of+Functional+Connectivity+Analysis+Methods+and+Their+Interpretational+Pitfalls.">A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls.</a> Frontiers in Systems Neuroscience. 9, 175 (2015).</li><li>Rubin, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Synsoplevede Figurer. , (1915).</li><li>Balderston, N. L., Schultz, D. H., Baillet, S., Helmstetter, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+to+detect+amygdala+activity+with+magnetoencephalography+using+source+imaging.">How to detect amygdala activity with magnetoencephalography using source imaging.</a> Journal of Visualized Experiments. (76), e50212 (2013).</li><li>Kleiner, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What's+new+in+psychtoolbox-3.">What's new in psychtoolbox-3.</a> Perception. 36 (14), 1-16 (2007).</li><li>Oostenveld, R., Fries, P., Maris, E., Schoffelen, J. -. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FieldTrip:+Open+source+software+for+advanced+analysis+of+MEG,+EEG,+and+invasive+electrophysiological+data.">FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.</a> Computational Intelligence and Neuroscience. 2011, 156869 (2011).</li><li>Rassi, E., Wutz, A., Mueller-Voggel, N., Weisz, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pre-stimulus+feedback+connectivity+biases+the+content+of+visual+experiences.">Pre-stimulus feedback connectivity biases the content of visual experiences.</a> BioRxiv. , (2019).</li><li>Nolte, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+true+brain+interaction+from+EEG+data+using+the+imaginary+part+of+coherency.">Identifying true brain interaction from EEG data using the imaginary part of coherency.</a> Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology. 115 (10), 2292-2307 (2004).</li><li>Maris, E., Oostenveld, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonparametric+statistical+testing+of+EEG-+and+MEG-data.">Nonparametric statistical testing of EEG- and MEG-data.</a> Journal of Neuroscience Methods. 164 (1), 177-190 (2007).</li><li>Hesselmann, G., Kell, C. A., Eger, E., Kleinschmidt, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spontaneous+local+variations+in+ongoing+neural+activity+bias+perceptual+decisions.">Spontaneous local variations in ongoing neural activity bias perceptual decisions.</a> Proceedings of the National Academy of Sciences of the United States of America. 105 (31), 10984-10989 (2008).</li><li>Kanwisher, N., McDermott, J., Chun, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+fusiform+face+area:+a+module+in+human+extrastriate+cortex+specialized+for+face+perception.">The fusiform face area: a module in human extrastriate cortex specialized for face perception.</a> The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 17 (11), 4302-4311 (1997).</li><li>Morey, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Confidence+Intervals+from+Normalized+Data:+A+correction+to+Cousineau.">Confidence Intervals from Normalized Data: A correction to Cousineau.</a> Tutorials in Quantitative Methods for Psychology. 4 (2), 61-64 (2008).</li><li>Miller, E. K., Nieder, A., Freedman, D. J., Wallis, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+correlates+of+categories+and+concepts.">Neural correlates of categories and concepts.</a> Current Opinion in Neurobiology. 13 (2), 198-203 (2003).</li><li>Kreiman, G., Koch, C., Fried, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Category-specific+visual+responses+of+single+neurons+in+the+human+medial+temporal+lobe.">Category-specific visual responses of single neurons in the human medial temporal lobe.</a> Nature Neuroscience. 3 (9), 946-953 (2000).</li></ol>

The authors have nothing to disclose.

