This work was funded by the Excellence Initiative of the German federal state and governments (ERS Seed Fund, OPSF449). The Hitachi NIRS system was supported by a funding of the German Research Foundation DFG (INST 948/18-1 FUGG).

Prior to participation, all parents / children provided informed consent / assent. The study was approved by the ethics committee of the Medical Faculty of RWTH Aachen University.
1. Preparation before the Participant Arrives
<ol>
	<li>Prepare NIRS caps.
	<ol>
		<li>Choose the cap sizes the same size or slightly larger than the participant&#8217;s head circumference.</li>
		<li>Cut 15 holes with a diameter of approximately 15 mm each, arranged in a horizontal 3x5 grid, into the forehead area of each of 2 raw EEG caps (see Table of Materials). Make sure that the holes are spaced 30 mm from each other in any direction, that the middle column of holes is located in the center of the forehead, i.e., above the nose, and that the bottom row is located above the eyebrows.</li>
		<li>In order to make the caps more comfortable and minimize pressure marks, attach soft foam material (e.g., adhesive window sealing tape or similar flat foam rubber material) at the inner side of the holder grid between the probe sockets and at the edges. Use double-faced adhesive tape or sewing thread if necessary.</li>
		<li>Mount an empty 3x5 probe holder grid (see Table of Materials) to each of the modified EEG caps such that the holder grid itself is placed on the inside of the cap and the holder sockets stick in the holes. 
		NOTE: The NIRS measurement system (see Table of Materials) has two separate probe sets, use one probe set for each participant.</li>
		<li>Gently insert the probes into the appropriate holder sockets on the grids such that only the first ridge of each probe is mounted in the socket, which results in one clicking sound.</li>
		<li>Open the probe set monitor window at the NIRS measurement system and select 2 probe sets arranged in a 3x5 grid each, one for the participating child and one for the adult. Ensure that the probe arrangements of the two caps corresponds to the arrangements in the probe set window (i.e., same location of the respective emitter and receiver probe numbers).</li>
	</ol>
	</li>
	<li>Prepare the experiment.
	<ol>
		<li>Start the NIRS measurement system with laser diodes switched on 30 min. before measuring, such that the system reaches a stable operating temperature.</li>
		<li>Set all necessary options at the NIRS measurement system. Make sure that the device is set to event-related measurement and that the RS232 serial input, necessary for receiving triggers from the experimental paradigm, is active. 
		Note: The experiment is an adapted version by a paradigm devised by Cui et al.12, programmed in the non-commercial Psychophysics Toolbox extensions, version 3.0.1116.</li>
		<li>Prepare the experimental paradigm by starting the technical computing software (see Table of Materials) that serves as base for the Psychophysics Toolbox extensions and setting the current directory to the folder that the paradigm is saved in.</li>
		<li>Place two&#160;chin rests in front of the computer screen to prevent head movements during the experiment.</li>
	</ol>
	</li>
</ol>
2. Participant Arrival in the Laboratory
<ol>
	<li>Prepare the participants.
	<ol>
		<li>Show and explain the experimental setup including the NIRS measurement system to the participants. Always make sure that the participants do not look directly into the laser beam of the NIRS measurement system as this may be harmful to the eye.</li>
		<li>Seat the participants next to each other in front of the computer screen. Adjust the height of the chin rests such that both participants sit comfortably.</li>
		<li>Instruct the participants and administer practice trials of both the cooperative and the competitive game. Give additional instructions during the practice trials if necessary.</li>
		<li>Measure and mark the Fpz point according to the 10-20 system, which is 10% of the distance between nasion and inion, on each participant&#8217;s head.</li>
		<li>Place the caps with the probes carefully on the participants&#8217; heads, with the laser turned off. Place the front of the cap, including the probe grid, on the participant&#8217;s forehead first and then pull down the back of the cap towards the neck. Make sure that the middle probe of the bottom row is placed on Fpz and the middle probe column is aligned along the sagittal reference curve.</li>
		<li>Place the fiber strings on the holder arm attached to the NIRS measurement system so that they hang loosely without contact with the participant or chair and that they do not pull on the caps. Use an additional holder (e.g., modified microphone stand or similar) for the second participant if necessary.</li>
		<li>Push each probe further into its socket until the small white nose in the center of the top of the probe casing is visible. 
		Note: The nose is pushed upwards by a coil spring mechanism as soon as the probe tip touches the participant&#8217;s scalp.</li>
		<li>Turn the laser on again and test the signal quality by clicking on the Auto Gain button in the probe set monitor window of the NIRS measurement system.</li>
		<li>If a channel does not have a sufficient signal (i.e., if it is marked in yellow), gently put the hair underneath the surrounding probe tip aside. If necessary, push the probes further into their sockets but ensure the comfort of the participant. Check whether the signal quality has improved (i.e., the channel is now marked in green) by clicking on the Auto Gain button again.</li>
		<li>If step 2.1.9. does not lead to a signal improvement, adjust the signal intensity. If there is too much signal (i.e., if the channel is marked in red), change the signal intensity to low signal intensity by repeatedly clicking on the respective probe&#8217;s symbol in the probe set monitor window of the NIRS measurement system. If there is not enough signal (i.e., if the channel is marked in yellow), change the signal intensity to high signal intensity, again by repeatedly clicking on the respective probe&#8217;s symbol.</li>
	</ol>
	</li>
	<li>Run the experiment
	<ol>
		<li>When there are no questions after the practice trials and a good signal quality is ensured, start the experimental paradigm.</li>
		<li>Place a towel over the participants&#8217; hands so that they cannot see the hand movements of their respective game partner.</li>
		<li>After the experiment, save the data and export the raw light intensity data as a text file by clicking on the Text File Out button. Do not apply any filters in the NIRS measurement system.</li>
		<li>Clean all necessary materials (probes, probe holders, chin rests) with ethanol. Wash the caps&#160;in a gentle cycle with mild detergent.</li>
	</ol>
	</li>
</ol>
3. Data Analysis
<ol>
	<li>Data Preprocessing 
	Note: There are several non-commercial software packages available for fNIRS data analysis, e.g., HomER17, NIRS Brain AnalyzIR18&#160;or SPM for fNIRS19. The latter was used for the following preprocessing steps. For more information on how to perform these steps, please see the toolbox manual.
	<ol>
		<li>Convert the data files to the SPM for fNIRS data format.</li>
		<li>Calculate oxy-Hb and deoxy-Hb concentration changes using the modified Beer-Lambert law by pressing the Convert button in the main window. Enter the age of the subject and the distance between source and detector (e.g., 3 cm). Accept the default values for the molar absorption coefficients of oxy-Hb and deoxy-Hb at wavelength (&#955;) 1 and &#955; 2 as well as the default values for the differential pathlength factor (DPF) at &#955; 1 and &#955; 2.</li>
		<li>Preprocess the time series of hemodynamic changes to reduce motion artifacts by selecting the MARA button (for more information on the MARA algorithm see Scholkman et al.20).</li>
		<li>Preprocess the time series to reduce slow drifts by selecting the DCT button.</li>
	</ol>
	</li>
	<li>Bad channel detection 
	Note: Bad channel detection can be performed before and / or after fNIRS data preprocessing. In this protocol, different objective criteria for detecting bad channels and visual inspection are combined. Please note that the proposed list of objective criteria is not exhaustive. For bad channel detection, self-written scripts were used (for the technical computing software see Table of Materials).
	<ol>
		<li>Exclude channels in which there is no signal change for several continuous samples, which is indicated by a flat line when plotting the time series.</li>
		<li>Calculate the coefficient of variation CV = SD/mean*100 for the raw attenuation data. Exclude channels in which the CV is above a predefined percentage (e.g., 10%; see for instance van der Kant et al.21).</li>
		<li>Plot the power spectrum of the signal. If there is no heartbeat visible in the signal spectrum around 1 Hz, as indicated by an increased power in this frequency band, exclude the channel from the analysis.</li>
		<li>Visually inspect all data before and/or after preprocessing. Decide whether to include the channel based on the objective criteria, described in 3.2.1 &#8211; 3.2.3, as well as on subjective visual detection of noisy channels.</li>
	</ol>
	</li>
	<li>Brain-to-brain connectivity 
	Note: Two different estimate types of brain connectivity can be distinguished: non-directed estimates, which quantify the strength of the connectivity, and directed estimates, which seek to establish statistical evidence for causation from the data22. Here the focus was on the wavelet transform coherence (WTC), a widely applied non-directed estimate for fNIRS brain-to-brain connectivity. Several non-commercial software solutions for the computation of the WTC are available, e.g., one by Grinsted and colleagues23&#160;or the ASToolbox24, which was used in this protocol for the following steps.
	<ol>
		<li>In the AWCO function of the ASToolbox, specify the mother wavelet (e.g., Generalized Morse Wavelet with its parameters beta and gamma), which is used to transform each time series into the time and frequency domain by the continuous wavelet transformation.</li>
		<li>Specify the smoothing window type (e.g., Hanning window) and the smoothing window size for the time and scale domain in the AWCO function.</li>
		<li>To examine the significance of the WTC coefficients and to calculate their p-values, specify the number of surrogate time series (n &#8805; 300) and the ARMA model (e.g., AR (1)) in the AWCO function.</li>
		<li>With the parameters specified in steps 3.3.1 to 3.3.3, calculate the wavelet coherence of two corresponding channels (the same channel in two participants).</li>
		<li>Choose a frequency band of interest in which the task-related brain-to-brain synchrony is expected to occur based on previous studies and visual inspection of the data (for an alternative approach see Nozawa et al. 25).</li>
		<li>Calculate the mean of the WTC coefficients and / or the percentage of significant WTC coefficients in the task-related frequency band for each task block in each channel and for each dyad. Use this value as an outcome measure of brain-to-brain synchrony for further statistical analysis (for more information see Reindl et al.11).</li>
	</ol>
	</li>
	<li>Comparison to Random Pairs 
	Note: To validate the results, we recommend comparing the WTC of the actual dyads to the WTC of random adult-child pairings, who did not play with each other but performed the same experimental task.
	<ol>
		<li>Calculate the WTC, as described in 3.3., for participant pairs who did not play together but performed the same experimental task (i.e., random pairs). Choose the number of random pairs (e.g., 300 for each condition) and calculate the WTC for each random pair.</li>
		<li>Compare the coherence of the random and actual pairs to avoid the detection of spurious synchronicity.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

In this protocol, we show how to conduct fNIRS hyperscanning experiments and one possible way to analyze brain-to-brain synchrony, measuring concentration changes of oxy-Hb and deoxy-Hb at frontal brain regions of two subjects simultaneously. FNIRS hyperscanning is relatively easy to apply: a single NIRS device is sufficient to measure brain activities of both subjects by splitting the optodes between them. Thus, no synchronization between different devices is necessary1. Moreover, since fNIRS does not require strict motion restriction, it is well suited for conducting hyperscanning experiments in a natural environment and in children. In the following, we highlight some critical issues when designing, analyzing and interpreting (fNIRS) hyperscanning experiments, discuss challenges as well as possible solutions.
Experimental Design. One important issue of hyperscanning studies concerns the experimental design. Two participants who complete the same experimental task independently of each other might show similar brain activities, which then might be detected as brain-to-brain synchrony26. To differentiate between brain-to-brain synchrony induced by the experimental task and by the social interaction, appropriate experimental control conditions are necessary. On the one hand, the cooperative and competitive tasks are very well suited because they differ only in the cooperative task component and not in the stimulus material and the participant&#39;s motor behavior. On the other hand, less standardized and more natural interactions (e.g., making a puzzle together) might induce more variance in social interactive behavior and might have a greater ecological validity.
Spatial registration of channels. One challenge in fNIRS hyperscanning is measuring hemodynamic activity in corresponding channels. Attaching emitter and detector probes at corresponding locations of two participants&#39; heads does not warrant that activity in two corresponding cortical regions is tapped, as individual brain anatomy is liable to differ across participants. Simultaneously measuring an adult and a child exacerbates this problem by introducing developmental differences on top of anatomical ones. Moreover, with an increasing number of channels, the placement of the channels is less reproducible across subjects because of variability in head shape and size27. One optional accessory to the ETG-4000 is a probe positioning unit which creates probe positions relative to fiducial points on the head in three-dimensional space. These data can then be co-registered to the structural MR image of the participant&#39;s brain27. Acquiring MR images and using the positioning unit will enable the experimenter to better control whether activity is actually measured in corresponding brain regions across two participants. Additionally, researchers could partly circumvent this problem by calculating an all-to-all connectivity model, measuring the connection between any two channels of the two participants.
Influence of the systemic physiology. Another important issue is that hemodynamic changes are known to be influenced not only by the effect of the neurovascular coupling, thus neuronal activity, but also by systemic changes, such as changes in heart rate, blood pressure, breathing rate, and autonomic nervous system activity28. Therefore, any synchrony detected in the hemodynamic changes of two cooperating participants may also be attributable to a synchrony of those factors. Previous studies have shown that two interacting partners do indeed synchronize their physiological activities29. Note, however, that in tasks with different experimental conditions which are directly compared to each other, this is only a confounder if physiological coupling is more prominent in one but not the other condition. Nevertheless, it can be helpful to acquire physiological data in hyperscanning studies to enable experimental control of these parameters. Another option, as demonstrated recently by Nozawa et al.25, is to add measurement channels with a short source-detector (S-D) separation (e.g., 1 cm), which are sensitive to the superficial skin blood-flow signal. The corresponding component can then be removed from the fNIRS signal obtained from measurement channels with a regular S-D separation (e.g., 3 cm), thus reducing the influence of physiological confounders. Such a dual or multi-distance approach has been shown to improve the sensitivity to task-enhanced (here: communication-enhanced) brain-to-brain synchrony.
Data Analysis. Hyperscanning results depend on an estimator to quantify brain-to-brain synchrony. In the current study, we calculated the WTC of oxy-Hb signals of corresponding channels as a measure of brain-to-brain synchrony. Wavelet-based methods have the advantage that they consider the oscillatory dynamics of time series in the time-frequency space. The WTC is a non-directed measure calculated from wavelet transformed time series, representing the strength of the relationship between two time series. In future studies, it would be interesting to additionally include directed measures, such as Granger causality, in order to examine which participant &#34;leads&#34; the activity (see for instance Pan et al.15). Furthermore, while many previous fNIRS-based hyperscanning studies examine brain-to-brain synchrony in only one signal (e.g., oxy-Hb), it is advisable to consider both oxy-Hb and deoxy-Hb (and possibly total-Hb) in order to take full advantage of the fNIRS technique15.
Limitations. Although fNIRS offers a promising, rapidly growing neuroimaging technique, some technical limitations associated with the device need to be considered when planning such a study (for a recent review see Pinti et al.30). In comparison to EEG and fMRI, fNIRS is more resistant to motion artifacts, yet, it still requires sufficient motion artifact control and detection. There are several potential causes of artifacts. First, some participants tend to move their head abruptly, in particular infants and children, and thereby might pull on the fiber tracts, affecting the optode contact. Developments of new fiberless devices are more robust to movement and thereby&#160;allow investigations of active tasks30. The use of a chin-rest can serve as an additional motion artifact control; however, it limits the ability to record brain activities in natural interactions. Second, acquiring an adequate optode contact can be hindered by dark, curly and/ or thick hair of the participant. Placing the optodes can thus be time-consuming and a perfect signal is not always guaranteed. Third, depending on the fNIRS system, wearing optodes for a longer period of time can put pressure on the participant&#39;s head, which can be experienced as unpleasant. This does not only limit the recording time of the experiment but might also lead to more movement and artifacts (e.g., smaller children might pull on the cap). In addition to motion artifacts, it is noteworthy that fNIRS provides measures of the cortical surface only. Finally, there are no standardized data analysis guidelines yet. Several toolboxes were developed over the past years and first attempts were made to analyze the effectiveness of various preprocessing techniques (e.g., Brigadoi et al.31&#160;and Cooper et al.32). Moreover, the analytical protocol presented in this article shows one way to analyze fNIRS hyperscanning data. Importantly, the selected parameters of the analysis should be understood as one possible option and not as a standard guideline. Several other analytical protocols for fNIRS hyperscanning have been developed in the last years by different research groups (see for instance Cui et al.12; Hirsch et al.33).
Conclusion. fNIRS hyperscanning is a promising technique to gain further insights into the neurobiological underpinnings of social interactions34. In the future, portable and fiberless NIRS devices may be particularly important when examining brain-to-brain synchrony in natural interactions and moving from the dyad towards larger groups of subjects. Finally, combining different neuroimaging techniques, e.g., EEG-fNIRS, may provide new insights, broadening our understanding of brain-to-brain synchrony.

In recent years, neuroscientists have started to study social interactions by recording the brain activities of two or more persons simultaneously, an approach termed hyperscanning1. This technique opens new opportunities to elucidate the neurobiological mechanisms underlying these interactions. To fully understand social interactions, it may not be sufficient to study single brains in isolation but rather the joint activities of brains of interacting persons2. Using different neuroimaging techniques, hyperscanning studies have shown that brain activities of interacting persons or groups synchronize, e.g., while they coordinate their actions3, make music4, communicate5, engage in classroom activities6 or cooperate7.
The article presents a protocol for conducting simultaneous recordings with functional near-infrared spectroscopy (fNIRS). Similar to functional magnetic resonance imaging (fMRI), fNIRS measures the hemodynamic response to brain activation. Changes in oxygenated and deoxygenated hemoglobin (oxy-Hb and deoxy-Hb) are calculated based on the amount of diffusively transmitted near-infrared light through tissue8. fNIRS is well suited for conducting hyperscanning experiments, especially with children, because it can be applied in less constrained and more natural settings than fMRI. Moreover, it is less prone to movement artifacts than both, fMRI and EEG9. In addition, fNIRS data can be acquired at high sampling frequencies (e.g., 10 Hz), thus it highly oversamples the relatively slow hemodynamic response and thereby potentially provides a more complete temporal picture of the brain hemodynamics10.
This protocol was developed within the study of Reindl et al.11 and has been slightly modified (in particular with respect to the channel placement and bad channel identification) more recently. The aim of the study was to examine synchronized brain activity of parent-child dyads. Using fNIRS hyperscanning, we assessed brain-to-brain synchrony in prefrontal brain areas of children (aged five to nine years) and their parents, mostly mothers, during a cooperative and a competitive computer task. Prefrontal brain regions were targeted as they had been identified as important regions for social interactive processes in previous hyperscanning studies1. The cooperative and competitive task were originally developed by Cui et al.12&#160;and recently employed by several previous studies13,14,15. For the study of Reindl et al.11, the tasks were modified to be suitable for children. Participants were instructed to either respond jointly via button presses in response to a target (cooperation) or to respond faster than the other player (competition). Each child performed each task once with the parent and once with an adult stranger of the same sex as the parent. Within each child-adult dyad, wavelet coherence was calculated for the oxy-Hb signals of corresponding channels as a measure of brain-to-brain synchrony.
This protocol describes the procedures to collect fNIRS hyperscanning data of parent and child during the cooperative and competitive game. The overall procedure, however, is not specific to this research design but is appropriate for different populations (e.g., adult strangers, romantic partners, siblings, etc.) and can be adapted for a number of different experimental tasks. This protocol also outlines one possible analytical procedure, which covers necessary and optional data analysis steps, including fNIRS data preprocessing, bad channel detection, wavelet coherence analysis and validation by random pair analysis.

<table border="0" cellpadding="0" cellspacing="0" style="width:872px;" width="873">
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:222px;">NIRS measurement system with probe sets and probe holder grids</td>
			<td style="width:125px;">Hitachi Medical Corporation, Tokyo, Japan</td>
			<td style="width:355px;">ETG-4000 Optical Topography System&#160;</td>
			<td style="width:171px;">The current study protocol requires an optional second adult probe set for 52 channels of measurement in total as well as two 3x5 probe holder grids.&#160;</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">raw EEG caps</td>
			<td>EASYCAP GmbH, Herrsching, Germany</td>
			<td>C-SCMS-56; C-SCMS-58</td>
			<td>Caps must be provided with holes for NIRS probes by the experimenter. Choose cap size the same size or slightly larger than participant&#39;s head circumference.</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Technical computing software</td>
			<td>The MathWorks, Inc., Natick, MA</td>
			<td>MATLAB R2014a (or later versions)</td>
			<td>Serves as base for Psychophysics Toolbox extensions (stimulus presentation), SPM for fNIRS toolbox&#160; (fNIRS data analysis), and ASToolbox (WTC computation).</td>
		</tr>
	</tbody>
</table>

conducting hyperscanning experiments with functional near-infrared spectroscopy

Concurrent brain recordings of two or more interacting persons, an approach termed hyperscanning, are gaining increasing importance for our understanding of the neurobiological underpinnings of social interactions, and possibly interpersonal relationships. Functional near-infrared spectroscopy (fNIRS) is well suited for conducting hyperscanning experiments because it measures local hemodynamic effects with a high sampling rate and, importantly, it can be applied in natural settings, not requiring strict motion restrictions. In this article, we present a protocol for conducting fNIRS hyperscanning experiments with parent-child dyads and for analyzing brain-to-brain synchrony. Furthermore, we discuss critical issues and future directions, regarding the experimental design, spatial registration of the fNIRS channels, physiological influences and data analysis methods. The described protocol is not specific to parent-child dyads, but can be applied to a variety of different dyadic constellations, such as adult strangers, romantic partners or siblings. To conclude, fNIRS hyperscanning has the potential to yield new insights into the dynamics of the ongoing social interaction, which possibly go beyond what can be studied by examining the activities of individual brains.

Representative data of one parent-child dyad during the cooperative condition are shown in Figure 1. The cooperative task consists of three 30 s rest blocks and two task blocks, with 20 trials each, presented in alternating order. In each trial, participants have to react as simultaneously as possible to a signal to earn a point11.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58807/58807fig1.jpg" /> 
Figure 1: Hyperscanning data analysis and representative results. Light intensity data is collected in 22 channels (CHs) of two participants. First, bad channels are detected and excluded from further analyses. Afterwards, light intensity data is converted to changes in oxy-hemoglobin (&#916; Oxy-Hb) and deoxy-hemoglobin (&#916; Deoxy-Hb). Signals are shown for one exemplary parent-child dyad in CH 8 during the cooperative condition. Data is preprocessed by reducing motion artifacts and slow drifts. Afterwards, the wavelet coherence is calculated from the preprocessed oxy-Hb signals. To estimate the significance of each wavelet coherence value, 300 surrogate time series are generated. If the observed wavelet coherence value is higher than 95% of the wavelet coherence values obtained from the surrogate time series at the same point in time and scale, it is regarded as significant. Significant wavelet coherence values are marked by solid lines surrounding the respective areas in the plot. Coherence in the task-related frequency band is depicted within the black box. Please note that the analysis steps and the parameterization depicted in the figure should be understood as an example. The optimal parameterization depends on the data, e.g., different parameters of the MARA algorithm might work best for different types of artifacts20, and there is no gold standard for any of the analysis steps yet. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58807/58807fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The results are exemplified for the fNIRS data of channel 8 of both participants of a parent-child dyad. Before preprocessing, raw light attenuation data, received from the fNIRS device, are converted to changes in oxy-Hb and deoxy-Hb for both participants. Next, fNIRS time series are preprocessed to reduce motion artifacts and drifts. Finally, the significant WTC is calculated from the preprocessed oxy-Hb signals of both participants.
Figure 1&#160;illustrates a real valued WTC matrix, which is composed of the coherence coefficients in time and frequency domain (here in period length). The coefficients can range between 0 and 1, with 1 indicating a perfect relationship at a specific time and frequency between both signals24. The coefficients are visualized using a color map ranging from blue (little or no coherence) to red (strong or maximum coherence). Significant coherence values are marked by solid black lines surrounding the respective areas in the plot. The beginning and end of each task block are indicated by vertical dashed lines.
Results show a strong coherence throughout the experiment in a high frequency band, until a period length of ~ 1 s (1 Hz). This likely results from the cardiac rhythms of parent and child. Additionally, results show a strong coherence in a lower frequency band between ~ 2 s and 8 s period length (0.5 - 0.125 Hz). Trial lengths differed due to pseudo-randomized variable cue durations (600 - 1500 ms) and participants&#39; individual reaction times but were around 7 s on average, assuming reaction times of about 1 s. Therefore, coherence in this low frequency range likely reflects a synchronization of brain activities of both subjects during the task.

Watch this Scientific Journal Video about Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy at JoVE.com

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

The present protocol describes how to conduct fNIRS hyperscanning experiments and analyze brain-to-brain synchrony. Further, we discuss challenges and possible solutions.

Concurrent brain recordings of two or more interacting persons, an approach termed hyperscanning, are gaining increasing importance for our understanding ...

conducting-hyperscanning-experiments-with-functional-near-infrared

Nanyang Technological University

Research

JoVE Journal

Neuroscience

9.7K Views.  RWTH Aachen University. The present protocol describes how to conduct fNIRS hyperscanning experiments and analyze brain-to-brain synchrony. Further, we discuss challenges and possible solutions.

Video: Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Functional Mapping with Simultaneous MEG and EEG

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

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

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

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments1. MazeSuite can be used to design and edit adapted virtual 3D environments, track a participants' behavioral performance within the virtual environment and synchronize with external devices for physiological and neuroimaging measures, including electroencephalogram and eye tracking.
Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables continuous, noninvasive, and portable monitoring of changes in cerebral blood oxygenation related to human brain functions2-7. Over the last decade fNIR is used to effectively monitor cognitive tasks such as attention, working memory and problem solving7-11. fNIR can be implemented in the form of a wearable and minimally intrusive device; it has the capacity to monitor brain activity in ecologically valid environments. 
Cognitive functions assessed through task performance involve patterns of brain activation of the prefrontal cortex (PFC) that vary from the initial novel task performance, after practice and during retention12. Using positron emission tomography (PET), Van Horn and colleagues found that regional cerebral blood flow was activated in the right frontal lobe during the encoding (i.e., initial na&iuml;ve performance) of spatial navigation of virtual mazes while there was little to no activation of the frontal regions after practice and during retention tests. Furthermore, the effects of contextual interference, a learning phenomenon related to organization of practice, are evident when individuals acquire multiple tasks under different practice schedules13,14. High contextual interference (random practice schedule) is created when the tasks to be learned are presented in a non-sequential, unpredictable order. Low contextual interference (blocked practice schedule) is created when the tasks to be learned are presented in a predictable order.
Our goal here is twofold: first to illustrate the experimental protocol design process and the use of MazeSuite, and second, to demonstrate the setup and deployment of the fNIR brain activity monitoring system using Cognitive Optical Brain Imaging (COBI) Studio software15. To illustrate our goals, a subsample from a study is reported to show the use of both MazeSuite and COBI Studio in a single experiment. The study involves the assessment of cognitive activity of the PFC during the acquisition and learning of computer maze tasks for blocked and random orders. Two right-handed adults (one male, one female) performed 315 acquisition, 30 retention and 20 transfer trials across four days. Design, implementation, data acquisition and analysis phases of the study were explained with the intention to provide a guideline for future studies.

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments. Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables noninvasive and portable monitoring of cerebral blood oxygenation changes. This paper summarizes collective use of MazeSuite and fNIR within a cognitive processing learning paradigm.

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments1. MazeSuite can be used to design and edit adapted ...

Micron-scale Resolution Optical Tomography of Entire Mouse Brains with Confocal Light Sheet Microscopy

Understanding the architecture of mammalian brain at single-cell resolution is one of the key issues of neuroscience. However, mapping neuronal soma and projections throughout the whole brain is still challenging for imaging and data management technologies. Indeed, macroscopic volumes need to be reconstructed with high resolution and contrast in a reasonable time, producing datasets in the TeraByte range. We recently demonstrated an optical method (confocal light sheet microscopy, CLSM) capable of obtaining micron-scale reconstruction of entire mouse brains labeled with enhanced green fluorescent protein (EGFP). Combining light sheet illumination and confocal detection, CLSM allows deep imaging inside macroscopic cleared specimens with high contrast and speed. Here we describe the complete experimental pipeline to obtain comprehensive and human-readable images of entire mouse brains labeled with fluorescent proteins. The clearing and the mounting procedures are described, together with the steps to perform an optical tomography on its whole volume by acquiring many parallel adjacent stacks. We showed the usage of open-source custom-made software tools enabling stitching of the multiple stacks and multi-resolution data navigation. Finally, we illustrated some example of brain maps: the cerebellum from an L7-GFP transgenic mouse, in which all Purkinje cells are selectively labeled, and the whole brain from a thy1-GFP-M mouse, characterized by a random sparse neuronal labeling.

In this article we describe the full experimental procedure to reconstruct, with high resolution, the fine brain anatomy of fluorescently labeled mouse brains. The described protocol includes sample preparation and clearing, specimen mounting for imaging, data post-processing and multi-scale visualization.

Understanding the architecture of mammalian  brain at single-cell resolution is one of the key issues of neuroscience.  However, mapping neuronal soma and ...

Functional Interrogation of Adult Hypothalamic Neurogenesis with Focal Radiological Inhibition

The functional characterization of adult-born neurons remains a significant challenge. Approaches to inhibit adult neurogenesis via invasive viral delivery or transgenic animals have potential confounds that make interpretation of results from these studies difficult. New radiological tools are emerging, however, that allow one to noninvasively investigate the function of select groups of adult-born neurons through accurate and precise anatomical targeting in small animals. Focal ionizing radiation inhibits the birth and differentiation of new neurons, and allows targeting of specific neural progenitor regions. In order to illuminate the potential functional role that adult hypothalamic neurogenesis plays in the regulation of physiological processes, we developed a noninvasive focal irradiation technique to selectively inhibit the birth of adult-born neurons in the hypothalamic median eminence. We describe a method for Computer tomography-guided focal irradiation (CFIR) delivery to enable precise and accurate anatomical targeting in small animals. CFIR uses three-dimensional volumetric image guidance for localization and targeting of the radiation dose, minimizes radiation exposure to nontargeted brain regions, and allows for conformal dose distribution with sharp beam boundaries. This protocol allows one to ask questions regarding the function of adult-born neurons, but also opens areas to questions in areas of radiobiology, tumor biology, and immunology. These radiological tools will facilitate the translation of discoveries at the bench to the bedside.

The function of adult-born mammalian neurons remains an active area of investigation. Ionizing radiation inhibits the birth of new neurons. Using computer tomography-guided focal irradiation (CFIR), three-dimensional anatomical targeting of specific neural progenitor populations can now be used to assess the functional role of adult neurogenesis.

The functional characterization of adult-born neurons remains a significant challenge. Approaches to inhibit adult neurogenesis via invasive viral ...

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy (NIRS) Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Portable neuroimaging approaches provide new advances to the study of brain function and brain development with previously inaccessible populations and in remote locations. This paper shows the development of field functional Near Infrared Spectroscopy (fNIRS) imaging to the study of child language, reading, and cognitive development in a rural village setting of C&#244;te d&#39;Ivoire. Innovation in methods and the development of culturally appropriate neuroimaging protocols allow&#160;a first-time look into the brain&#39;s development and children&#39;s learning outcomes in understudied environments. This paper demonstrates protocols for transporting and setting up a mobile laboratory, discusses considerations for field versus laboratory neuroimaging, and presents a guide for developing neuroimaging consent procedures and building meaningful long-term collaborations with local government and science partners. Portable neuroimaging methods can be used to study complex child development contexts, including the impact of significant poverty and adversity on brain development. The protocol presented here has been developed for use in C&#244;te d&#39;Ivoire, the world&#39;s primary source of cocoa, and where reports of child labor in the cocoa sector are common. Yet, little is known about the impact of child labor on brain development and learning. Field neuroimaging methods have the potential to yield new insights into such urgent issues, and the development of children globally.

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy NIRS Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Portable neuroimaging approaches (functional Near Infrared Spectroscopy) provide advances to the study of the brain in previously inaccessible regions; here, rural C&#244;te d&#39;Ivoire. Innovation in methods and development of culturally-appropriate neuroimaging protocols permits novel study of the brain&#39;s development and children&#39;s learning outcomes in environments with significant poverty and adversity.

Portable neuroimaging approaches provide new advances to the study of brain function and brain development with previously inaccessible populations and in ...

Lipid Bilayer Experiments with Contact Bubble Bilayers for Patch-Clampers

Lipid bilayers provide a unique experimental platform for functional studies of ion channels, allowing the examination of channel-membrane interactions under various membrane lipid compositions. Among them, the droplet interface bilayer has gained popularity; however, the large membrane size hinders the recording of low electrical background noise. We have established a contact bubble bilayer (CBB) method that combines the benefits of planar lipid bilayer and patch-clamp methods, such as the ability to vary the lipid composition and to manipulate the bilayer mechanics, respectively. Using the setup for conventional patch-clamp experiments, CBB-based experiments can be readily performed. In brief, an electrolyte solution in a glass pipette is blown into an organic solvent phase (hexadecane), and the pipette pressure is maintained to obtain a stable bubble size. The bubble is spontaneously lined with a lipid monolayer (pure lipids or mixed lipids), which is provided from liposomes in the bubbles. Next, the two monolayer-lined bubbles (~50 &#181;m in diameter) at the tip of the glass pipettes are docked for bilayer formation. Introduction of channel-reconstituted liposomes into the bubble leads to the incorporation of channels in the bilayer, allowing for single-channel current recording with a signal-to-noise ratio comparable to that of patch-clamp recordings. CBBs with an asymmetric lipid composition are readily formed. The CBB is renewed repeatedly by blowing out the previous bubbles and forming new ones. Various chemical and physical perturbations (e.g., membrane perfusion and bilayer tension) can be imposed on the CBBs. Herein, we present the basic procedure for CBB formation.

Here, we present a protocol for the formation of lipid bilayers using a contact bubble bilayer method. A water bubble is blown into an organic solvent, whereby a monolayer is formed at the water-oil interface. Two pipettes are manipulated to dock the bubbles to form a bilayer.

Lipid bilayers provide a unique experimental platform for functional studies of ion channels, allowing the examination of channel-membrane interactions ...

