Addressing Arbitrary Choices of Frequency Band of Interest in fNIRS Hyperscanning

Addressing Arbitrary Choices of Frequency Band of Interest in fNIRS Hyperscanning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Addressing Arbitrary Choices of Frequency Band of Interest in fNIRS Hyperscanning Xin Zhou, Florrie Ng, Patrick Wong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7854624/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 4 You are reading this latest preprint version Abstract Neuroimaging hyperscanning—the monitoring of brain activity of two or more persons simultaneously—has emerged as a popular tool to uncover the neural mechanisms of social interactions. The use of functional near-infrared spectroscopy (fNIRS)—a non-invasive, child-friendly technique tolerant of motion artifacts—has significantly advanced the research of social interactions. Despite its popularity, the field has yet to agree on best practices for quantifying inter-brain connections (IBC) during social interactions, including the frequency band of interest (FOI) for signal analysis. Consequently, past research findings have often been inconsistent. In this study, we reviewed various methods used and their corresponding FOI results in previous fNIRS hyperscanning research focused on the topics of cooperation. Additionally, we propose a new methodology to quantify FOI that aims to point to the origin of synchronization between brains. We tested the proposed method on three independent fNIRS hyperscanning datasets. The three datasets involved three different populations and three types of social interactions commonly studied in the literature. We examined the effect of sample sizes and data exclusion rates on the calculation of FOIs and statistical results. We offer a method for testing and adoption within the fNIRS community, aimed at eliminating arbitrary FOI selections and potentially enhancing the reproducibility of results in future fNIRS hyperscanning research. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology fNIRS hyperscanning inter-brain connection frequency band of interest Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Hyperscanning, by simultaneously monitoring brain activities between two or more people, has emerged as a hot approach to uncover the mechanisms underlying social interactions. Hyperscanning research analyzes inter-brain connections (IBC) to reveal how changes in one person’s brain activity align with the changes in another person’s brain activity and to quantify inter-personal brain synchronization. In this review, we concentrated on hyperscanning research by employing functional near-infrared spectroscopy (fNIRS), a non-invasive, child-friendly, and motion-tolerant neuroimaging technique 1 , which has become an essential tool for investigating the complexities of human social behavior in naturalistic settings 2 . One of the primary areas of focus within fNIRS hyperscanning research is the study of cooperative interactions 3 . This is because cooperation is a fundamental aspect of human social behavior and a social norm 4 . Prior fNIRS hyperscanning studies have revealed important information about the neural mechanisms that underpin cooperation and how social interactions can influence cognitive processes among different populations, including romantic partners 5 , parent-child dyads 6 , and schoolers with varying autistic traits 7 . Despite its promise in studying social behaviors in real-world setting, a major problem of fNIRS is that it has poor signal-to-noise ratios. The fNIRS signals comprise stimulus-evoked and non-stimulus-evoked, systemic and neuronal responses from the extracerebral and cerebral tissues 8 . The systemic responses comprise concentration changes in hemoglobin related to cardiac activity, respiration, changes in blood pressure, and vasomotion 9 . Signals of interest to researchers—concentration changes in hemoglobin associated with functional brain activity due to neurovascular coupling—are exclusive to the cerebral tissue and represent a minor part of the overall fNIRS data. The fNIRS Society has established best practices for fNIRS publications involving single-person research to improve signal-to-noise ratios 10 . However, single-person and hyperscanning fNIRS research differ in the metrics utilized to assess brain signals of interest based on the changes in hemoglobin data. The former computes task-specific cortical responses within individuals, whereas the latter focuses on the relation between two or more brains during naturalistic social interactions. Thus, the pipeline of signal preprocessing recommended for single-person fNIRS research may not be suitable for hyperscanning research. To date, no consensus has been achieved on how to analyze fNIRS signal for hyperscanning research. Further, various methods have been used to calculate IBC 11 . Specifically, prior studies have employed varied and arbitrary selections of the frequency band of interest (FOI) for calculating IBC, which complicates the comparison and may be a major reason why previous studies have shown mixed findings regarding the synchronization of two brains. We will discuss these findings in detail below. The overarching goal of the current study is to address the arbitrary choices of FOIs and enhance the reproducibility of results in the fNIRS hyperscanning literature. To this end, we first reviewed the various rationales for selecting FOIs and the mixed FOI results in prior studies. Second, we propose an innovative methodology to select FOI based on the neurophysiology of the fNIRS signals when quantifying IBC. Specifically, we focus on the wavelet transform coherence (WTC) method to measure IBC 12 . IBC reveals how well changes in brain activity in one person align with changes in brain activity in another person across time and frequencies. This is the most widely used technique in fNIRS hyperscanning literature, with 70% of studies employing this method 11 . We provide guidance on the sample sizes required and channel exclusions to achieve stable statistical results. 1.1 Review of cooperation studies using fNIRS hyperscanning We carried out a review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 13 . Studies utilizing fNIRS hyperscanning techniques to investigate cooperation were discovered through searches on PubMed and Scopus, using the search terms: (“cooperation” OR “collaboration” OR “coordination”) AND (hyperscanning OR “two-person” OR “synchronization” OR “interbrain” OR “inter-brain”) AND (fNIRS OR NIRS OR “functional near-infrared spectroscopy”). We also identified a few references from citation search (Fig. 1 ). 1.2 Various methods used to determine FOIs As of November 2024, there have been 96 published fNIRS hyperscanning studies investigating the topics of cooperation, collaboration, and/or coordination (Fig. 1 ). Among the 96 studies, seven different approaches were used and researchers presumably adopted whatever approach they deemed most appropriate to decide the FOIs (Fig. 2 ). Specifically, 63 studies (65.6%) involved using a temporal or spectral filter to avoid confounds from the neurophysiological signals. Among those 63 studies, 15 only used a filter, while the rest combined filtering with one of below approaches to determine FOI. Among the 96 studies, 32 (33.3%) decided the FOI based on the duration of the trials or tasks; 20 (20.4%) utilized a data-driven analysis. Additionally, 12 studies chose the FOI based on visualization (n = 3), information from prior studies (n = 5), or the frequency of neurophysiological signals (n = 4). In 21 (21.9%) studies, the rationale for choosing FOI was not mentioned. The first method utilized a temporal or spatial filter. Among the 96 studies, 63 (65.6%) reported applying a filter during signal pre-processing before calculating inter-brain connections. The reported filters included a low-pass (n = 4), band-pass (n = 37), high-pass (n = 7) filter, or a spatial filter based on principal component analysis (n = 10) to minimize the influence of neurophysiological signals like respiration, heartbeat, or Mayer waves (periodic changes in arterial blood pressure), and to reduce the effect of motion artefacts. The frequency ranges of the filters reported in previous studies included [~ 0.01, 0.2] Hz 14–18 , [0.02, 0.2] Hz 19 , [0.06, 0.2] Hz 20 , [0.015, 0.15] Hz 21–24 , [0.01 0.15] Hz 25,26 , and [~ 0.01 0.1] Hz 20,27 . An upper cutoff threshold set at 0.1 Hz was to further reduce the impact of Mayer waves (~ 0.1 Hz) on the calculation of IBCs 28 . The rationale for filtering high-frequency physiological signals has been well documented in single-person fNIRS studies 10 . However, they raise questions about the neural basis of IBCs. Specifically, would social interactions elicit synchronization in neurophysiological signals between interactive partners, regardless of whether they are strangers or different in ages? Moreover, as previously mentioned, naturalistic social interactions elicit neuronal responses to continuous and diverse external stimuli. Consequently, the responses of interest may significantly differ from those observed in single-person neuroscience studies, which typically reveal cortical responses to a few specific and periodic stimuli. Concentrating on investigations within a limited frequency range, such as below 0.2 Hz, may result in overlooking significant findings at higher frequencies that support social interactions, potentially leading to false negatives. Whereas 27 out of 96 studies specifically mentioned that no filtering was involved, because the WTC method by looking at the phase-locked signals between multiple brains is robust against motion artefacts and the neurological interference. In the rest 6 out of 96 papers, it was unclear whether a filter was used. The method of determining the FOI based on task duration, which was the second most widely used approach and used by a third of the studies reviewed (Fig. 2 ), was first introduced in a study where pairs of participants engaged in computer-based cooperation versus competition tasks 29 . The trial durations ranged from 3.2 to 12.8 seconds, with resting breaks between trials, representing a traditional event-related design. The FOI was defined as the inverse of the task periods, calculated as 1/[12.8, 3.2], i.e., [0.08, 0.31] Hz, when assessing IBC using the WTC method 12 . Despite its popularity, this method can be challenging to implement and the results can be difficult to interpret for four reasons. First, among studies using different tasks, different FOIs were also reported, for instance, [0.02, 0.3] Hz 30 for an Etch-A-Sketch task, [0.03, 0.33] Hz 31 for a pattern-board color-filling task, [0.08, 0.16] Hz 32 for a cybermall task, [0.04, 0.16] Hz 33 for an arithmetic operation task, ~[0.04, 0.08] Hz 34–36 for build-up (e.g., Jenga tower) and joint-drawing games, [0.02, 0.1] Hz 37–39 for parent-child problem-solving tasks, and [0.137, 0.145] Hz 40 for a Jigsaw puzzle game. Even when using the same computer-based task 29 or a modified version, various FOIs were reported, including [0.016, 5] Hz 6 , [0.08, 0.5] Hz 41,42 , [0.125, 0.5] Hz 43 , [0.08, 0.31] Hz 5,29,44–55 , and [0.08, 0.16] Hz 56–58 , in correspondence with varying task durations. Second, the concept of a trial is often inapplicable to continuous social interactions in ecologically valid settings where the task formats are substantially different from those in traditional event-related designs. For instance, a lecture in a real-world classroom is a composite of interrelated events, the duration of which is not easily defined. Third, the hemodynamic response function (HRF) is not perfectly elicited by external stimulation, and so whilst FOI may approximately be the inverse of the task duration, it may not be exact, challenging the underlying premise of this method. Fourth, naturalistic social interactions encompass a variety of external stimuli, resulting in changes in hemodynamic responses that would be a convolution of multiple HRFs with various periods, rather than a fixed period of identical stimulation. The third most common approach is the data-driven analysis method, which has become quite popular in the last few years and involves three specific steps. A WTC would first be used to calculate the IBC between two participants across frequencies and time points. Then, a bin-by-bin analysis would be conducted across multiple experimental conditions, against the baseline control condition for each frequency bin or a randomly generated null distribution. The FOI was identified as any three or more consecutive frequency bins that demonstrated significance, or any frequency bins that showed significance after applying a multiple comparison statistical control method. Finally, hypotheses would be tested on the identified FOIs. Although this method seems beneficial for uncovering study-specific IBCs, four major issues have been observed. First, to simplify the multiple comparison correction process when conducting bin-by-bin analyses, a few studies investigated IBCs within specific initial frequency ranges by applying filters in signal pre-processing, instead of testing the hypothesis across the entire frequency range. Among the 20 studies that determined FOIs using the data-driven analysis (Fig. 2 ), three reported initial frequency ranges of [~ 0.01, 1] Hz 59–61 , six studies in [~ 0.01, 0.7] Hz 62–67 , two studies in [0.01, 0.5] Hz 7,68 , one in the range of [0.01, 0.3] Hz 69 and [0.01, 0.2] Hz 70 , and four studies below 0.1 Hz 71–74 . Because bin-by-bin data-driven analyses depend on fNIRS signals, varying initial frequency range selections might have led to inconsistent results in previous studies, particularly when sample sizes were small or signal-to-noise ratios were suboptimal. Second, this method raises the potential concern of double-dipping or circularity 75 , as it uses the same dataset for both FOI selection and selective analysis. Consequently, the resulting statistics are not inherently independent of the selection criteria. For instance, in a study that examined the differences in IBC in triads of students who learned poems cooperatively or independently 71 , the FOI was chosen by computing the differences in IBC between the two learning conditions and further compared with a null distribution. The increases in IBC from independent to cooperative learning were then correlated with their communicative behaviors during the cooperation. As the differences in IBC between the two conditions—cooperative and independent learning of idioms were strongly tied to the communicative behaviors in the cooperative learning mode, such correlational analyses were susceptible to circularity. Third, past studies that used this methodology have reported very different FOIs and often with smaller ranges of FOI (mean: 0.042 Hz, 95% CI [0.035, 0.049] Hz), compared to the FOI from studies determined based on task duration (Fig. 2 ). Among the 20 studies, 8 identified two or three FOIs of significance per task 7 , 59 , 61 , 65 , 66 , 68 , 71 , 76 , which introduced complexities in further hypothesis testing and made result dissemination challenging. Fourth, depending on the contrasts—whether between experimental conditions with different manipulations or compared with a rest session involving minimal social interaction between participants—the bin-by-bin data-driven analysis may yield different results within the same study and across different studies as discussed above. 1.3 An innovative approach to determine the FOIs To tackle the issues mentioned above regarding the selection of FOI for fNIRS hyperscanning research, the current study proposes an innovative method. When assessing IBCs during social interactions in multi-person neuroscience, we aim to observe the synchronization between two or more brains associated with neuronal activities, which are only present in the cerebral tissue. The regular fNIRS channels measure event-evoked and non-event-evoked systemic responses from both the extracerebral and cerebral tissues and neuronal responses from the cerebral tissue. Adhering to current best practices for single-person neuroscience studies utilizing the fNIRS technique 10 , short channels that measure signals from extracerebral tissue 8 , 77 , have been employed to minimize the impact of extracerebral signals on the calculation of task-related neuronal signals of interest. The latter is only present in the regular channels. In this study, we propose to compare the frequency of signals measured between the regular and short channels. The frequency components that showed significantly greater IBC in the regular channels compared to the short channels should indicate the concentration changes in hemodynamic responses associated with neuronal activity, which are not detectable by the short channels. This method offers an innovative approach to investigating the origins of neural synchronization in multi-person contexts, mitigating the risk of false findings that plagued earlier studies due to their methodological restrictions. We tested this methodology on three independent datasets from three experiments, each involving different types of social interactions across various populations. 2 Methods 2.1 Participants Experiment 1 involved fifty-eight triads of participants, with one instructor and two school-aged children per triad. Each instructor participated in at least one session, involving 24 instructors (mean ± SD of age: 34.9 ± 9.8 years, 4 males) and 116 students (mean ± SD of age: 8.2 ± 0.5 years, 64 males). Experiment 2 involved fifty-five dyads of mothers (mean ± SD of age: 42.1 ± 4.3 years) and their children 9–12 years of age (mean ± SD of age: 9.8 ± 0.9 years; 31 females). Experiment 3 involved forty-four dyads of young adults (38 males and 50 females, mean and standard deviation (SD) of age: 20.6 ± 1.5 years). All three studies were approved by the local Research Ethics Committee (information masked for peer review). The study protocols were carried out following the Declaration of Helsinki. All participants (or parents of the child participants) provided written consent and were reimbursed for their participation. 