Presenting a unique stimulus which can be interpreted as more than one object over time, but as only one object at any given time, allows for investigating pre-stimulus effects on object perception. In this way one is able to relate pre-stimulus brain states to subjective reports of the perceived objects. In a laboratory setting, ambiguous images which can be interpreted in one of two ways, such as the Rubin vase illusion, provide an optimal case which allows for straightforward contrasts of brain activity between two trial types: those perceived one way (e.g., &#39;face&#39;) and those perceived the other way (e.g., &#39;vase&#39;).
Presenting these stimuli briefly (&#60;200 ms) ensures that people see and subsequently report only one of the two possible interpretations of the stimulus on a given trial. Counterbalancing (randomly alternating) between the black vase/white faces and white vase/black faces versions of the stimulus across participants reduces the influence of low-level stimulus features on the subsequent analysis. Presenting a mask immediately after the stimulus prevents after-images from forming and biasing participants&#39;&#160;responses. Because analyzing the period after stimulus onset is not of interest, no matching between low frequency features of the stimulus and mask is required. Finally, alternating the response buttons across participants (e.g., left for vase, right for face, or vice versa) prevents activity due to motor preparation from factoring into the contrasts.
Given the millisecond resolution of MEG, a pre-stimulus interval of as short as 1 s is sufficient to estimate measures such as spectral power and connectivity. Given the short duration of each resulting trial, a large number of trials can be accommodated in an experimental session, ensuring a high signal-to-noise ratio when averaging MEG signals across trials.
Specific category-sensitive regions of interest have been shown to be active during object perception24,25. For example, FFA is widely reported to be involved in face perception22. To investigate the effects of measured activity stemming from specific sources, one can source-reconstruct MEG data. To investigate connectivity between sources, source reconstruction is necessary. To facilitate source data analysis, single-trial source-level data can be represented by &#39;virtual sensors&#39;. Representing the data in this way enables one to analyze single-trial source data in the exact same way in source space and sensor space (that is, using the same analysis functions, for example using the Fieldtrip toolbox). This then enables testing hypotheses about the activity of specified regions of interests in a straightforward manner.
While pre-stimulus oscillatory power has been shown to influence stimulus detection near perceptual threshold (perceived vs not perceived), whether it influences the content of what is seen is less known. Here we contrasted pre-stimulus oscillatory power in FFA between trials on which people reported face vs vase, and found no statistical differences. We subsequently tested whether the connectivity between V1 and FFA influences the upcoming perceptual report, and found that face trials were preceded by enhanced connectivity between V1 and FFA in the alpha frequency range around 700 ms prior to stimulus onset. That we found no effect in alpha power, but rather in connectivity in the alpha band, suggests that while pre-stimulus alpha power might influence stimulus detection7,8, it does not necessarily influence object categorization. Our results therefore show that for a more complete understanding of the oscillatory dynamics preceding object perception and their subsequent influence on object perception, simply analyzing oscillatory power in regions of interest is not sufficient. Rather, connectivity between regions of interest must be taken into account, as the ongoing fluctuations in the strength of these connections can bias subsequent perception18. Finally, despite the less-than-optimal spatial resolution of MEG, our protocol demonstrates that one is able to clearly identify regions of interest and investigate their relationships. MEG can supersede Electroencephalography (EEG) because it offers superior spatial resolution, and can supersede function MRI because it offers superior temporal resolution. Therefore, MEG combined with source reconstruction is ideally suited to investigate fast and localized neural processes.

Brain states preceding stimuli presentation influence the way stimuli are perceived as well as the neural responses associated with perception1,2,3,4. For example, when a stimulus is presented with an intensity close to perceptual threshold (near-threshold), pre-stimulus neural oscillatory power, phase, and connectivity can influence whether the upcoming stimulus will be perceived or not perceived5,6,7,8,9,10. These pre-stimulus signals could also influence other aspects of perception, such as perceptual object content.
Presenting people with an ambiguous image which can be interpreted in one of two ways is an ideal way to probe object perception11. This is because the subjective content of perception can be one of two objects, while the actual stimulus remains unchanged. One can therefore assess the differences in recorded brain signals between trials on which people reported perceiving one versus the other possible interpretation of the stimulus. Given the reports, one can also investigate whether there were any differences in the brain states prior to stimulus onset.
Magnetoencephalography (MEG) is a functional neuroimaging technique that records magnetic fields produced by electrical currents in the brain. While blood-oxygenation level dependent (BOLD) responses resolve at a timescale of seconds, MEG provides millisecond resolution and therefore allows investigating brain mechanisms that occur at very fast timescales. A related advantage of MEG is that it allows characterizing brain states from short periods of recorded data, meaning experimental trials can be shortened such that many trials fit into an experimental session. Further, MEG allows for frequency-domain analyses which can uncover oscillatory activity.
In addition to its high temporal resolution, MEG offers good spatial resolution. With source reconstruction techniques12, one can project sensor-level data to source space. This then enables testing hypotheses about the activity of specified regions of interests. Finally, while signals in sensor-space are highly correlated and therefore the connectivity between sensors cannot be accurately assessed, source reconstruction allows for the assessment of connectivity between regions of interest because it reduces the correlations between source signals13. These connectivity estimates can be resolved in both the time and frequency domains.
Given these advantages, MEG is ideally suited to investigate pre-stimulus effects on object perception in specified regions of interest. In the present report we will illustrate how to design such an experiment and the MEG acquisition set-up, as well as how to apply source reconstruction and assess oscillatory activity and connectivity.

