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>

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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 border="0" cellpadding="0" cellspacing="0" style="width:1273px;" width="1273">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:199px;">Data analysis sowftware</td>
			<td style="width:404px;">Elekta Oy, Stockholm, SWEDEN</td>
			<td style="width:164px;">NM23321N</td>
			<td style="width:507px;">Elekta standard data analysis software including MaxFilter release 2.2&#160;</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Data analysis workstation</td>
			<td>Elekta Oy, Stockholm, SWEDEN</td>
			<td>NM20998N</td>
			<td>MEG recoding PC and software</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Head position coil kit</td>
			<td>Elekta Oy, Stockholm, SWEDEN</td>
			<td>NM23880N</td>
			<td>5 Head Position Indicator (HPI) coils&#160;</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Neuromag TRIUX</td>
			<td>Elekta Oy, Stockholm, SWEDEN</td>
			<td>NM23900N</td>
			<td>306-channel magnetoencephalograph system</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:199px;">Polhemus Fastrak 3D</td>
			<td style="width:404px;">Polhemus, VT, USA</td>
			<td></td>
			<td>3D head digitization system</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">PROPixx</td>
			<td>VPixx Technologies Inc., QC, CANADA</td>
			<td>VPX-PRO-5001C</td>
			<td>Projector and data acquisition system</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">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 height="40">
			<td height="40" style="height:40px;">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 height="40">
			<td height="40" style="height:40px;">VacuumSchmelze AK-3</td>
			<td>VacuumSchmelze GmbH &#38; Co. KG, Hanau, GERMANY</td>
			<td>NM23122N</td>
			<td>Two-layer magnetically-shielded room</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:199px;">Software</td>
			<td style="width:404px;"></td>
			<td style="width:164px;">Version</td>
			<td style="width:507px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Fieldtrip</td>
			<td>Open Source</td>
			<td>FTP-181005</td>
			<td>fieldtriptoolbox.org</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Matlab</td>
			<td>MathWorks, MA, USA</td>
			<td>R2018b</td>
			<td>mathworks.com/products/matlab</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">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 ...

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JoVE Journal

Neuroscience

6.7K 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

State-Dependency Effects on TMS:  A Look at Motive Phosphene Behavior

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.1,2 Within the field of TMS, the term state dependency refers to the initial, baseline condition of the particular neural region targeted for stimulation. As can be inferred, the effects of TMS can (and do) vary according to this primary susceptibility and responsiveness of the targeted cortical area.3,4,5

In this experiment, we will examine this concept of state dependency through the elicitation and subjective experience of motive phosphenes. Phosphenes are visually perceived flashes of small lights triggered by electromagnetic pulses to the visual cortex. These small lights can assume varied characteristics depending upon which type of visual cortex is being stimulated. In this particular study, we will be targeting motive phosphenes as elicited through the stimulation of V1/V2 and the V5/MT+ complex visual regions.6

In this article, we examine the effects of visually relevant state dependency on TMS induced motive phosphenic presentations.

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate ...

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2.
Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings.
First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints.
Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases.
Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms.
Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics15,16. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects9,13. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be 'printed' as haptic objects using a conventional 3-D prototyper.
We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a 'proof of principle' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have.
Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.

We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic objects.

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object ...

Using Affordable LED Arrays for Photo-Stimulation of Neurons

Standard slice electrophysiology has allowed researchers to probe individual components of neural circuitry by recording electrical responses of single cells in response to electrical or pharmacological manipulations1,2. With the invention of methods to optically control genetically targeted neurons (optogenetics), researchers now have an unprecedented level of control over specific groups of neurons in the standard slice preparation. In particular, photosensitive channelrhodopsin-2 (ChR2) allows researchers to activate neurons with light3,4. By combining careful calibration of LED-based photostimulation of ChR2 with standard slice electrophysiology, we are able to probe with greater detail the role of adult-born interneurons in the olfactory bulb, the first central relay of the olfactory system. Using viral expression of ChR2-YFP specifically in adult-born neurons, we can selectively control young adult-born neurons in a milieu of older and mature neurons. Our optical control uses a simple and inexpensive LED system, and we show how this system can be calibrated to understand how much light is needed to evoke spiking activity in single neurons. Hence, brief flashes of blue light can remotely control the firing pattern of ChR2-transduced newborn cells.