2.2 fNIRS data acquisition The fNIRS data were collected using one continuous-wave NIRS instrument (NIRSport2 devices, NIRx medical technologies, LLC) per participant. Each device had 16 LED light sources and 16 avalanche photodiode (APD) detectors, with a sampling frequency of 5.08 Hz. Each LED light source emitted near-infrared light with wavelengths of 760 nm and 850 nm. A light source paired with detectors located at about 30-mm distance provided regular fNIRS channels that collected signals. Experiment 1, Experiment 2, and Experiment 3 included 42, 36, and 40 regular channels per participant, respectively. In addition, 8 short-channel detectors were individually connected to 8 light sources, resulting in 8 short channels per person at an approximate distance of 8 mm (Fig. 3 ). In Experiment 1, an adult instructor taught two school-aged children mathematics (i.e., friction and perimeter) in two separate sessions. Each session started with a rest condition, followed by a pre-test, a lecture, and a post-test condition. Data from the rest and lecture conditions were reported in the current study. In Experiment 2, children performed a map task and story-telling task independently (with no interaction with their mothers) and then together with their mothers. In Experiment 3, dyads of college students engaged in a dual-task paradigm across four sessions. The primary task involved playing a Jenga game, while the secondary task required them to listen to Cantonese stories with varying levels of background noises. 2.3 fNIRS data analysis The fNIRS data were imported into and analyzed in MATLAB (The MathWorks, Natick, MA) with scripts from HOMER2 software 78 and written by the authors to preprocess data, involving the following four steps (Fig. 4 C). The selection of FOI utilized short channels of 8 mm (Fig. 4 A, B), which were specifically designed to penetrate only to the depth to measure cortical responses from extracerebral tissues 79 . In Step 1, signal pre-processing was conducted consisting of 1) removing step-like noise, 2) converting light intensity to optical density, and 3) calculating DHbO and DHbR with age-dependent differential pathlength factors (DPFs) being used. No filtering or other denoising method was involved in the pre-processing step, as the WTC method was quite robust to motion artifacts 29 . Further, an inappropriate choice of filters may introduce distortion to the data. We refrained from pre-selecting any specific frequency range to ensure we did not overlook significant results. Further, our analyses focused on the IBC between the oxygenated hemoglobin (DHbO) data but not the deoxygenated hemoglobin (DHbR). This was because our prior study showed that IBC values calculated from the DHbO and DHbR data were highly correlated 7 , 80 , and most previous fNIRS hyperscanning research has reported IBC results calculated from the DHbO data. Reporting the same measures enhances the comparability of our findings with those in past studies. In Step 2, we examined the differences in IBC between regular and short channels, focusing on the eight light sources that were connected with short-channel detectors, i.e., short-channel sources, each connected to 2–4 regular detectors (blue circles; Fig. 3 ). To calculate the IBC between participants’ regular channels, we first computed the WTC between the regular channels linked to a short-channel source on one participant and those linked to a short-channel source on the other. The WTC values—a time-frequency matrix for each channel pair were averaged across time per frequency bin. We then computed the WTC of the two short channels (one from each participant), averaged across time per frequency bin, and subtracted them from all pairs of regular channels connected with the same short-channel sources in the two participants. Step 2 results in ΔIBC across frequency bins. In Step 3, a one-sample t-test was conducted on DIBC across channels and dyads of participants for each frequency bin within the frequency range of 0.01-2 Hz, with a total of 92 frequency bins. For each dyad, experimental data from multiple conditions of the same task were combined, assuming that the neuronal signals driving the synchronization between two or more brains would share the same FOI, and may or may not differ in amplitude across these conditions. A Bonferroni method was used for multiple comparison corrections (n = 92 bins) 81 . The FOI was then defined as the range where three or more consecutive frequency bins exhibited greater than zero ΔIBC with adjusted p-values < 0.05 at the group level. Selecting three or more consecutive frequency bins was aimed at minimizing false positives. In Step 4, we further investigated the impact of fNIRS data quality and sample size on the estimation of FOI and the subsequent statistical outcomes of IBC analysis. To quantify fNIRS signal quality, the scalp coupling index (SCI)—the correlation between heartbeat signals recorded from the two wavelengths for each fNIRS channel was computed per channel. An SCI of 1 signifies a perfect correlation between the fNIRS signals measured at the two wavelengths, indicating excellent signal quality. However, it is unknown whether and how signal quality and channel exclusion affect the IBC results in hyperscanning research. To address this issue, we systematically investigated the FOI by excluding channels at various SCI cutoff thresholds. The SCI cutoff values were calculated at each percentile of SCI values across all participants and sessions. With a SCI cutoff threshold of 100th percentile, all the channels were excluded from this step of analysis. We assessed the impact of sample size by calculating the FOI across a range of sample sizes (from 2 to 40 dyads, in steps of 2), using the data from Experiment 2 and Experiment 3, both of which consisted of dyadic data with smaller sample sizes than Experiment 1. For each sample size, we randomly selected the number of dyads from the pool and repeated the above steps for FOI calculation 500 times. We assessed robustness for each sample size and FOI by requiring that the frequency bins show significance in more than 475 of the 500 repetitions (exceeding a 95% threshold) at the 95% confidence level. 3 Results 3.1 The frequency band of interest (FOI) results Across the three experiments, the regular channels showed greater IBC results than the shorter channels in three - four comparable frequency FOIs. As summarized in Table 1 , across all studies with various tasks, the regular channels showed greater IBC in the high-frequency FOI of [1.03, 2] Hz, which corresponds to the frequency range of heart rate signals. Experiment 1 and Experiment 3 (in the interactive sessions) showed a wide mid-frequency FOI of starting from 0.073 Hz to around 0.5 Hz. Experiment 2 (in the independent sessions) and Experiment 3 showed increased IBC in two FOIs, which covered a mid-frequency range similar to the one above but were separated by a gap between 0.163 and approximately 0.22 Hz. All sessions except the interactive session in Experiment 2 showed increased IBC in the very-low frequency FOI, i.e., under 0.04 Hz. Table 1 Summary of the study information and statistical results for the frequency band of interest (FOI) in each study. Experiment 1 Experiment 2 Experiment 3 Participants 56 triads (1 teacher- 2 students) 55 dyads (mother-child) 44 dyads (university students) Task Rest Lecture Independent Interactive Dual-task Conditions & durations N = 2 (2 mins each) N = 2 (5–10 mins each) N = 2 (5 mins each) N = 2 (5 and 8 mins) N = 4 (~ 6 mins each) FOI FOI1: [1.03 2] Hz FOI1: [1.03 2] Hz FOI1: [1.03 2] Hz FOI1: [1.03 2] Hz FOI1: [1.03 2] Hz FOI2: [0.073 0.435] Hz FOI2: [0.073 0.73] Hz FOI2: [0.218 0.435] Hz FOI2: [0.073 0.489] Hz FOI2: [0.259 0.435] Hz FOI3: [0.082 0.163] Hz FOI3: [0.073 0.163] Hz FOI3: [0.024 0.031] Hz FOI3: [0.014 0.019] Hz FOI4: [0.020 0.024] Hz FOI4: [0.019 0.039] Hz 3.2 The effect of channel exclusion and sample size To examine the impact of sample size and channel exclusion, we computed the robustness of the significance based on 500 repeated iterations for every sample size and each exclusion rate for both Experiment 2 and Experiment 3. The robustness results from Experiment 3 are shown in Fig. 5 ; yellow colors represent that regular channels showed greater IBC than the short channels, with robustness exceeding a 95% threshold. As the sample size increased, three to four FOIs emerged that showed consistent results (see Table 1 ). As the exclusion rate increased, the robustness of the FOI decreased, this was particularly the case when sample sizes were small. Robustness results stabilized once the sample size reached a sufficient threshold (e.g., n ≥ 32). Similar results were observed in Experiment 1 and Experiment 2 (see supplementary materials Fig. S1 ). To pinpoint the sample size required and an optimal channel exclusion rate, we focused on the FOIs below 0.5 Hz for Experiment 2 and Experiment 3. For each FOI, we measured how many robust frequency bins (exceeding the 95% threshold) remained in each cluster as we changed the sample size and exclusion rate. As shown in Fig. 6 , for both Experiment 2 (Fig. 6 A) and Experiment 3 (Fig. 6 B), for the two FOIs between ~[0.21 0.46] Hz and ~[0.07 0.17] Hz, with the sample size increasing to 32, the number of frequency bins within each FOI that demonstrated significance stabilized. With such a sample size, channel exclusion rate increasing did not alter the number of frequency bins within the two FOIs until 10%. With the channel exclusion rate increasing further, the sample size required to maintain a high robustness undoubtedly increased. In contrast, for the low-frequency FOI of [0.019 0.038] Hz, various channel exclusion rates resulted in quite different numbers of frequency bins remaining significant. The statistical results of this FOI remained relatively stable for an exclusion rate between 5%-7% for both studies. Similar results were found in Experiment 1. Please see the supplementary materials for the results (Fig. S2). 4 Discussion Previous fNIRS hyperscanning research has reported inconsistent findings of IBC during social interactions, likely due to the varying selections of FOIs and various ways to compute IBC. To date, no consensus has been reached regarding the rationale or method for determining the FOI in fNIRS hyperscanning research. The current study proposes a new methodology for selecting FOIs to calculate IBC in fNIRS hyperscanning research. This method utilizes short channels, which are recommended as part of current best practices in single-person fNIRS research to reduce noise signals, such as systemic responses from extracerebral tissue, and enhance neuronal signal-to-noise ratios 10 , 77 . This method where neural synchronization during social interactions is supposed to originate, potentially paving the way for utilizing fNIRS hyperscanning techniques in multi-person neuroscience. 4.1 The neurophysiology driving the inter-brain coherence within four FOIs We evaluated the proposed method using three independent datasets that featured distinct samples from different age groups and three types of social interactions: instructor-learner interactions involving young children in Experiment 1, mother-child interactions in Experiment 2, and cooperation among young adults in Experiment 3. The results revealed four comparable FOIs across the three experiments. The greater IBC between the regular channels compared to the shorter channels in the relatively high FOI—[1 2] Hz—likely indicates similarities in heart rate signals between participants. As heart rate signals are the dominant signals in fNIRS data, and our data comprised children above 9 years of age and adults who have comparable heart rates, it is not surprising that we observed IBC between them when co-present in the same room and/or performing the same tasks. For the mid-frequency FOI—[0.22, 0.49 Hz]—across the three experiments, and up to 0.7 Hz in Experiment 1 during the lecture conditions, greater IBC could be due to three reasons. First, it might be attributed to verbal communication between interaction partners, which was present across three experiments in the interactive tasks. In line with this result, two prior studies that involved instructor-learner verbal communications also reported increased IBC in relatively high-frequency bins 82 , 83 . Both studies employed bin-by-bin data-driven analyses to identify FOIs by comparing the teaching sessions to a resting state. The first study involved an instructor teaching psychological concepts using two strategies: explanation and scaffolding 82 and reported increased IBC in an FOI of [0.45, 0.57] Hz 82 . The second study involved an instructor teaching numerical concepts using three teaching styles: lecturing, interactive methods, and pre-recorded videos 83 ; it identified two significant FOIs—[0.5, 0.7] Hz that was related to teaching outcomes, and [0.3, 0.4] Hz that was associated with teaching styles 83 . These results were comparable with the results reported in the current study. It is worth noting that these mid-frequency FOIs, including the FOI from the current study, were higher than the FOIs in the prior studies reviewed above, which involved cooperation or coordination but mostly without verbal communication (Fig. 2 ), highlighting the role of verbal communication on IBC during cooperative interactions. Second, the mid-frequency FOI could also be attributed to motor synchrony, occurring at the pace of 2–4 seconds. Both Experiment 2 and Experiment 3 involved dyadic cooperation between participants. Specifically, mother-child dyads participated in a map task in Experiment 2 in two conditions, and dyads of college students performed a dual-task paradigm with the primary task of playing Jenga in Experiment 3. During both Experiments, the synchronized movements of fingers, hands, arms, and bodies as participants take turns performing the tasks (e.g., pointing to the map, holding a block), as well as observing these actions from their partner, may lead to elevated synchronizations between two brains due to the activities of mirror neurons. In line with results in the current study, our previous study 8 , which involved school-aged children playing a Jenga game and employed a bin-by-bin data-driven analysis method, also reported an FOI of [0.29, 0.35] Hz. Third, the increased IBC within this FOI could also be due to Respiratory Sinus Arrhythmia (RSA), occurring around 0.12–0.45 Hz. RSA refers to heart rate variability in synchrony with respiration due to rhythmic changes in cardiac parasympathetic activity 84 . RSA synchrony has been observed during rest and is task dependent, with greater changes in the synchrony associated with tasks that involve challenging behavior and emotion regulation 85 . In the developmental literature, mother-child RSA synchrony is believed to be closely related to children’s brain maturation and ability to form interpersonal attachments, self-regulate, and engage positively with their environment 86 . Likewise, RSA synchrony has been studied in romantic relationships to understand its association with relationship functioning 87 . Interestingly, greater IBC in the regular channels than in the short channels was also observed in this mid-frequency FOI during rest in Experiment 1. Taken together, these results suggest that verbal communications and motor behaviors may foster synchronization between interaction partners through modifying their RSA activities, manifested as synchrony in largely comparable FOIs across the three experiments. The low-frequency FOIs of [0.07, 0.16] Hz across the three studies are below the range of respirational signals but overlap with the Mayer waves, defined as oscillations of arterial pressure occurring spontaneously in conscious subjects 9 . Mayer waves are amplified during sympathetic activation and relatively consistent within the same species. The result that participants showed increased IBC in this range across studies with and without interaction was not surprising; it points to the effect of mere presence on the synchronization between two partners 7 . This FOI ([0.07, 0.16] Hz) also overlaps with the mean frequency across all the FOIs reviewed above (Fig. 2 ). Excluding the three studies that examined frequencies up to 4 Hz 82,83 and the study that specifically focused on the IBC between heartbeats 82 , the mean values and standard error of the mean (SEM) frequencies across the identified FOIs across the 93 studies were 0.14 Hz and 0.01 Hz, respectively, with the mean range of the FOIs being 0.16 Hz. We suspect that the low-frequency FOIs were driven by the slow concentration changes in hemoglobin from the cerebral tissues associated with neuronal activity through neurovascular coupling. Finding analogous activities across three experiments in the current study, each involving different social interactions among diverse groups, suggests the presence of shared mechanisms that align multiple brains and support general social interactions. This hypothesis should be explored in future research. The very-low frequency FOI of [0.02, 0.04] Hz suggests similarities in the slow changes in fNIRS signals. This frequency range has been reported in multiple studies that used data-driven analyses and the reverse of task durations, i.e., 25–50 s (Fig. 2 ). Increased IBC in this frequency band has been proposed to be associated with prolonged eye contact during social interaction 88 , which was often not observable when participants had their eyes closed. Because this FOI captures very slow neural activity, significant IBC implies that participants’ brains were synchronized over a sustained duration, such as 25 to 50 seconds. Possibly due to this reason, the IBC results in this IBC varied a lot across sample sizes (Fig. 6 ) and populations (Table 1 ). Findings from the current study shed light on our earlier question of how social interactions may influence the synchronization of systemic signals, including heart rates, parasympathetic nervous signals, and Mayer waves between different brains. We would like to highlight that the FOIs may be different in the few prior studies that used tasks that were comparable with those in the current studies, due to the different methods used to determine FOIs. For instance, in two studies involving dyadic cooperation, different FOIs were mentioned based on data-driven analyses 82 , 83 . Between the two studies, this variation could be attributed to the differing experimental designs: one study employed a mixed design 82 , while the other used a within-subject design 83 , thus complicating the comparisons of IBC results. This example exemplifies how different methodological choices for determining FOIs can lead to varying outcomes. 4.2 Effect of channel exclusions and sample sizes on the calculation of IBC Given the poor signal-to-noise ratios of fNIRS data, which can be further compromised by hair artifacts or inadequate contact between the fNIRS optodes and the skin, we investigated how fNIRS signal quality and channel exclusion influence the calculation of IBC. We calculated the SCI—the correlation between heartbeat signals recorded in the two wavelengths per channel—to indicate the signal quality 89 . However, prior studies have used different SCI cutoff thresholds, leading to various portions of data being excluded. Some studies adhered to the initial recommendation by using an SCI cutoff threshold of 0.75 89,90 . Others preferred a more conservative approach, excluding minimal channels and selecting a lower SCI cutoff threshold 91 , 92 . A few studies chose an intermediate SCI cutoff threshold to ensure a certain number of short channels included for further analysis 7 , 77 , 93 , 94 . Our prior study demonstrated that both lower (SCI = 0.15) and higher (SCI = 0.75) SCI cutoff thresholds yielded comparable statistical results in single-person fNIRS research with well-controlled block-design stimulation. We must highlight that variations in signal quality can cause the same SCI cutoff threshold to result in different proportions of channels being excluded. To date, no study has investigated the impact of channel exclusion on the calculation of IBC results in fNIRS hyperscanning research, which often occurs in ecologically valid settings. Our results (Fig. 5 and Fig. 6 ) revealed that channel exclusion affected the statistical results more when the sample sizes were relatively small. When the sample sizes are sufficient, such as 32 for studies involving dyadic settings (Experiment 2 and Experiment 3), channel exclusion rates may affect the statistical results for the mid-frequency ([0.22, 0.49] Hz) and low-frequency FOI ([0.073, 0.17] Hz) less. In contrast, the very-low frequency FOI (under 0.04 Hz) results varied a lot across various sample sizes and various exclusion rates. Across three FOIs under 0.5 Hz (Table 1 ), a sample size of 32 or above, with a channel exclusion rate of 5–7% achieved robust results across data obtained from three independent experiments. Differences in signal quality (hence various channel exclusions) and different sample sizes in prior studies could have contributed to inconsistent results across studies using a similar paradigm and methodology to determine FOI. 4.3 Limitations of the current method The proposed methodology offers the advantage of addressing the neural origins of inter-brain synchronization during social interactions in ecologically valid settings, while circumventing the circularity issues often observed in the bin-by-bin frequency analyses as discussed above. We were able to utilize short-channel detectors from NIRx devices, which are among the most popular for fNIRS research. However, not all commercially available fNIRS systems include short-channel detectors, limiting the generalization of this methodology. Further, the participant pool for all three studies was exclusively Asian. Since darker, denser hair attenuates light more effectively, this can introduce greater artefacts (and relatively higher ratios of channel exclusions) in fNIRS data compared to data acquired from individuals with light-colored hair. Further research is needed to confirm these findings in individuals with light-colored hair. 5 Conclusion Previous fNIRS hyperscanning studies have reported varied selections of frequency bands of interest (FOIs), potentially contributing to inconsistent findings of inter-brain coherence (IBC). In the current study, we briefly reviewed the different methods previously used to determine FOIs in studies focused on cooperation and coordination and discussed the associated challenges. We then proposed a method including short channels to identify FOIs and to address the neural origins of inter-personal brain synchronization. Testing this method on three independent datasets, we found that three datasets identified comparable yet slightly different FOIs that showed greater IBC between different populations and across various social interaction contexts. The shared and unique FOIs of signals driving the IBC between two or more brains could provide a common ground for the comparisons of IBC results in future hyperscanning studies. Our findings further revealed the effect of fNIRS signal quality (hence channel exclusion) and sample size on detecting FOIs and calculating IBC. Future studies may consider reporting data quality and detailing the number of excluded channels to enhance the transparency and reproducibility of the research results. Declarations Additional Information The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contribution **XZ** : Conceptualization; Data acquisition, analysis, and interpretation; Writing – original draft.**FFYN** : Conceptualization; Resources; Supervision; Writing – review & editing.**PCMW** : Conceptualization; Funding acquisition; Resources; Supervision; Writing – review & editing. Acknowledgement We sincerely thank all the participants for participating in this research. We have used AI tools such as Deepseek-V3.2 to help polish and improve some of the writing. Data Availability MATLAB code used for the data analyses is stored on Open Science Framework and available for peer review through the following link [https://osf.io/r4s73/?view_only=6c14eb3ca218417489e939bbd45f7197] . The summary (de-identified) data supporting the conclusions of this article will be made available by the authors, without undue reservation. References Ferrari, M. & Quaresima, V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage 63 , 921–935. 10.1016/j.neuroimage.2012.03.049 (2012). Quaresima, V. & Ferrari, M. Functional Near-Infrared Spectroscopy (fNIRS) for Assessing Cerebral Cortex Function During Human Behavior in Natural/Social Situations: A Concise Review. Organizational Res. Methods . 22 , 46–68. 10.1177/1094428116658959 (2019). Czeszumski, A. et al. Cooperative Behavior Evokes Interbrain Synchrony in the Prefrontal and Temporoparietal Cortex: A Systematic Review and Meta-Analysis of fNIRS Hyperscanning Studies. Eneuro 9, (2022). 10.1523/Eneuro.0268-21.2022 Nowak, M. & Highfield, R. Supercooperators: Altruism, evolution, and why we need each other to succeed (Simon and Schuster, 2012). Pan, Y., Cheng, X., Zhang, Z., Li, X. & Hu, Y. Cooperation in lovers: An fNIRS-based hyperscanning study. Hum. Brain Mapp. 38 , 831–841. 10.1002/hbm.23421 (2017). Reindl, V., Gerloff, C., Scharke, W. & Konrad, K. Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning. NeuroImage 178, 493–502, (2018). 10.1016/j.neuroimage.2018.05.060 Zhou, X., Hong, X. C. & Wong, P. C. M. Autistic Traits Modulate Social Synchronizations Between School-Aged Children: Insights From Three fNIRS Hyperscanning Experiments. Psychol. Sci. 35 , 840–857. 10.1177/09567976241237699 (2024). Tachtsidis, I. & Scholkmann, F. False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward. Neurophotonics 3 , 031405. 10.1117/1.NPh.3.3.031405 (2016). Julien, C. The enigma of Mayer waves: Facts and models. Cardiovasc. Res. 70 , 12–21. 10.1016/j.cardiores.2005.11.008 (2006). Yücel, M. A. et al. Best practices for fNIRS publications. Neurophotonics 8 , 012101 (2021). Hakim, U. et al. Quantification of inter-brain coupling: A review of current methods used in haemodynamic and electrophysiological hyperscanning studies. NeuroImage 280 , 120354. 10.1016/j.neuroimage.2023.120354 (2023). Grinsted, A., Moore, J. C. & Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Proc. Geoph . 11 , 561–566. 10.5194/npg-11-561-2004 (2004). Liberati, A. et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. Bmj-Brit Med. J. 339 10.1136/bmj.b2700 (2009). Balters, S., Miller, J. G., Li, R., Hawthorne, G. & Reiss, A. L. Virtual (Zoom) Interactions Alter Conversational Behavior and Interbrain Coherence. J. Neurosci. 43 , 2568–2578. 10.1523/JNEUROSCI.1401-22.2023 (2023). Song, X. et al. Influence of interpersonal distance on collaborative performance in the joint Simon task-An fNIRS-based hyperscanning study. NeuroImage 285 , 120473. 10.1016/j.neuroimage.2023.120473 (2024). Azhari, A., Bizzego, A. & Esposito, G. Parent-child dyads with greater parenting stress exhibit less synchrony in posterior areas and more synchrony in frontal areas of the prefrontal cortex during shared play. Soc. Neurosci. 17 , 520–531. 10.1080/17470919.2022.2162118 (2022). Zhang, M., Jia, H. & Zheng, M. Interbrain Synchrony in the Expectation of Cooperation Behavior: A Hyperscanning Study Using Functional Near-Infrared Spectroscopy. Front. Psychol. 11 , 542093. 10.3389/fpsyg.2020.542093 (2020). Greaves, D. A. et al. Exploring Theater Neuroscience: Using Wearable Functional Near-infrared Spectroscopy to Measure the Sense of Self and Interpersonal Coordination in Professional Actors. J. Cogn. Neurosci. 34 , 2215–2236. 10.1162/jocn_a_01912 (2022). Balters, S., Miller, J. G. & Reiss, A. L. Expressing appreciation is linked to interpersonal closeness and inter-brain coherence, both in person and over Zoom. Cereb. Cortex . 33 , 7211–7220. 10.1093/cercor/bhad032 (2023). Hu, Y. et al. Musical Meter Induces Interbrain Synchronization during Interpersonal Coordination. Eneuro 9 10.1523/ENEURO.0504-21.2022 (2022). Lin, F. R. et al. Hearing loss and cognitive decline in older adults. JAMA Intern. Med. 173 , 293–299 (2013). Mayseless, N., Hawthorne, G. & Reiss, A. L. Real-life creative problem solving in teams: fNIRS based hyperscanning study. NeuroImage 203 , 116161. 10.1016/j.neuroimage.2019.116161 (2019). Huang, C. et al. Disrupted inter-brain synchronization in the prefrontal cortex between adolescents and young adults with gaming disorders during the real-world cooperating video games. J. Affect. Disorders . 352 , 386–394 (2024). Shamay-Tsoory, S. G., Marton-Alper, I. Z. & Markus, A. Post-interaction neuroplasticity of inter-brain networks underlies the development of social relationship. iScience 27 , 108796. 10.1016/j.isci.2024.108796 (2024). Zhang, H., Wang, H., Long, Y., Jiang, Y. & Lu, C. Interpersonal neural synchronization underlies mnemonic similarity during collaborative remembering. Neuropsychologia 191 , 108732. 10.1016/j.neuropsychologia.2023.108732 (2023). Lu, K., Teng, J. & Hao, N. Gender of partner affects the interaction pattern during group creative idea generation. Exp. Brain Res. 238 , 1157–1168. 10.1007/s00221-020-05799-7 (2020). Liu, D. et al. A cost-effective instrument of distributed functional near-infrared spectroscopy for hyperscanning real-world interactions. IEEE Trans. Instrum. Measurement (2023). Yücel, M. A. et al. Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy. Biomedical Opt. express . 7 , 3078–3088 (2016). Cui, X., Bryant, D. M. & Reiss, A. L. NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. NeuroImage 59 , 2430–2437. 10.1016/j.neuroimage.2011.09.003 (2012). Liu, S. et al. Parenting links to parent-child interbrain synchrony: a real-time fNIRS hyperscanning study. Cereb. Cortex . 34 10.1093/cercor/bhad533 (2024). Zheng, Y., Liu, S., Tian, B., Zhang, Y. & Wang, D. in IEEE World Haptics Conference (WHC). 176–182 (IEEE). 176–182 (IEEE). (2023). Jiao, Z., Song, J., Yang, X., Chen, Y. & Han, G. Social pain sharing boosts interpersonal brain synchronization in female cooperation. Acta Psychol. (Amst) . 243 , 104138. 10.1016/j.actpsy.2024.104138 (2024). Sun, B. H. et al. Behavioral and brain synchronization differences between expert and novice teachers when collaborating with students. Brain Cogn. 139 10.1016/j.bandc.2019.105513 (2020). Li, Y. et al. Dyad sex composition effect on inter-brain synchronization in face-to-face cooperation. Brain Imaging Behav. 15 , 1667–1675. 10.1007/s11682-020-00361-z (2021). Liu, N. et al. NIRS-based hyperscanning reveals inter-brain neural synchronization during cooperative Jenga game with face-to-face communication. Front. Hum. Neurosci. 10 , 82. 10.3389/fnhum.2016.00082 (2016). Li, L. et al. Interpersonal Neural Synchronization During Cooperative Behavior of Basketball Players: A fNIRS-Based Hyperscanning Study. Front. Hum. Neurosci. 14 , 169. 10.3389/fnhum.2020.00169 (2020). Nguyen, T. et al. The effects of interaction quality on neural synchrony during mother-child problem solving. Cortex 124 , 235–249. 10.1016/j.cortex.2019.11.020 (2020). Nguyen, T., Hoehl, S. & Vrticka, P. A. Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis. Sensors-Basel 21, (2021). 10.3390/s21124075 Nguyen, T., Kungl, M. T., Hoehl, S., White, L. O. & Vrticka, P. Visualizing the invisible tie: Linking parent-child neural synchrony to parents' and children's attachment representations. Dev. Sci. 27 , e13504. 10.1111/desc.13504 (2024). Zhou, S. J. et al. The Effect of Task Performance and Partnership on Interpersonal Brain Synchrony during Cooperation. Brain Sci. 12 10.3390/brainsci12050635 (2022). Kruppa, J. A. et al. Brain and motor synchrony in children and adolescents with ASD-a fNIRS hyperscanning study. Soc. Cogn. Affect. Neur . 16 , 103–116. 10.1093/scan/nsaa092 (2021). Guo, L. et al. Decreased inter-brain synchronization in the right middle frontal cortex in alcohol use disorder during social interaction: An fNIRS hyperscanning study. J. Affect. Disord . 329 , 573–580. 10.1016/j.jad.2023.02.072 (2023). Reindl, V. et al. Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy. J. Vis. Exp. 10.3791/58807 (2019). Baker, J. M. et al. Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning. Sci. Rep. 6 , 26492. 10.1038/srep26492 (2016). Duan, H. et al. Is the creativity of lovers better? A behavioral and functional near-infrared spectroscopy hyperscanning study. Curr. Psychol. 1–14. 10.1007/s12144-020-01093-5 (2020). Cheng, X. J., Pan, Y. F., Hu, Y. Y. & Hu, Y. Coordination Elicits Synchronous Brain Activity Between Co-actors: Frequency Ratio Matters. Front. NeuroSci. 13 10.3389/fnins.2019.01071 (2019). Cheng, X., Li, X. & Hu, Y. Synchronous brain activity during cooperative exchange depends on gender of partner: A fNIRS-based hyperscanning study. Hum. Brain Mapp. 36 , 2039–2048. 10.1002/hbm.22754 (2015). Li, Y. Z., Chen, M., Zhang, R. Q. & Li, X. C. Experiencing happiness together facilitates dyadic coordination through the enhanced interpersonal neural synchronization. Soc. Cogn. Affect. Neur . 17 , 447–460. 10.1093/scan/nsab114 (2022). Miller, J. G. et al. Inter-brain synchrony in mother-child dyads during cooperation: An fNIRS hyperscanning study. Neuropsychologia 124 , 117–124. 10.1016/j.neuropsychologia.2018.12.021 (2019). Osaka, N. et al. How Two Brains Make One Synchronized Mind in the Inferior Frontal Cortex: fNIRS-Based Hyperscanning During Cooperative Singing. Front. Psychol. 6 10.3389/fpsyg.2015.01811 (2015). Tang, Y. et al. Different strategies, distinguished cooperation efficiency, and brain synchronization for couples: An fNIRS-based hyperscanning study. Brain Behav. 10 , e01768. 10.1002/brb3.1768 (2020). Wang, Q. et al. Autism Symptoms Modulate Interpersonal Neural Synchronization in Children with Autism Spectrum Disorder in Cooperative Interactions. Brain Topogr . 33 , 112–122. 10.1007/s10548-019-00731-x (2020). Feng, X. et al. Self-other overlap and interpersonal neural synchronization serially mediate the effect of behavioral synchronization on prosociality. Soc. Cogn. Affect. Neurosci. 15 , 203–214. 10.1093/scan/nsaa017 (2020). Tang, Y. et al. Children with autism spectrum disorder perform comparably to their peers in a parent-child cooperation task. Exp. Brain Res. 241 , 1905–1917. 10.1007/s00221-023-06626-5 (2023). Lu, H. et al. Increased interbrain synchronization and neural efficiency of the frontal cortex to enhance human coordinative behavior: A combined hyper-tES and fNIRS study. NeuroImage 282, 120385, (2023). 10.1016/j.neuroimage.2023.120385 Wang, C. et al. Dynamic interpersonal neural synchronization underlying pain-induced cooperation in females. Hum. Brain Mapp. 40 , 3222–3232. 10.1002/hbm.24592 (2019). Zhang, R. et al. Effects of acute psychosocial stress on interpersonal cooperation and competition in young women. Brain Cogn. 151 , 105738. 10.1016/j.bandc.2021.105738 (2021). Wei, Y. et al. Reduced interpersonal neural synchronization in right inferior frontal gyrus during social interaction in participants with clinical high risk of psychosis: An fNIRS-based hyperscanning study. Prog Neuropsychopharmacol. Biol. Psychiatry . 120 , 110634. 10.1016/j.pnpbp.2022.110634 (2023). Ni, J., Yang, J. & Ma, Y. Social bonding in groups of humans selectively increases inter-status information exchange and prefrontal neural synchronization. PLoS Biol. 22 , e3002545. 10.1371/journal.pbio.3002545 (2024). Zhao, H. et al. Acute stress makes women’s group decisions more rational: A functional near-infrared spectroscopy (fNIRS)–based hyperscanning study. J. Neurosci. Psychol. Econ. 14 , 20 (2021). Zhou, C., Cheng, X., Liu, C. & Li, P. Interpersonal coordination enhances brain-to-brain synchronization and influences responsibility attribution and reward allocation in social cooperation. NeuroImage 252 , 119028. 10.1016/j.neuroimage.2022.119028 (2022). Lu, K., Xue, H., Nozawa, T. & Hao, N. Cooperation makes a group be more creative. Cereb. Cortex . 29 , 3457–3470. 10.1093/cercor/bhy215 (2019). Lu, K., Qiao, X. & Hao, N. Praising or keeping silent on partner's ideas: Leading brainstorming in particular ways. Neuropsychologia 124 , 19–30. 10.1016/j.neuropsychologia.2019.01.004 (2019). Wang, X. et al. Dynamic brain networks in spontaneous gestural communication. Npj Sci. Learn. 9 10.1038/s41539-024-00274-2 (2024). Lu, K., Qiao, X., Yun, Q. & Hao, N. Educational diversity and group creativity: Evidence from fNIRS hyperscanning. NeuroImage 243, 118564, (2021). 10.1016/j.neuroimage.2021.118564 Yin, J. T., Pan, Y. F., Zhang, Y. X., Hu, Y. Y. & Luo, J. L. Distinct inter-brain synchronization patterns during group creativity under threats in cooperative and competitive contexts. Think. Skills Creat . 49 10.1016/j.tsc.2023.101366 (2023). Lu, K. L. & Hao, N. When do we fall in neural synchrony with others? Soc. Cogn. Affect. Neur . 14 , 253–261 (2019). Du, B. et al. Higher or lower? Interpersonal behavioral and neural synchronization of movement imitation in autistic children. Autism Res. 17 , 1876–1901 (2024). Zhang, Y. et al. Exploring the role of mutual prediction in inter-brain synchronization during competitive interactions: an fNIRS hyperscanning investigation. Cereb. Cortex . 34 10.1093/cercor/bhad483 (2024). Zhang, M. et al. Neural mechanisms distinguishing two types of cooperative problem-solving approaches: An fNIRS hyperscanning study. NeuroImage 291 , 120587. 10.1016/j.neuroimage.2024.120587 (2024). Pan, Y., Cheng, X. & Hu, Y. Three heads are better than one: cooperative learning brains wire together when a consensus is reached. Cereb. Cortex . 10.1093/cercor/bhac127 (2022). Xue, H., Lu, K. L. & Hao, N. Cooperation makes two less-creative individuals turn into a highly-creative pair. NeuroImage 172, 527–537, (2018). 10.1016/j.neuroimage.2018.02.007 Wang, H., Cong, Y., Zhao, W., Li, X. & Li, L. A study of trust behavior and its neural basis in athletes under long-term exercise training. Neurosci. Lett. 805 , 137218. 10.1016/j.neulet.2023.137218 (2023). Deng, X., Hosseini, S., Miyake, Y. & Nozawa, T. Cooperativeness as a Personality Trait and Its Impact on Cooperative Behavior in Young East Asian Adults Who Synchronized in Casual Conversations. Behav. Sci-Basel . 14 , 987 (2024). Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. & Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12 , 535–540 (2009). Liu, Q. et al. Inter-brain neural mechanism and influencing factors underlying different cooperative behaviors: a hyperscanning study. Brain Struct. Funct. 229 , 75–95. 10.1007/s00429-023-02700-4 (2024). Zhou, X., Sobczak, G., McKay, C. M. & Litovsky, R. Y. Comparing fNIRS signal qualities between approaches with and without short channels. PloS one . 15 , e0244186. 10.1371/journal.pone.0244186 (2020). Huppert, T. J., Diamond, S. G., Franceschini, M. A. & Boas, D. A. HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl. Opt. 48 , D280–298. 10.1364/ao.48.00d280 (2009). Brigadoi, S. & Cooper, R. J. How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy. Neurophotonics 2 , 025005–025005 (2015). Zhou, X., Hong, X. & Wong, P. C. M. Exploring inter-brain coherence between fathers and infants during maternal storytelling: an fNIRS hyperscanning study. Infant Child. Dev (2025). Aickin, M. & Gensler, H. Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. Am. J. Public Health . 86 , 726–728 (1996). Pan, Y. F. et al. Instructor-learner brain coupling discriminates between instructional approaches and predicts learning. NeuroImage 211, (2020). 10.1016/j.neuroimage.2020.116657 Zheng, L. et al. Enhancement of teaching outcome through neural prediction of the students' knowledge state. Hum. Brain. Mapp. 39 , 3046–3057. 10.1002/hbm.24059 (2018). Yasuma, F. & Hayano, J. Respiratory sinus arrhythmia - Why does the heartbeat synchronize with respiratory rhythm? Chest 125, 683–690, doi: (2004). 10.1378/chest.125.2.683 Miller, J. G., Armstrong-Carter, E., Balter, L. & Lorah, J. A meta‐analysis of mother–child synchrony in respiratory sinus arrhythmia and contextual risk. Dev. Psychobiol. 65 , e22355 (2023). Feldman, R. The Neurobiology of Human Attachments. Trends Cogn. Sci. 21 , 80–99. 10.1016/j.tics.2016.11.007 (2017). Han, S. C., Baucom, B., Timmons, A. C. & Margolin, G. A Systematic Review of Respiratory Sinus Arrhythmia in Romantic Relationships. Fam Process. 60 , 441–456. 10.1111/famp.12644 (2021). Guglielmini, S., Bopp, G., Marcar, V. L., Scholkmann, F. & Wolf, M. Systemic physiology augmented functional near-infrared spectroscopy hyperscanning: a first evaluation investigating entrainment of spontaneous activity of brain and body physiology between subjects. Neurophotonics 9 10.1117/1.NPh.9.2.026601 (2022). Pollonini, L. et al. Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy. Hear. Res. 309 , 84–93. 10.1016/j.heares.2013.11.007 (2014). Weder, S., Zhou, X., Shoushtarian, M., Innes-Brown, H. & McKay, C. Cortical Processing Related to Intensity of a Modulated Noise Stimulus—a Functional Near-Infrared Study. J. Assoc. Res. Otolaryngol. 19 , 273–286. 10.1007/s10162-018-0661-0 (2018). Lawrence, R. J., Wiggins, I. M., Anderson, C. A., Davies-Thompson, J. & Hartley, D. E. Cortical correlates of speech intelligibility measured using functional near-infrared spectroscopy (fNIRS). Hear. Res. 370 , 53–64. 10.1016/j.heares.2018.09.005 (2018). Wijayasiri, P., Hartley, D. E. H. & Wiggins, I. M. Brain activity underlying the recovery of meaning from degraded speech: A functional near-infrared spectroscopy (fNIRS) study. Hear. Res. 351 , 55–67. 10.1016/j.heares.2017.05.010 (2017). Zhou, X., Wang, L., Hong, X. & Wong, P. C. M. Infant-directed speech facilitates word learning through attentional mechanisms: an fNIRS study of toddlers. Dev. Sci. 10.1111/desc.13424 (2023). Zhou, X. et al. Inhibitory Control in Children 4–10 Years of Age: Evidence From Functional Near-Infrared Spectroscopy Task-Based Observations. Front. Hum. Neurosci. 15 , 798358. 10.3389/fnhum.2021.798358 (2021). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 14 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:07:32","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187191,"visible":true,"origin":"","legend":"","description":"","filename":"972dd611197842589180603c048a18a71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/7f9ad189b77596dd9eeacc1b.xml"},{"id":93574179,"identity":"14de39a8-32e4-433a-8953-086dc8873a63","added_by":"auto","created_at":"2025-10-15 09:15:32","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209921,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/dc383d7a083538d408d3a4ab.html"},{"id":93572140,"identity":"e316fa16-0a95-468a-9763-91ee70ad93a2","added_by":"auto","created_at":"2025-10-15 09:07:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238743,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flowchart of paper inclusions/exclusions.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/63f623fead1a8aef259c7773.png"},{"id":93575413,"identity":"cbfe5636-ab8d-4cb0-afb2-b5809aeaefef","added_by":"auto","created_at":"2025-10-15 09:23:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":742978,"visible":true,"origin":"","legend":"\u003cp\u003eVarying frequency of interest (FOI) results in the prior fNIRS hyperscanning research about cooperation/collaboration.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/cc4286bc3a6c1d5e9711b6b7.png"},{"id":93572144,"identity":"e782038a-6422-4d25-8427-c969e97e2b5d","added_by":"auto","created_at":"2025-10-15 09:07:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1028781,"visible":true,"origin":"","legend":"\u003cp\u003efNIRS montages three experiments. Panels (A), (B), and (C) plot the montage in experiments 1, 2, and 3, respectively. Red, blue, and green circles represent light sources (n=16), detectors (n=15), and short-channel detectors (n=8), respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/0d4b56e184f008410276b8e8.png"},{"id":93572149,"identity":"2cdfcea7-0ca3-40fd-8bce-6367a5b3bed4","added_by":"auto","created_at":"2025-10-15 09:07:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":816970,"visible":true,"origin":"","legend":"\u003cp\u003eSignal processing pipeline. Panels (A) and (B) show an example of the short-channel detector and its connection, adapted from our prior study\u003csup\u003e79\u003c/sup\u003e. Panel (C) shows the example pipeline of signal preprocessing, the calculation of inter-brain connections (IBC), and the examination of frequency of interest (FOI) for fNIRS data. Panel (D) plots the group mean (solid lines) and standard error of means (shaded areas) of the IBC in the regular (pink) and short (grey) channels. \u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/4a5b9a5363f6fdfed80d2c75.png"},{"id":93572142,"identity":"c4889f8c-bb43-4de9-93a4-ca9ad329c6a4","added_by":"auto","created_at":"2025-10-15 09:07:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":511859,"visible":true,"origin":"","legend":"\u003cp\u003eSignificance of FOI across various channel exclusion rates and sample sizes. This plot was based on data from experiment 3. Yellow colors indicate robustness above 95% among 500 repetitions.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/f9be083f73b9c3cbaa96e9a8.png"},{"id":93574178,"identity":"b86b9483-98d3-46c9-9703-fa858c6a7723","added_by":"auto","created_at":"2025-10-15 09:15:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":514943,"visible":true,"origin":"","legend":"\u003cp\u003eSignificance at the FOI level. Panels (A) and (B) plot the results from Experiment 2 and Experiment 3, respectively.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/b2e6ff158494965d4ad5298b.png"},{"id":108439254,"identity":"495438f0-bcec-4b16-acce-5f5aff1ffbd5","added_by":"auto","created_at":"2026-05-04 16:17:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3393107,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/9e578803-c469-4cec-94ab-b6626e4bfa57.pdf"},{"id":93572152,"identity":"39fa7c9e-ac5a-44f4-8856-12173b23e9eb","added_by":"auto","created_at":"2025-10-15 09:07:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":880979,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7854624/v1/ae8d83a670a69718b9929642.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Addressing Arbitrary Choices of Frequency Band of Interest in fNIRS Hyperscanning","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHyperscanning, by simultaneously monitoring brain activities between two or more people, has emerged as a hot approach to uncover the mechanisms underlying social interactions. Hyperscanning research analyzes inter-brain connections (IBC) to reveal how changes in one person\u0026rsquo;s brain activity align with the changes in another person\u0026rsquo;s brain activity and to quantify inter-personal brain synchronization. In this review, we concentrated on hyperscanning research by employing functional near-infrared spectroscopy (fNIRS), a non-invasive, child-friendly, and motion-tolerant neuroimaging technique\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, which has become an essential tool for investigating the complexities of human social behavior in naturalistic settings\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. One of the primary areas of focus within fNIRS hyperscanning research is the study of cooperative interactions\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This is because cooperation is a fundamental aspect of human social behavior and a social norm\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Prior fNIRS hyperscanning studies have revealed important information about the neural mechanisms that underpin cooperation and how social interactions can influence cognitive processes among different populations, including romantic partners\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, parent-child dyads\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, and schoolers with varying autistic traits\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite its promise in studying social behaviors in real-world setting, a major problem of fNIRS is that it has poor signal-to-noise ratios. The fNIRS signals comprise stimulus-evoked and non-stimulus-evoked, systemic and neuronal responses from the extracerebral and cerebral tissues\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The systemic responses comprise concentration changes in hemoglobin related to cardiac activity, respiration, changes in blood pressure, and vasomotion\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Signals of interest to researchers\u0026mdash;concentration changes in hemoglobin associated with functional brain activity due to neurovascular coupling\u0026mdash;are exclusive to the cerebral tissue and represent a minor part of the overall fNIRS data. The fNIRS Society has established best practices for fNIRS publications involving single-person research to improve signal-to-noise ratios\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, single-person and hyperscanning fNIRS research differ in the metrics utilized to assess brain signals of interest based on the changes in hemoglobin data. The former computes task-specific cortical responses within individuals, whereas the latter focuses on the relation between two or more brains during naturalistic social interactions. Thus, the pipeline of signal preprocessing recommended for single-person fNIRS research may not be suitable for hyperscanning research. To date, no consensus has been achieved on how to analyze fNIRS signal for hyperscanning research. Further, various methods have been used to calculate IBC\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Specifically, prior studies have employed varied and arbitrary selections of the frequency band of interest (FOI) for calculating IBC, which complicates the comparison and may be a major reason why previous studies have shown mixed findings regarding the synchronization of two brains. We will discuss these findings in detail below.\u003c/p\u003e\u003cp\u003eThe overarching goal of the current study is to address the arbitrary choices of FOIs and enhance the reproducibility of results in the fNIRS hyperscanning literature. To this end, we first reviewed the various rationales for selecting FOIs and the mixed FOI results in prior studies. Second, we propose an innovative methodology to select FOI based on the neurophysiology of the fNIRS signals when quantifying IBC. Specifically, we focus on the wavelet transform coherence (WTC) method to measure IBC\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. IBC reveals how well changes in brain activity in one person align with changes in brain activity in another person across time and frequencies. This is the most widely used technique in fNIRS hyperscanning literature, with 70% of studies employing this method\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. We provide guidance on the sample sizes required and channel exclusions to achieve stable statistical results.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Review of cooperation studies using fNIRS hyperscanning\u003c/h2\u003e\u003cp\u003eWe carried out a review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Studies utilizing fNIRS hyperscanning techniques to investigate cooperation were discovered through searches on PubMed and Scopus, using the search terms: (\u0026ldquo;cooperation\u0026rdquo; OR \u0026ldquo;collaboration\u0026rdquo; OR \u0026ldquo;coordination\u0026rdquo;) AND (hyperscanning OR \u0026ldquo;two-person\u0026rdquo; OR \u0026ldquo;synchronization\u0026rdquo; OR \u0026ldquo;interbrain\u0026rdquo; OR \u0026ldquo;inter-brain\u0026rdquo;) AND (fNIRS OR NIRS OR \u0026ldquo;functional near-infrared spectroscopy\u0026rdquo;). We also identified a few references from citation search (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Various methods used to determine FOIs\u003c/h2\u003e\u003cp\u003eAs of November 2024, there have been 96 published fNIRS hyperscanning studies investigating the topics of cooperation, collaboration, and/or coordination (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 96 studies, seven different approaches were used and researchers presumably adopted whatever approach they deemed most appropriate to decide the FOIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, 63 studies (65.6%) involved using a temporal or spectral filter to avoid confounds from the neurophysiological signals. Among those 63 studies, 15 only used a filter, while the rest combined filtering with one of below approaches to determine FOI. Among the 96 studies, 32 (33.3%) decided the FOI based on the duration of the trials or tasks; 20 (20.4%) utilized a data-driven analysis. Additionally, 12 studies chose the FOI based on visualization (n\u0026thinsp;=\u0026thinsp;3), information from prior studies (n\u0026thinsp;=\u0026thinsp;5), or the frequency of neurophysiological signals (n\u0026thinsp;=\u0026thinsp;4). In 21 (21.9%) studies, the rationale for choosing FOI was not mentioned.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe first method utilized a temporal or spatial filter. Among the 96 studies, 63 (65.6%) reported applying a filter during signal pre-processing before calculating inter-brain connections. The reported filters included a low-pass (n\u0026thinsp;=\u0026thinsp;4), band-pass (n\u0026thinsp;=\u0026thinsp;37), high-pass (n\u0026thinsp;=\u0026thinsp;7) filter, or a spatial filter based on principal component analysis (n\u0026thinsp;=\u0026thinsp;10) to minimize the influence of neurophysiological signals like respiration, heartbeat, or Mayer waves (periodic changes in arterial blood pressure), and to reduce the effect of motion artefacts. The frequency ranges of the filters reported in previous studies included [~\u0026thinsp;0.01, 0.2] Hz\u003csup\u003e14\u0026ndash;18\u003c/sup\u003e, [0.02, 0.2] Hz\u003csup\u003e19\u003c/sup\u003e, [0.06, 0.2] Hz\u003csup\u003e20\u003c/sup\u003e, [0.015, 0.15] Hz\u003csup\u003e21\u0026ndash;24\u003c/sup\u003e, [0.01 0.15] Hz\u003csup\u003e25,26\u003c/sup\u003e, and [~\u0026thinsp;0.01 0.1] Hz\u003csup\u003e20,27\u003c/sup\u003e. An upper cutoff threshold set at 0.1 Hz was to further reduce the impact of Mayer waves (~\u0026thinsp;0.1 Hz) on the calculation of IBCs\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The rationale for filtering high-frequency physiological signals has been well documented in single-person fNIRS studies\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, they raise questions about the neural basis of IBCs. Specifically, would social interactions elicit synchronization in neurophysiological signals between interactive partners, regardless of whether they are strangers or different in ages? Moreover, as previously mentioned, naturalistic social interactions elicit neuronal responses to continuous and diverse external stimuli. Consequently, the responses of interest may significantly differ from those observed in single-person neuroscience studies, which typically reveal cortical responses to a few specific and periodic stimuli. Concentrating on investigations within a limited frequency range, such as below 0.2 Hz, may result in overlooking significant findings at higher frequencies that support social interactions, potentially leading to false negatives. Whereas 27 out of 96 studies specifically mentioned that no filtering was involved, because the WTC method by looking at the phase-locked signals between multiple brains is robust against motion artefacts and the neurological interference. In the rest 6 out of 96 papers, it was unclear whether a filter was used.\u003c/p\u003e\u003cp\u003eThe method of determining the FOI based on task duration, which was the second most widely used approach and used by a third of the studies reviewed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), was first introduced in a study where pairs of participants engaged in computer-based cooperation versus competition tasks\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The trial durations ranged from 3.2 to 12.8 seconds, with resting breaks between trials, representing a traditional event-related design. The FOI was defined as the inverse of the task periods, calculated as 1/[12.8, 3.2], i.e., [0.08, 0.31] Hz, when assessing IBC using the WTC method\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Despite its popularity, this method can be challenging to implement and the results can be difficult to interpret for four reasons. First, among studies using different tasks, different FOIs were also reported, for instance, [0.02, 0.3] Hz\u003csup\u003e30\u003c/sup\u003e for an Etch-A-Sketch task, [0.03, 0.33] Hz\u003csup\u003e31\u003c/sup\u003e for a pattern-board color-filling task, [0.08, 0.16] Hz\u003csup\u003e32\u003c/sup\u003e for a cybermall task, [0.04, 0.16] Hz\u003csup\u003e33\u003c/sup\u003e for an arithmetic operation task, ~[0.04, 0.08] Hz\u003csup\u003e34\u0026ndash;36\u003c/sup\u003e for build-up (e.g., Jenga tower) and joint-drawing games, [0.02, 0.1] Hz\u003csup\u003e37\u0026ndash;39\u003c/sup\u003e for parent-child problem-solving tasks, and [0.137, 0.145] Hz\u003csup\u003e40\u003c/sup\u003e for a Jigsaw puzzle game. Even when using the same computer-based task\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e or a modified version, various FOIs were reported, including [0.016, 5] Hz\u003csup\u003e6\u003c/sup\u003e, [0.08, 0.5] Hz\u003csup\u003e41,42\u003c/sup\u003e, [0.125, 0.5] Hz\u003csup\u003e43\u003c/sup\u003e, [0.08, 0.31] Hz\u003csup\u003e5,29,44\u0026ndash;55\u003c/sup\u003e, and [0.08, 0.16] Hz\u003csup\u003e56\u0026ndash;58\u003c/sup\u003e, in correspondence with varying task durations. Second, the concept of a trial is often inapplicable to continuous social interactions in ecologically valid settings where the task formats are substantially different from those in traditional event-related designs. For instance, a lecture in a real-world classroom is a composite of interrelated events, the duration of which is not easily defined. Third, the hemodynamic response function (HRF) is not perfectly elicited by external stimulation, and so whilst FOI may approximately be the inverse of the task duration, it may not be exact, challenging the underlying premise of this method. Fourth, naturalistic social interactions encompass a variety of external stimuli, resulting in changes in hemodynamic responses that would be a convolution of multiple HRFs with various periods, rather than a fixed period of identical stimulation.\u003c/p\u003e\u003cp\u003eThe third most common approach is the data-driven analysis method, which has become quite popular in the last few years and involves three specific steps. A WTC would first be used to calculate the IBC between two participants across frequencies and time points. Then, a bin-by-bin analysis would be conducted across multiple experimental conditions, against the baseline control condition for each frequency bin or a randomly generated null distribution. The FOI was identified as any three or more consecutive frequency bins that demonstrated significance, or any frequency bins that showed significance after applying a multiple comparison statistical control method. Finally, hypotheses would be tested on the identified FOIs. Although this method seems beneficial for uncovering study-specific IBCs, four major issues have been observed. First, to simplify the multiple comparison correction process when conducting bin-by-bin analyses, a few studies investigated IBCs within specific initial frequency ranges by applying filters in signal pre-processing, instead of testing the hypothesis across the entire frequency range. Among the 20 studies that determined FOIs using the data-driven analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), three reported initial frequency ranges of [~\u0026thinsp;0.01, 1] Hz\u003csup\u003e59\u0026ndash;61\u003c/sup\u003e, six studies in [~\u0026thinsp;0.01, 0.7] Hz\u003csup\u003e62\u0026ndash;67\u003c/sup\u003e, two studies in [0.01, 0.5] Hz\u003csup\u003e7,68\u003c/sup\u003e, one in the range of [0.01, 0.3] Hz\u003csup\u003e69\u003c/sup\u003e and [0.01, 0.2] Hz\u003csup\u003e70\u003c/sup\u003e, and four studies below 0.1 Hz\u003csup\u003e71\u0026ndash;74\u003c/sup\u003e. Because bin-by-bin data-driven analyses depend on fNIRS signals, varying initial frequency range selections might have led to inconsistent results in previous studies, particularly when sample sizes were small or signal-to-noise ratios were suboptimal. Second, this method raises the potential concern of double-dipping or circularity\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, as it uses the same dataset for both FOI selection and selective analysis. Consequently, the resulting statistics are not inherently independent of the selection criteria. For instance, in a study that examined the differences in IBC in triads of students who learned poems cooperatively or independently\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, the FOI was chosen by computing the differences in IBC between the two learning conditions and further compared with a null distribution. The increases in IBC from independent to cooperative learning were then correlated with their communicative behaviors during the cooperation. As the differences in IBC between the two conditions\u0026mdash;cooperative and independent learning of idioms were strongly tied to the communicative behaviors in the cooperative learning mode, such correlational analyses were susceptible to circularity. Third, past studies that used this methodology have reported very different FOIs and often with smaller ranges of FOI (mean: 0.042 Hz, 95% CI [0.035, 0.049] Hz), compared to the FOI from studies determined based on task duration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the 20 studies, 8 identified two or three FOIs of significance per task\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, which introduced complexities in further hypothesis testing and made result dissemination challenging. Fourth, depending on the contrasts\u0026mdash;whether between experimental conditions with different manipulations or compared with a rest session involving minimal social interaction between participants\u0026mdash;the bin-by-bin data-driven analysis may yield different results within the same study and across different studies as discussed above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 An innovative approach to determine the FOIs\u003c/h2\u003e\u003cp\u003eTo tackle the issues mentioned above regarding the selection of FOI for fNIRS hyperscanning research, the current study proposes an innovative method. When assessing IBCs during social interactions in multi-person neuroscience, we aim to observe the synchronization between two or more brains associated with neuronal activities, which are only present in the cerebral tissue. The regular fNIRS channels measure event-evoked and non-event-evoked systemic responses from both the extracerebral and cerebral tissues and neuronal responses from the cerebral tissue. Adhering to current best practices for single-person neuroscience studies utilizing the fNIRS technique\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, short channels that measure signals from extracerebral tissue\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, have been employed to minimize the impact of extracerebral signals on the calculation of task-related neuronal signals of interest. The latter is only present in the regular channels. In this study, we propose to compare the frequency of signals measured between the regular and short channels. The frequency components that showed significantly greater IBC in the regular channels compared to the short channels should indicate the concentration changes in hemodynamic responses associated with neuronal activity, which are not detectable by the short channels. This method offers an innovative approach to investigating the origins of neural synchronization in multi-person contexts, mitigating the risk of false findings that plagued earlier studies due to their methodological restrictions. We tested this methodology on three independent datasets from three experiments, each involving different types of social interactions across various populations.\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eExperiment 1 involved fifty-eight triads of participants, with one instructor and two school-aged children per triad. Each instructor participated in at least one session, involving 24 instructors (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of age: 34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 years, 4 males) and 116 students (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of age: 8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 years, 64 males). Experiment 2 involved fifty-five dyads of mothers (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of age: 42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 years) and their children 9\u0026ndash;12 years of age (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of age: 9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 years; 31 females). Experiment 3 involved forty-four dyads of young adults (38 males and 50 females, mean and standard deviation (SD) of age: 20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 years). All three studies were approved by the local Research Ethics Committee (information masked for peer review). The study protocols were carried out following the Declaration of Helsinki. All participants (or parents of the child participants) provided written consent and were reimbursed for their participation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 fNIRS data acquisition\u003c/h2\u003e\u003cp\u003eThe fNIRS data were collected using one continuous-wave NIRS instrument (NIRSport2 devices, NIRx medical technologies, LLC) per participant. Each device had 16 LED light sources and 16 avalanche photodiode (APD) detectors, with a sampling frequency of 5.08 Hz. Each LED light source emitted near-infrared light with wavelengths of 760 nm and 850 nm. A light source paired with detectors located at about 30-mm distance provided regular fNIRS channels that collected signals. Experiment 1, Experiment 2, and Experiment 3 included 42, 36, and 40 regular channels per participant, respectively. In addition, 8 short-channel detectors were individually connected to 8 light sources, resulting in 8 short channels per person at an approximate distance of 8 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Experiment 1, an adult instructor taught two school-aged children mathematics (i.e., friction and perimeter) in two separate sessions. Each session started with a rest condition, followed by a pre-test, a lecture, and a post-test condition. Data from the rest and lecture conditions were reported in the current study. In Experiment 2, children performed a map task and story-telling task independently (with no interaction with their mothers) and then together with their mothers. In Experiment 3, dyads of college students engaged in a dual-task paradigm across four sessions. The primary task involved playing a Jenga game, while the secondary task required them to listen to Cantonese stories with varying levels of background noises.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 fNIRS data analysis\u003c/h2\u003e\u003cp\u003eThe fNIRS data were imported into and analyzed in MATLAB (The MathWorks, Natick, MA) with scripts from HOMER2 software\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e and written by the authors to preprocess data, involving the following four steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The selection of FOI utilized short channels of 8 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B), which were specifically designed to penetrate only to the depth to measure cortical responses from extracerebral tissues\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn Step 1, signal pre-processing was conducted consisting of 1) removing step-like noise, 2) converting light intensity to optical density, and 3) calculating DHbO and DHbR with age-dependent differential pathlength factors (DPFs) being used. No filtering or other denoising method was involved in the pre-processing step, as the WTC method was quite robust to motion artifacts\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Further, an inappropriate choice of filters may introduce distortion to the data. We refrained from pre-selecting any specific frequency range to ensure we did not overlook significant results. Further, our analyses focused on the IBC between the oxygenated hemoglobin (DHbO) data but not the deoxygenated hemoglobin (DHbR). This was because our prior study showed that IBC values calculated from the DHbO and DHbR data were highly correlated\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e, and most previous fNIRS hyperscanning research has reported IBC results calculated from the DHbO data. Reporting the same measures enhances the comparability of our findings with those in past studies.\u003c/p\u003e\u003cp\u003eIn Step 2, we examined the differences in IBC between regular and short channels, focusing on the eight light sources that were connected with short-channel detectors, i.e., short-channel sources, each connected to 2\u0026ndash;4 regular detectors (blue circles; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To calculate the IBC between participants\u0026rsquo; regular channels, we first computed the WTC between the regular channels linked to a short-channel source on one participant and those linked to a short-channel source on the other. The WTC values\u0026mdash;a time-frequency matrix for each channel pair were averaged across time per frequency bin. We then computed the WTC of the two short channels (one from each participant), averaged across time per frequency bin, and subtracted them from all pairs of regular channels connected with the same short-channel sources in the two participants. Step 2 results in ΔIBC across frequency bins.\u003c/p\u003e\u003cp\u003eIn Step 3, a one-sample t-test was conducted on DIBC across channels and dyads of participants for each frequency bin within the frequency range of 0.01-2 Hz, with a total of 92 frequency bins. For each dyad, experimental data from multiple conditions of the same task were combined, assuming that the neuronal signals driving the synchronization between two or more brains would share the same FOI, and may or may not differ in amplitude across these conditions. A Bonferroni method was used for multiple comparison corrections (n\u0026thinsp;=\u0026thinsp;92 bins)\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The FOI was then defined as the range where three or more consecutive frequency bins exhibited greater than zero ΔIBC with adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at the group level. Selecting three or more consecutive frequency bins was aimed at minimizing false positives.\u003c/p\u003e\u003cp\u003eIn Step 4, we further investigated the impact of fNIRS data quality and sample size on the estimation of FOI and the subsequent statistical outcomes of IBC analysis. To quantify fNIRS signal quality, the scalp coupling index (SCI)\u0026mdash;the correlation between heartbeat signals recorded from the two wavelengths for each fNIRS channel was computed per channel. An SCI of 1 signifies a perfect correlation between the fNIRS signals measured at the two wavelengths, indicating excellent signal quality. However, it is unknown whether and how signal quality and channel exclusion affect the IBC results in hyperscanning research. To address this issue, we systematically investigated the FOI by excluding channels at various SCI cutoff thresholds. The SCI cutoff values were calculated at each percentile of SCI values across all participants and sessions. With a SCI cutoff threshold of 100th percentile, all the channels were excluded from this step of analysis. We assessed the impact of sample size by calculating the FOI across a range of sample sizes (from 2 to 40 dyads, in steps of 2), using the data from Experiment 2 and Experiment 3, both of which consisted of dyadic data with smaller sample sizes than Experiment 1. For each sample size, we randomly selected the number of dyads from the pool and repeated the above steps for FOI calculation 500 times. We assessed robustness for each sample size and FOI by requiring that the frequency bins show significance in more than 475 of the 500 repetitions (exceeding a 95% threshold) at the 95% confidence level.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 The frequency band of interest (FOI) results\u003c/h2\u003e\u003cp\u003eAcross the three experiments, the regular channels showed greater IBC results than the shorter channels in three - four comparable frequency FOIs. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, across all studies with various tasks, the regular channels showed greater IBC in the high-frequency FOI of [1.03, 2] Hz, which corresponds to the frequency range of heart rate signals. Experiment 1 and Experiment 3 (in the interactive sessions) showed a wide mid-frequency FOI of starting from 0.073 Hz to around 0.5 Hz. Experiment 2 (in the independent sessions) and Experiment 3 showed increased IBC in two FOIs, which covered a mid-frequency range similar to the one above but were separated by a gap between 0.163 and approximately 0.22 Hz. All sessions except the interactive session in Experiment 2 showed increased IBC in the very-low frequency FOI, i.e., under 0.04 Hz.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the study information and statistical results for the frequency band of interest (FOI) in each study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eExperiment 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eExperiment 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExperiment 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e56 triads\u003c/p\u003e\u003cp\u003e(1 teacher- 2 students)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e55 dyads\u003c/p\u003e\u003cp\u003e(mother-child)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44 dyads\u003c/p\u003e\u003cp\u003e(university students)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLecture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInteractive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDual-task\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConditions \u0026amp; durations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003cp\u003e(2 mins each)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003cp\u003e(5\u0026ndash;10 mins each)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003cp\u003e(5 mins each)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003cp\u003e(5 and 8 mins)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e\u003cp\u003e(~\u0026thinsp;6 mins each)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eFOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFOI1: [1.03 2] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFOI1: [1.03 2] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFOI1: [1.03 2] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFOI1: [1.03 2] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFOI1: [1.03 2] Hz\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFOI2: [0.073 0.435] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFOI2: [0.073 0.73] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFOI2: [0.218 0.435] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFOI2: [0.073 0.489] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFOI2: [0.259 0.435] Hz\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFOI3: [0.082 0.163] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFOI3: [0.073 0.163] Hz\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFOI3: [0.024 0.031] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFOI3: [0.014 0.019] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFOI4: [0.020 0.024] Hz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFOI4: [0.019 0.039] Hz\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 The effect of channel exclusion and sample size\u003c/h2\u003e\u003cp\u003eTo examine the impact of sample size and channel exclusion, we computed the robustness of the significance based on 500 repeated iterations for every sample size and each exclusion rate for both Experiment 2 and Experiment 3. The robustness results from Experiment 3 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; yellow colors represent that regular channels showed greater IBC than the short channels, with robustness exceeding a 95% threshold. As the sample size increased, three to four FOIs emerged that showed consistent results (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As the exclusion rate increased, the robustness of the FOI decreased, this was particularly the case when sample sizes were small. Robustness results stabilized once the sample size reached a sufficient threshold (e.g., n\u0026thinsp;\u0026ge;\u0026thinsp;32). Similar results were observed in Experiment 1 and Experiment 2 (see supplementary materials Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo pinpoint the sample size required and an optimal channel exclusion rate, we focused on the FOIs below 0.5 Hz for Experiment 2 and Experiment 3. For each FOI, we measured how many robust frequency bins (exceeding the 95% threshold) remained in each cluster as we changed the sample size and exclusion rate. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, for both Experiment 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and Experiment 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), for the two FOIs between ~[0.21 0.46] Hz and ~[0.07 0.17] Hz, with the sample size increasing to 32, the number of frequency bins within each FOI that demonstrated significance stabilized. With such a sample size, channel exclusion rate increasing did not alter the number of frequency bins within the two FOIs until 10%. With the channel exclusion rate increasing further, the sample size required to maintain a high robustness undoubtedly increased. In contrast, for the low-frequency FOI of [0.019 0.038] Hz, various channel exclusion rates resulted in quite different numbers of frequency bins remaining significant. The statistical results of this FOI remained relatively stable for an exclusion rate between 5%-7% for both studies. Similar results were found in Experiment 1. Please see the supplementary materials for the results (Fig. S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003ePrevious fNIRS hyperscanning research has reported inconsistent findings of IBC during social interactions, likely due to the varying selections of FOIs and various ways to compute IBC. To date, no consensus has been reached regarding the rationale or method for determining the FOI in fNIRS hyperscanning research. The current study proposes a new methodology for selecting FOIs to calculate IBC in fNIRS hyperscanning research. This method utilizes short channels, which are recommended as part of current best practices in single-person fNIRS research to reduce noise signals, such as systemic responses from extracerebral tissue, and enhance neuronal signal-to-noise ratios\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. This method where neural synchronization during social interactions is supposed to originate, potentially paving the way for utilizing fNIRS hyperscanning techniques in multi-person neuroscience.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 The neurophysiology driving the inter-brain coherence within four FOIs\u003c/h2\u003e\u003cp\u003eWe evaluated the proposed method using three independent datasets that featured distinct samples from different age groups and three types of social interactions: instructor-learner interactions involving young children in Experiment 1, mother-child interactions in Experiment 2, and cooperation among young adults in Experiment 3. The results revealed four comparable FOIs across the three experiments.\u003c/p\u003e\u003cp\u003eThe greater IBC between the regular channels compared to the shorter channels in the relatively high FOI\u0026mdash;[1 2] Hz\u0026mdash;likely indicates similarities in heart rate signals between participants. As heart rate signals are the dominant signals in fNIRS data, and our data comprised children above 9 years of age and adults who have comparable heart rates, it is not surprising that we observed IBC between them when co-present in the same room and/or performing the same tasks.\u003c/p\u003e\u003cp\u003eFor the mid-frequency FOI\u0026mdash;[0.22, 0.49 Hz]\u0026mdash;across the three experiments, and up to 0.7 Hz in Experiment 1 during the lecture conditions, greater IBC could be due to three reasons. First, it might be attributed to verbal communication between interaction partners, which was present across three experiments in the interactive tasks. In line with this result, two prior studies that involved instructor-learner verbal communications also reported increased IBC in relatively high-frequency bins\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Both studies employed bin-by-bin data-driven analyses to identify FOIs by comparing the teaching sessions to a resting state. The first study involved an instructor teaching psychological concepts using two strategies: explanation and scaffolding\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e and reported increased IBC in an FOI of [0.45, 0.57] Hz\u003csup\u003e82\u003c/sup\u003e. The second study involved an instructor teaching numerical concepts using three teaching styles: lecturing, interactive methods, and pre-recorded videos\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e; it identified two significant FOIs\u0026mdash;[0.5, 0.7] Hz that was related to teaching outcomes, and [0.3, 0.4] Hz that was associated with teaching styles\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. These results were comparable with the results reported in the current study. It is worth noting that these mid-frequency FOIs, including the FOI from the current study, were higher than the FOIs in the prior studies reviewed above, which involved cooperation or coordination but mostly without verbal communication (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), highlighting the role of verbal communication on IBC during cooperative interactions. Second, the mid-frequency FOI could also be attributed to motor synchrony, occurring at the pace of 2\u0026ndash;4 seconds. Both Experiment 2 and Experiment 3 involved dyadic cooperation between participants. Specifically, mother-child dyads participated in a map task in Experiment 2 in two conditions, and dyads of college students performed a dual-task paradigm with the primary task of playing Jenga in Experiment 3. During both Experiments, the synchronized movements of fingers, hands, arms, and bodies as participants take turns performing the tasks (e.g., pointing to the map, holding a block), as well as observing these actions from their partner, may lead to elevated synchronizations between two brains due to the activities of mirror neurons. In line with results in the current study, our previous study\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, which involved school-aged children playing a Jenga game and employed a bin-by-bin data-driven analysis method, also reported an FOI of [0.29, 0.35] Hz. Third, the increased IBC within this FOI could also be due to Respiratory Sinus Arrhythmia (RSA), occurring around 0.12\u0026ndash;0.45 Hz. RSA refers to heart rate variability in synchrony with respiration due to rhythmic changes in cardiac parasympathetic activity\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. RSA synchrony has been observed during rest and is task dependent, with greater changes in the synchrony associated with tasks that involve challenging behavior and emotion regulation\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. In the developmental literature, mother-child RSA synchrony is believed to be closely related to children\u0026rsquo;s brain maturation and ability to form interpersonal attachments, self-regulate, and engage positively with their environment\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Likewise, RSA synchrony has been studied in romantic relationships to understand its association with relationship functioning\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. Interestingly, greater IBC in the regular channels than in the short channels was also observed in this mid-frequency FOI during rest in Experiment 1. Taken together, these results suggest that verbal communications and motor behaviors may foster synchronization between interaction partners through modifying their RSA activities, manifested as synchrony in largely comparable FOIs across the three experiments.\u003c/p\u003e\u003cp\u003eThe low-frequency FOIs of [0.07, 0.16] Hz across the three studies are below the range of respirational signals but overlap with the Mayer waves, defined as oscillations of arterial pressure occurring spontaneously in conscious subjects\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Mayer waves are amplified during sympathetic activation and relatively consistent within the same species. The result that participants showed increased IBC in this range across studies with and without interaction was not surprising; it points to the effect of mere presence on the synchronization between two partners\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This FOI ([0.07, 0.16] Hz) also overlaps with the mean frequency across all the FOIs reviewed above (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Excluding the three studies that examined frequencies up to 4 Hz\u003csup\u003e82,83\u003c/sup\u003e and the study that specifically focused on the IBC between heartbeats\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, the mean values and standard error of the mean (SEM) frequencies across the identified FOIs across the 93 studies were 0.14 Hz and 0.01 Hz, respectively, with the mean range of the FOIs being 0.16 Hz. We suspect that the low-frequency FOIs were driven by the slow concentration changes in hemoglobin from the cerebral tissues associated with neuronal activity through neurovascular coupling. Finding analogous activities across three experiments in the current study, each involving different social interactions among diverse groups, suggests the presence of shared mechanisms that align multiple brains and support general social interactions. This hypothesis should be explored in future research.\u003c/p\u003e\u003cp\u003eThe very-low frequency FOI of [0.02, 0.04] Hz suggests similarities in the slow changes in fNIRS signals. This frequency range has been reported in multiple studies that used data-driven analyses and the reverse of task durations, i.e., 25\u0026ndash;50 s (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Increased IBC in this frequency band has been proposed to be associated with prolonged eye contact during social interaction\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, which was often not observable when participants had their eyes closed. Because this FOI captures very slow neural activity, significant IBC implies that participants\u0026rsquo; brains were synchronized over a sustained duration, such as 25 to 50 seconds. Possibly due to this reason, the IBC results in this IBC varied a lot across sample sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and populations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFindings from the current study shed light on our earlier question of how social interactions may influence the synchronization of systemic signals, including heart rates, parasympathetic nervous signals, and Mayer waves between different brains. We would like to highlight that the FOIs may be different in the few prior studies that used tasks that were comparable with those in the current studies, due to the different methods used to determine FOIs. For instance, in two studies involving dyadic cooperation, different FOIs were mentioned based on data-driven analyses\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Between the two studies, this variation could be attributed to the differing experimental designs: one study employed a mixed design\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, while the other used a within-subject design\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, thus complicating the comparisons of IBC results. This example exemplifies how different methodological choices for determining FOIs can lead to varying outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Effect of channel exclusions and sample sizes on the calculation of IBC\u003c/h2\u003e\u003cp\u003eGiven the poor signal-to-noise ratios of fNIRS data, which can be further compromised by hair artifacts or inadequate contact between the fNIRS optodes and the skin, we investigated how fNIRS signal quality and channel exclusion influence the calculation of IBC. We calculated the SCI\u0026mdash;the correlation between heartbeat signals recorded in the two wavelengths per channel\u0026mdash;to indicate the signal quality\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. However, prior studies have used different SCI cutoff thresholds, leading to various portions of data being excluded. Some studies adhered to the initial recommendation by using an SCI cutoff threshold of 0.75\u003csup\u003e89,90\u003c/sup\u003e. Others preferred a more conservative approach, excluding minimal channels and selecting a lower SCI cutoff threshold\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. A few studies chose an intermediate SCI cutoff threshold to ensure a certain number of short channels included for further analysis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Our prior study demonstrated that both lower (SCI\u0026thinsp;=\u0026thinsp;0.15) and higher (SCI\u0026thinsp;=\u0026thinsp;0.75) SCI cutoff thresholds yielded comparable statistical results in single-person fNIRS research with well-controlled block-design stimulation. We must highlight that variations in signal quality can cause the same SCI cutoff threshold to result in different proportions of channels being excluded.\u003c/p\u003e\u003cp\u003eTo date, no study has investigated the impact of channel exclusion on the calculation of IBC results in fNIRS hyperscanning research, which often occurs in ecologically valid settings. Our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed that channel exclusion affected the statistical results more when the sample sizes were relatively small. When the sample sizes are sufficient, such as 32 for studies involving dyadic settings (Experiment 2 and Experiment 3), channel exclusion rates may affect the statistical results for the mid-frequency ([0.22, 0.49] Hz) and low-frequency FOI ([0.073, 0.17] Hz) less. In contrast, the very-low frequency FOI (under 0.04 Hz) results varied a lot across various sample sizes and various exclusion rates. Across three FOIs under 0.5 Hz (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), a sample size of 32 or above, with a channel exclusion rate of 5\u0026ndash;7% achieved robust results across data obtained from three independent experiments. Differences in signal quality (hence various channel exclusions) and different sample sizes in prior studies could have contributed to inconsistent results across studies using a similar paradigm and methodology to determine FOI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Limitations of the current method\u003c/h2\u003e\u003cp\u003eThe proposed methodology offers the advantage of addressing the neural origins of inter-brain synchronization during social interactions in ecologically valid settings, while circumventing the circularity issues often observed in the bin-by-bin frequency analyses as discussed above. We were able to utilize short-channel detectors from NIRx devices, which are among the most popular for fNIRS research. However, not all commercially available fNIRS systems include short-channel detectors, limiting the generalization of this methodology. Further, the participant pool for all three studies was exclusively Asian. Since darker, denser hair attenuates light more effectively, this can introduce greater artefacts (and relatively higher ratios of channel exclusions) in fNIRS data compared to data acquired from individuals with light-colored hair. Further research is needed to confirm these findings in individuals with light-colored hair.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003ePrevious fNIRS hyperscanning studies have reported varied selections of frequency bands of interest (FOIs), potentially contributing to inconsistent findings of inter-brain coherence (IBC). In the current study, we briefly reviewed the different methods previously used to determine FOIs in studies focused on cooperation and coordination and discussed the associated challenges. We then proposed a method including short channels to identify FOIs and to address the neural origins of inter-personal brain synchronization. Testing this method on three independent datasets, we found that three datasets identified comparable yet slightly different FOIs that showed greater IBC between different populations and across various social interaction contexts. The shared and unique FOIs of signals driving the IBC between two or more brains could provide a common ground for the comparisons of IBC results in future hyperscanning studies. Our findings further revealed the effect of fNIRS signal quality (hence channel exclusion) and sample size on detecting FOIs and calculating IBC. Future studies may consider reporting data quality and detailing the number of excluded channels to enhance the transparency and reproducibility of the research results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAdditional Information\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003e**XZ** : Conceptualization; Data acquisition, analysis, and interpretation; Writing \u0026ndash; original draft.**FFYN** : Conceptualization; Resources; Supervision; Writing \u0026ndash; review \u0026amp; editing.**PCMW** : Conceptualization; Funding acquisition; Resources; Supervision; Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe sincerely thank all the participants for participating in this research. We have used AI tools such as Deepseek-V3.2 to help polish and improve some of the writing.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eMATLAB code used for the data analyses is stored on Open Science Framework and available for peer review through the following link [https://osf.io/r4s73/?view_only=6c14eb3ca218417489e939bbd45f7197] . The summary (de-identified) data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFerrari, M. \u0026amp; Quaresima, V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e63\u003c/b\u003e, 921\u0026ndash;935. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2012.03.049\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2012.03.049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuaresima, V. \u0026amp; Ferrari, M. Functional Near-Infrared Spectroscopy (fNIRS) for Assessing Cerebral Cortex Function During Human Behavior in Natural/Social Situations: A Concise Review. \u003cem\u003eOrganizational Res. Methods\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e, 46\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1094428116658959\u003c/span\u003e\u003cspan address=\"10.1177/1094428116658959\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCzeszumski, A. et al. Cooperative Behavior Evokes Interbrain Synchrony in the Prefrontal and Temporoparietal Cortex: A Systematic Review and Meta-Analysis of fNIRS Hyperscanning Studies. \u003cem\u003eEneuro\u003c/em\u003e 9, (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/Eneuro.0268-21.2022\u003c/span\u003e\u003cspan address=\"10.1523/Eneuro.0268-21.2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNowak, M. \u0026amp; Highfield, R. \u003cem\u003eSupercooperators: Altruism, evolution, and why we need each other to succeed\u003c/em\u003e (Simon and Schuster, 2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan, Y., Cheng, X., Zhang, Z., Li, X. \u0026amp; Hu, Y. Cooperation in lovers: An fNIRS-based hyperscanning study. \u003cem\u003eHum. Brain Mapp.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 831\u0026ndash;841. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.23421\u003c/span\u003e\u003cspan address=\"10.1002/hbm.23421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReindl, V., Gerloff, C., Scharke, W. \u0026amp; Konrad, K. Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning. \u003cem\u003eNeuroImage\u003c/em\u003e 178, 493\u0026ndash;502, (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2018.05.060\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2018.05.060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, X., Hong, X. C. \u0026amp; Wong, P. C. M. Autistic Traits Modulate Social Synchronizations Between School-Aged Children: Insights From Three fNIRS Hyperscanning Experiments. \u003cem\u003ePsychol. Sci.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 840\u0026ndash;857. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/09567976241237699\u003c/span\u003e\u003cspan address=\"10.1177/09567976241237699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTachtsidis, I. \u0026amp; Scholkmann, F. False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward. \u003cem\u003eNeurophotonics\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 031405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1117/1.NPh.3.3.031405\u003c/span\u003e\u003cspan address=\"10.1117/1.NPh.3.3.031405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJulien, C. The enigma of Mayer waves: Facts and models. \u003cem\u003eCardiovasc. Res.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e, 12\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cardiores.2005.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.cardiores.2005.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eY\u0026uuml;cel, M. A. et al. Best practices for fNIRS publications. \u003cem\u003eNeurophotonics\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 012101 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHakim, U. et al. Quantification of inter-brain coupling: A review of current methods used in haemodynamic and electrophysiological hyperscanning studies. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e280\u003c/b\u003e, 120354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2023.120354\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2023.120354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrinsted, A., Moore, J. C. \u0026amp; Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. \u003cem\u003eNonlinear Proc. Geoph\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 561\u0026ndash;566. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/npg-11-561-2004\u003c/span\u003e\u003cspan address=\"10.5194/npg-11-561-2004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiberati, A. et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. \u003cem\u003eBmj-Brit Med. J.\u003c/em\u003e \u003cb\u003e339\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.b2700\u003c/span\u003e\u003cspan address=\"10.1136/bmj.b2700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalters, S., Miller, J. G., Li, R., Hawthorne, G. \u0026amp; Reiss, A. L. Virtual (Zoom) Interactions Alter Conversational Behavior and Interbrain Coherence. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 2568\u0026ndash;2578. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/JNEUROSCI.1401-22.2023\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1401-22.2023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong, X. et al. Influence of interpersonal distance on collaborative performance in the joint Simon task-An fNIRS-based hyperscanning study. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e285\u003c/b\u003e, 120473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2023.120473\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2023.120473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzhari, A., Bizzego, A. \u0026amp; Esposito, G. Parent-child dyads with greater parenting stress exhibit less synchrony in posterior areas and more synchrony in frontal areas of the prefrontal cortex during shared play. \u003cem\u003eSoc. Neurosci.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 520\u0026ndash;531. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/17470919.2022.2162118\u003c/span\u003e\u003cspan address=\"10.1080/17470919.2022.2162118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, M., Jia, H. \u0026amp; Zheng, M. Interbrain Synchrony in the Expectation of Cooperation Behavior: A Hyperscanning Study Using Functional Near-Infrared Spectroscopy. \u003cem\u003eFront. Psychol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 542093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2020.542093\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2020.542093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreaves, D. A. et al. Exploring Theater Neuroscience: Using Wearable Functional Near-infrared Spectroscopy to Measure the Sense of Self and Interpersonal Coordination in Professional Actors. \u003cem\u003eJ. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 2215\u0026ndash;2236. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1162/jocn_a_01912\u003c/span\u003e\u003cspan address=\"10.1162/jocn_a_01912\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalters, S., Miller, J. G. \u0026amp; Reiss, A. L. Expressing appreciation is linked to interpersonal closeness and inter-brain coherence, both in person and over Zoom. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e33\u003c/b\u003e, 7211\u0026ndash;7220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cercor/bhad032\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhad032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu, Y. et al. Musical Meter Induces Interbrain Synchronization during Interpersonal Coordination. \u003cem\u003eEneuro\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/ENEURO.0504-21.2022\u003c/span\u003e\u003cspan address=\"10.1523/ENEURO.0504-21.2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin, F. R. et al. Hearing loss and cognitive decline in older adults. \u003cem\u003eJAMA Intern. Med.\u003c/em\u003e \u003cb\u003e173\u003c/b\u003e, 293\u0026ndash;299 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayseless, N., Hawthorne, G. \u0026amp; Reiss, A. L. Real-life creative problem solving in teams: fNIRS based hyperscanning study. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e203\u003c/b\u003e, 116161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2019.116161\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2019.116161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, C. et al. Disrupted inter-brain synchronization in the prefrontal cortex between adolescents and young adults with gaming disorders during the real-world cooperating video games. \u003cem\u003eJ. Affect. Disorders\u003c/em\u003e. \u003cb\u003e352\u003c/b\u003e, 386\u0026ndash;394 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShamay-Tsoory, S. G., Marton-Alper, I. Z. \u0026amp; Markus, A. Post-interaction neuroplasticity of inter-brain networks underlies the development of social relationship. \u003cem\u003eiScience\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 108796. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.isci.2024.108796\u003c/span\u003e\u003cspan address=\"10.1016/j.isci.2024.108796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, H., Wang, H., Long, Y., Jiang, Y. \u0026amp; Lu, C. Interpersonal neural synchronization underlies mnemonic similarity during collaborative remembering. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cb\u003e191\u003c/b\u003e, 108732. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuropsychologia.2023.108732\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropsychologia.2023.108732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, K., Teng, J. \u0026amp; Hao, N. Gender of partner affects the interaction pattern during group creative idea generation. \u003cem\u003eExp. Brain Res.\u003c/em\u003e \u003cb\u003e238\u003c/b\u003e, 1157\u0026ndash;1168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00221-020-05799-7\u003c/span\u003e\u003cspan address=\"10.1007/s00221-020-05799-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, D. et al. A cost-effective instrument of distributed functional near-infrared spectroscopy for hyperscanning real-world interactions. \u003cem\u003eIEEE Trans. Instrum. Measurement\u003c/em\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eY\u0026uuml;cel, M. A. et al. Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy. \u003cem\u003eBiomedical Opt. express\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 3078\u0026ndash;3088 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCui, X., Bryant, D. M. \u0026amp; Reiss, A. L. NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e59\u003c/b\u003e, 2430\u0026ndash;2437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2011.09.003\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2011.09.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, S. et al. Parenting links to parent-child interbrain synchrony: a real-time fNIRS hyperscanning study. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cercor/bhad533\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhad533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng, Y., Liu, S., Tian, B., Zhang, Y. \u0026amp; Wang, D. in \u003cem\u003eIEEE World Haptics Conference (WHC).\u003c/em\u003e 176\u0026ndash;182 (IEEE). 176\u0026ndash;182 (IEEE). (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiao, Z., Song, J., Yang, X., Chen, Y. \u0026amp; Han, G. Social pain sharing boosts interpersonal brain synchronization in female cooperation. \u003cem\u003eActa Psychol. (Amst)\u003c/em\u003e. \u003cb\u003e243\u003c/b\u003e, 104138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.actpsy.2024.104138\u003c/span\u003e\u003cspan address=\"10.1016/j.actpsy.2024.104138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun, B. H. et al. Behavioral and brain synchronization differences between expert and novice teachers when collaborating with students. \u003cem\u003eBrain Cogn.\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bandc.2019.105513\u003c/span\u003e\u003cspan address=\"10.1016/j.bandc.2019.105513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, Y. et al. Dyad sex composition effect on inter-brain synchronization in face-to-face cooperation. \u003cem\u003eBrain Imaging Behav.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1667\u0026ndash;1675. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11682-020-00361-z\u003c/span\u003e\u003cspan address=\"10.1007/s11682-020-00361-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, N. et al. NIRS-based hyperscanning reveals inter-brain neural synchronization during cooperative Jenga game with face-to-face communication. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2016.00082\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2016.00082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, L. et al. Interpersonal Neural Synchronization During Cooperative Behavior of Basketball Players: A fNIRS-Based Hyperscanning Study. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2020.00169\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2020.00169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, T. et al. The effects of interaction quality on neural synchrony during mother-child problem solving. \u003cem\u003eCortex\u003c/em\u003e \u003cb\u003e124\u003c/b\u003e, 235\u0026ndash;249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cortex.2019.11.020\u003c/span\u003e\u003cspan address=\"10.1016/j.cortex.2019.11.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, T., Hoehl, S. \u0026amp; Vrticka, P. A. Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis. \u003cem\u003eSensors-Basel\u003c/em\u003e 21, (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/s21124075\u003c/span\u003e\u003cspan address=\"10.3390/s21124075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, T., Kungl, M. T., Hoehl, S., White, L. O. \u0026amp; Vrticka, P. Visualizing the invisible tie: Linking parent-child neural synchrony to parents' and children's attachment representations. \u003cem\u003eDev. Sci.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, e13504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/desc.13504\u003c/span\u003e\u003cspan address=\"10.1111/desc.13504\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, S. J. et al. The Effect of Task Performance and Partnership on Interpersonal Brain Synchrony during Cooperation. \u003cem\u003eBrain Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/brainsci12050635\u003c/span\u003e\u003cspan address=\"10.3390/brainsci12050635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKruppa, J. A. et al. Brain and motor synchrony in children and adolescents with ASD-a fNIRS hyperscanning study. \u003cem\u003eSoc. Cogn. Affect. Neur\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 103\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/scan/nsaa092\u003c/span\u003e\u003cspan address=\"10.1093/scan/nsaa092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo, L. et al. Decreased inter-brain synchronization in the right middle frontal cortex in alcohol use disorder during social interaction: An fNIRS hyperscanning study. \u003cem\u003eJ. Affect. Disord\u003c/em\u003e. \u003cb\u003e329\u003c/b\u003e, 573\u0026ndash;580. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2023.02.072\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2023.02.072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReindl, V. et al. Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy. \u003cem\u003eJ. Vis. Exp.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3791/58807\u003c/span\u003e\u003cspan address=\"10.3791/58807\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaker, J. M. et al. Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 26492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep26492\u003c/span\u003e\u003cspan address=\"10.1038/srep26492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan, H. et al. Is the creativity of lovers better? A behavioral and functional near-infrared spectroscopy hyperscanning study. \u003cem\u003eCurr. Psychol.\u003c/em\u003e 1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12144-020-01093-5\u003c/span\u003e\u003cspan address=\"10.1007/s12144-020-01093-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng, X. J., Pan, Y. F., Hu, Y. Y. \u0026amp; Hu, Y. Coordination Elicits Synchronous Brain Activity Between Co-actors: Frequency Ratio Matters. \u003cem\u003eFront. NeuroSci.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2019.01071\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2019.01071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng, X., Li, X. \u0026amp; Hu, Y. Synchronous brain activity during cooperative exchange depends on gender of partner: A fNIRS-based hyperscanning study. \u003cem\u003eHum. Brain Mapp.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 2039\u0026ndash;2048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.22754\u003c/span\u003e\u003cspan address=\"10.1002/hbm.22754\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, Y. Z., Chen, M., Zhang, R. Q. \u0026amp; Li, X. C. Experiencing happiness together facilitates dyadic coordination through the enhanced interpersonal neural synchronization. \u003cem\u003eSoc. Cogn. Affect. Neur\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, 447\u0026ndash;460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/scan/nsab114\u003c/span\u003e\u003cspan address=\"10.1093/scan/nsab114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller, J. G. et al. Inter-brain synchrony in mother-child dyads during cooperation: An fNIRS hyperscanning study. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cb\u003e124\u003c/b\u003e, 117\u0026ndash;124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuropsychologia.2018.12.021\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropsychologia.2018.12.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsaka, N. et al. How Two Brains Make One Synchronized Mind in the Inferior Frontal Cortex: fNIRS-Based Hyperscanning During Cooperative Singing. \u003cem\u003eFront. Psychol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2015.01811\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2015.01811\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, Y. et al. Different strategies, distinguished cooperation efficiency, and brain synchronization for couples: An fNIRS-based hyperscanning study. \u003cem\u003eBrain Behav.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, e01768. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/brb3.1768\u003c/span\u003e\u003cspan address=\"10.1002/brb3.1768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, Q. et al. Autism Symptoms Modulate Interpersonal Neural Synchronization in Children with Autism Spectrum Disorder in Cooperative Interactions. \u003cem\u003eBrain Topogr\u003c/em\u003e. \u003cb\u003e33\u003c/b\u003e, 112\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10548-019-00731-x\u003c/span\u003e\u003cspan address=\"10.1007/s10548-019-00731-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng, X. et al. Self-other overlap and interpersonal neural synchronization serially mediate the effect of behavioral synchronization on prosociality. \u003cem\u003eSoc. Cogn. Affect. Neurosci.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 203\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/scan/nsaa017\u003c/span\u003e\u003cspan address=\"10.1093/scan/nsaa017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, Y. et al. Children with autism spectrum disorder perform comparably to their peers in a parent-child cooperation task. \u003cem\u003eExp. Brain Res.\u003c/em\u003e \u003cb\u003e241\u003c/b\u003e, 1905\u0026ndash;1917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00221-023-06626-5\u003c/span\u003e\u003cspan address=\"10.1007/s00221-023-06626-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, H. et al. Increased interbrain synchronization and neural efficiency of the frontal cortex to enhance human coordinative behavior: A combined hyper-tES and fNIRS study. \u003cem\u003eNeuroImage\u003c/em\u003e 282, 120385, (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2023.120385\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2023.120385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, C. et al. Dynamic interpersonal neural synchronization underlying pain-induced cooperation in females. \u003cem\u003eHum. Brain Mapp.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 3222\u0026ndash;3232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.24592\u003c/span\u003e\u003cspan address=\"10.1002/hbm.24592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, R. et al. Effects of acute psychosocial stress on interpersonal cooperation and competition in young women. \u003cem\u003eBrain Cogn.\u003c/em\u003e \u003cb\u003e151\u003c/b\u003e, 105738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bandc.2021.105738\u003c/span\u003e\u003cspan address=\"10.1016/j.bandc.2021.105738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei, Y. et al. Reduced interpersonal neural synchronization in right inferior frontal gyrus during social interaction in participants with clinical high risk of psychosis: An fNIRS-based hyperscanning study. \u003cem\u003eProg Neuropsychopharmacol. Biol. Psychiatry\u003c/em\u003e. \u003cb\u003e120\u003c/b\u003e, 110634. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pnpbp.2022.110634\u003c/span\u003e\u003cspan address=\"10.1016/j.pnpbp.2022.110634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNi, J., Yang, J. \u0026amp; Ma, Y. Social bonding in groups of humans selectively increases inter-status information exchange and prefrontal neural synchronization. \u003cem\u003ePLoS Biol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, e3002545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pbio.3002545\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.3002545\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao, H. et al. Acute stress makes women\u0026rsquo;s group decisions more rational: A functional near-infrared spectroscopy (fNIRS)\u0026ndash;based hyperscanning study. \u003cem\u003eJ. Neurosci. Psychol. Econ.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 20 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, C., Cheng, X., Liu, C. \u0026amp; Li, P. Interpersonal coordination enhances brain-to-brain synchronization and influences responsibility attribution and reward allocation in social cooperation. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e252\u003c/b\u003e, 119028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2022.119028\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2022.119028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, K., Xue, H., Nozawa, T. \u0026amp; Hao, N. Cooperation makes a group be more creative. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e, 3457\u0026ndash;3470. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cercor/bhy215\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhy215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, K., Qiao, X. \u0026amp; Hao, N. Praising or keeping silent on partner's ideas: Leading brainstorming in particular ways. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cb\u003e124\u003c/b\u003e, 19\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuropsychologia.2019.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropsychologia.2019.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, X. et al. Dynamic brain networks in spontaneous gestural communication. \u003cem\u003eNpj Sci. Learn.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41539-024-00274-2\u003c/span\u003e\u003cspan address=\"10.1038/s41539-024-00274-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, K., Qiao, X., Yun, Q. \u0026amp; Hao, N. Educational diversity and group creativity: Evidence from fNIRS hyperscanning. \u003cem\u003eNeuroImage\u003c/em\u003e 243, 118564, (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2021.118564\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2021.118564\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin, J. T., Pan, Y. F., Zhang, Y. X., Hu, Y. Y. \u0026amp; Luo, J. L. Distinct inter-brain synchronization patterns during group creativity under threats in cooperative and competitive contexts. \u003cem\u003eThink. Skills Creat\u003c/em\u003e. \u003cb\u003e49\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tsc.2023.101366\u003c/span\u003e\u003cspan address=\"10.1016/j.tsc.2023.101366\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, K. L. \u0026amp; Hao, N. When do we fall in neural synchrony with others? \u003cem\u003eSoc. Cogn. Affect. Neur\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 253\u0026ndash;261 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDu, B. et al. Higher or lower? Interpersonal behavioral and neural synchronization of movement imitation in autistic children. \u003cem\u003eAutism Res.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 1876\u0026ndash;1901 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Y. et al. Exploring the role of mutual prediction in inter-brain synchronization during competitive interactions: an fNIRS hyperscanning investigation. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cercor/bhad483\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhad483\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, M. et al. Neural mechanisms distinguishing two types of cooperative problem-solving approaches: An fNIRS hyperscanning study. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e291\u003c/b\u003e, 120587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2024.120587\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2024.120587\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan, Y., Cheng, X. \u0026amp; Hu, Y. Three heads are better than one: cooperative learning brains wire together when a consensus is reached. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cercor/bhac127\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhac127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue, H., Lu, K. L. \u0026amp; Hao, N. Cooperation makes two less-creative individuals turn into a highly-creative pair. \u003cem\u003eNeuroImage\u003c/em\u003e 172, 527\u0026ndash;537, (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2018.02.007\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2018.02.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, H., Cong, Y., Zhao, W., Li, X. \u0026amp; Li, L. A study of trust behavior and its neural basis in athletes under long-term exercise training. \u003cem\u003eNeurosci. Lett.\u003c/em\u003e \u003cb\u003e805\u003c/b\u003e, 137218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neulet.2023.137218\u003c/span\u003e\u003cspan address=\"10.1016/j.neulet.2023.137218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng, X., Hosseini, S., Miyake, Y. \u0026amp; Nozawa, T. Cooperativeness as a Personality Trait and Its Impact on Cooperative Behavior in Young East Asian Adults Who Synchronized in Casual Conversations. \u003cem\u003eBehav. Sci-Basel\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 987 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKriegeskorte, N., Simmons, W. K., Bellgowan, P. S. \u0026amp; Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 535\u0026ndash;540 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, Q. et al. Inter-brain neural mechanism and influencing factors underlying different cooperative behaviors: a hyperscanning study. \u003cem\u003eBrain Struct. Funct.\u003c/em\u003e \u003cb\u003e229\u003c/b\u003e, 75\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00429-023-02700-4\u003c/span\u003e\u003cspan address=\"10.1007/s00429-023-02700-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, X., Sobczak, G., McKay, C. M. \u0026amp; Litovsky, R. Y. Comparing fNIRS signal qualities between approaches with and without short channels. \u003cem\u003ePloS one\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, e0244186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0244186\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0244186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuppert, T. J., Diamond, S. G., Franceschini, M. A. \u0026amp; Boas, D. A. HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. \u003cem\u003eAppl. Opt.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e, D280\u0026ndash;298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1364/ao.48.00d280\u003c/span\u003e\u003cspan address=\"10.1364/ao.48.00d280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrigadoi, S. \u0026amp; Cooper, R. J. How short is short? Optimum source\u0026ndash;detector distance for short-separation channels in functional near-infrared spectroscopy. \u003cem\u003eNeurophotonics\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 025005\u0026ndash;025005 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, X., Hong, X. \u0026amp; Wong, P. C. M. Exploring inter-brain coherence between fathers and infants during maternal storytelling: an fNIRS hyperscanning study. \u003cem\u003eInfant Child. Dev\u003c/em\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAickin, M. \u0026amp; Gensler, H. Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. \u003cem\u003eAm. J. Public Health\u003c/em\u003e. \u003cb\u003e86\u003c/b\u003e, 726\u0026ndash;728 (1996).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan, Y. F. et al. Instructor-learner brain coupling discriminates between instructional approaches and predicts learning. \u003cem\u003eNeuroImage\u003c/em\u003e 211, (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2020.116657\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2020.116657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng, L. et al. Enhancement of teaching outcome through neural prediction of the students' knowledge state. \u003cem\u003eHum. Brain. Mapp.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, 3046\u0026ndash;3057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.24059\u003c/span\u003e\u003cspan address=\"10.1002/hbm.24059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYasuma, F. \u0026amp; Hayano, J. Respiratory sinus arrhythmia - Why does the heartbeat synchronize with respiratory rhythm? \u003cem\u003eChest\u003c/em\u003e 125, 683\u0026ndash;690, doi: (2004). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1378/chest.125.2.683\u003c/span\u003e\u003cspan address=\"10.1378/chest.125.2.683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller, J. G., Armstrong-Carter, E., Balter, L. \u0026amp; Lorah, J. A meta‐analysis of mother\u0026ndash;child synchrony in respiratory sinus arrhythmia and contextual risk. \u003cem\u003eDev. Psychobiol.\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, e22355 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeldman, R. The Neurobiology of Human Attachments. \u003cem\u003eTrends Cogn. Sci.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 80\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tics.2016.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.tics.2016.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan, S. C., Baucom, B., Timmons, A. C. \u0026amp; Margolin, G. A Systematic Review of Respiratory Sinus Arrhythmia in Romantic Relationships. \u003cem\u003eFam Process.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 441\u0026ndash;456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/famp.12644\u003c/span\u003e\u003cspan address=\"10.1111/famp.12644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuglielmini, S., Bopp, G., Marcar, V. L., Scholkmann, F. \u0026amp; Wolf, M. Systemic physiology augmented functional near-infrared spectroscopy hyperscanning: a first evaluation investigating entrainment of spontaneous activity of brain and body physiology between subjects. \u003cem\u003eNeurophotonics\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1117/1.NPh.9.2.026601\u003c/span\u003e\u003cspan address=\"10.1117/1.NPh.9.2.026601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePollonini, L. et al. Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy. \u003cem\u003eHear. Res.\u003c/em\u003e \u003cb\u003e309\u003c/b\u003e, 84\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heares.2013.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.heares.2013.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeder, S., Zhou, X., Shoushtarian, M., Innes-Brown, H. \u0026amp; McKay, C. Cortical Processing Related to Intensity of a Modulated Noise Stimulus\u0026mdash;a Functional Near-Infrared Study. \u003cem\u003eJ. Assoc. Res. Otolaryngol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 273\u0026ndash;286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10162-018-0661-0\u003c/span\u003e\u003cspan address=\"10.1007/s10162-018-0661-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawrence, R. J., Wiggins, I. M., Anderson, C. A., Davies-Thompson, J. \u0026amp; Hartley, D. E. Cortical correlates of speech intelligibility measured using functional near-infrared spectroscopy (fNIRS). \u003cem\u003eHear. Res.\u003c/em\u003e \u003cb\u003e370\u003c/b\u003e, 53\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heares.2018.09.005\u003c/span\u003e\u003cspan address=\"10.1016/j.heares.2018.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWijayasiri, P., Hartley, D. E. H. \u0026amp; Wiggins, I. M. Brain activity underlying the recovery of meaning from degraded speech: A functional near-infrared spectroscopy (fNIRS) study. \u003cem\u003eHear. Res.\u003c/em\u003e \u003cb\u003e351\u003c/b\u003e, 55\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heares.2017.05.010\u003c/span\u003e\u003cspan address=\"10.1016/j.heares.2017.05.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, X., Wang, L., Hong, X. \u0026amp; Wong, P. C. M. Infant-directed speech facilitates word learning through attentional mechanisms: an fNIRS study of toddlers. \u003cem\u003eDev. Sci.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/desc.13424\u003c/span\u003e\u003cspan address=\"10.1111/desc.13424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, X. et al. Inhibitory Control in Children 4\u0026ndash;10 Years of Age: Evidence From Functional Near-Infrared Spectroscopy Task-Based Observations. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 798358. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2021.798358\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2021.798358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"fNIRS hyperscanning, inter-brain connection, frequency band of interest","lastPublishedDoi":"10.21203/rs.3.rs-7854624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7854624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeuroimaging hyperscanning\u0026mdash;the monitoring of brain activity of two or more persons simultaneously\u0026mdash;has emerged as a popular tool to uncover the neural mechanisms of social interactions. The use of functional near-infrared spectroscopy (fNIRS)\u0026mdash;a non-invasive, child-friendly technique tolerant of motion artifacts\u0026mdash;has significantly advanced the research of social interactions. Despite its popularity, the field has yet to agree on best practices for quantifying inter-brain connections (IBC) during social interactions, including the frequency band of interest (FOI) for signal analysis. Consequently, past research findings have often been inconsistent. In this study, we reviewed various methods used and their corresponding FOI results in previous fNIRS hyperscanning research focused on the topics of cooperation. Additionally, we propose a new methodology to quantify FOI that aims to point to the origin of synchronization between brains. We tested the proposed method on three independent fNIRS hyperscanning datasets. The three datasets involved three different populations and three types of social interactions commonly studied in the literature. We examined the effect of sample sizes and data exclusion rates on the calculation of FOIs and statistical results. We offer a method for testing and adoption within the fNIRS community, aimed at eliminating arbitrary FOI selections and potentially enhancing the reproducibility of results in future fNIRS hyperscanning research.\u003c/p\u003e","manuscriptTitle":"Addressing Arbitrary Choices of Frequency Band of Interest in fNIRS Hyperscanning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 09:07:27","doi":"10.21203/rs.3.rs-7854624/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-16T11:15:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-16T07:58:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-16T07:56:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-14T06:03:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55cf5d94-7b41-4464-b17a-cd607fa270e8","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56249143,"name":"Biological sciences/Neuroscience"},{"id":56249144,"name":"Biological sciences/Psychology"},{"id":56249145,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-04T16:17:42+00:00","versionOfRecord":{"articleIdentity":"rs-7854624","link":"https://doi.org/10.1038/s41598-026-50540-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-27 15:56:54","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2025-10-15 09:07:27","video":"","vorDoi":"10.1038/s41598-026-50540-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-50540-z","workflowStages":[]},"version":"v1","identity":"rs-7854624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7854624","identity":"rs-7854624","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}