<table><tbody><tr><td>Data analysis sowftware</td><td>Elekta Oy, Stockholm, SWEDEN</td><td>NM23321N</td><td>Elekta standard data analysis software including MaxFilter release 2.2&amp;nbsp;</td></tr><tr><td>Data analysis workstation</td><td>Elekta Oy, Stockholm, SWEDEN</td><td>NM20998N</td><td>MEG recoding PC and software</td></tr><tr><td>Head position coil kit</td><td>Elekta Oy, Stockholm, SWEDEN</td><td>NM23880N</td><td>5 Head Position Indicator (HPI) coils&amp;nbsp;</td></tr><tr><td>Neuromag TRIUX</td><td>Elekta Oy, Stockholm, SWEDEN</td><td>NM23900N</td><td>306-channel magnetoencephalograph system</td></tr><tr><td>Polhemus Fastrak 3D</td><td>Polhemus, VT, USA</td><td></td><td>3D head digitization system</td></tr><tr><td>PROPixx</td><td>VPixx Technologies Inc., QC, CANADA</td><td>VPX-PRO-5001C</td><td>Projector and data acquisition system</td></tr><tr><td>RESPONSEPixx</td><td>VPixx Technologies Inc., QC, CANADA</td><td>VPX-ACC-4910</td><td>MEG-compatible response collection handheld control pad system</td></tr><tr><td>Screen</td><td>VPixx Technologies Inc., QC, CANADA</td><td>VPX-ACC-5180</td><td>MEG-compatible rear projection screen with frame and stand</td></tr><tr><td>VacuumSchmelze AK-3</td><td>VacuumSchmelze GmbH &amp;amp; Co. KG, Hanau, GERMANY</td><td>NM23122N</td><td>Two-layer magnetically-shielded room</td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td>&lt;strong&gt;Version&lt;/strong&gt;</td><td></td></tr><tr><td>Fieldtrip</td><td>Open Source</td><td>FTP-181005</td><td>fieldtriptoolbox.org</td></tr><tr><td>Matlab</td><td>MathWorks, MA, USA</td><td>R2018b</td><td>mathworks.com/products/matlab</td></tr><tr><td>Psychophysics Toolbox</td><td>Open Source</td><td>PTB-3.0.13</td><td>psychtoolbox.org</td></tr></tbody></table>

detecting pre-stimulus source-level effects on object perception with magnetoencephalography

Pre-stimulus oscillatory brain activity influences upcoming perception. The characteristics of this pre-stimulus activity can predict whether a near-threshold stimulus will be perceived or not perceived, but can they also predict which one of two competing stimuli with different perceptual contents is perceived? Ambiguous visual stimuli, which can be seen in one of two possible ways at a time, are ideally suited to investigate this question. Magnetoencephalography (MEG) is a neurophysiological measurement technique that records magnetic signals emitted as a result of brain activity. The millisecond temporal resolution of MEG allows for a characterization of oscillatory brain states from as little as 1 second of recorded data. Presenting an empty screen around 1 second prior to the ambiguous stimulus onset therefore provides a time window in which one can investigate whether pre-stimulus oscillatory activity biases the content of upcoming perception, as indicated by participants&#39;&#160;reports. The spatial resolution of MEG is not excellent, but sufficient to localise sources of brain activity at the centimetre scale. Source reconstruction of MEG activity then allows for testing hypotheses about the oscillatory activity of specific regions of interest, as well as the time- and frequency-resolved connectivity between regions of interest. The described protocol enables a better understanding of the influence of spontaneous, ongoing brain activity on visual perception.