Functional Magnetic Resonance Spectroscopy at 7 T in the Rat Barrel Cortex During Whisker Activation

Nuclear magnetic resonance (NMR) spectroscopy offers the opportunity to measure cerebral metabolite contents in vivo and noninvasively. Thanks to technological developments over the last decade and the increase in magnetic field strength, it is now possible to obtain good resolution spectra in vivo in the rat brain. Neuroenergetics (i.e., the study of brain metabolism) and, especially, metabolic interactions between the different cell types have attracted more and more interest in recent years. Among these metabolic interactions, the existence of a lactate shuttle between neurons and astrocytes is still debated. It is, thus, of great interest to perform functional proton magnetic resonance spectroscopy (1H-MRS) in a rat model of brain activation and monitor lactate. However, the methyl lactate peak overlaps lipid resonance peaks and is difficult to quantify. The protocol described below allows metabolic and lactate fluctuations to be monitored in an activated brain area. Cerebral activation is obtained by whisker stimulation and 1H-MRS is performed in the corresponding activated barrel cortex, whose area is detected using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI). All steps are fully described: the choice of anesthetics, coils, and sequences, achieving efficient whisker stimulation directly in the magnet, and data processing.

After checking by blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) that the corresponding somatosensory barrel field cortex area (called S1BF) is correctly activated, the main goal of this study is to quantify lactate content fluctuations in the activated rat brains by localized proton magnetic resonance spectroscopy (1H-MRS) at 7 T.

Nuclear magnetic resonance (NMR) spectroscopy offers the opportunity to measure cerebral metabolite contents in vivo and noninvasively. Thanks to ...

Using Near-infrared Fluorescence and High-resolution Scanning to Measure Protein Expression in the Rodent Brain

Neuroscience is the study of how cells in the brain mediate various functions. Measuring protein expression in neurons and glia is critical for the study of neuroscience as cellular function is determined by the composition and activity of cellular proteins. In this article, we describe how immunocytochemistry can be combined with near-infrared high-resolution scanning to provide a semi-quantitative measure of protein expression in distinct brain regions. This technique can be used for single or double protein expression in the same brain region. Measuring proteins in this fashion can be used to obtain a relative change in protein expression with an experimental manipulation, molecular signature of learning and memory, activity in molecular pathways, and neural activity in multiple brain regions. Using the correct proteins and statistical analysis, functional connectivity among brain regions can be determined as well. Given the ease of implementing immunocytochemistry in a laboratory, using immunocytochemistry with near-infrared high-resolution scanning can expand the ability of the neuroscientist to examine neurobiological processes at a systems level.

Here, we present a protocol that uses near-infrared dyes in conjunction with immunohistochemistry and high-resolution scanning to assay proteins in brain regions.

Neuroscience is the study of how cells in the brain mediate various functions. Measuring protein expression in neurons and glia is critical for the study ...

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Concurrent EEG and fNIRS recordings offer an excellent opportunity to gain a full understanding of the neural mechanism of cognitive processing by inspecting the relationship between the neural and hemodynamic signals. EEG is an electrophysiological technology that can measure the rapid neuronal activity of the cortex, whereas fNIRS relies on the hemodynamic responses to infer brain activation. The combination of EEG and fNIRS neuroimaging techniques can identify more features and reveal more information associated with the functioning of the brain. In this protocol, fused EEG-fNIRS measurements were performed for concurrent recordings of evoked-electrical potentials and hemodynamic responses during a Flanker task. In addition, the critical steps for setting up the hardware and software system as well as the procedures for data acquisition and analysis were provided and discussed in detail. It is expected that the present protocol can pave a new avenue for improving the understanding of the neural mechanisms underlying various cognitive processes by using the EEG and fNIRS signals.

The present protocol describes how to perform concurrent EEG and fNIRS recordings and how to inspect the relationship between the EEG and fNIRS data.&#160;

Concurrent EEG and fNIRS recordings offer an excellent opportunity to gain a full understanding of the neural mechanism of cognitive processing by ...

Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical treatment. Among the many neuroimaging technologies used to measure brain activity, functional near-infrared spectroscopy (fNIRS) enables the evaluation of dynamic cortical activities by measuring the local hemoglobin levels similar to functional magnetic resonance imaging (fMRI). Also, due to lesser physical restriction in fNIRS, multiple variants of sensorimotor tasks can be evaluated. Many laboratories have developed several methods for fNIRS data analysis; however, despite the fact that the general principles are the same, there is no universally standardized method. Here, we present the qualitative and comparative analytic methods of data obtained from a multi-channel fNIRS experiment using a block design. For qualitative analysis, we used a software for NIRS as a mass-univariate approach based on the generalized linear model. The NIRS-SPM analysis shows qualitative results for each session by visualizing the activated area during the task. In addition, the non-invasive three-dimensional digitizer can be used to estimate the fNIRS channel locations relative to the brain. To corroborate the NIRS-SPM findings, the amplitude of the changes in hemoglobin levels induced by the sensorimotor task can be statistically analyzed by comparing the data obtained from two different sessions (before and after intervention) of the same study subject using a multi-channel hierarchical mixed model. Our methods can be used to measure the pre- vs. post-intervention analysis in a variety of neurological disorders such as movement disorders, cerebrovascular diseases, and neuropsychiatric disorders.

We describe the analysis of continuous-wave functional near-infrared spectroscopy experiment using a block design with a sensorimotor task. To increase the reliability of the data analysis, we used the qualitative general linear model-based statistical parametric mapping and the comparative hierarchical mixed models for multi-channels.

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical ...