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>

<ol>
	<li>Babiloni, F., Astolfi, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Social+neuroscience+and+hyperscanning+techniques:+past,+present+and+future.">Social neuroscience and hyperscanning techniques: past, present and future.</a> Neuroscience &amp; Biobehavioral Reviews. 44, 76-93 (2014).</li><li>Hari, R., Henriksson, L., Malinen, S., Parkkonen, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Centrality+of+social+interaction+in+human+brain+function.">Centrality of social interaction in human brain function.</a> Neuron. 88 (1), 181-193 (2015).</li><li>Funane, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synchronous+activity+of+two+people's+prefrontal+cortices+during+a+cooperative+task+measured+by+simultaneous+near-infrared+spectroscopy.">Synchronous activity of two people's prefrontal cortices during a cooperative task measured by simultaneous near-infrared spectroscopy.</a> Journal of Biomedical Optics. 16 (7), 077011 (2011).</li><li>Lindenberger, U., Li, S. -. C., Gruber, W., M&#252;ller, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brains+swinging+in+concert:+cortical+phase+synchronization+while+playing+guitar.">Brains swinging in concert: cortical phase synchronization while playing guitar.</a> BMC Neuroscience. 10, 22 (2009).</li><li>Jiang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+synchronization+during+face-to-face+communication.">Neural synchronization during face-to-face communication.</a> Journal of Neuroscience. 32 (45), 16064-16069 (2012).</li><li>Dikker, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-to-brain+synchrony+tracks+real-world+dynamic+group+interactions+in+the+classroom.">Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom.</a> Current Biology. 27 (9), 1375-1380 (2017).</li><li>Liu, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NIRS-based+hyperscanning+reveals+inter-brain+neural+synchronization+during+cooperative+Jenga+game+with+face-to-face+communication.">NIRS-based hyperscanning reveals inter-brain neural synchronization during cooperative Jenga game with face-to-face communication.</a> Frontiers in Human Neuroscience. 10, 82 (2016).</li><li>Hoshi, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+near-infrared+spectroscopy:+current+status+and+future+prospects.">Functional near-infrared spectroscopy: current status and future prospects.</a> Journal of Biomedical Optics. 12 (6), 062106 (2007).</li><li>Lloyd-Fox, S., Blasi, A., Elwell, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Illuminating+the+developing+brain:+the+past,+present+and+future+of+functional+near+infrared+spectroscopy.">Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy.</a> Neuroscience &amp; Biobehavioral Reviews. 34 (3), 269-284 (2010).</li><li>Huppert, T. J., Hoge, R. D., Diamond, S. G., Franceschini, M. A., Boas, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+temporal+comparison+of+BOLD,+ASL,+and+NIRS+hemodynamic+responses+to+motor+stimuli+in+adult+humans.">A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans.</a> NeuroImage. 29 (2), 368-382 (2006).</li><li>Reindl, V., Gerloff, C., Scharke, W., Konrad, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-to-brain+synchrony+in+parent-child+dyads+and+the+relationship+with+emotion+regulation+revealed+by+fNIRS-based+hyperscanning.">Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning.</a> NeuroImage. 178, 493-502 (2018).</li><li>Cui, X., Bryant, D. M., Reiss, A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NIRS-based+hyperscanning+reveals+increased+interpersonal+coherence+in+superior+frontal+cortex+during+cooperation.">NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation.</a> NeuroImage. 59 (3), 2430-2437 (2012).</li><li>Baker, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sex+differences+in+neural+and+behavioral+signatures+of+cooperation+revealed+by+fNIRS+hyperscanning.">Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning.</a> Scientific Reports. 6, 26492 (2016).</li><li>Cheng, X., Li, X., Hu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synchronous+brain+activity+during+cooperative+exchange+depends+on+gender+of+partner:+a+fNIRS-based+hyperscanning+study.">Synchronous brain activity during cooperative exchange depends on gender of partner: a fNIRS-based hyperscanning study.</a> Human Brain Mapping. 36 (6), 2039-2048 (2015).</li><li>Pan, Y., Cheng, X., Zhang, Z., Li, X., Hu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cooperation+in+lovers:+an+fNIRS-based+hyperscanning+study.">Cooperation in lovers: an fNIRS-based hyperscanning study.</a> Human Brain Mapping. 38 (2), 831-841 (2017).</li><li>Kleiner, M., Brainard, D., Pelli, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What's+new+in+Psychtoolbox-3?.">What's new in Psychtoolbox-3?.</a> Perception. 36, (2007).</li><li>Huppert, T. J., Diamond, S. G., Franceschini, M. A., Boas, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HomER:+a+review+of+time-series+analysis+methods+for+near-infrared+spectroscopy+of+the+brain.">HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain.</a> Applied Optics. 48 (10), D280-D298 (2009).</li><li>Santosa, H., Zhai, X., Fishburn, F., Huppert, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+NIRS+Brain+AnalyzIR+Toolbox.">The NIRS Brain AnalyzIR Toolbox.</a> Algorithms. 11 (5), 73 (2018).</li><li>Tak, S., Uga, M., Flandin, G., Dan, I., Penny, W. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sensor+space+group+analysis+for+fNIRS+data.">Sensor space group analysis for fNIRS data.</a> Journal of Neuroscience Methods. 264, 103-112 (2016).</li><li>Scholkmann, F., Spichtig, S., Muehlemann, T., Wolf, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+to+detect+and+reduce+movement+artifacts+in+near-infrared+imaging+using+moving+standard+deviation+and+spline+interpolation.">How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation.</a> Physiological Measurement. 31 (5), 649-662 (2010).</li><li>van der Kant, A., Biro, S., Levelt, C., Huijbregts, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Negative+affect+is+related+to+reduced+differential+neural+responses+to+social+and+non-social+stimuli+in+5-to-8-month-old+infants:+a+functional+near-infrared+spectroscopy-study.">Negative affect is related to reduced differential neural responses to social and non-social stimuli in 5-to-8-month-old infants: a functional near-infrared spectroscopy-study.</a> Developmental Cognitive Neuroscience. 30, 23-30 (2018).</li><li>Bastos, A. M., Schoffelen, J. -. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+tutorial+review+of+functional+connectivity+analysis+methods+and+their+interpretational+pitfalls.">A tutorial review of functional connectivity analysis methods and their interpretational pitfalls.</a> Frontiers in Systems Neuroscience. 9, 175 (2016).</li><li>Grinsted, A., Moore, J. C., Jevrejeva, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+the+cross+wavelet+transform+and+wavelet+coherence+to+geophysical+time+series.">Application of the cross wavelet transform and wavelet coherence to geophysical time series.</a> Nonlinear Processes in Geophysics. 11, 561-566 (2004).</li><li>Aguiar-Conraria, L., Soares, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+continuous+wavelet+transform:+moving+beyond+uni-and+bivariate+analysis.">The continuous wavelet transform: moving beyond uni-and bivariate analysis.</a> Journal of Economic Surveys. 28 (2), 344-375 (2014).</li><li>Nozawa, T., Sasaki, Y., Sakaki, K., Yokoyama, R., Kawashima, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpersonal+frontopolar+neural+synchronization+in+group+communication:+an+exploration+toward+fNIRS+hyperscanning+of+natural+interactions.">Interpersonal frontopolar neural synchronization in group communication: an exploration toward fNIRS hyperscanning of natural interactions.</a> NeuroImage. 133, 484-497 (2016).</li><li>Burgess, A. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+interpretation+of+synchronization+in+EEG+hyperscanning+studies:+a+cautionary+note.">On the interpretation of synchronization in EEG hyperscanning studies: a cautionary note.</a> Frontiers in Human Neuroscience. 7, 881 (2013).</li><li>Tsuzuki, D., Dan, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+registration+for+functional+near-infrared+spectroscopy:+from+channel+position+on+the+scalp+to+cortical+location+in+individual+and+group+analyses.">Spatial registration for functional near-infrared spectroscopy: from channel position on the scalp to cortical location in individual and group analyses.</a> NeuroImage. 85, 92-103 (2014).</li><li>Tachtsidis, I., Scholkmann, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=False+positives+and+false+negatives+in+functional+near-infrared+spectroscopy:+issues,+challenges,+and+the+way+forward.">False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward.</a> Neurophotonics. 3 (3), 031405 (2016).</li><li>Palumbo, R. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpersonal+autonomic+physiology:+a+systematic+review+of+the+literature.">Interpersonal autonomic physiology: a systematic review of the literature.</a> Personality and Social Psychology Review. 21 (2), 99-141 (2016).</li><li>Pinti, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+present+and+future+use+of+functional+near-infrared+spectroscopy+(fNIRS)+for+cognitive+neuroscience.">The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience.</a> Annals of the New York Academy of Sciences. , (2018).</li><li>Brigadoi, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motion+artifacts+in+functional+near-infrared+spectroscopy:+a+comparison+of+motion+correction+techniques+applied+to+real+cognitive+data.">Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data.</a> NeuroImage. 85 (1), 181-191 (2014).</li><li>Cooper, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+systematic+comparison+of+motion+artifact+correction+techniques+for+functional+near-infrared+spectroscopy.">A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy.</a> Frontiers in Neuroscience. 6, 147 (2012).</li><li>Hirsch, J., Zhang, X., Noah, J. A., Ono, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frontal+temporal+and+parietal+systems+synchronize+within+and+across+brains+during+live+eye-to-eye+contact.">Frontal temporal and parietal systems synchronize within and across brains during live eye-to-eye contact.</a> NeuroImage. 157, 314-330 (2017).</li><li>Scholkmann, F., Holper, L., Wolf, U., Wolf, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+methodical+approach+in+neuroscience:+assessing+inter-personal+brain+coupling+using+functional+near-infrared+imaging+(fNIRI)+hyperscanning.">A new methodical approach in neuroscience: assessing inter-personal brain coupling using functional near-infrared imaging (fNIRI) hyperscanning.</a> Frontiers in Human Neuroscience. 7, 813 (2013).</li></ol>