We presented the Rubin face/vase illusion to participants briefly and repeatedly and asked participants to report their percept (face or vase?) following each trial (Figure 1). Each trial was preceded by at least 1 s of a blank screen (with fixation cross); this was the pre-stimulus interval of interest.
We asked whether pre-stimulus oscillatory power in regions of interest or pre-stimulus connectivity between regions of interest influenced the perceptual report of the upcoming ambiguous stimulus. Therefore, as a first step, we projected our data to source space such that we could extract signals from the relevant ROIs.
Based on previous literature investigating face and object perception with both ambiguous21 and unambiguous22 stimuli, we determined the FFA to be our ROI. We subsequently analyzed the low-frequency (1-40 Hz) spectral components of the FFA source signal and contrasted the spectral estimates from trials reported as &#39;face&#39;&#160;with those from trials reported as &#39;vase&#39;. A cluster-based permutation test, clustering over the frequencies 1-40 Hz, contrasting spectral power on trials where people reported face vs vase, revealed no significant differences between the 2 trial types. Nevertheless, descriptively, the power spectra showed the expected oscillatory alpha band peak in the range of 8-13 Hz, and to a lesser extent beta band activity in the range of 13-25 Hz (Figure 2).
Having found no differences in pre-stimulus spectral power, we next investigated whether there were differences in pre-stimulus connectivity between the trial types. In addition to FFA, we determined V1 to be our second ROI due to its ubiquitous involvement in vision. Based on the results of the power analysis, we determined the frequencies 8-13 Hz to be our frequencies of interest. We calculated the time- and frequency-resolved imaginary part of coherency between our two ROIs, separately for face and vase trials, and averaged the result across the frequencies of interest. This measure reflects synchrony of oscillatory phase amongst brain regions and conservatively controls against volume conduction effects in MEG reconstructed sources19, so it was the method of choice for assessing functional coupling. A cluster-based permutation test, clustering over the time-points -1 to 0 s, contrasting imaginary coherency between V1 and FFA on trials where people reported face vs vase, revealed that face trials had stronger pre-stimulus connectivity compared to vase trials, around 700 ms prior to stimulus onset (Figure 3).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60120/60120fig1.jpg" /> 
Figure 1: Example trial structure and raw data. Bottom panel: A trial starts with the display of a fixation cross. After 1 to 1.8 s, the Rubin stimulus appears for 150 ms followed by a mask for 200 ms. A response screen then appears to prompt participants to respond with &#39;face&#39;&#160;or &#39;vase&#39;. Top panel: Multi-channel raw data from an example participant, time-locked to stimulus onset and averaged across trials. This is a schematic to highlight the data in the pre-stimulus analysis window (-1 s to 0 s; highlighted in pink), which will be the target interval for analysis. <a href="https://www.jove.com/files/ftp_upload/60120/60120fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60120/60120fig2.jpg" /> 
Figure 2: Spectral power in FFA. Spectral power estimates from source-localized FFA signals on face and vase trials. <a href="https://www.jove.com/files/ftp_upload/60120/60120fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60120/60120fig3.jpg" /> 
Figure 3: Connectivity between V1 and FFA. Imaginary part of coherency between source-localized V1 and FFA signals on face and vase trials, in the frequency range of 8-13 Hz. Shaded regions represent the standard error of the mean for within-subjects designs23. <a href="https://www.jove.com/files/ftp_upload/60120/60120fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography at JoVE.com

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

This article describes how to set up an experiment that allows detecting pre-stimulus source-level influences on object perception using magnetoencephalography (MEG). It covers stimulus material, experimental design, MEG recording, and data analysis.

Pre-stimulus oscillatory brain activity influences upcoming perception. The characteristics of this pre-stimulus activity can predict whether a ...

detecting-pre-stimulus-source-level-effects-on-object-perception-with

Research

JoVE Journal

Neuroscience

6.8K Views.  Paris-Lodron Universität Salzburg. This article describes how to set up an experiment that allows detecting pre-stimulus source-level influences on object perception using magnetoencephalography (MEG). It covers stimulus material, experimental design, MEG recording, and data analysis.

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

Functional Mapping with Simultaneous MEG and EEG

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the processing of simple sensory stimuli. We will use somatosensory stimuli to locate the hand somatosensory areas, auditory stimuli to locate the auditory cortices, visual stimuli in four quadrants of the visual field to locate the early visual areas. These type of experiments are used for functional mapping in epileptic and brain tumor patients to locate eloquent cortices. In basic neuroscience similar experimental protocols are used to study the orchestration of cortical activity. The acquisition protocol includes quality assurance procedures, subject preparation for the combined MEG/EEG study, and acquisition of evoked-response data with somatosensory, auditory, and visual stimuli. We also demonstrate analysis of the data using the equivalent current dipole model and cortically-constrained minimum-norm estimates. Anatomical MRI data are employed in the analysis for visualization and for deriving boundaries of tissue boundaries for forward modeling and cortical location and orientation constraints for the minimum-norm estimates.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to map brain areas involved in the processing of simple sensory stimuli.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the ...