Adult-born neurons expressing ChR2 can be manipulated in slice electrophysiological preparations in order to examine their contribution towards the function of olfactory neural circuits.

Standard slice electrophysiology has allowed researchers to probe individual components of neural circuitry by recording electrical responses of single ...

Selective Viral Transduction of Adult-born Olfactory Neurons for Chronic in vivo Optogenetic Stimulation

Local interneurons are continuously regenerated in the olfactory bulb of adult rodents1-3. In this process, called adult neurogenesis, neural stem cells in the walls of the lateral ventricle give rise to neuroblasts that migrate for several millimeters along the rostral migratory stream (RMS) to reach and incorporate into the olfactory bulb. To study the different steps and the impact of adult-born neuron integration into preexisting olfactory circuits, it is necessary to selectively label and manipulate the activity of this specific population of neurons. The recent development of optogenetic technologies offers the opportunity to use light to precisely activate this specific cohort of neurons without affecting surrounding neurons4,5. Here, we present a series of procedures to virally express Channelrhodopsin2(ChR2)-YFP in a temporally restricted cohort of neuroblasts in the RMS before they reach the olfactory bulb and become adult-born neurons. In addition, we show how to implant and calibrate a miniature LED for chronic in vivo stimulation of ChR2-expressing neurons.

Selective Viral Transduction of Adult-born Olfactory Neurons for Chronic in vivo Optogenetic Stimulation

Adult-born neurons of the olfactory bulb can be optogenetically controlled using Channelrhodopsin2-expressing lentiviral injection in the rostral migratory stream and chronic photostimulation with an implanted miniature LED.

Local interneurons are continuously regenerated in the olfactory bulb of adult rodents1-3. In this process, called adult neurogenesis, neural stem cells ...

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Path integration is a process in which self-motion is integrated over time to obtain an estimate of one's current position relative to a starting point 1. Humans can do path integration based exclusively on visual 2-3, auditory 4, or inertial cues 5. However, with multiple cues present, inertial cues - particularly kinaesthetic - seem to dominate 6-7. In the absence of vision, humans tend to overestimate short distances (&lt;5 m) and turning angles (&lt;30&deg;), but underestimate longer ones 5. Movement through physical space therefore does not seem to be accurately represented by the brain.
Extensive work has been done on evaluating path integration in the horizontal plane, but little is known about vertical movement (see 3 for virtual movement from vision alone). One reason for this is that traditional motion simulators have a small range of motion restricted mainly to the horizontal plane. Here we take advantage of a motion simulator 8-9 with a large range of motion to assess whether path integration is similar between horizontal and vertical planes. The relative contributions of inertial and visual cues for path navigation were also assessed.
16 observers sat upright in a seat mounted to the flange of a modified KUKA anthropomorphic robot arm. Sensory information was manipulated by providing visual (optic flow, limited lifetime star field), vestibular-kinaesthetic (passive self motion with eyes closed), or visual and vestibular-kinaesthetic motion cues. Movement trajectories in the horizontal, sagittal and frontal planes consisted of two segment lengths (1st: 0.4 m, 2nd: 1 m; &plusmn;0.24 m/s2 peak acceleration). The angle of the two segments was either 45&deg; or 90&deg;. Observers pointed back to their origin by moving an arrow that was superimposed on an avatar presented on the screen.
Observers were more likely to underestimate angle size for movement in the horizontal plane compared to the vertical planes. In the frontal plane observers were more likely to overestimate angle size while there was no such bias in the sagittal plane. Finally, observers responded slower when answering based on vestibular-kinaesthetic information alone. Human path integration based on vestibular-kinaesthetic information alone thus takes longer than when visual information is present. That pointing is consistent with underestimating and overestimating the angle one has moved through in the horizontal and vertical planes respectively, suggests that the neural representation of self-motion through space is non-symmetrical which may relate to the fact that humans experience movement mostly within the horizontal plane.

An efficient way to gain insight into how humans navigate themselves in three dimensions is described. The method takes advantage of a motion simulator capable of moving observers in ways unattainable by traditional simulators. Results confirm that movement in the horizontal plane is underestimated, while vertical movement is overestimated.

Path integration is a process in which self-motion is integrated over time to obtain an estimate of one's current position relative to a starting point 1. ...

Visualizing the Effects of a Positive Early Experience, Tactile Stimulation, on Dendritic Morphology and Synaptic Connectivity with Golgi-Cox Staining

To generate longer-term changes in behavior, experiences must be producing stable changes in neuronal morphology and synaptic connectivity. Tactile stimulation is a positive early experience that mimics maternal licking and grooming in the rat. Exposing rat pups to this positive experience can be completed easily and cost-effectively by using highly accessible materials such as a household duster. Using a cross-litter design, pups are either stroked or left undisturbed, for 15 min, three times per day throughout the perinatal period. To measure the neuroplastic changes related to this positive early experience, Golgi-Cox staining of brain tissue is utilized. Owing to the fact that Golgi-Cox impregnation stains a discrete number of neurons rather than all of the cells, staining of the rodent brain with Golgi-Cox solution permits the visualization of entire neuronal elements, including the cell body, dendrites, axons, and dendritic spines. The staining procedure is carried out over several days and requires that the researcher pay close attention to detail. However, once staining is completed, the entire brain has been impregnated and can be preserved indefinitely for ongoing analysis. Therefore, Golgi-Cox staining is a valuable resource for studying experience-dependent plasticity.

This paper describes the procedures for tactile stimulation of rat pups and subsequent Golgi-Cox staining of neuronal morphology. Tactile stimulation is a positive experience that is administered in the perinatal period by stroking pups with a household duster. Golgi-cox staining is a reliable procedure permitting the visualization of entire neurons.

To generate longer-term changes in behavior, experiences must be producing stable changes in neuronal morphology and synaptic connectivity. Tactile ...

Effects of Transcranial Alternating Current Stimulation on the Primary Motor Cortex by Online Combined Approach with Transcranial Magnetic Stimulation

Transcranial Alternating Current Stimulation (tACS) is a neuromodulatory technique able to act through sinusoidal electrical waveforms in a specific frequency and in turn modulate ongoing cortical oscillatory activity. This neurotool allows the establishment of a causal link between endogenous oscillatory activity and behavior. Most of the tACS studies have shown online effects of tACS. However, little is known about the underlying action mechanisms of this technique because of the AC-induced artifacts on Electroencephalography (EEG) signals. Here we show a unique approach to investigate online physiological frequency-specific effects of tACS of the primary motor cortex (M1) by using single pulse Transcranial Magnetic Stimulation (TMS) to probe cortical excitability changes. In our setup, the TMS coil is placed over the tACS electrode while Motor Evoked Potentials (MEPs) are collected to test the effects of the ongoing M1-tACS. So far, this approach has mainly been used to study the visual and motor systems. However, the current tACS-TMS setup can pave the way for future investigations of cognitive functions. Therefore, we provide a step-by-step manual and video guidelines for the procedure.

Transcranial Alternating Current Stimulation (tACS) allows the modulation of cortical excitability in a frequency-specific fashion. Here we show a unique approach which combines online tACS with single pulse Transcranial Magnetic Stimulation (TMS) in order to "probe" cortical excitability by means of Motor Evoked Potentials.

Transcranial Alternating Current Stimulation (tACS) is a neuromodulatory technique able to act through sinusoidal electrical waveforms in a specific ...

Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits

The purpose of this protocol is to characterize the effect of gene knockdown on presynaptic function within intact neuronal circuits. We describe a workflow on how to combine artificial microRNA (miR)-mediated RNA interference with optogenetics to achieve selective stimulation of manipulated presynaptic boutons in acute brain slices. The experimental approach involves the use of a single viral construct and a single neuron-specific promoter to drive the expression of both an optogenetic probe and artificial miR(s) against presynaptic gene(s). When stereotactically injected in the brain region of interest, the expressed construct makes it possible to stimulate with light exclusively the neurons with reduced expression of the gene(s) under investigation. This strategy does not require the development and maintenance of genetically modified mouse lines and can in principle be applied to other organisms and to any neuronal gene of choice. We have recently applied it to investigate how the knockdown of alternative splice isoforms of presynaptic P/Q-type voltage-gated calcium channels (VGCCs) regulates short-term synaptic plasticity at CA3 to CA1 excitatory synapses in acute hippocampal slices. A similar approach could also be used to manipulate and probe the neuronal circuitry in vivo.

This protocol provides a workflow on how to combine artificial microRNA-mediated RNA interference with optogenetics to stimulate specifically presynaptic boutons with reduced expression of selective gene(s) within intact neuronal circuits.

The purpose of this protocol is to characterize the effect of gene knockdown on presynaptic function within intact neuronal circuits. We describe a ...

Effects of Blast-induced Neurotrauma on Pressurized Rodent Middle Cerebral Arteries

Though there have been studies on the histopathological and behavioral effects of blast exposure, fewer have been dedicated to blast&#39;s cerebral vascular effects. Impact (i.e., non-blast) traumatic brain injury (TBI) is known to decrease pressure autoregulation in the cerebral vasculature in both humans and experimental animals. The hypothesis that blast-induced traumatic brain injury (bTBI), like impact TBI, results in impaired cerebral vascular reactivity was tested by measuring myogenic dilatory responses to reduced intravascular pressure in rodent middle cerebral arterial (MCA) segments from rats subjected to mild bTBI using an Advanced Blast Simulator (ABS) shock tube. Adult, male Sprague-Dawley rats were anesthetized, intubated, ventilated and prepared for Sham bTBI (identical manipulation and anesthesia except for blast injury) or mild bTBI. Rats were randomly assigned to receive Sham bTBI or mild bTBI followed by sacrifice 30 or 60 min post-injury. Immediately after bTBI, righting reflex (RR) suppression times were assessed, euthanasia at the time points post-injury was completed, the brain was harvested and the individual MCA segments were collected, mounted and pressurized. As the intraluminal pressure perfused through the arterial segments was reduced in 20 mmHg increments from 100 to 20 mmHg, MCA diameters were measured and recorded. With decreasing intraluminal pressure, MCA diameters steadily increased significantly above baseline in the Sham bTBI groups while MCA dilator responses were significantly reduced (p &#60; 0.05) in both bTBI groups as evidenced by the impaired, smaller MCA diameters recorded for the bTBI groups. In addition, RR suppression in the bTBI groups was significantly (p &#60; 0.05) higher than in the Sham bTBI groups. MCA&#39;s collected from the Sham bTBI groups exhibited typical vasodilatory properties to decreases in intraluminal pressure while MCA&#39;s collected following bTBI exhibited significantly impaired myogenic vasodilatory responses to reduced pressure that persisted for at least 60 min after bTBI.

Here, we present a protocol to describe methods for ex vivo vascular reactivity determination following a primary blast traumatic brain injury (bTBI) using isolated, pressurized, rodent middle cerebral arterial (MCA) segments. bTBI induction is accomplished using a shock tube, also known as an Advanced Blast Simulator (ABS) device.

Though there have been studies on the histopathological and behavioral effects of blast exposure, fewer have been dedicated to blast&#39;s cerebral ...

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 ...