Method Article

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

DOI:

10.3791/62801

⸱

September 8th, 2021

 ,  ,  ,  ,  ,  , 
1Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, 2Department of Musicology, Harbin Conservatory of Music, 3Department of Clinical Neuroscience, Karolinska Institutet, 4School of Psychology, Shenzhen University

In This Article

Summary

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The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each other, mostly using simultaneous-recording signals of brains (namely hyperscanning) with fNIRS. In fNIRS hyperscanning studies, IBS has been commonly assessed through the wavelet transform coherence (WTC) method because of its advantage on expanding time series into time-frequency space where oscillations can be seen in a highly intuitive way. The observed IBS can be further validated via the permutation-based random pairing of the trial, partner, and condition. Here, a protocol is presented to describe how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation in a hyperscanning study. Further, we discuss the critical issues when using the above methods, including the choice of fNIRS signals, methods of data preprocessing, and optional parameters of computations. In summary, using the WTC method and permutation is a potentially standard pipeline for analyzing IBS in fNIRS hyperscanning studies, contributing to both the reproducibility and reliability of IBS.

Abstract

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The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each other, mostly using simultaneous-recording signals of brains (namely hyperscanning) with fNIRS. In fNIRS hyperscanning studies, IBS has been commonly assessed through the wavelet transform coherence (WTC) method because of its advantage on expanding time series into time-frequency space where oscillations can be seen in a highly intuitive way. The observed IBS can be further validated via the permutation-based random pairing of the trial, partner, and condition. Here, a protocol is presented to describe how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation in a hyperscanning study. Further, we discuss the critical issues when using the above methods, including the choice of fNIRS signals, methods of data preprocessing, and optional parameters of computations. In summary, using the WTC method and permutation is a potentially standard pipeline for analyzing IBS in fNIRS hyperscanning studies, contributing to both the reproducibility and reliability of IBS.

Introduction

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When people coordinate with others, their brains and bodies become a coupled unit through continuous mutual adaption. The coupling between brains can be represented by inter-brain synchronization (IBS) through the hyperscanning approach, which simultaneously records two or more individuals' brain signals1. Indeed, a growing body of fNIRS/EEG hyperscanning studies has found IBS in various collaboration contexts, including finger tapping2, group walking3, playing drums4, guitar playing5, and singing/humming6. fNIRS is widely used for the research of IBS during social interaction, as it less restricts head/body motions in relatively natural settings (compared to fMRI/EEG)7.

The article presents a protocol for calculating IBS via wavelet transform coherence (WTC) method in an fNIRS hyperscanning study. WTC is a method for assessing the cross-correlation between two movement signals on the time-frequency plane and, therefore, can give more information than the traditional correlation analysis (e.g., Pearson correlation and cross-correlation), which is only in the time domain8. In addition, hemodynamic signals are transformed into wavelet components, which can effectively remove the low-frequency noise. Although WTC is time-consuming, it has been the most commonly used method of calculating IBS in action imitation9, cooperative behavior10, verbal communication11, decision making12, and interactive learning13.

The article also presents how to validate IBS with the permutation-based random paring of trials, conditions, and participants. The IBS in hyperscanning studies is always proposed to track online social interaction between individuals, while it can also be interpreted by other explanations, such as the stimulus similarity, motion similarity, or condition similarity14. Permutation test, also called randomization test, can be leveraged to test the above-mentioned null hypotheses through resampling the observed data15. By using permutation, it is useful to investigate whether the identified IBS is specific to interactive behavior, ranging from modulation of IBS within dyads to between groups of partners16.

The protocol described here details how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation testing in a hyperscanning study. This study aims to examine whether privileged IBS is elicited by music meters during social coordination. The brain signals were recorded in the frontal cortex, based on the location of the IBS in a previous finding1. The experimental task was originally developed by Konvalinka and her colleges17, in which participants were asked to tap their fingers with the auditory feedback from the partner or themselves after listening to the meter or non-meter stimuli.

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Protocol

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The protocol presented here was approved by the University Committee on Human Research Protection of East China Normal University.

1. Preparation for the experiment

  1. Participants
    1. Recruit a group of undergraduate and graduate students with monetary compensation by the campus advertising.
    2. Ensure that the participants are right-handed and have normal or corrected-to-normal vision and hearing. Ensure that they have not studied music or have studied it for fewer than 3 years before.
    3. Randomly match the students in dyads. To control the potential effect of partner familiarity on social coordination18, ensure that the members of each dyad have not seen or known each other before.
  2. Experimental stimulus
    1. Create the auditory stimuli (440 Hz, 660 ms pure tones) by any free music composition and notation software.
    2. Repeat the tones with an interval of 500-1000 ms and combine them into a tone sequence. Each tone sequence is 12 s longer and composed of 12 tones.
    3. For one tone sequence, accent every first tone (+6 dB) to create the pattern of downbeats and upbeats, defined as the meter stimulus (Supplementary Audio 1). In the second tone sequence, unaccent the tones with equal intensity (40 dB above individual sensation threshold, collected before the experiment task), which corresponded to the non-meter stimulus (Supplementary Audio 2).
  3. Experimental task
    1. Program the experimental task by using a psychological software tool.
    2. Arrange two stages for the experimental task (Figure 1A) as described in steps 1.3.3-1.3.6.
    3. second resting state: Ask participants to remain as motionless as possible with their minds relaxed and eyes closed.
    4. Finger tapping task: Request the participants to complete two parts: a coordination part and an independence part.
    5. During the coordination part, provide auditory feedback (i.e., a drip sound corresponding to one tap) to each participant only for the response generated by the other member of the dyad. Ask the participants to try their best to respond synchronously with the other member.
    6. For the independence part, ensure both participants received the auditory feedback (i.e., a drip sound corresponding to one tap) of their own responses and ask them to respond synchronously with the auditory stimulus as precisely as possible.
      NOTE: Combined with the meter and non-meter stimuli, participants found themselves in one of four different conditions: (i) meter coordination - both participants heard meters, and the responses from each other; (ii) non-meter coordination - both participants heard non-meters and the responses from each other; (iii) meter independence - both participants heard meters and the responses from themselves; (iv) non-meter independence - both participants heard non-meters and the responses from themselves.
    7. For each trial, let the participants first hear a piece of the auditory stimulus (12 s) followed by a sound (262 Hz, 1000 ms) that serves as a cue to start to tap the finger.
    8. Ask the participants to reproduce the stimulus they heard before by tapping their right index finger on the keyboard (participant #1: "f"; participant #2: "j"). Participants must tap 12 times while keeping the same time interval between tones as the previously presented stimulus.
      NOTE: There were 60 trials assigned equally in 4 blocks corresponding to the 4 experimental conditions, namely 15 trials in one block. The order of the blocks was counterbalanced. The total duration of the tapping task was about 26 min.
    9. Between blocks, let the participants rest for 30 s.
    10. During the whole experiment, do not allow the participants to communicate through any language or movement. Separate the participants with the computer monitor to block any visual information that might deliver messages between them.
  4. Homemade fNIRS caps: Buy two elastic swimming caps of normal size. To cover the brain region of interest, mend the swimming caps as described in the following steps:
    1. Put one swimming cap on a headform, and then put a standard 10-20 EEG cap on the swimming cap.
    2. Mark the location of FCz on the swimming cap with a red magic marker.
    3. Take off the EEG cap from the headform.
    4. Put one optode probe patch (3 x 5 setup) on the swimming cap, aligning the middle one of the second probe row of the patch with the marked location of FCz.
      NOTE: The optode probe patch included 15 locations of optode probes (i.e., 8 emitters and 7 detectors), forming 22 measurement channels with 3 cm optode separation (Figure 1B).
    5. Mark the locations of the 15 probes of the patch on the swimming cap.
    6. Take off the patch and the swimming cap from the headform.
    7. Cut 15 small holes on the marked locations of the 15 probes with a pair of scissors.
    8. Mount the patch to the modified swimming cap by embedding the locations of 15 probes into the appropriate 15 holes.
    9. Mend the other swimming cap according to the above process.
      NOTE: The Homemade fNIRS cap, in which the locations of optodes are as per the standard EEG locations, were employed as there were no applicable standard EEG caps for the fNIRS system used in this study. There is no need to make an fNIRS cap if there are suitable standard EEG caps with the fNIRS system.

2. Before participants arrive

NOTE: Ensure to follow steps 2.1-2.5 before participants arrive at the laboratory.

  1. Remind the two participants of one dyad to come to the laboratory per the agreed time schedule.
  2. Start the fNIRS system at least 30 min in advance, leaving the laser turned off.
  3. Insert the optode probes from the fNIRS system into the optode probe patches.
  4. Examine the parameters of fNIRS measurement (i.e., subject ID, the event-related mode, the optode probe arrangement).
  5. Set the experimental apparatus with one table, two chairs, two 19-in computer monitors, and two pairs of headphones (Figure 1C).

3. Participant arrival in the laboratory

NOTE: Sincerely appreciate the two participants of one dyad when they arrive at the fNIRS lab. Request them to put their phone on silent mode and temporarily leave their personal belongings in the cabinet. Then conduct the following processes in sequence:

  1. Before the participants sit down, reconfirm that the two participants have not seen each other before. Ensure that they did not communicate with each other through any language or movement while in the lab.
  2. Provide the participants with informed consent forms approved by the University Committee on Human Research.
  3. Instruct the participants on the details of the experimental task. Ask them to wear headphones and give them several practicing trials.
  4. In the practice trials, allow the two participants of each dyad to practice together.
    NOTE: Before wearing the fNIRS cap, it is worthwhile to measure and determine the head size for each participant by using a flexible rule. Then select the right size cap for the participant according to his/her head size. In this study, such a step was missed as one-size swimming caps were used. It is better to conduct this step on the day before the experiment because the relative operations (i.e., measuring head size, selecting the right size swimming cap, mounting the optode probe patches to the swimming cap, and inserting the optode probes into the probe patches) are time-consuming (about 20-30 min).
  5. Put the fNIRS cap on the head of participants with the center of the cap pointing at the location of CZ, and place the middle optode probe of the second probe row of the patch at FCz.
  6. Operate the fNIRS system to perform the signal calibration with the laser turned on.
  7. If there is an insufficient signal at some channel, adjust the signal intensity with a fiber stick to gently put the hair underneath the surrounding probe tip aside.
  8. If necessary, press the probes gently but ensure not to hurt the participants.
  9. Repeat steps 3.5-3.8 until the quality of the signal is accessible. Make sure that participants feel comfortable during the whole process of signal calibration.
  10. Help the participants to find a comfortable posture for themselves (e.g., comfortable body positions). Remind the participants to keep their heads as motionless as possible during the whole experimental task (i.e., about 26 min).
  11. Examine the quality of NIRS signals again. If there are sufficient signals in all channels, run the experiment procedure on the desktop computer.
  12. Help the participants take off the headphones and fNIRS cap on completion of the experimental procedure. Return their personal belongings and thank them with monetary compensation.
  13. Operate the fNIRS system to save data. Use a disc to export raw fNIRS data (.csv) and use a USB to copy the behavioral data from the computer.
  14. Close the fNIRS system and the computer if no more experimental arrangement.
  15. Keep the lab notebook ready to note down any events, especially abnormalities during the whole experiment.

4. Data analysis

NOTE: Perform all data analysis by using MATLAB software, with the following toolboxes: HOMER219, Hitachi2nirs20, xjView21, Cross Wavelet and Wavelet Coherence toolbox22, and Groppe's scripts in MathWork23.

  1. Data preprocessing
    1. To check the data quality, follow steps 4.1.2-4.1.3
    2. Read the data files (.csv) for each participant with the readHitachData function of xjView.
      NOTE: In this way, the Hitachi measurement data (csv format) is converted into oxyHb/deoxyHb/marker data with the information saved in the measurement (i.e., wavelength, timedata, and channel list).
    3. Visually check the quality in oxyHb and deoxyHb values by plotting all channels' time series in one figure, with the function plotTraces of xjView.
      NOTE: It is easy to identify abnormalities in the data. The channel that has much noise can be excluded in subsequent analysis.
    4. Convert Hitachi files (.csv) to .nirs files format with the csv2nirs function of Hitachi2nirs, which supports further data preprocessing with Homer2.
    5. Transform the raw data to optical density with the function hmrIntensity2OD of Homer2.
    6. Use principal component analyses (PCA)24 to remove the fNIRS global physiological noise by using the function enPCAFilter (nSV = 0.8, that is 80% of the covariance of the data was removed) of Homer2.
    7. Use correlation-based signal improvement method (CBSI)25 to remove head motion artifacts using the function hmrMotionCorrect_Cbsi of Homer2.
    8. Use modified Beer-Lembert law to transform the processed optical density into oxyHb and deoxyHb values with the hmrOD2Conc function of Homer2.
  2. Calculating IBS
    NOTE: For the preprocessed oxyHb values, use WTC to calculate the coherence values for the channel pair that are from the same location of the dyad, including the following pipeline:
    1. Adopt the wtc function of Cross Wavelet and Wavelet Coherence toolbox with default parameters to compute the coherence values at each time and frequency point to obtain a two-axis matrix of coherence values.
    2. For the default parameters, use morlet mother wavelet, to transform each time series into the time and frequency domain by the continuous wavelet transformation.
    3. Select MonteCarloCount to represent the number of surrogate data sets in the significance calculation, and select Auto AR1 to calculate the autocorrelation coefficients of the time series.
    4. Choose frequency band of interest (FOI) as mentioned in steps 4.2.5-4.2.8.
    5. Select and average the coherence values of the frequency band between 0.5-1 Hz (respectively corresponding to period 2 s and 1 s), according to the frequency band used in the finger-motion task of a previous fNIRS hyperscanning study9. Such FOI also corresponded to the period of one tap in the experimental task. Thus, obtain one column of coherence values for each pair.
      NOTE: To further statistically confirm FOI, calculate the coherence values for each dyad across the full frequency range (i.e., 0.008-10 Hz for the data), rather than just confining the selected frequency band (i.e., 0.5-1 Hz).
    6. Average the coherence values of the targeted time windows (same as 4.2.3) for each frequency point.
    7. Next, analyze the average coherence values following the pipeline described in steps 4.2.9-4.2.11 and subsequent statistics (i.e., 4.3.1 - 4.3.2) for each frequency point.
    8. Last, visually inspect FOI by plotting the statistical z values of each channel across frequency.
    9. Select and average the coherence values of the time window during the resting state (time window for 20 s-resting-state) and each experimental condition (i.e., meter coordination, non-meter coordination, meter independence, and non-meter independence), respectively, using the information of mark. Thus, obtain five coherence values for each dyad.
    10. For the task session, only select the duration during which participants tapped to reproduce the auditory stimulus, about 12 s for each trial, thus total 180 s (i.e., 12 s x 15 trials) for each experimental condition.
      NOTE: IBS was calculated as coherence increase (the larger subtracted coherence values than zero), namely the larger coherence values in the task session compared to those in the resting-state session.
    11. Subtract the resting coherence value from the task-related coherence value, respectively, in which the coherence value during the resting-state was used as a baseline in this experiment.
      NOTE: By repeating the above steps (4.2.1-4.2.11) across channels (i.e., 22 channels) and dyads (i.e., 16 dyads), the subtracted coherence values for each dyad at each channel were obtained finally.
  3. Statistics
    1. Compare the subtracted coherence values with zero at each channel for each experimental condition, using the paired samples permutation t-test with the mult_comp_perm_t1 function of Groppe's work (5000 permutations to estimate the distribution of the null hypothesis; desired family-wise alpha level- 0.05; two-tailed test, which means the alternative hypothesis is that the mean of the data is different from 0) as abnormal data distribution and limited sample size in the current experiment26.
      NOTE: The paired samples permutation t-test here is similar to paired t-test, but the latter assumes that the data is normally distributed, whereas the former does not. Such test begins the same way as paired t-test, that is, by computing a t score (i.e., real t score) for the coherence values in different groups (one is the subtracted coherence values in the task condition, the other is zeros). Then, a permutation is generated by exchanging the coherence values of different groups, and a new t score is calculated for the subtracted coherence values and zeros following this permutation. Such permutation is conducted 5000 times. Thus, 5000 t scores are obtained. In the distribution of the 5000 t scores, the relative location of the real t score generates the p-value for the subtracted coherence values.
    2. Correct the p values (i.e., due to the multiple comparison problem, and generate from the comparisons across 22 channels in one patch) by False Discovery Rate method (p < 0.05)27. Perform this correction via the mafdr function of MATLAB toolbox.
      NOTE: If the p-value at any channel was significant (i.e., p < 0.05) after FDR correction, there is IBS at that channel.
    3. Compare the coherence values between different task conditions at the channel where IBS existed, using the paired samples permutation t-test with the mult_comp_perm_t1 function of Groppe's work (same parameters as mentioned in step 4.3.1).
      NOTE: To intuitively examine the IBS during interpersonal coordination regarding meter vs. non-meter stimuli, compare the coherence values of different conditions directly (i.e., meter coordination vs. non-meter coordination; meter coordination vs. meter independence).
    4. Calculate the behavioral performance by the absolute difference between the partners' response time divided by the sum of both partners' responses56.
    5. Evaluate the relationship between the IBS and behavioral performance through the permutation test based on Pearson linear correlation analysis (i.e., the mult_comp_perm_corr function of Groppe's work).
  4. Validating IBS
    NOTE: To exclude the explanations that similar stimuli, motions, or conditions induced the demonstrated IBS, use a permutation test as a validation approach, with three permutations (i.e., within the dyad, between dyad, and between condition permutations), included the followings:
    1. Randomize the label of trials in the meter coordination condition (i.e., within dyad permutation, such as trial #1 and trial #13 in dyad #1) for one dyad at each channel via the randperm function of MATLAB.
    2. Follow the above pipeline of calculating IBS and statistics (i.e., sections 4.2 and 4.3, but excluding the sensitivity analysis for FOI) for the randomized trial label.
      NOTE: Calculate coherence values of the fake pair for each condition separately, and compute coherence increase for the fake pair (i.e., subtract the resting coherence value from the task-related coherence value for the fake pair).
    3. Conduct the permutation 1000 times, followed by the pipeline of calculating IBS and statistics (sections 4.2 and 4.3).
    4. Plot the distribution of statistical z values generated within dyad permutation.
    5. Conduct steps 4.4.2-4.4.4 by randomizing pairing of the participants of the same trial in the meter coordination condition (i.e., between dyad permutation, such as participant #1 in dyad #1 and participant #1 in dyad #3).
    6. Conduct steps 4.4.2-4.4.4 by randomizing the label of conditions for the same members of one dyad in the same trial (i.e., between condition permutation, such as participant #1 in the meter coordination condition and participant #2 in the meter independence condition).

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Results

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The results showed that there was IBS at channel 5 in the meter coordination condition, whereas no IBS existed in other conditions (i.e., meter independence, non-meter coordination, non-meter independence; Figure 2A). At channel 5, the IBS in the meter coordination condition was significantly higher than the coherence values in the non-meter coordination and meter independence condition (Figure 2B). Channel 5 approximately belonged to the left dorsolateral prefr...

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Discussion

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This protocol provides a step-by-step procedure to calculate and validate IBS, using the fNIRS hyperscanning approach to simultaneously collect two participants' brain signals. Some critical issues involved in fNIRS data preprocessing, IBS calculation, statistics, and IBS validation are discussed below.

Data Preprocessing
It is necessary to preprocess fNIRS data in hyperscanning studies to extract real signals from the possible noise (i.e., motion artifacts, systemic...

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Disclosures

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The authors have nothing to disclose.

Acknowledgements

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This research was supported by: National Natural Science Foundation of China (31872783, 31800951).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
ComputerHewlett-Packard Development Company, L.P.HP S01-pF157mcn
EarphoneRoyal Philips Electronics, Eindhoven, The NetherlandsSHE2405BK/00
EEG capCompumedics Neuroscan, Charlotte, USA64-channel Quik-Cap
E-Prime softwarePsychology Software Tools, Inc., Pittsburgh, USAE-Prime 3
fNIRS systemHitachi Medical Corporation, Tokyo, JapanETG-7100 Optical Topography System
MATLAB 2014bThe MathWorks, Inc., Natick, MAMATLAB 2014b
MuseScoreMusescore Company, BelgiumMuseScore 3.6.2.548021803
Swimming capDecathlon Group, Villeneuve-d'Ascq, France1681552

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Inter Brain SynchronizationfNIRS HyperscanningWavelet Transform CoherencePermutation AnalysisBrain Signal ProcessingPrincipal Component AnalysisMotion Artifact CorrectionFrequency Band SelectionFalse Discovery RateDyad Synchronization

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