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

In trace fear conditioning a conditional stimulus (CS) predicts the occurrence of the unconditional stimulus (UCS), which is presented after a brief stimulus free period (trace interval)1. Because the CS and UCS do not co-occur temporally, the subject must maintain a representation of that CS during the trace interval. In humans, this type of learning requires awareness of the stimulus contingencies in order to bridge the trace interval2-4. However when a face is used as a CS, subjects can implicitly learn to fear the face even in the absence of explicit awareness*. This suggests that there may be additional neural mechanisms capable of maintaining certain types of "biologically-relevant" stimuli during a brief trace interval. Given that the amygdala is involved in trace conditioning, and is sensitive to faces, it is possible that this structure can maintain a representation of a face CS during a brief trace interval.
It is challenging to understand how the brain can associate an unperceived face with an aversive outcome, even though the two stimuli are separated in time. Furthermore investigations of this phenomenon are made difficult by two specific challenges. First, it is difficult to manipulate the subject's awareness of the visual stimuli. One common way to manipulate visual awareness is to use backward masking. In backward masking, a target stimulus is briefly presented (&lt; 30 msec) and immediately followed by a presentation of an overlapping masking stimulus5. The presentation of the mask renders the target invisible6-8. Second, masking requires very rapid and precise timing making it difficult to investigate neural responses evoked by masked stimuli using many common approaches. Blood-oxygenation level dependent (BOLD) responses resolve at a timescale too slow for this type of methodology, and real time recording techniques like electroencephalography (EEG) and magnetoencephalography (MEG) have difficulties recovering signal from deep sources.
However, there have been recent advances in the methods used to localize the neural sources of the MEG signal9-11. By collecting high-resolution MRI images of the subject's brain, it is possible to create a source model based on individual neural anatomy. Using this model to "image" the sources of the MEG signal, it is possible to recover signal from deep subcortical structures, like the amygdala and the hippocampus*.

This article describes how to record amygdala activity with magnetoencephalography (MEG). In addition this article will describe how to conduct trace fear conditioning without awareness, a task that activates the amygdala. It will cover 3 topics: 1) Designing a trace conditioning paradigm using backward masking to manipulate awareness. 2) Recording brain activity during the task using magnetoencephalography. 3) Using source imaging to recover signal from subcortical structures.

In trace fear conditioning a conditional  stimulus (CS) predicts the occurrence of the unconditional stimulus (UCS),  which is presented after a brief ...

Behavior

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.

We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the ...

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

The effect of a stimulus to the brain depends not only on the parameters of the stimulus but also on the dynamics of brain activity at the time of the stimulation. The combination of electroencephalography (EEG) and transcranial magnetic stimulation (TMS) in a real-time brain state-dependent stimulation system allows the study of relations of dynamics of brain activity, cortical excitability, and plasticity induction. Here, we demonstrate a newly developed method to synchronize the timing of brain stimulation with the phase of ongoing EEG oscillations using a real-time data analysis system. This real-time EEG-triggered TMS of the human motor cortex, when TMS is synchronized with the surface EEG negative peak of the sensorimotor &#181;-alpha (8-14 Hz) rhythm, has shown differential corticospinal excitability and plasticity effects. The utilization of this method suggests that real-time information about the instantaneous brain state can be used for efficacious plasticity induction. Additionally, this approach enables personalized EEG-synchronized brain stimulation which may lead to the development of more effective therapeutic brain stimulation protocols.

This paper describes real-time electroencephalography-triggered transcranial magnetic stimulation to study and modulate human brain networks.

The effect of a stimulus to the brain depends not only on the parameters of the stimulus but also on the dynamics of brain activity at the time of the ...

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator&#39;s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.

This paper describes a method for collecting and analyzing electroencephalography (EEG) data during concurrent transcranial magnetic stimulation (TMS) guided by activations revealed with functional magnetic resonance imaging (fMRI). A method for TMS artifact removal and extraction of event related potentials is described as well as considerations in paradigm design and experimental setup.

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological ...