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><tbody><tr><td>NIRS measurement system with probe sets and probe holder grids</td><td>Hitachi Medical Corporation, Tokyo, Japan</td><td>ETG-4000 Optical Topography System&amp;nbsp;</td><td>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.&amp;nbsp;</td></tr><tr><td>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&#039;s head circumference.</td></tr><tr><td>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&amp;nbsp; (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

10.2K Views.  RWTH Aachen University. Door hersenactiviteiten van twee of meer mensen tegelijk te meten, kan deze methode licht werpen op de neurobiologische mechanismen die ten grondslag liggen aan sociale interacties. Functionele nabij-infrarood spectroscopie, of fNIRS, is zeer geschikt voor het uitvoeren van dergelijke hyperscanning experimenten omdat het meet de hersenen hemodynamische respons met een hoge sampling rate en kan worden toegepast in relatief natuurlijke omgevingen. Hoewel dit protocol laat zien hoe gelijktijdige...

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 ...

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Experimental paradigms are valuable insofar as the timing and other parameters of their stimuli are well specified and controlled, and insofar as they yield data relevant to the cognitive processing that occurs under ecologically valid conditions. These two goals often are at odds, since well controlled stimuli often are too repetitive to sustain subjects' motivation. Studies employing electroencephalography (EEG) are often especially sensitive to this dilemma between ecological validity and experimental control: attaining sufficient signal-to-noise in physiological averages demands large numbers of repeated trials within lengthy recording sessions, limiting the subject pool to individuals with the ability and patience to perform a set task over and over again. This constraint severely limits researchers' ability to investigate younger populations as well as clinical populations associated with heightened anxiety or attentional abnormalities. Even adult, non-clinical subjects may not be able to achieve their typical levels of performance or cognitive engagement: an unmotivated subject for whom an experimental task is little more than a chore is not the same, behaviourally, cognitively, or neurally, as a subject who is intrinsically motivated and engaged with the task. A growing body of literature demonstrates that embedding experiments within video games may provide a way between the horns of this dilemma between experimental control and ecological validity. The narrative of a game provides a more realistic context in which tasks occur, enhancing their ecological validity (Chaytor &amp; Schmitter-Edgecombe, 2003). Moreover, this context provides motivation to complete tasks. In our game, subjects perform various missions to collect resources, fend off pirates, intercept communications or facilitate diplomatic relations. In so doing, they also perform an array of cognitive tasks, including a Posner attention-shifting paradigm (Posner, 1980), a go/no-go test of motor inhibition, a psychophysical motion coherence threshold task, the Embedded Figures Test (Witkin, 1950, 1954) and a theory-of-mind (Wimmer &amp; Perner, 1983) task. The game software automatically registers game stimuli and subjects' actions and responses in a log file, and sends event codes to synchronise with physiological data recorders. Thus the game can be combined with physiological measures such as EEG or fMRI, and with moment-to-moment tracking of gaze. Gaze tracking can verify subjects' compliance with behavioural tasks (e.g. fixation) and overt attention to experimental stimuli, and also physiological arousal as reflected in pupil dilation (Bradley et al., 2008). At great enough sampling frequencies, gaze tracking may also help assess covert attention as reflected in microsaccades - eye movements that are too small to foveate a new object, but are as rapid in onset and have the same relationship between angular distance and peak velocity as do saccades that traverse greater distances. The distribution of directions of microsaccades correlates with the (otherwise) covert direction of attention (Hafed &amp; Clark, 2002).

Procedures for recording high-density EEG and gaze data during computer game-based cognitive tasks are described. Using a video game to present cognitive tasks enhances ecological validity without sacrificing experimental control.

Experimental paradigms are valuable insofar as the timing and other parameters of their stimuli are well specified and controlled, and insofar as they ...

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is relatively flexible with respect to participant positioning and allows for some head movements during tasks. Additionally, it is inexpensive, light weight, and portable, with very few contraindications to its use. This presents a unique opportunity to study functional brain activity during motor tasks in individuals who are typically developing, as well as those with movement disorders, such as cerebral palsy. An additional consideration when studying movement disorders, however, is the quality of actual movements performed and the potential for additional, unintended movements. Therefore, concurrent monitoring of both blood flow changes in the brain and actual movements of the body during testing is required for appropriate interpretation of fNIRS results. Here, we show a protocol for the combination of fNIRS with muscle and kinematic monitoring during motor tasks. We explore gait, a unilateral multi-joint movement (cycling), and two unilateral single-joint movements (isolated ankle dorsiflexion, and isolated hand squeezing). The techniques presented can be useful in studying both typical and atypical motor control, and can be modified to investigate a broad range of tasks and scientific questions.

Monitoring brain activity during upright motor tasks is of great value when investigating the neural source of movement disorders. Here, we demonstrate a protocol that combines functional near infrared spectroscopy with continuous monitoring of muscle and kinematic activity during 4 types of motor tasks.

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is ...

Behavior

Exploring Cognitive Functions in Babies, Children &amp; Adults with Near Infrared Spectroscopy

An explosion of functional Near Infrared Spectroscopy (fNIRS) studies investigating cortical activation in relation to higher cognitive processes, such as language1,2,3,4,5,6,7,8,9,10, memory11, and attention12 is underway worldwide involving adults, children and infants 3,4,13,14,15,16,17,18,19 with typical and atypical cognition20,21,22. The contemporary challenge of using fNIRS for cognitive neuroscience is to achieve systematic analyses of data such that they are universally interpretable23,24,25,26, and thus may advance important scientific questions about the functional organization and neural systems underlying human higher cognition.
Existing neuroimaging technologies have either less robust temporal or spatial resolution. Event Related Potentials and Magneto Encephalography (ERP and MEG) have excellent temporal resolution, whereas Positron Emission Tomography and functional Magnetic Resonance Imaging (PET and fMRI) have better spatial resolution. Using non-ionizing wavelengths of light in the near-infrared range (700-1000 nm), where oxy-hemoglobin is preferentially absorbed by 680 nm and deoxy-hemoglobin is preferentially absorbed by 830 nm (e.g., indeed, the very wavelengths hardwired into the fNIRS Hitachi ETG-400 system illustrated here), fNIRS is well suited for studies of higher cognition because it has both good temporal resolution (~5s) without the use of radiation and good spatial resolution (~4 cm depth), and does not require participants to be in an enclosed structure27,28. Participants cortical activity can be assessed while comfortably seated in an ordinary chair (adults, children) or even seated in mom s lap (infants). Notably, NIRS is uniquely portable (the size of a desktop computer), virtually silent, and can tolerate a participants subtle movement. This is particularly outstanding for the neural study of human language, which necessarily has as one of its key components the movement of the mouth in speech production or the hands in sign language. 
The way in which the hemodynamic response is localized is by an array of laser emitters and detectors. Emitters emit a known intensity of non-ionizing light while detectors detect the amount reflected back from the cortical surface. The closer together the optodes, the greater the spatial resolution, whereas the further apart the optodes, the greater depth of penetration. For the fNIRS Hitachi ETG-4000 system optimal penetration / resolution the optode array is set to 2cm. 
Our goal is to demonstrate our method of acquiring and analyzing fNIRS data to help standardize the field and enable different fNIRS labs worldwide to have a common background.

Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy

Here we describe a data collection and data analysis method for functional Near Infrared Spectroscopy (fNIRS), a novel non-invasive brain imaging system used in cognitive neuroscience, particularly in studying child brain development. This method provides a universal standard of data acquisition and analysis vital to data interpretation and scientific discovery.

An explosion of functional Near Infrared Spectroscopy (fNIRS) studies investigating cortical activation in relation to higher cognitive processes, such as ...

Biology

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 ...

Cerebrovascular Reactivity Measurement with Functional Near Infrared Spectroscopy

Cerebrovascular reactivity (CVR) is the capacity of blood vessels in the brain to alter cerebral blood flow (either with dilation or constriction) in response to chemical or physical stimuli. The amount of reactivity in the cerebral microvasculature depends on the integrity of the capacitance vasculature and is the primary function of endothelial cells. CVR is, therefore, an indicator of the microvasculature&#8217;s physiology and overall health. Imaging methods that can measure CVR are available but can be costly, and require magnetic resonance imaging centers and technical expertise. In this study, we used fNIRS technology to monitor changes of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the cerebral microvasculature to assess the CVR of 15 healthy controls (HC) in response to a vasoactive stimulus (inhaled 5% carbon dioxide or CO2). Our results suggest that this is a promising imaging technology that offers a non-invasive, accurate, portable, and cost-effective method of mapping cortical CVR and associated microvasculature function, resulting from a traumatic brain injury or other conditions associated with cerebral microvasculopathy.

Presented here is a protocol for imaging and measurement of cerebrovascular reactivity in humans with functional Near Infrared Spectroscopy (fNIRS). fNIRS is a novel imaging modality that captures the concentration changes of hemoglobin species in the brain&#8217;s outermost cortex under specific stimuli.

Cerebrovascular reactivity (CVR) is the capacity of blood vessels in the brain to alter cerebral blood flow (either with dilation or constriction) in ...

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a noninvasive method particularly suitable for measuring cerebral cortex activation in multiple subjects, which is relevant for studying group interpersonal interactions in ecological settings. Although many fNIRS systems technically offer the possibility to monitor more than two individuals simultaneously, establishing easy-to-implement setup procedures and reliable paradigms to track hemodynamic and behavioral responses in group interaction is still required. The present protocol combines fNIRS and video-based observation to measure interpersonal synchronization in quartets during a cooperative task.This protocol provides practical recommendations for data acquisition and paradigm design, as well as guiding principles for an illustrative data analysis example. The procedure is designed to assess differences in brain and behavior interpersonal responses between social and non-social conditions inspired by a well-known ice-breaker activity, the Collaborative Face Drawing Task. The described procedures can guide future studies to adapt group naturalistic social interaction activities to the fNIRS environment.

The present protocol combines functional near-infrared spectroscopy (fNIRS) and video-based observationto measure interpersonal synchronization in quartets during a collaborative drawing task.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive method particularly suitable for measuring cerebral cortex activation in multiple subjects, ...

Revolutionizing fNIRS Studies&#58; A Novel Approach to Neural Coupling

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals using wavelet transform coherence (WTC) seems to ignore the directionality of the interaction. The field is currently lacking a framework that allows researchers to determine whether a high coherence value obtained using a WTC function reflects in-phase synchronization (i.e., neural activation is seen in both members of the dyad at the same time), lagged synchronization (i.e., neural activation is seen in one member of the dyad prior to the other member), or anti-phase synchronization (i.e., neural activation is increased in one member of the dyad and decreased in the other). To address this need, a complementary and more sensitive approach for analyzing the phase coherence of two neural signals is proposed in this work. The toolbox allows investigators to estimate the coupling directionality by classifying the phase angle values obtained using traditional WTC into in-phase synchronization, lagged synchronization, and anti-phase synchronization. The toolbox also allows researchers to assess how the dynamics of interactions develop and change throughout the task. Using this novel WTC approach and the toolbox will advance our understanding of complex social interactions through their uses in fNIRS hyperscanning studies.

Author Spotlight: Unlocking New Insights in fNIRS Studies -  A Novel Framework for Inter-Brain Synchrony Analysis 

Wavelet transform coherence (WTC) is a common methodology for assessing the coupling between signals that is used in functional near-infrared spectroscopy (fNIRS) hyperscanning studies. A toolbox for assessing the directionality of the signal interaction is presented in this work.

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals ...

fNIRS Hyperscanning Protocol to Analyze Inter-Brain Synchrony in Counseling

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Functional Near-Infrared Spectroscopy (fNIRS) hyperscanning is an innovative technique that enables real-time monitoring of brain activity among multiple individuals engaged in social interactions. Researchers in this field quantify concurrent brain activities through the index of inter-brain synchrony (IBS). In psychological counseling research, the use of fNIRS to measure IBS has garnered attention for its potential to illuminate the dynamics of counselor-client interactions. Nevertheless, the field currently lacks a standardized protocol for precisely measuring IBS between counselors and clients, which would facilitate the revelation of real-time interaction patterns during counseling sessions. To address this need, this paper proposes a detailed standardized protocol, outlining the procedural steps for conducting fNIRS hyperscanning in psychological counseling settings, focusing on the acquisition of brain signals, calculation of IBS between counselors and clients, and analysis of lead-lag patterns of IBS throughout counseling sessions. Implementing this standardized fNIRS hyperscanning pipeline not only enhances the reproducibility and reliability of IBS measurements in psychological counseling research but also facilitates deeper insights into the neural mechanisms underlying working alliance. By integrating fNIRS hyperscanning into naturalistic counseling environments, researchers can advance understanding of how IBS correlates with counseling outcomes, potentially informing personalized approaches to mental health treatment.

The protocol aims to provide a detailed description of the methodology for conducting an fNIRS hyperscanning study in psychological counseling. This includes the preparations for the experiment, the procedure for collecting data, and the subsequent data analysis process.

Functional Near-Infrared Spectroscopy (fNIRS) hyperscanning is an innovative technique that enables real-time monitoring of brain activity among multiple ...

Functional Near-Infrared Spectroscopy for Recording Brain Activity During Social Interactions

Source:&#160;<a style='text-decoration:none;' href='https://dx-doi-org.remotexs.ntu.edu.sg/10.3791/58807'><span style='-webkit-text-decoration-skip:none;font-family:Arial,sans-serif;font-size:12pt;font-style:normal;font-variant:normal;font-weight:400;text-decoration-skip-ink:none;vertical-align:baseline;white-space:pre-wrap;'>Reindl, V.,&#160;et al<span style='-webkit-text-decoration-skip:none;font-family:Arial,sans-serif;font-size:12pt;font-style:normal;font-variant:normal;font-weight:400;text-decoration-skip-ink:none;vertical-align:baseline;white-space:pre-wrap;'>. Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy. J. Vis. Exp. (2019)</a> &#160;This video demonstrates the use of functional near-infrared spectroscopy (fNIRS) to measure brain activity during social interactions. Participants engage in a coordinated task that increases activity in the prefrontal cortex, a region critical for social behavior, while the system detects changes in light absorption due to blood oxygenation. By analyzing the synchrony of brain activity between participants, the study assesses neural dynamics underlying social interaction.

Source:&#160;Reindl, V.,&#160;et al. Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy. J. Vis. Exp. (2019)&#160;This video ...

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

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