The authors gratefully acknowledge Luc&#237;a Zepeda, Grace A. Whitaker, and Nicolas Nieto for their contributions to video production. This work was supported in part by CONICYT programs BASAL FB0008, MEC 80170124 and PhD scholarship 21171741, as well as the National Institute on Deafness and Other Communication Disorders of the National Institutes of Health under award number P50DC015446. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The present study was performed under approval of the Research and Ethics Committee of the Universidad de Valpara&#237;so, Chile (assessment statement code CEC170-18), confirmed to the national guidelines for research with human subjects.
1. Preparation
<ol>
	<li>Welcome the subject.</li>
	<li>Explain the aims and relevance of the study. Provide a description of relevant technical details. Answer all questions thoroughly.</li>
	<li>Explicitly mention that she/he can interrupt the experimental session at any time if desired.</li>
	<li>Ask the volunteer to read the Subject Informed Consent and sign the corresponding form. Interrupt the experimental session if the informed consent is not obtained.</li>
</ol>
2. Subject preparation
<ol>
	<li>Ask the subject to sit in a laboratory chair in a comfortable position.
	<ol>
		<li>Clean the scalp with ethanol (a solution at 95%) to remove the layer of dead skin cells and sebum that cover it. This step is important to reduce impedance between the electrodes and scalp.</li>
	</ol>
	</li>
	<li>Measure the head circumference with a measuring tape to define the size of the electrode cap to be used.
	<ol>
		<li>Ask the subject to wear the electrode cap. Provide the instructions for comfortable but correct positioning of the cap.</li>
		<li>Measure the distance between the nasion (Nz, the middle point of the nasofrontal suture, which can be identified by the depression between the eyes and the top of the nose) and the inion (Iz, the prominence of the occipital bone) using a measuring tape.</li>
		<li>Measure the distance between the left and right pre-auricular points (identified as the depression just before the auricle of the ears) using a measuring tape.</li>
		<li>Correct the position of the electrode cap, so the intersection between the imaginary lines defined in the previous steps correspond to the vertex of the head. Make sure the subject is comfortable after the adjustments.</li>
	</ol>
	</li>
	<li>Put conductive gel in the electrode holders, according to the locations considered for the experiment.
	<ol>
		<li>Use 64 scalp locations following the International 10-20 system31 to use the outcome of the protocol to perform source localization analysis. The higher number of the electrode locations (128) on the scalp can be used if needed.</li>
		<li>Implement clinical or ambulatory settings (with only a few electrodes) if source localization analysis is not planned. Use occipital locations to record SSVEP, temporal locations to acquire ASSR, and parietal locations to record SSSEP.</li>
		<li>Push the electrodes in the electrode holders. Make sure that the label of the electrode matches the label of location in the cap.</li>
		<li>Accompany the volunteer to the experimental room (preferably, a shielded, sound-attenuated chamber). Ask the subject to sit in a chair inside the room, in a comfortable position.</li>
	</ol>
	</li>
	<li>Place external electrodes on the nose and earlobes if a physical reference (different from the scalp electrodes) will be used for re-reference the EEG recording (in step 3.8.1).</li>
	<li>Place external electrodes in periocular locations.
	<ol>
		<li>Place electrodes on the cheek and the frontal region of the head, approximately 1 cm above the eyebrow, to record blinking (in step 2.6.1).</li>
		<li>Place electrodes on the outer canthus of the eyes, approximately 1 cm above/below the midline, to record eye movements (in step 2.6.1). 
		NOTE: The electrooculogram (EOG) will be used in the step 3.8.5 for removing EGG artifacts induced by blinking and eye movements.</li>
	</ol>
	</li>
	<li>Turn the EEG acquisition system on and check the electrode impedance if a low-impedance system is used for recording the EEG. Correct the impedance, as needed, as per the manufacturer&#39;s directions. Impedance should be kept below 10 k&#937;32.
	<ol>
		<li>Ask the subject to blink and move the eyes in different directions to ensure that EOG is being correctly recorded.</li>
	</ol>
	</li>
	<li>To analyze the dynamic of SSVEP, adjust the location of the screen in the vertical direction, to match the view angle of the subject. Dim the lights of the room until a comfortable level is achieved. Adjust the luminance level of the screen to the upper limit of the participant&#180;s comfort level.
	<ol>
		<li>To analyze the dynamics of ASSR, insert the earphones using the correct foam inserts, so the earphones fit the ear canal. Check that sounds are delivered at the desired intensity (e.g., a psychophysical comfortable level33).</li>
	</ol>
	</li>
</ol>
3. EEG acquisition and pre-processing
<ol>
	<li>Set the stimulus parameters defined in the experimental design. Refer to the user manual provided by the manufacturer of the stimulation system for details about the software. 
	NOTE: For comprehensive explanations of the stimulus used for generation of SSVEP and ASSR, see Norcia et al.8 and Rance34, respectively.</li>
	<li>Instruct the subject to pay attention to the stimulation, in the case that visual entrainment is the topic of the experiment.
	<ol>
		<li>Present a subtitled movie with the sound off when auditory entrainment is the topic of the experiment. 
		NOTE: Presentation of a silent movie allows deflection of attention from the acoustic stimulation while maintaining arousal level25.</li>
	</ol>
	</li>
	<li>Present stimuli longer than 90 s, as have been done to investigate the time evolution of SSVEPs and ASSR in both humans and animal models21,22,25,26. 
	NOTE: Present stimuli shorter in duration if a pilot study has been performed.</li>
	<li>Pause the stimulation for 2 min if only one experimental condition is being tested. Interact with the subject to check awareness. 
	NOTE: The duration of the pause depends on the duration of the stimulation. Pauses 3x longer than the stimulation intervals will ensure that a response elicited by one stimulus is not affected by the previous stimulation. Longer pauses are permitted if the subject so requests.
	<ol>
		<li>Pause the stimulation for at least 10 s when different experimental conditions are tested since alternating stimulation with pauses of 10 s has been proposed to decrease extra adaptation effects and reduce the length of the experiment25.</li>
	</ol>
	</li>
	<li>Repeat the presentation steps (steps 3.3-3.4) at least 30x to ensure the high SNR of the measurements after the averaging of epochs (step 4.4).</li>
	<li>Record the EEG using standard procedures35. Create a separate EEG file for each experimental run. 
	NOTE: Refer to the user manual of the acquisition system for details about the software.</li>
	<li>Monitor the EEG recording to detect sleep periods based on the level of alpha activity and the frequency at which blinking artifacts appear. Pause the experiment when increased alpha levels accompanied by decreased blinking frequencies are detected, which is indicative of sleepiness. Reject the experimental run from further analysis when sleep periods are detected.
	<ol>
		<li>Compute the amplitude of the steady-state response at the end of each experimental run, following the instruction provided in the user manual of the acquisition software used in the experiment.</li>
		<li>Monitor the attentional level of the subject by comparing the amplitude of steady-state responses obtained at the end of each experimental run. Set the steady-state amplitude obtained in the first experimental runs as a reference amplitude.</li>
		<li>Set a rejection threshold (a decrease in the response amplitude of 5% regarding the reference amplitude). Reject the experimental runs in which the amplitude of the steady-state response meets the rejection criterium.</li>
		<li>Finish the experimental session after acquiring the number of runs defined in the experimental design.</li>
	</ol>
	</li>
	<li>Pre-process the EEG data offline using standard EEG procedures35 described in the next steps per the manufacturer&#39;s directions.
	<ol>
		<li>Re-reference the recording using an average reference (average of all recording electrodes) or the average of a subset of electrodes. Alternatively, use a physical reference (e.g., external electrodes placed on the nose and earlobes described in step 2.4).</li>
		<li>Convert the electrode coordinates to the international 10/20 system if the radial coordinate system was used during the EEG acquisitions. Refer to the manufacturer manual for details on conversion.</li>
		<li>Band-pass filter the EEG signal between 0.5-300 Hz. Set a notch-filter (centered at 50 Hz or 60 Hz) if necessary.</li>
		<li>Down-sample the EEG signal to decrease the execution time of the algorithm selected for removing ocular artifacts (step 3.8.5). 
		NOTE: A sampling frequency of 512 Hz is adequate to analyze brain oscillations of frequency below 40 Hz35.</li>
		<li>Remove the ocular artifacts. 
		NOTE: To this end, different techniques can be used (see Urig&#252;en and Garcia-Zapirain35&#160;for an extensive review on artifact removal algorithms). Among them, independent component analysis is one of the most extended methodologies and is implemented in both commercial and free analysis softwares37,38,39.</li>
		<li>Segment the EEG data in epochs time-locked to stimulation. Select the epochs length according to the aim of the experiment. 
		NOTE: Epochs should be sufficiently long to allow for analysis of the steady-state response in the frequency-domain with an adequate spectral resolution.</li>
		<li>Do not run artifact rejection algorithms at this stage to detect and remove epochs containing artifacts. 
		NOTE: Removing epoch at this stage will induce errors when the dataset is organized to run the column-wise averaging of epochs (steps 4.2 and 4.4). Rejection algorithms are implemented at a later processing step (step 4.1.4).</li>
		<li>Run the DC-detrend function to calculate DC-trends in individual EEG epochs and correct them.</li>
		<li>Run the baseline correction function to correct the baseline of the recording. Select pre-stimulus time intervals longer than 200 ms. 
		NOTE: Baseline correction consists of averaging the data in the selected time interval. The average is calculated for each channel and subtracted from each data point in every epoch.</li>
	</ol>
	</li>
</ol>
4. Computation of the response amplitudes
<ol>
	<li>Enter the parameters needed for the computation of the steady-state responses (Figure 1A). 
	NOTE: The in-house code used for processing the data is freely available at &#60;https://figshare.com/ projects/Steady-state_visually_evoked_potentials_SSVEP_elicited_in_humans_by_continuos_light_modulated_in_amplitude_at_10Hz/62573&#62;. Refer to the help text inside the code for further instructions. Similarly, a subset of the data used in this study is available.
	<ol>
		<li>Enter the number of recordings (experimental runs) of the experiment.</li>
		<li>Enter the length of the epochs to segment the individual recordings.</li>
		<li>Enter the sampling frequency of the experiment.</li>
		<li>Select artifact rejection algorithms to detect and remove epochs containing artifacts. The available selection criteria are 1) gradient (absolute difference between two consecutive samples), 2) max-min (the difference between the maximum and minimum amplitude in the epoch), and 3) amplitude (absolute maximum and minimum amplitudes).</li>
		<li>Run the processing code. 
		NOTE: Steps 4.2-4.7 are automatically performed when this option is selected. Run the steps manually if appropriate.</li>
	</ol>
	</li>
	<li>Re-arrange the epochs into a data matrix of n rows and m columns, in which n represents the number of recordings (experimental runs) and m the number of epochs (Figure 1B).</li>
	<li>Weight the epoch to attenuate the effect of motion and muscular artifacts. 
	NOTE: Weighted EEG epochs are obtained by dividing each voltage sample by the amplitude variance of the epoch they belong to, so that variance is used as a measure of amplitude variability and weighting factor40.</li>
	<li>Column-wise average the dataset. To this end, time-domain average the epochs corresponding to the same time window in the different recordings. 
	NOTE: This step allows the computation of the steady-state amplitude in recordings with a remarkably high signal-to-noise ratio (SNR).</li>
	<li>Export the time series resulting from the averaging for further analysis of the time evolution of the entrainment in external software.
	<ol>
		<li>Compute the amplitude of the steady-state response in each epoch resulting from the column-wise averaging, using the fast Fourier transform (FFT). 
		NOTE: The FFT length should correspond to the length of one epoch. The implementation of a windowing technique is not mandatory. The amplitude of the steady-state response is defined as the spectral amplitude obtained at the frequency of the amplitude modulation of the sensory stimuli.</li>
	</ol>
	</li>
	<li>Vector average the amplitude of an ad-hoc number of FFT bins at each side of the frequency of the response to calculate the residual noise level (RNL). The number of FFT bins should correspond with a frequency band of about 3 Hz, at each side of the frequency of the response. 
	NOTE: The high frequency-specificity of the steady-state responses makes the response amplitude independent of those background oscillations with similar frequencies, which in turn distributes uniformly in a relatively narrow frequency band41,42,43.</li>
	<li>Plot the amplitude of the steady-state response and the RNL as a function of column index (i.e., the number of the acquired epoch) to explore the evolution of the steady-state response during the stimulation interval.</li>
</ol>

The authors have nothing to disclose.

This work describes an experimental procedure for analyzing the dynamics of oscillatory brain responses. Such methodology consists of acquiring a sufficient number of independent experimental runs of the same experimental condition, and time-domain averaging epochs corresponding to the same time window in the different recordings (columns-wise averaging in Figure 1B). The amplitude computed in the averaged data represent the instantaneous amplitude of the oscillatory response. Plotting these amplitudes as a function of time (or the number of columns in the dataset) allows analyzing the time evolution of the oscillatory response time-locked to the stimulation. This methodology is a modification of that proposed by Ritter et al.23 for analyzing the adaptation of transient cortical evoked potentials. The method has been used to analyze the dynamic of auditory evoked potentials in both&#160;humans24 and animal models20,21.
From a methodological point of view, the combination of parameters used to elicit the steady-state response and those implemented to extract the neural response from background noise is critical to analyze the time evolution of steady-state evoked potentials22. The stimulus length used in the experiment presented here (40 s) was selected based on results obtained in a pilot study. This stimulus length was sufficient to analyze the adaptation of ASSR generated in the rat brainstem21,22. Furthermore, the stimulus length should exceed the time at which the asymptotic instantaneous band power of SSVEPs is reached (Figure 1 in Labecki et al.26). Nevertheless, the asymptotic instantaneous band power of SSVEPs can be reached beyond 60s in some cases (Figure 2 in Labecki et al.26). Therefore, running a small-sample pilot study is recommended to define the stimulus length of the stimulation. Otherwise, a stimulus length longer than 90 s is recommended to achieve complete representation of the time evolution of the response. Using adequately long pauses between consecutive recordings implies considering consecutive experimental runs as to be statistically independent (i.e., different, independent measures of the same variable). To the best of our knowledge, no experiments have been performed to analyze the optimum pause between runs (minimum pause required to make runs independent from each other). The criterium of using pauses at least 3x longer than the stimulus length is conservative enough to ensure that the steady-state response recorded in any given run is not affected by the previous stimulation.
Recently, alternating stimuli (experimental conditions) has been proposed as a choice to reduce the pause between experimental runs, avoiding extra adaptation effect25. Likewise, the number of experimental runs (30) implemented in this experimental protocol is conservative, since the asymptotic RNL and pSNR are typically reached after averaging 20 experimental runs, approximately. When stimuli fall within the middle-upper region of the dynamic range of the response (high sensation levels), lower numbers of runs are likely needed to analyze the dynamics of the evoked response. Nevertheless, in cases in which different experimental conditions are tested, having the same number of experimental runs is crucial for making comparisons among conditions (i.e., different sensation levels).
In addition to the column-wise averaging of epochs, the dynamics of oscillatory evoked potentials has been analyzed by filtering the single-trial measurements in a narrow frequency band around the frequency of interest and computing the envelope of the power signal using low-pass filtering26. Likewise, single trial analysis has been implemented to characterize the transition period that precedes the stable region of SSVEP48, and the changes in amplitude and phase of the SSVEP during the stable region of the response49. While single trial analyses allow discrimination of relatively fast fluctuations in response amplitude, experimental designs to analyze the average response in blocks separated with a given inter-block interval only account for long-term variations in the amplitude of the evoked potential50,51. The column-wise averaging of epochs stands between these two options. Converting the averaged signal to the frequency-domain using the FFT implies analyzing the dynamics of the response with a resolution equal to the length of the epoch. In the example presented here, the SSVEP was reported every 4 s. Although 4 s of resolution is adequate to describe dynamics occurring at intervals of time surpassing tens of seconds, such as that of the SSVEP26, partially overlapping epochs in the original recordings allows to describe the time evolution of the steady-state response in a more refined manner25.
Dynamics of the steady-state responses obtained after column-wise averaging of epochs mainly represent evolution of the oscillatory activity that is synchronized among the averaged EEG segments (those which survive the averaging). Therefore, a major issue regarding the feasibility of the methodology is the possible attenuation of response amplitudes due to variations in the phase of neural oscillations from one independent experimental run to another (i.e., among recordings). This topic needs to be addressed experimentally. However, evidence indicates that the phase of brain oscillatory responses is less variable than expected. In fact, several studies have reported a regularity in the expected phase of the human 80 Hz ASSR47,48,49. When latencies are estimated based on the phase of the oscillatory activity, the predictable effect of the intensity and the carrier frequency of the acoustic stimuli on the latency of the auditory responses has been observed (i.e., the latency decrease as the intensity and carrier frequency increase)52,53,54. Furthermore, typical maturational changes in amplitude and the left-to-right asymmetry in the hearing levels have been also observed when latencies are estimated from the phase of the ASSR47,55,56,57,58. When describing the time evolution of SSVEP using single-trial analysis, Labecki et al.26 observed that although inter-trial variability of the response amplitudes within the same subject was considerably high, variability of the phase was significantly less pronounced.
Based on their observations, Labecki et al.26&#160;suggested that a minimum of 50 trials should be averaged to obtain a reliable estimation of the mean power envelope of the response. These results indicate that, even when the amplitude of the response is computed in single trials, averaging (of envelopes in that case) is needed to report trustworthy results. Moreover, the inter-trial variability in the amplitude of SSVEP reported by Labecki et al.26 suggests that the computation of this parameter in single trials can be highly influenced by background noise. Considering the evolution of the signal-to-noise ratio presented in Figure 2, the computation of the response in the averaged signal instead of single trials significantly reduces the number of EEG segments needed to be processed to obtain reliable measurements. Additionally, the low variability in phase obtained by Labecki et al.26 supports the idea that the column-wise averaging of epochs presented here is a valid procedure for computing the dynamics of oscillatory evoked potentials.
Averaging the data at different levels leads to different interpretation of the results. Regarding oscillatory evoked potentials, computing the response amplitude after the time-domain averaging of independent runs implies analyzing only time-locked oscillations (i.e., those that survive the averaging). This procedure may filter relevant information regarding the dynamics of the response in individual trials. However, it guaranties a sufficiently high signal-to-noise ratio of the measurements. This aspect might be of significance when the responses are close to the electrophysiological threshold, a condition in which the detection of the entrainment can be compromised due to low signal-to-noise ratio of the measurement.

When recorded from the scalp, brain electrical activity is observed as continuous and regular changes in voltages over time. This electrical activity is called electroencephalogram (EEG) and was first described by Hans Berger in the late twenties of the last century1. Subsequent seminal studies described the EEG as a compound time series, in which different rhythmic or repetitive patterns can be observed2,3,4. Nowadays, the EEG is typically divided into five well-established frequency bands, delta, theta, alpha, beta, and gamma, which are associated with the different sensory and cognitive process.
For years, the study of brain oscillations using EEG was restricted to either analysis of the spectrum in the ongoing activity or changes in oscillatory activity elicited by non-periodic sensory events. In the last decades, different methodologies have been implemented for modulating ongoing EEG oscillations and exploring the effects of such modulations on perceptual and cognitive processes, including the presentation of rhythmic sensory stimulation for inducing neural entrainment. The term neural entrainment refers to the synchronization of neural activity with the periodic properties of sensory stimuli. This process leads to the generation of steady-state evoked potentials (i.e., EEG oscillations locked to the periodic properties of the driving stimuli). Steady-state evoked potentials are most commonly elicited by visual, auditory, and vibrotactile stimulation, using either transient stimuli presented at a constant rate or continuous stimulation modulated in amplitude at the frequency of interest. Whereas somatosensory steady-state evoked potentials (SSSEPs) are recorded in response to repetitive tactile stimulation5,6, steady-state visually evoked potentials (SSVEPs) are generally elicited by the periodic presentation of luminance flickers, pictures, and faces7,8. Auditory steady-state responses (ASSRs) are usually generated by trains of transient acoustic stimuli or by the continuous presentation of amplitude-modulated tones9,10.
The extraction of steady-state evoked potentials from the measured EEG essentially relies on averaging subsequently acquired EEG epochs time-locked to the stimulus11. Due to the periodicity of the responses, they can be analyzed in both time and frequency domains. After the frequency-domain transformation, the sensory response is observed as peaks of amplitude at the presentation rate or modulation frequency of the external stimuli, and their corresponding harmonics. These procedures (time-domain averaging and the subsequent frequency-domain transformation) have been essential for developing a hearing test based on the detection of ASSR methods with clinical purposes12,13,14,15,16.
Furthermore, the classical time-domain averaging of EEG epochs has been extremely useful for analyzing physiological processes such as the generation and extinction of SSVEP17,18. Presenting consecutive trains of flicker lights and averaging subsequent epochs within a recording, Wacker et al.19 observed that the phase-locking index of the SSVEP rapidly increased during the first 400 ms of stimulation and remained high afterwards. They also reported that robust visual entrainment was established between 700-1 100 ms after stimulus onset. A certain degree of entrainment remained effective after the offset of the stimulation train, which lasted approximately three periods of the oscillatory response17,19. Those behaviors have been interpreted as the engaging/disengaging effect of the observed oscillations, which is a consequence of the nonlinear information processing in the human visual system17. Alternatively, it is known that under certain experimental conditions, the flicker stimulation can elicit on-responses at the beginning, and off-responses at the end of stimulation trains instead of neural entrainment18.
The main assumption to average consecutively acquired EEG epochs is that the EEG signal represents a linear combination of the sensory response and the background noise20. Furthermore, the amplitude, frequency, and phase of the oscillatory response are assumed to be stationary, whereas the background noise is considered as a random activity. However, in cases in which this assumption is not met, the response amplitude computed after several epochs do not necessarily correspond to the instantaneous amplitude of the evoked potential.
It has been recently reported that the ASSR generated in the brainstem of rats adapts to the continuous presentation of amplitude-modulated tones (i.e., the response amplitude decrease exponentially over time)21,22. Adaptation has been interpreted as a neural mechanism that reflects the loss of novelty of a monotonously repetitive sensory stimulus, increasing the sensitivity to relevant fluctuations in the acoustic environment23,24. In the auditory pathway, adaptation may enhance speech comprehension in noisy environments. Furthermore, this process may be a part of existing mechanisms to monitor the auditory feedback of one&#39;s own voice to control the speech production.
Analyzing the time evolution of the 40 Hz ASSR in humans, Van Eeckhoutte et al.25 observed a significant but small decrease in the response&#160;amplitude over time (around -0.0002 &#181;V/s based on the group analysis, when assuming a linear decrease over time). Consequently, these authors concluded that the 40 Hz ASSR in humans does not adapt to the stimulation. In humans, non-stationary behaviors have been observed when analyzing the stability of the SSVEP26. These authors observed that the amplitude of the fundamental frequency and the second harmonic of the SSVEP were stationary in only 30% and 66.7% of the subjects they tested, respectively. The phases of both SSVEP frequency components, although relatively stable over time, exhibited small drifts26.
Therefore, although the classical time-domain averaging of subsequently acquired epochs allows exploring of stationary properties of the neural entrainment, this methodology needs to be revised when long-term dynamics of the entrainment is the focus of the research, or when the averaging of short-term dynamics is corrupted by the occurrence of long-term dynamics. To characterize non-stationary behaviors of the steady-state responses, the evoked response computed at a given time window should not be compromised by those computed in the preceding EEG segments. In other words, the evoked potential should be extracted from the background noise without epochs being time-domain averaged with the preceding EEG segments.
In this study, a method for assessing the dynamics of neural entrainment is presented. Steady-state responses are repetitively recorded in response to the same stimulation, where consecutive recordings are interleaved by a resting interval of three times the length of the experimental run. Considering that if the time evolution of the physiological response is the same in different independent experimental runs (independent recordings), recordings are column-wise averaged. In other words, epochs corresponding to the same location in the different recordings are averaged, without averaging epochs within a recording. Consequently, the response amplitude computed at any stimulation interval will correspond to the instantaneous amplitude of the evoked potential. The sensory responses can be either analyzed in the time-domain or transformed into the frequency-domain, depending on the aim of the experiment. In any case, the amplitudes can be plotted as a function of time to analyze time evolution of the steady-state response. Generation and extinction of the steady-state evoked potentials can be assessed by restricting the analysis to the first and last epochs of the recordings.
The dynamics of the neural entrainment can be analyzed using other approaches, such as narrowband filtering single-trial measurements around the frequency of interest and computing the envelope of the power signal using low-pass filtering25 and the Hilbert transformation27. Compared to these methodologies, the column-wise averaging of epochs allows computing steady-state parameters based on signals with the higher signal-to-noise ratio (SNR). Recently, Kalman filtering has emerged as a promising technique for the estimation of 40-Hz ASSR amplitudes28,29,30. Implementation of Kalman filtering can improve the detection of steady-state responses closer to the electrophysiological threshold and reduce the time of the hearing test29. Furthermore, stationary responses are not needed to be assumed when a Kalman filtering approach is used to estimate the ASSR amplitude30. Nevertheless, only one study has analyzed the time evolution of ASSRs using Kalman filtering25. The conclusion of the study is that the 40-Hz ASSR amplitude is stable over the stimulation interval. Therefore, Kalman filtering needs to be tested in conditions under which the ASRR is not stationary.
Although time consuming, the column-wise averaging method is model-free and does not need initialization values and/or a priori definitions of the noise behavior. Furthermore, since it does not involve convergence times, the column-wise averaging may provide a more reliable representation of the onset of neural entrainment. Therefore, the results obtained with the column-wise averaging method can be considered as the ground truth for analyzing dynamics of the neural entrainment using Kalman filtering.
This description of the protocol is based on an example of SSVEP. However, it is important to note that the method presented here is modality-independent, such that it&#160;can also be used to analyze the time evolution of SSSEP and ASSR.

<table><tbody><tr><td>Active electrodes&amp;nbsp;</td><td>Biosemi</td><td>P32-1020-32ACMS (ABC)</td><td>for channels 1-32</td></tr><tr><td>Active electrodes&amp;nbsp;</td><td>Biosemi</td><td>P32-1020-32A (ABC)</td><td>for channels 33-64</td></tr><tr><td>Active electrodes&amp;nbsp;</td><td>Biosemi</td><td>8 x TP FLAT</td><td>external electrodes</td></tr><tr><td>Active-Two adquisition system</td><td>Biosemi</td><td>version 7.0</td><td>EEG adquisition system</td></tr><tr><td>alcohol</td><td>Salcobrand</td><td>Code:&amp;nbsp; 3309011</td><td>for cleaning the scalp</td></tr><tr><td>Electrode cap 64 channels&amp;nbsp;</td><td>Biosemi</td><td>CAP MS xx yy</td><td>cap&amp;nbsp;</td></tr><tr><td>Electrode cap 64 channels&amp;nbsp;</td><td>Biosemi</td><td>CAP ML xx yy</td><td>cap&amp;nbsp;</td></tr><tr><td>gel</td><td>Biosemi</td><td>SIGNA BOX12</td><td>conductive gel</td></tr><tr><td>Laptop</td><td>Asus</td><td>Core i7 1TB DD + 128GB SSD 8GB RAM&amp;nbsp;</td><td>computer for stimulation</td></tr><tr><td>Laptop</td><td>Asus</td><td>Core i7 1TB DD + 128GB SSD 8GB RAM&amp;nbsp;</td><td>computer for recording</td></tr><tr><td>LED screen</td><td>in-house production</td><td>-</td><td>The screen consists of four light-emitting diodes (LEDs) situated on the center of a 50x50 cm black screen, as vertexes of a square of 5x5 cm</td></tr><tr><td>sterile gauze</td><td>Salcobrand</td><td>Code:&amp;nbsp; 8730277</td><td>for cleaning the scalp</td></tr></tbody></table>

a method for tracking the time evolution of steady-state evoked potentials

Neural entrainment refers to the synchronization of neural activity to the periodicity of sensory stimuli. This synchronization defines the generation of steady-state evoked responses (i.e., oscillations in the electroencephalogram phase-locked to the driving stimuli). The classic interpretation of the amplitude of the steady-state evoked responses assumes a stereotypical time-invariant neural response plus random background fluctuations, such that averaging over repeated presentations of the stimulus recovers the stereotypical response. This approach ignores the dynamics of the steady-state, as in the case of the adaptation elicited by prolonged exposures to the stimulus. To analyze the dynamics of steady-state responses, it can be assumed that the time evolution of the response amplitude is the same in different stimulation runs separated by sufficiently long breaks. Based on this assumption, a method to characterize the time evolution of steady-state responses is presented. A sufficiently large number of recordings are acquired in response to the same experimental condition. Experimental runs (recordings) are column-wise averaged (i.e., runs are averaged but epoch within recordings are not averaged with the preceding segments). The column-wise averaging allows analysis of steady-state responses in recordings with remarkably high signal-to-noise ratios. Therefore, the averaged signal provides an accurate representation of the time evolution of the steady-state response, which can be analyzed in both the time and frequency domains. In this study, a detailed description of the method is provided, using steady-state visually evoked potentials as an example of a response. Advantages and caveats are evaluated based on a comparison with single-trial methods designed to analyze neural entrainment.

SSVEP was elicited by continuous visual stimuli of 40 s in length, where the light intensity was modulated by a sinusoidal wave of 10 Hz (modulation depth of 90%). Stimuli were delivered by four light-emitting diodes (LEDs) situated in the center of a 50 cm x 50 cm black screen, as vertexes of a 5 cm x 5 cm square. When the participant sat 70 cm from the screen, the area of the square of LEDs subtends a visual angle of about 4&#176;. The LED screen was designed using an USB-based microcontroller development system and four super bright white LEDs of 10 mm of diameter. The pulse width modulation (PWM) technique was used to control the power supplied to the LEDs. This technique controlled the LEDs intensities at a given frequency and generate the final sinusoidal envelope. A PWM frequency of 40 kHz was used to avoid a perceivable flicker effect.
Thirty recordings were obtained, which were segmented in epochs of 4 s. Therefore, a dataset composed of 10 columns (number of EEG epochs within recordings) and 30 rows (number of recordings, number of experimental runs) was obtained.
The neural oscillation time-locked to the stimulation became evident as the column-wise averaging was performed (Figure 2). Significantly, the interval at which the SSVEP is generated can be observed in traces corresponding with column 1. In that column, 0.2 s of pre-stimulus baseline are plotted in addition to the first 0.8 s of neural entrainment. Therefore, the procedure described here allows characterization of 1) the dynamics of the oscillatory response once neural entrainment is already established and 2) the engagement of neural oscillations. One or more epochs recorded after the end of stimulation can also be included in the data matrix to study extinction of the steady-state response after stimulus offset.
During the column-wise averaging of epochs, the mean amplitude of the SSVEP (spectral amplitude at 10 Hz, computed by applying the FFT) decreased during the averaging of the first epochs of the columns and tended to stabilize afterward (Figure 3A). This result agrees with previous studies analyzing the evolution of ASSR during the averaging of sequentially acquired epochs21,22,40,43,44. The behavior of the response amplitude during averaging is usually explained by the relatively high contribution of unaveraged noise to the response amplitude computed in the first epochs, which is attenuated as averaging is performed13,44,45,46,47. Noteworthy, the SSVEP amplitude variability significantly decreased as averaging progressed.
We also analyzed the RNL of the measurements during the column-wise averaging of epochs (Figure 3B). The RNL was computed in a narrow frequency band (3 Hz) at both sides of the frequency of the SSVEP. Although this procedure is not common when SSVEP are analyzed, vector-averaging a given number of frequency bins around that of the neural entrainment is the standard for estimating the RNL in ASSR measurements41,42,43. As expected, the RNL progressively decreased as the number of averaged epochs increased and reached the asymptotic level after about 20 epochs were processed. Unlike that observed when the SSVEP amplitude was analyzed, the standard deviation of the RNL remained relatively constant as the number of averaged epochs increased, which suggests that the recording conditions were stable along the experimental session.
The results presented above determined the changes in the peak signal-to-noise ratio (pSNR) of measurements during the column-wise averaging of epoch (Figure 3C). This term is defined here as the ratio (in dB) between the square amplitude of the response (SSVEP) and square amplitude of the RNL. As averaging progressed, the pSNR increased as the number of averaged epochs increased up to 18, approximately. Further increments in the number of averaged epochs did not significantly impact the quality of the signal. The variability of the pSNR decreased as more epochs were averaged.
Finally, the dynamics of the SSVEP amplitude and the RNL are represented in Figure 4. These time evolutions were obtained by plotting the response parameters computed at the end of the column-wise averaging of epochs as a function of the number of columns (as a function of time). As demonstrated by Labecki et al.26, the dynamics of SSVEP can significantly vary among subjects. Since the results presented in Figure 4 correspond to a single individual, generalizations cannot be made. In this subject, the amplitude of the SSVEP displayed a relatively complex behavior (Figure 4A). The response amplitude gradually increased during the first 12 seconds following the stimulus onset (time which corresponds to the length of 3 epochs). As the stimulus persisted, the SSVEP consistently decreased during the following 12 seconds, and remained relatively constant afterwards. These results cannot be explained by the behavior of the RNL, since this parameter was relatively constant during the stimulation interval (Figure 4B). The increase in the SSVEP amplitude following the stimulus onset is evident in the traces presented in Figure 2 and can be explained by integration processes, which result in stabilization of the neural entrainment. The subsequent decrease in amplitude suggests the adaptation of SSVEP to the sustained stimulation. Nevertheless, these hypotheses need to be tested in controlled experiments with appropriated sample sizes.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59898/59898fig1v2.jpg" /> 
Figure 1: Critical steps for extracting the time evolution of the amplitude of steady-state responses. (A) Screenshot of the processing code, where analysis parameters are defined. (B) Representative diagram illustrating the organization of the dataset. A data matrix composed of 30 recordings of 10 epochs is represented. The column-wise averaging of epochs is highlighted in the first column. The vertical line represents the direction of the averaging. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59898/59898fig1large.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/59898/59898fig2v2.jpg" /> 
Figure 2: Changes in the waveform of steady-state visually evoked potentials (SSVEP) during the column-wise averaging of epochs. Responses were elicited by the continuous presentation of light modulated in amplitude at 10 Hz. The rows show the waveforms obtained after averaging all previous recordings (i.e., row 1 is the first recording, row 5 is the waveform obtained after averaging the first five recordings, and the last row is the average of all recordings). More reliable waveforms of SSVEP were observed in each column as the number of averaging runs increased. To provide clarity (to make the oscillations of the SSVEP visible), only the first second of the epochs is represented. The exceptions are traces in the first column of the data set, for which 0.2 seconds of pre-stimulus baseline are displayed. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59898/59898fig2large.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/59898/59898fig3v2.jpg" /> 
Figure 3: Changes in the response and recording parameters during the column-wise averaging of epochs. (A)&#160;Evolution of the SSVEP amplitude. (B) Behavior of the RNL. (C) Changes in the pSNR. Black lines represent the mean values obtained for each column (n = 10) and the grey shadow represents the area covered by &#177; one standard deviation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59898/59898fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59898/59898fig4v2.jpg" /> 
Figure 4: Time evolution of the SSVEP elicited by the presentation of continuous visual stimulation, modulated in amplitude at 10 Hz. (A) Time course of the&#160;SSVEP amplitude. (B) Time course of the RNL. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59898/59898fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about A Method for Tracking the Time Evolution of Steady-State Evoked Potentials at JoVE.com

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A protocol to assess the time evolution of the neural entrainment to external repetitive stimuli is presented. Steady-state recordings of the same experimental condition are acquired and averaged in the time-domain. The steady-state dynamics are analyzed by plotting the response amplitude as a function of time.

Neural entrainment refers to the synchronization of neural activity to the periodicity of sensory stimuli. This synchronization defines the generation of ...

a-method-for-tracking-time-evolution-steady-state-evoked

Nanyang Technological University

Research

Education

JoVE Core

JoVE Journal

Electrical Engineering

Neuroscience

Unit 1

First and Second-Order Circuits

SCIENTISTS IN ACTION

8.4K Views.  Universidad Técnica Federico Santa María. Neurale entrainment is de synchronisatie van de neurale activiteit naar de periodiciteit van de zintuiglijke stimuli. Deze synchronisatie genereert het steady-state opgeroepen potentieel, dat wil zeggen oscillaties in het elektro-encefalogram fase-vergrendeld op de zintuiglijke stimuli. De klassieke interpretatie van de amplitude van de steady-state opgeroepen reacties, gaat uit van een constante neurale respons fase-vergrendeld om de stimulus, plus een willekeurig achtergrondgeluid niet gere...

Video: A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

An Electrophysiology Protocol to Measure Reward Anticipation and Processing in Children

We present a protocol designed to measure the neural correlates of reward in children. The protocol allows researchers to measure both reward anticipation and processing. Its purpose is to create a reward task that is appropriate for young children with and without autism while controlling reward properties between two conditions: social and nonsocial. The current protocol allows for comparisons of brain activity between social and nonsocial reward conditions while keeping the reward itself identical between conditions. Using this protocol, we found evidence that neurotypical children demonstrate enhanced anticipatory brain activity during the social condition. Furthermore, we found that neurotypical children anticipate social reward more robustly than children with autism diagnoses. As the task uses snacks as a reward, it is most appropriate for young children. However, the protocol may be adapted for use with adolescent or adult populations if snacks are replaced by monetary incentives. The protocol is designed to measure electrophysiological events (event-related potentials), but it may be customized for use with eye-tracking or fMRI.

This protocol is designed to measure reward anticipation and processing in young children with and without autism. Specifically, the protocol is designed to study the neural correlates of reward during social and nonsocial conditions while controlling for reward between conditions.

We present a protocol designed to measure the neural correlates of reward in children. The protocol allows researchers to measure both reward anticipation ...

Behavior

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 ...

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

The electroretinogram (ERG) and visual evoked potential (VEP) are commonly used to assess the integrity of the visual pathway. The ERG measures the electrical responses of the retina to light stimulation, while the VEP measures the corresponding functional integrity of the visual pathways from the retina to the primary visual cortex following the same light event. The ERG waveform can be broken down into components that reflect responses from different retinal neuronal and glial cell classes. The early components of the VEP waveform represent the integrity of the optic nerve and higher cortical centers. These recordings can be conducted in isolation or together, depending on the application. The methodology described in this paper allows simultaneous assessment of retinal and cortical visual evoked electrophysiology from both eyes and both hemispheres. This is a useful way to more comprehensively assess retinal function and the upstream effects that changes in retinal function can have on visual evoked cortical function.

This protocol describes simultaneous measurement of electroretinogram and visual evoked potentials in anesthetized rats.

The electroretinogram (ERG) and visual evoked potential (VEP) are commonly used to assess the integrity of the visual pathway. The ERG measures the ...

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations over time in the blood-oxygenation level dependent signal. rs-fcMRI has been applied extensively to identify abnormalities in brain connectivity in different neurologic and psychiatric diseases. However, the relationship among rs-fcMRI connectivity abnormalities, brain electrophysiology and disease state is unknown, in part because the causal significance of alterations in functional connectivity in disease pathophysiology has not been established. Transcranial Magnetic Stimulation (TMS) is a technique that uses electromagnetic induction to noninvasively produce focal changes in cortical activity. When combined with electroencephalography (EEG), TMS can be used to assess the brain's response to external perturbations. Here we provide a protocol for combining rs-fcMRI, TMS and EEG to assess the physiologic significance of alterations in functional connectivity in patients with neuropsychiatric disease. We provide representative results from a previously published study in which rs-fcMRI was used to identify regions with abnormal connectivity in patients with epilepsy due to a malformation of cortical development, periventricular nodular heterotopia (PNH). Stimulation in patients with epilepsy resulted in abnormal TMS-evoked EEG activity relative to stimulation of the same sites in matched healthy control patients, with an abnormal increase in the late component of the TMS-evoked potential, consistent with cortical hyperexcitability. This abnormality was specific to regions with abnormal resting-state functional connectivity. Electrical source analysis in a subject with previously recorded seizures demonstrated that the origin of the abnormal TMS-evoked activity co-localized with the seizure-onset zone, suggesting the presence of an epileptogenic circuit. These results demonstrate how rs-fcMRI, TMS and EEG can be utilized together to identify and understand the physiological significance of abnormal brain connectivity in human diseases.

Resting-state functional-connectivity MRI has identified abnormalities in patients with a wide range of neuropsychiatric disorders, including epilepsy due to malformations of cortical development. Transcranial Magnetic Stimulation in combination with EEG can demonstrate that patients with epilepsy have cortical hyperexcitability in regions with abnormal connectivity.

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations ...

Medicine

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Micro-electrode arrays (MEAs) can be used to investigate drug toxicity, design paradigms for next-generation personalized medicine, and study network dynamics in neuronal cultures. In contrast with more traditional methods, such as patch-clamping, which can only record activity from a single cell, MEAs can record simultaneously from multiple sites in a network, without requiring the arduous task of placing each electrode individually. Moreover, numerous control and stimulation configurations can be easily applied within the same experimental setup, allowing for a broad range of dynamics to be explored. One of the key dynamics of interest in these in vitro studies has been the extent to which cultured networks display properties indicative of learning. Mouse neuronal cells cultured on MEAs display an increase in response following training induced by electrical stimulation. This protocol demonstrates how to culture neuronal cells on MEAs; successfully record from over 95% of the plated dishes; establish a protocol to train the networks to respond to patterns of stimulation; and sort, plot, and interpret the results from such experiments. The use of a proprietary system for stimulating and recording neuronal cultures is demonstrated. Software packages are also used to sort neuronal units. A custom-designed graphical user interface is used to visualize post-stimulus time histograms, inter-burst intervals, and burst duration, as well as to compare the cellular response to stimulation before and after a training protocol. Finally, representative results and future directions of this research effort are discussed.

Mouse neuronal cells cultured on multi-electrode arrays display an increase in response following electrical stimulation. This protocol demonstrates how to culture neurons, how to record activity, and how to establish a protocol to train the networks to respond to patterns of stimulation.

Micro-electrode arrays (MEAs) can be used to investigate drug toxicity, design paradigms for next-generation personalized medicine, and study network ...

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the neurogenesis of EEG is still very limited. Local field potentials (LFPs) recorded via a multi-laminar microelectrode can provide a more detailed account of simultaneous neural activity across different cortical layers in the neocortex, but the technique is invasive. Combining EEG and LFP measurements in a pre-clinical model can greatly enhance understanding of the neural mechanisms involved in the generation of EEG signals, and facilitate the derivation of a more realistic and biologically accurate mathematical model of EEG. A simple procedure for acquiring concurrent and co-localized EEG and multi-laminar LFP signals in the anesthetized rodent is presented here. We also investigated whether EEG signals were significantly affected by a burr hole drilled in the skull for the insertion of a microelectrode. Our results suggest that the burr hole has a negligible impact on EEG recordings.

This protocol describes a simple method for concurrent recording of co-localized electroencephalography (EEG) and multi-laminar local field potential in an anesthetized rat. A burr hole drilled in the skull for the insertion of a microelectrode is shown to produce negligible distortion of the EEG signal.

Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the ...

Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor

For scalp EEG research environments with laboratory mice, we designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable because of the plunger-spring-barrel structural facet and mechanical strengths resulting from metal materials. The whole process for acquiring the VEP responses in vivo from a mouse consists of four steps: (1) sensor assembly, (2) animal preparation, (3) VEP measurement, and (4) signal processing. This paper presents representative measurements of VEP responses from multiple mice with a submicro-voltage signal resolution and sub-hundred millisecond temporal resolution. Although the proposed method is safer and more convenient compared to other previously reported animal EEG acquiring methods, there are remaining issues including how to enhance the signal-to-noise ratio and how to apply this technique with freely moving animals. The proposed method utilizes easily available resources and shows a repetitive VEP response with a satisfactory signal quality. Therefore, this method could be utilized for longitudinal experimental studies and reliable translational research exploiting non-invasive paradigms.

We designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable. This paper describes the whole process from manufacturing the proposed EEG electrode to signal processing of visual evoked potential (VEP) signals measured on a mouse scalp using a dry non-invasive multi-channel EEG sensor.

For scalp EEG research environments with laboratory mice, we designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable ...

Recording and Modulation of Epileptiform Activity in Rodent Brain Slices Coupled to Microelectrode Arrays

Temporal lobe epilepsy (TLE) is the most common partial complex epileptic syndrome and the least responsive to medications. Deep brain stimulation (DBS) is a promising approach when pharmacological treatment fails or neurosurgery is not recommended. Acute brain slices coupled to microelectrode arrays (MEAs) represent a valuable tool to study neuronal network interactions and their modulation by electrical stimulation. As compared to conventional extracellular recording techniques, they provide the added advantages of a greater number of observation points and a known inter-electrode distance, which allow studying the propagation path and speed of electrophysiological signals. However, tissue oxygenation may be greatly impaired during MEA recording, requiring a high perfusion rate, which comes at the cost of decreased signal-to-noise ratio and higher oscillations in the experimental temperature. Electrical stimulation further stresses the brain tissue, making it difficult to pursue prolonged recording/stimulation epochs. Moreover, electrical modulation of brain slice activity needs to target specific structures/pathways within the brain slice, requiring that electrode mapping be easily and quickly performed live during the experiment. Here, we illustrate how to perform the recording and electrical modulation of 4-aminopyridine (4AP)-induced epileptiform activity in rodent brain slices using planar MEAs. We show that the brain tissue obtained from mice outperforms rat brain tissue and is thus better suited for MEA experiments. This protocol guarantees the generation and maintenance of a stable epileptiform pattern that faithfully reproduces the electrophysiological features observed with conventional field potential recording, persists for several hours, and outlasts sustained electrical stimulation for prolonged epochs. Tissue viability throughout the experiment is achieved thanks to the use of a small-volume custom recording chamber allowing for laminar flow and quick solution exchange even at low (1 mL/min) perfusion rates. Quick MEA mapping for real-time monitoring and selection of stimulating electrodes is performed by a custom graphic user interface (GUI).

We illustrate how to perform recording and electrical modulation of 4-aminopyridine-induced epileptiform activity in rodent brain slices using microelectrode arrays. A custom recording chamber maintains tissue viability throughout prolonged experimental sessions. Live electrode mapping and selection of stimulating pairs are performed by a custom graphical user interface.

Temporal lobe epilepsy (TLE) is the most common partial complex epileptic syndrome and the least responsive to medications. Deep brain stimulation (DBS) ...

Topographical EEG Recordings of Visual Evoked Potentials in Mice using Multichannel Thin-film Electrodes

Visual evoked potentials (VEP) allow the characterization of visual function in preclinical mouse models. Various methods exist to measure VEPs in mice, from non-invasive EEG, subcutaneous single-electrodes, and ECoG to fully invasive intracortical multichannel visual cortex recordings. It can be useful to acquire a global, topographical EEG-level characterization of visual responses previous to local intracortical microelectrode measurements in acute experimental settings. For example, one use case is&#160;to assess global cross-modal changes in VEP topography in deafness models before studying its effects on a local intracortical level. Multichannel epicranial EEG is a robust method to acquire such an overview measure of cortical visual activity.&#160;Multichannel epicranial EEG provides comparable results through a standardized, consistent approach to, for example, identify cross-modal, pathological, or age-related changes in cortical visual function. The current study presents a method to obtain the topographical distribution of flash-evoked VEPs with a 32-channel thin-film EEG electrode array in anesthetized mice. Combined with analysis in the time and frequency domain, this approach allows fast characterization and screening of the topography and basic visual properties of mouse cortical visual function, which can be combined with various acute experimental settings.

The present protocol describes a simple procedure to acquire and analyze the topography of epicranial visual evoked potentials with 32-multichannel thin-film electrodes in the mouse.

Visual evoked potentials (VEP) allow the characterization of visual function in preclinical mouse models. Various methods exist to measure VEPs in mice, ...

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 ...

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Samenvatting

Meer video's verkennen