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency Oscillations (HFOs) have emerged as potential presurgical biomarkers for the identification of the EZ in addition to Interictal Epileptiform Discharges (IEDs) and ictal activity. Although they are promising to localize the EZ, they are not yet suited for the diagnosis or monitoring of epilepsy in clinical practice. Primary barriers remain: the lack of a formal and global definition for HFOs; the consequent heterogeneity of methodological approaches used for their study; and the practical difficulties to detect and localize them noninvasively from scalp recordings. Here, we present a methodology for the recording, detection, and localization of interictal HFOs from pediatric patients with refractory epilepsy. We report representative data of HFOs detected noninvasively from interictal scalp EEG and MEG from two children undergoing surgery.
The underlying generators of HFOs were localized by solving the inverse problem and their localization was compared to the Seizure Onset Zone (SOZ) as this was defined by the epileptologists. For both patients, Interictal Epileptogenic Discharges (IEDs) and HFOs were localized with source imaging at concordant locations. For one patient, intracranial EEG (iEEG) data were also available. For this patient, we found that the HFOs localization was concordant between noninvasive and invasive methods. The comparison of iEEG with the results from scalp recordings served to validate these findings. To our best knowledge, this is the first study that presents the source localization of scalp HFOs from simultaneous EEG and MEG recordings comparing the results with invasive recordings. These findings suggest that HFOs can be reliably detected and localized noninvasively with scalp EEG and MEG. We conclude that the noninvasive localization of interictal HFOs could significantly improve the presurgical evaluation for pediatric patients with epilepsy.

High Frequency Oscillations (HFOs) have emerged as presurgical biomarkers for the identification of the epileptogenic zone in pediatric patients with medically refractory epilepsy. A methodology for the noninvasive recording, detection, and localization of HFOs with simultaneous scalp electroencephalography (EEG) and magnetoencephalography (MEG) is presented.

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency ...

Medicine

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen in one of two adjacent rooms. These rooms are separated by a glass window through which participants communicate to maintain feelings of togetherness while being fitted with the EEG cap. After checking for adequate EEG signals, the glass is covered by a curtain to prevent visual communication. Then, partners must be silent but are instructed to try to feel in the presence of their partner during the entire experiment. Just before it starts, participants are told that each of them will be presented with one image at a time and that these images will occur at the same time for both of them on their own screen. They are also instructed that, for each trial, the simultaneous images will always be different. However, unbeknownst to them, trials are randomized: only half of them are consistent with this instruction and actually include two different images. These trials form the DSC, that is, the different-stimuli condition. The other half of the trials are inconsistent with the instruction. They include two identical images and form the ISC (identical-stimuli condition). After the experiment, participants are sorted into two groups: those who reported that they felt in the presence of their partner during the majority of the trials and those who reported they did not. The impact of the stimulus processing of the partner is found by subtracting the mean voltages of the ERPs of the ISC (inconsistent with the instructions) from the ERPs of the DSC (consistent with the instructions) in at least two time windows (TWs): firstly, in the 75 to 150 ms TW, where the absolute values of these subtractions are greater, especially at right frontal sites, in those who felt in the presence of their partner than in those who did not; secondly, in the LPP time window (i.e., from 650 to 950 ms post onset), where ERPs are significantly less positive in the DSC than in the ISC in those in whom the raw results of the early (75-150ms) subtractions are negative.

This protocol describes key steps involved in assessing the sensitivity of the brain of one person to the stimulus processing of a close other by selecting pairs of partners, recording their electroencephalogram (EEG) simultaneously and computing their event-related brain potentials (ERPs).

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen ...

Studying Brain Function in Children Using Magnetoencephalography

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique which directly measures magnetic fields produced by the electrical activity of the human brain. MEG is quiet and less likely to induce claustrophobia compared with magnetic resonance imaging (MRI). It is therefore a promising tool for investigating brain function in young children. However, analysis of MEG data from pediatric populations is often complicated by head movement artefacts which arise as a consequence of the requirement for a spatially-fixed sensor array that is not affixed to the child's head. Minimizing head movements during MEG sessions can be particularly challenging as young children are often unable to remain still during experimental tasks. The protocol presented here aims to reduce head movement artefacts during pediatric MEG scanning. Prior to visiting the MEG laboratory, families are provided with resources that explain the MEG system and the experimental procedures in simple, accessible language. An MEG familiarization session is conducted during which children are acquainted with both the researchers and the MEG procedures. They are then trained to keep their head still whilst lying inside an MEG simulator. To help children feel at ease in the novel MEG environment, all of the procedures are explained through the narrative of a space mission. To minimize head movement due to restlessness, children are trained and assessed using fun and engaging experimental paradigms. In addition, children's residual head movement artefacts are compensated for during the data acquisition session using a real-time head movement tracking system. Implementing these child-friendly procedures is important for improving data quality, minimizing participant attrition rates in longitudinal studies, and ensuring that families have a positive research experience.

This article introduces a child-friendly research protocol designed to improve data quality by reducing head movement during pediatric magnetoencephalography (MEG). We familiarize families with the MEG environment, train children to remain still using an MEG simulator, and correct for residual head movement artefacts using a real-time head movement detection system.

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique which directly measures magnetic fields produced by the electrical activity of the ...

Stimulating Mouse Brain with a Low-Cost EEG and Millimeter-Sized Coil

Low-Cost Electroencephalographic Recording System Combined with a Millimeter-Sized Coil to Transcranially Stimulate the Mouse Brain In Vivo

A low-cost electroencephalographic (EEG) recording system is proposed here to drive transcranial magnetic stimulation (TMS) of the mouse brain in vivo, utilizing a millimeter-sized coil. Using conventional screw electrodes combined with a custom-made, flexible, multielectrode array substrate, multi-site recording can be carried out from the mouse brain. In addition, we explain how a millimeter-sized coil is produced using low-cost equipment usually found in laboratories. Practical procedures for fabricating the flexible multielectrode array substrate and the surgical implantation technique for screw electrodes are also presented, which are necessary to produce low-noise EEG signals. Although the methodology is useful for recording from the brain of any small animal, the present report focuses on electrode implementation in an anesthetized mouse skull. Furthermore, this method can be easily extended to an awake small animal that is connected with tethered cables via a common adapter and fixed with a TMS device to the head during recording.The present version of the EEG-TMS system, which can include a maximum of 32 EEG channels (a device with 16 channels is presented as an example with fewer channels) and one TMS channel device, is described. Additionally, typical results obtained by the application of the EEG-TMS system to anesthetized mice are briefly reported.

Author Spotlight: Low-Cost Electroencephalographic Recording System Combined with a Millimeter-Sized Coil to Transcranially Stimulate the Mouse Brain In Vivo  

A low-cost electroencephalographic recording system combined with a millimeter-sized coil is proposed to drive transcranial magnetic stimulation of the mouse brain in vivo. Using conventional screw electrodes with a custom-made, flexible, multielectrode array substrate, multi-site recording can be carried out from the mouse brain in response to transcranial magnetic stimulation.

A low-cost electroencephalographic (EEG) recording system is proposed here to drive transcranial magnetic stimulation (TMS) of the mouse brain in vivo, ...

Integrating MEG and HD-EEG in Pediatric Epilepsy Localization

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic zone (EZ) while preserving the eloquent brain areas. The development of a reliable and noninvasive localization method that provides clinically useful information for the localization of the EZ is, therefore, crucial to achieving successful surgical outcomes. Electric and magnetic source imaging (ESI and MSI) have been increasingly utilized in the presurgical evaluation of these patients showing promising findings in the delineation of epileptogenic as well as eloquent brain areas. Moreover, the combination of ESI and MSI into a single solution, namely electromagnetic source imaging (EMSI), performed on simultaneous high-density electroencephalography (HD-EEG) and magnetoencephalography (MEG) recordings has shown higher source localization accuracy than either modality alone. Despite these encouraging findings, such techniques are performed in only a few tertiary epilepsy centers, are rarely recorded simultaneously, and are underutilized in pediatric cohorts. This study illustrates the experimental setup for recording simultaneous MEG and HD-EEG data as well as the methodological framework for analyzing these data aiming to localize the irritative zone, the seizure onset zone, and eloquent brain areas in children with DRE. More specifically, the experimental setups are presented for (i) recording and localizing interictal and ictal epileptiform activity during sleep and (ii) recording visual-, motor-, auditory-, and somatosensory-evoked responses and mapping relevant eloquent brain areas (i.e., visual, motor, auditory, and somatosensory) during visuomotor task, as well as auditory and somatosensory stimulations. Detailed steps of the data analysis pipeline are further presented for performing EMSI as well as individual ESI and MSI using equivalent current dipole (ECD) and dynamic statistical parametric mapping (dSPM).

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Magnetoencephalography (MEG) and high-density electroencephalography (HD-EEG) are rarely recorded simultaneously, although they yield confirmatory and complementary information. Here, we illustrate the experimental setup for recording simultaneous MEG and HD-EEG and the methodology for analyzing these data aiming to localize epileptogenic and eloquent brain areas in children with drug-resistant epilepsy.

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic ...