Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

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

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

In recent years, using hyperscanning techniques to explore shared brain activities during dyadic or group interactions has become a popular research direction. Researchers often employ electroencephalogram (EEG)¹, functional magnetic resonance imaging (fMRI)², or functional near-infrared spectroscopy (fNIRS)³ to monitor the neural and brain activities of multiple subjects simultaneously. The neuroscience metric of inter-brain synchrony (IBS)⁴ is thus introduced to quantify the degree of brain activity coupling between people with meticulous analysis of the phase and amplitude alignment of neural or hemodynamic signals across time⁵. IBS refers to the phenomenon where the brain activities of two or more individuals become aligned or synchronized during social interactions. This synchronization can occur in various forms, such as in the phase, frequency, or amplitude of brain oscillations^6,7,8.

In the realm of naturalistic social interactions involving multiple participants, a vast array of research has illuminated the phenomenon of IBS, particularly in contexts as diverse as parent-child dynamics⁹, educator-student exchanges¹⁰, romantic partnerships¹¹, and audience-performer engagements¹². Notably, IBS exhibits heightened levels within intimate relationships such as parent-child and romantic partnerships, compared to interactions with strangers^13,14, underscoring its sensitivity to the depth of emotional connection. Concurrently, this heightened IBS frequently coincides with enhanced collaboration efficiency and behavioral improvements, suggesting a functional role in facilitating positive social outcomes¹⁵.

In the context of counseling, the working alliance -- a pivotal construct closely tied to counseling efficacy¹⁶ -- embodies a distinct interpersonal dynamic that gradually evolves between the counselor and client during the therapeutic process. At its essence, this alliance rests upon the fostering of profound emotional ties and the establishment of efficient collaborative frameworks¹⁷. Therefore, exploring IBS within counseling interactions provides a new perspective that enhances the understanding of the complexities and quality of these therapeutic relationships.

Counselor empathy, as perceived by the client, contributes to the development of a working alliance¹⁸. This indicates that the establishment of the working alliance may arise from the mutual understanding and corresponding neural activities between the counselor and the client. Empathy can be dissociated into two components: affective empathy and cognitive empathy. The inferior frontal gyrus (IFG) is implicated in affective empathy and is also associated with the neural processes underlying face-to-face communication¹⁹. The right temporal-parietal junction (rTPJ), an important part of the Theory of Mind network, is closely linked to cognitive empathy, particularly in understanding the mental states of others²⁰. Consequently, early brain synchronization studies in counseling prioritized these two regions as regions of interest (ROIs) and identified IBS primarily in the rTPJ²¹. Subsequent research has thus focused predominantly on the rTPJ²². Studies have found that neural synchronization in the rTPJ between clients and therapists during counseling is significantly higher than in conversational contexts. There is a positive correlation between increased synchronous neural activity in the rTPJ and the strength of the therapeutic alliance²¹. The unique activity patterns in counseling may result from the in-depth exploration of emotional expressions and personal experiences. This suggests that IBS warrants further investigation within counseling. Additionally, the correlation between enhanced rTPJ activity and the strength of the working alliance indicates that IBS could serve as a neurobiological basis for assessing counseling relationships, offering a novel evaluation metric.

While these findings underscore the promising role of IBS in counselor-client dynamics, they also emphasize the need for further clarification regarding the direct causal link between brain synchronization, counseling effectiveness, and the working alliance. To advance this burgeoning field, developing standardized hyperscanning protocols and rigorous data analysis methodologies is paramount. By refining the methodological toolkit, it is possible to more precisely map the neural underpinnings of effective counseling, ultimately enhancing the quality of therapeutic interventions and their outcomes.

This article provides a protocol on how to conduct an fNIRS-based hyperscanning study and how to observe and analyze the IBS between the counselor-client dyads. fNIRS is a non-invasive imaging technique used to measure brain activity. It works by detecting changes in blood oxygenation and blood volume within the brain, which are indirect markers of neural activity. This is achieved by emitting near-infrared light into the brain and measuring the amount of light absorbed or scattered by the blood cells²³. Thus, the hemodynamic/oxygenation activity is measured. Comparatively, fNIRS offers higher temporal resolution than fMRI, and it is less vulnerable to motion artifacts than EEG, rendering it well suited for studying social interactions in natural settings such as psychological counseling⁸.

This article also presents the specific steps of computing IBS via the method of wavelet transform coherence (WTC)²⁴. WTC is an analytical technique that measures the relationship between two signals across different frequencies over time. It is beneficial for identifying areas of synchrony between brain regions or between participants in a study. It calculates the coherence between two time series by analyzing their cross-spectrum using wavelet transforms. To contextualize the importance of WTC, it is essential to first understand the foundational concepts of Wavelet Transform (WT)²⁵, Coherence²⁶, and how they converge in the framework of WTC²⁷.

Wavelet Transform, a mathematical tool, excels at decomposing complex signals into their constituent time-frequency components, enabling the analysis of both localized changes in frequency over time and the overall frequency content of a signal²⁷. This characteristic is particularly advantageous when studying neural activity, which is inherently non-stationary and exhibits dynamic changes across different frequencies. Coherence, on the other hand, quantifies the degree to which two signals share similar frequency components and phase relationships, serving as a metric of synchronization between them²⁶. By combining these two concepts, WTC provides a powerful means to assess IBS, capturing both the temporal evolution and frequency specificity of neural coupling between individuals and providing insights into how different parts of the brain or brains interact dynamically throughout a task or stimulus²⁴.

While the traditional WTC framework merely tests the correlation between the brain signals of different individuals, a method considering the directionality of the interaction between the counselor and client is presented here. There are different lead-lag patterns,where one signal consistently precedes variations in the other by a specific time interval, indicating a temporal relationship in IBS according to previous studies^28,29. The IBS may not occur simultaneously between the counselor and the client during counseling. Thus, a comprehensive method is needed to explore the directionality of IBS. The method clarifies the role that counselors play throughout various phases of counseling (leading the IBS, in-phase IBS with the client, or led by the client).

This study proposes a detailed and implementable protocol based on the research question of whether IBS scores between counselors and clients can serve as potential biomarkers for assessing alliance quality or outcomes among clients with different adult attachment styles. The protocol outlines the utilization of fNIRS hyperscanning technology to investigate IBS between psychological counselors and clients within a counseling context. It provides comprehensive descriptions of the experimental procedures, precautions for each step, and subsequent data processing methods. It is anticipated that this protocol will offer valuable insights and guidance to future scholars interested in exploring IBS within the realm of psychological counseling.The specific protocol for data collection and processing is presented as follows.

All participants signed a written informed consent form before participating and were remunerated with approximately 60 yuan (Chinese currency) after the experiment. The study procedure outlined above was approved by the University Committee on Human Research Protection of East China Normal University (HR 425-2020).

1. Preparation for the experiment

1. Measures included in the study
   1. Adult attachment style
      1. Prepare the revised Chinese version of the Experiences in Close Relationships-Revised (ECR-R^30,31) on a Chinese online survey platform called Wenjuanxing (similar to SurveyMonkey or Qualtrics in other parts of the world; see Table of Materials) to prescreen participants.
         NOTE: The Chinese version of the ECR-R consists of 18 items measuring two dimensions of attachment style (attachment avoidance and attachment anxiety). Use a 7-point self-report Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). This scale is for Chinese-speaking participants. Researchers can choose the ECR³² or a suitable revised version based on participants' language and cultural background.
      2. Prepare a Chinese revised version of the Relationship Questionnaire (RQ)³⁰ to ensure that participants fit a typical attachment style.
         NOTE: The RQ comprises four short paragraphs describing four different attachment styles. Require participants to choose the paragraph that fits them best using a 7-point self-report Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). It is necessary to find a second scale measuring the same dimensions to clarify the attachment style of participants because typical participants are needed. This questionnaire is for Chinese-speaking participants. Researchers can choose the RQ³³ or a suitable revised version based on participants' language and cultural background.
   2. Working alliance
      1. Prepare the Chinese version of the Working Alliance Inventory-Short Form Revised (client version)^34,35 (WAI-SR) to measure client-reported working alliances after the experiment.
         NOTE: The Chinese version of WAI-SR consists of a total of 12 items measuring three aspects of the therapeutic working alliances, including (a) agreement on the tasks of therapy, (b) agreement on the goals of therapy, and (c) development of an affective bond. The scale was based on a 5-point Likert scale ranging from 1 = "never" to 5 = "always", with higher scores representing better-working alliance. This questionnaire is for Chinese-speaking participants. Researchers can choose the WAI-SR³⁶ or a suitable revised version based on participants' language and cultural background.
   3. Clinical outcome
      1. Prepare the Chinese revised version of the Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM)^37,38for recruiting participants.
         NOTE: The Chinese revised version of CORE-OM includes four dimensions: subjective well-being, problems/symptoms, life/social functioning, and risk to self and others, comprising a total of 34 items. Ensure that these domains reflect different areas of distress and dysfunction, with "item clusters" addressing symptom domains such as anxiety, depression, physical problems, and trauma. This questionnaire is for Chinese-speaking participants. Researchers can choose the CORE-OM³⁷ or a suitably revised version based on the participants' language and cultural background.
      2. Use the revised Chinese version of the Clinical Outcomes in Routine Evaluation-10 (CORE-10)38 to assess symptom severity before and after the experiment in order to alleviate participants' cognitive load.
         NOTE: The CORE-10 is a shortened version of the CORE-OM, which consists of 10 items on a 5-point scale ranging from 0 to 4, with higher scores indicating a higher level of psychological distress. Assess clients with pre and post-tests before and after the counseling process, and note that the difference in pre and post-test scores indicates the clinical effectiveness of the first counseling session. Calculate the amount of change in the CORE-10 (pre-test score minus post-test score) to demonstrate improvement in the client's symptoms. This questionnaire is for Chinese-speaking participants. Researchers can choose the CORE-10³⁸ or a suitable revised version based on participants' language and cultural background.
2. Participants
   1. Clients
      1. According to previous findings, recruit clients and counselors of the same sex(female) to avoid gender effect^39,40 in synchronous brain activity.
      2. Recruit female college students experiencing psychological distress on campus through the Wenjuanxing platform (see Table of Materials) and ask participants to report their chief complaint for counseling. Ensure that they have a desire to seek help on their own.
      3. Ensure the student clients are right-handed and have normal or corrected-to-normal vision and hearing. Ensure that the student clients have no known previous or current psychiatric or physical diagnoses. Make sure that they do not have other psychological counseling in progress.
      4. Use the established cut-off score to ensure the CORE-OM score for female samples remains below 1.17, confirming that they have not met clinical diagnostic criteria for psychological distress in the past week and maintaining control over the clients' mental health levels. Classify clients into secure or dismissing attachment categories based on their self-evaluation of the Chinese version of the RQ.
      5. According to the scores of ECR-R, select the top 27% of all participants with high levels of avoidance in the dismissing attachment category to form the dismissing group; select the top 27% of all participants with low levels of anxiety in the secure attachment category to participate in the secure group.
      6. After successfully recruiting the participants, confirm with the counselors whether the sessions focus on the issues reported by the participants and assess the severity of the reported problems.
         NOTE: The prescreening process involved 252 college student clients, ultimately resulting in the selection of 37 participants facing moderate academic stress, school adjustment issues, or interpersonal relationship difficulties for the formal experiment. The mean age of the client participants was 20.46 years (SD = 2.17), and all participants were female college students. No significant differences were observed between dismissing clients (n = 16) and secure clients (n = 21) regarding age (t(35) = 0.51, p = .62) or in the problems/symptoms domain of the CORE-OM(t(35) = −1.76, p = .09).
   2. Counselors
      1. Recruit several counselors from the college mental health center.
      2. Ensure that the counselors are right-handed with normal or corrected vision and hearing, registered with the Chinese Psychological Association, and possess 2–10 years of counseling experience.
      3. Ensure that the counselors have received college counseling training programs and use the same Counseling Integration Orientation Therapy method⁴¹ for semi-structured counseling, focusing on the visitor's emotional state, current distress, and counseling goals.
         NOTE: A total of 7 female counselors from the Chinese Psychological Association (CPS) participated in this study, with a mean age of 34.42 years (SD = 5.09). Among the counselors, 6 were self-identified as securely attached, and 1 was self-identified as dismissingly attached. However, the dismissing counselor's ratings on the alliance, BS, and IBS did not significantly differ from those of the other 6 counselors (all p > .05).
   3. Random matching of counselors and clients
      1. Randomly match the counselors and student clients in dyads. Due to the small number of counselors, each counselor is paired with several clients at different times. Ensure that a counselor sees only one visitor at a time.
3. Homemade fNIRS caps
   NOTE: Homemade fNIRS caps are unnecessary if there are suitable standard caps with the fNIRS system.
   1. Prepare two medium-sized swimming caps made of nylon fabric to place the optode holder grid and cover the brain region of interest (see Table of Materials). Mend the swimming caps using the following steps to meet the needs of the experiment.
      NOTE: Given the different head sizes of participants, binder clips of different sizes should be prepared to keep the optodes close to the participant's head during the experimental process.
   2. To anchor the reference optodes on the swimming cap according to the standard international 10-20 system⁴², use a standard 10-20 EEG cap (see Table of Materials ). Place the EEG cap on a head mold and then one of the swimming caps on the EEG cap.
      NOTE: Since the swimming cap and the EEG cap may not be exactly the same size, make sure neither of the caps is put on crooked.
   3. Mark reference optodes (Cz, T3, T4) with a red marker on the swimming cap through the EEG cap electrodes. Then, mark reference optodes of Regions of Interest (ROI).
      NOTE: In the study, the right temporal-parietal junction (rTPJ) is selected as the ROI, with a 4 x 4 optode probe patch placed over the right temporoparietal region. The reference optode of ROI is placed at P6.
   4. Reference the patch at P6 on the swimming cap. Place P6 at the second optode from the back of the column near T4 on the Patch. Mark the positions of other optodes and then cut small holes with a diameter of about 15 mm at the marked positions to ensure the grid holder fits in.
      NOTE: The 4 x 4 patch includes eight emitters and eight detectors, comprising 24 measurement channels (CH1-CH24). The fNIRS system is an optical topography system designed to collect fNIRS data by simultaneously measuring changes in the concentrations of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) (see Table of Materials), provides a standard separation of 30 mm. Additionally, the corresponding anatomical structure of each channel (see Figure 1) would be determined in a standard Montreal Neurological Institute coordinate space using the MATLAB (see Table of Materials) toolbox of SPM8 later.
   5. Embed the probes into the holes to appropriately mount the patches to the modified swimming caps. Mend the other swimming cap according to the above steps. Finally, set the layout of a 4 x 4 probe set for each participant through the fNIRS measurement system, corresponding to the probe arrangements of the two caps.

2. Before the participants arrive

1. Start the fNIRS system at least 30 min in advance to ensure a stable normal temperature range of 5 °C to 35 °C during the experiment.
   NOTE: It is unnecessary to turn the laser on.
2. In the fNIRS system, activate the event-related measurement mode and ensure that the recording of different states can be triggered by pressing specific keys. Check other parameters, such as subject ID, to ensure proper fNIRS measurement. Connect the fNIRS caps to the fNIRS system by inserting the corresponding optode probes into the optode probe patches on the caps.
   1. Specifically, set the experiment procedure here as an event-related design that includes two parts: the resting state session and the counseling session. In the NIRSPort system (NIRx), set the F1 key as the marker for the resting state session and the F2 key as the marker for the counseling session.
      NOTE: For users of other fNIRS systems, the procedure for marking the sessions may differ, and it is important to consult the specific system's manual or settings to ensure correct session marking.
3. Prepare informed consent forms and the questionnaires mentioned in step 1.1 for participants. Prepare a stopwatch to remind the participants that the resting state is over. Prepare a clock to remind the counselor of the counseling time limit. Prepare some lighted probes to move participants' hair aside in case hair blocks the signal.
4. Set the laboratory as a standard counseling room in a real-life scenario, with the counselor and the client sitting at 90° to each other in both groups, with a distance of 40 cm between the two chairs. The counselor would be seated on the right and the client on the left.

3. Data collection process

1. Provide instructions to the participants
   1. When both participants arrive, ensure they do not know each other again. Remind the participants to keep their mobile phones on silent.
   2. Ask the client participants to read and sign the informed consent forms, complete some demographic information, and fill out the CORE-10 to assess their psychological well-being upon arrival at the laboratory. This process typically takes about 5 min.
   3. Seat the participants. Turn on the laser. Then, put the fNIRS caps on the participants.
      NOTE: Locate the center of the cap at CZ on the participant's head, with the 4 x 4 patch covering the rTPJ.
   4. Remind the participants that they can adjust their postures if they feel uncomfortable when adjusting optodes. Organize optical fiber bundles and place them on the arm of the chair without touching the participants in case they feel heavy or tired.
      1. Remind the participants not to adjust the position of the cap or make strenuous head movements during the experiment to prevent damage to the optical fibers or changing the position of the optodes.
   5. Calibrate signals. Click AUTO GAIN in the fNIRS system to check the quality of the signals. For poor signals, first ensure probe tips are fully seated. Then, use binder clips to close gaps from hats and a lighted probe to clear hair obstructions in case hair blocks the signals. Repeat until all channels show green, indicating acceptable signal quality.
      NOTE: In the NIRSPort system (NIRx), a poor signal at a channel is indicated in yellow, while a sufficient signal is indicated in green. Users of other fNIRS systems should refer to their specific system instructions for appropriate adjustments.
2. Run the experiment
   1. Obtain the participant's consent, then turn on the camera to record the counseling process.
   2. Verify signal quality and initiate fNIRS recording. Instruct participants to rest with eyes closed for 5 min, marking the start with a predefined key (e.g., F1) and using a stopwatch to time the resting period.
   3. Remind the participants to stop resting 5 min later. Press F1 again to mark that the resting state is over. Recheck the quality of signals.
   4. Remind the participants that it is a 40-min counseling and where the clock is. Tell the participants that they can start counseling. Press F2 as set up previously to mark the start of the counseling.
   5. Leave the participants in the laboratory until 40 min later.
3. After the experiment
   1. Knock on the laboratory door to ensure that the participants have finished their conversation. Press F2 to mark the end of the experiment. End the camera recording. Help the participants take off the caps.
      NOTE: Review the video afterward to confirm that the counseling proceeded as expected.
   2. Invite the client to fill out the WAI-SR and the CORE-10. This process typically takes about 5 min. Thank the participants and provide them with some monetary compensation.
   3. Save the data. Use a disc and click Text File Out to export the raw fNIRS data. Turn off the fNIRS system. Unplug the optode probes.
      NOTE: This step is specific to the NIRSPort system (NIRx). For other systems, please make the necessary adjustments according to the system's instructions.
   4. Wipe the probes and the probe holders with ethanol. Regularly wash the caps (with the probe holder unplugged) with mild detergent and air dry them.

4. Data analysis

1. Data preprocessing
   NOTE: The software MATLAB (see Table of Materials ) was used to perform all data analysis with the following toolboxes: Homer 2⁴³ and Hitachi2nirs⁴⁴. Homer 2 is used when the temporal information is of interest, i.e., when the activation is present in both conditions, to compare the difference between the response functions of the two conditions in terms of mean amplitude and latency. Hitachi2nirs is a MATLAB script to convert the raw .csv Hitachi ETG4000 output file into a .nirs file for use with Homer2⁴⁴.
   1. Copy the dataset from the disc and convert raw .csv files to. nirs format using csv2nirs in Hitachi2nirs. Then, launch the Homer2 toolbox in MATLAB by typing Homer2_UI and convert light intensity data to optical density (OD) measurements with the hmrIntensity2OD function. Average the OD points of each channel for each participant. Reject the channels within which the OD signals are too strong or too weak (exceeding five standard deviations (SD).
   2. Use the hmrMotionArtifact function to detect motion artifacts using a wavelet transform with Daubechies 5 (db5) wavelet and a tuning parameter of 0.1^45,46 for optimal sensitivity. After detecting the artifacts, use the hmrMotionCorrectSpline to correct them through spline interpolation, smoothing the signal and reducing motion-related noise for improved data quality.
   3. Band-pass filter the OD signal using the hmrBandpassFilt function with a selected frequency range of 0.01–0.1 Hz to remove low-frequency drift and high-frequency noise.
   4. Use the hmrR_OD2conc function of Homer2 to convert the OD data into oxygenated hemoglobin (Oxy-Hb) and deoxygenated hemoglobin ( DeOxy-Hb ) values according to the modified Beer-Lambert law⁴⁷.
      NOTE: The Oxy-HB concentration changes are focused on conducting all the data analysis because the indicator can reflect changes in blood flow during brain activity^48,49, has a high signal-to-noise ratio, and has been more widely used in social interaction studies based on fNIRS hyperscanning^50,51.
   5. Use the hmrMotionCorrectGlobal function to remove global physiological noise, such as blood pressure, with a wavelet transform-based (WT-based) method.
      NOTE: The WT-based method is more sensitive to the temporal property of the data. If the focus is on the overall interaction pattern among participants rather than detailed changes at each time point, the principal components analysis (PCA)³ is a better choice. The PCA method, proposed by Zhang et al., primarily involves several steps, including decomposing the signal, conducting spatial smoothing, and reconstructing the signal in order to remove non-neural global components. The enPCAFilter function can be used to remove global physiological noise from fNIRS data using PCA. The WT-based method proposed by Duan and colleagues²⁷ is adopted here.
      1. Use wavelet transform coherence ( WTC )^24,52 to detect the time-frequency points contaminated by the global physiological noise. The method allows the detection of the coherence of two signals on different time scales and is suitable for analyzing complex dynamic relationships in time series data.
         NOTE: Specifically, the time-frequency distributed WTC map (also known as scalogram⁵³) between the current channel signal and unfiltered signals from every other channel is first calculated. Then, convert these WTC maps into binary form based on the significance of the WTC value at each time-frequency pixel. Subsequently, these WTC maps are averaged, creating a globally co-varying time-frequency map. The value at each pixel of this composite map indicates the degree to which the current channel is globally synchronized with other channels at that specific time-frequency point. Ultimately, a denoising mask for the current channel is produced by setting a threshold k on this globally co-varying time-frequency map.
      2. Use WT to decompose the signal of the current denoising channel into time-frequency space.
      3. Apply the mask derived to the wavelet coefficients to suppress the wavelet energy at the time-frequency points contaminated by noise.
      4. Reconstruct the signal by using the inverse WT.
      5. Repeat the above steps per channel to complete the global physiological noise removal.
2. Calculating the client-counselor inter-brain synchrony
   1. To compute the correlation between the signals in the time-frequency domain measured in each channel of the two participants, use the function of Wavelet Transform Coherence.
   2. Use the default setting of the mother wavelet (i.e., Generalized Morse Wavelet with its parameters beta and gamma), a fundamental waveform from which a family of wavelets can be derived by dilation (scaling) and translation⁵⁴. Perform continuous wavelet transforms to convert the time series data into time-frequency space.
   3. Set MonteCarloCount as a representation of the number of surrogate datasets used for significance testing and use Auto AR1 to compute the autocorrelation coefficients of the time series.
   4. Use the Wavelet Coherence function to calculate the correlation between two signals in time-frequency space. Repeat the steps until the 24 WTC matrices are generated from the 24 recording channels.
   5. Determine the frequency of interest (FOI), which is sensitive to psychological counseling.
      1. Select and average the coherence values for the frequency range between 0.01 Hz and 0.1 Hz (which correspond to periods of 100 s and 10 s, respectively) based on the frequency range utilized in a previous fNIRS hyperscanning study focused on psychological counseling tasks⁵⁵.
         NOTE: Further statistic confirmation needs to be performed rather than simply confining the selected frequency band.
      2. Standardize the WTC values by conducting a time-average of the WTC values in resting and counseling stages, respectively, for each channel combination, helping in standardizing the data and preparing it for comparison. This standardization is crucial for reducing variability and focusing on task-specific effects.
      3. Set rest-stage WTC values as baseline-level WTC and task-stage WTC values as task-level WTC.
         NOTE: Rest-stage WTC values are used as a baseline to represent the normal, non-task-related state. In contrast, task-stage WTC values reflect the state during psychological counseling. This differentiation allows for isolating the specific impact of counseling on brain activity.
      4. Use the mult_comp_perm_t1 function of Groppe's work. Conduct paired-sample t-tests to compare baseline-level WTC and task-level WTC at each frequency point.
         NOTE: This step helps to statistically determine which frequency points exhibit significant differences between the baseline and task states. The comparison helps identify the specific frequency ranges where counseling has a measurable effect.
      5. Determine frequency bins where the task effect is significant (counseling > resting, p < 0.0005⁵⁶).
         NOTE: This step involves identifying the frequency bins that show significant increases in coherence during counseling compared to resting. The threshold p < 0.0005 is used to control for multiple comparisons and ensure the robustness of the findings.
      6. Determine the FOI as the frequency points with p values below 0.0005 and their nearest frequency points (p < 0.01).
         NOTE: This criterion ensures that the selected frequency bands are not only significant but also relevant to the observed counseling effects.
      7. Calculate the mean WTC values within the specified FOI for each channel across each pair in the study.
      8. Perform Fisher-Z statistical transformations on the inter-brain synchrony values obtained for each period in the two groups of subjects to get a normal distribution of the WTC values, which can be an index for analyzing IBS.
3. Further statistics
   1. Determine the task-related channels.
      1. Gain task-related WTC values by subtracting baseline-level WTC from task-level WTC.
      2. Conduct one-sample t-tests for each channel, utilizing the mean task-related WTC values across the specified frequencies of interest.
      3. Use the mafdr function to apply the method of False Discovery Rate ( p < 0.05 )⁵⁷ correction to multiple comparisons.
      4. Determine the task-related channels as channels with adjusted p values below 0.05.
   2. Compare IBS between different task conditions. Conduct a one-sample t-test between the WTC values of different conditioned groups (i.e., the secure and dismissing groups) at each task-related channel.
   3. Further, identify the differences in IBS between the two groups throughout the psychological counseling process. Divide the counseling into two stages: early stage (0–15 min) and late stage (15–35 min).
   4. Perform one-sample t-tests separately on the task-related WTC values for corresponding stages and the increments of task-related WTC (calculated as late-stage values minus early-stage values) across different task conditions.
   5. Check for the time lag effect in IBS. Shift the counselor's brain activity forward or backward to clients' by 2–12 s (step = 2 s) and re-calculate task-related WTC values according to the above steps. Check if there are differences between counselor-led IBS, client-led IBS, and in-phase IBS.
   6. Evaluate the relationship between the IBS and behavioral data using multiple regression analysis.
      NOTE: WTC computaion code is provided as Supplementary File 1.

The results showed that there was a marginally significant effect that the secure group had higher task-related WTC increments than the dismissing group (t = 2.50, adjusted p = 0.07) at channel 19 in the angular gyrus (ANG; see Figure 2). The WTC values at CH19 were selected for further analysis of IBS. As to the time lag effect in IBS, significantly higher late-stage counselor-led IBS was observed in the dismissing group (M = 0.04, SD = 0.07) compared to the secure group (M = -0.02, SD = 0.07), t (31) = 6.18, p = 0.018, Cohen's d = 0.86. Similarly, significantly higher late-stage client-led IBS was found in the dismissing group (M = 0.04, SD = 0.07) compared to the secure group (M = -0.02, SD = 0.07), t (31) = 5.97, p = 0.020, Cohen's d = 0.86. (see Table 1). No other IBS indicators showed any significant differences.

Within the secure group, significant correlations were observed between increases in CORE score changes and increases in no-lag IBS, both at the early stage (r = 0.552, p = 0.018) and across the whole stage (r = 0.489, p = 0.039). In contrast, these correlations were not significant in the dismissing group. Conversely, within the dismissing group, a significant negative correlation was found between increases in no-lag IBS at the late stage and across the whole stage and a decrease in the task dimension of alliance (r = -0.612, p = 0.015 for late-stage; r = -0.522, p = 0.046 for whole-stage). These correlations were not significant within the secure group (see Figure 3).

Using multiple regression analysis, adult attachment was found to moderate the correlation between both early-stage (p = 0.031) and whole-stage no-lag IBS (p = 0.022) with changes in CORE-10 scores (see Table 2). No significant correlations or moderators were found between IBS indicators and behavioral data aside from those previously mentioned.

The study revealed an increase in IBS in the ANG, a region that is pivotal for attention, memory, language, and social processing^58,59. This finding further reinforces the notion that during psychological counseling sessions, the coupling of brain regions may be related to the mentalizing system between counselors and their secure clients.

This study revealed significantly higher late-stage counselor-led and client-led IBS in ANG among dismissing dyads compared to secure dyads. This suggests that clients' attachment styles influence the dynamics of IBS during counseling sessions.

Only for secure dyads were early-stage and whole-stage IBS significantly positively correlated with changes in CORE scores. This suggests that an increase in IBS for secure clients may indicate a smoother development of the psychological counseling process. Adult attachment style significantly moderated the correlation between early-stage and whole-stage no-lag IBS with changes in CORE-10 scores (Figure 4). This suggests that the complex and nonlinear relationship between IBS and counseling outcomes was influenced by the heterogeneity in client composition, particularly their adult attachment styles.

The research findings show that within the dismissing group, increases in late-stage and whole-stage no-lag IBS were significantly associated with a decrease in the task dimension of the alliance. This may be related to the fact that patients who tend to dismiss or avoid their negative feelings require more emotional responsiveness rather than guidance from their counselors⁶⁰. To clarify whether synchrony is beneficial or detrimental to dyadic regulation, future studies should investigate the timing and direction of synchrony during the process. This study suggests that IBS may help identify unique interaction patterns between dismissing clients and their counselors, indicating its potential as a biomarker for assessing alliance quality in these clients.

Figure 1: The environmental setup of the experiment. Please click here to view a larger version of this figure.

Figure 2: Optodes probe set. A probe set covers the right temporoparietal regions. This figure has been modified with permission from Dai et al.²². Please click here to view a larger version of this figure.

Figure 3: T-map of the difference in task-related WTC increment between the secure group and the dismissing group. In the secure group, stronger WTC value increments were found at channels with positive values; while in the dismissing group, stronger WTC value increments were found at channels with negative values. Higher absolute values are shown in darker colors. Please click here to view a larger version of this figure.

Figure 4: Correlation between IBS and CORE-10 score. (A) Correlation between IBS in the early stage and CORE-10 score changes of the two attachment groups. (B) Correlation between IBS in the late stage and the task dimension of the working alliance of the two attachment groups. (C) Correlation between whole-stage IBS and CORE-10 score changes of the two attachment groups. This figure has been modified with permission from Dai et al.²². Please click here to view a larger version of this figure.

Table 1: Comparison of IBS in two groups. *p < 0.05.

Table 2: Multiple linear regression with bond dimension of alliance as outcome variable *p < 0.05.

Supplementary File 1: wtc_computaion.m Please click here to download this file.

In the present protocol, the specific steps of how to conduct an fNIRS hyperscanning experiment in the natural setting of psychological counseling and how to calculate the IBS between counselor and client, as well as how to determine the lead-lag patterns of IBS across the counseling are described. The detailed operation can help researchers repeat an fNIRS hyperscanning experiment and further research in open science. Some critical issues about the experiment design, experiment conducting, and data analysis are discussed below.

The fNIRS experiments can be designed using a block design, an event-related design, or a mixed design of both. The current study employs an event-related design to explore real-time neural dynamics between counselors and clients during counseling sessions conducted in a natural setting. In this design, stimuli or tasks (e.g., the counselor's or client's reaction) are presented discretely and randomly, allowing researchers to capture responses to individual events. This approach offers flexibility in experimental design and enables detailed analyses of how different stimuli and cognitive processes manifest in brain activity⁶¹. While in a block design, stimuli or tasks are presented in continuous blocks, with each block containing multiple trials of the same condition. This method enhances the signal-to-noise ratio and produces robust hemodynamic responses, making it easier to analyze⁶¹. By alternating these blocks with controlled condition blocks, researchers can systematically examine the prolonged effects of counseling interactions on brain activity. Unlike event-related designs that focus on immediate responses to specific moments, block designs can reveal sustained neural processes throughout the entire counseling process. Future research could consider employing block design or mixed designs to delve deeper into changes in IBS during long-term counseling processes. By integrating these designs, researchers can comprehensively understand the impact of counseling on brain function and neural mechanisms.

At the same time, it is worth noting that the experiment discussed herein deviates from the standard 50-min counseling session, lasting only 40 min. This abbreviated duration stems primarily from the discomfort participants experience when wearing the fNIRS cap with optodes for extended periods and the difficulty in maintaining stillness throughout the counseling process. With this adjustment, an improvement in the quality of the data signals collected is anticipated, ensuring both high reliability and validity.

Moreover, given the established gender effect in IBS, as evidenced by previous studies^39,40, this study specifically recruited only female participants to mitigate this influence. Focusing exclusively on females allows for more precise isolation and analysis of the effects of other variables, thereby minimizing the confounding impact of gender on synchronous brain activity during cooperative interactions. Further research may explore whether different gender combinations elicit distinct brain synchrony patterns during counseling.

In fNIRS hyperscanning experiments, ensuring signal quality is paramount. Experimenters must undergo comprehensive training to prepare for situations where signals may be blocked or degraded. Given the involvement of multiple participants, an adequate number of experimenters are required to correctly fit and adjust the fNIRS caps to achieve high-quality signals. Immediately after placement, channel signals should be checked and confirmed before the experiment commences to ensure everything is in order.

Given the confidentiality of the counseling process, the experimenters' presence is not ideal. Consequently, ensuring signal quality during the experimental recording poses a challenge. Remote monitoring techniques can be explored to allow experimenters to oversee the process without compromising privacy. Furthermore, the development of automated signal quality checks and alerts can help identify potential issues in real-time, enabling prompt corrective actions and enhancing data integrity and reliability.

The data analysis presented here includes three parts: data preprocessing, IBS calculation, and further statistics. The process of data preprocessing aims to remove the possible noise (i.e., motion artifacts, optical artifacts). Appropriate filters and algorithms should be used to reduce the impact of these interferences. In the current study, a wavelet-based method is used to remove the global physiological noise since it is more sensitive to the temporal property of the data. Other methods, such as the principal components analysis (PCA)³, could also be used to remove global components such as brain activity not specific to the task when an overall pattern of interaction between the participants is more concerned rather than detailed changes at each time point.

The method of WTC is adopted to calculate IBS. This method is chosen for the following main advantages: First, it provides detailed insights into the time-varying frequency content of signals, allowing researchers to observe how coherence between two signals changes over time and across different frequencies. Moreover, it helps detect and quantify the degree of synchronization between different brain regions or subjects in a hyperscanning setup. In addition, it is particularly suited for analyzing non-stationary data, which is common in fNIRS data due to physiological and experimental variations. All in all, it can identify periods and frequencies where significant relationships occur, making it easier to link neural dynamics to cognitive or behavioral events.

Furthermore, the study presented here explored the directionality of IBS between the participants by applying the time lag function to the fNIRS data, which deepened the understanding of interaction characteristics between counselors and clients. Other methods, such as Granger causality analysis (GCA)⁶², can also be utilized to detect the directionality of IBS by characterizing the direction of information flow and causal relationships between two signal sequences using vector autoregression models. When using this method, it is important to note that Granger causality analysis (GCA) assumes a linear relationship between the variables during data analysis. This assumption may limit its ability to capture more complex nonlinear relationships, thereby affecting the accuracy and comprehensiveness of the analysis results. In the existing literature on fNIRS hyperscanning studies, GCA has been employed to estimate IBS in various tasks, including cooperation⁶³ and imitation⁶⁴. Future applications of this method in the field of psychological counseling may also be considered.

Several limitations of this study need to be noted. Firstly, the ecological validity of this study is limited. Considering that participants experience discomfort wearing the fNIRS probe cap for extended periods and have difficulty remaining motionless during counseling, the session duration was adjusted to 40 min. However, typical counseling sessions in real-life settings often range from 50 min to 60 min. Future research should focus on developing more comfortable and convenient data collection technologies and exploring more flexible and diverse study designs to better reflect the true complexity of counseling processes. Secondly, according to previous studies, there is a gender effect^39,40 in IBS; the present study thus recruits only female participants to avoid this effect. Further research explores whether different gender combinations produce distinct brain synchrony patterns during counseling. Finally, the fNIRS used in this study has a limitation: it only detects changes in blood flow concentration at the cortical level. This constraint restricts the exploration of neural events related to the development of relationships between clients and counselors during the counseling process. Consequently, this study focused solely on the rTPJ, which could be further extended to other brain regions in the future. Additionally, the study unexpectedly observed results in the angular gyrus. While there is some overlap between the rTPJ and the angular gyrus, the unique functions of each warrant further attention, and future studies should explore this in greater depth.

The protocol provides a pipeline of experiment conducting and data processing in a real-time psychological counseling scenario, exploring leading-lag patterns in counselor-client IBS. Such a pipeline provides a standard guide in the field, allowing researchers to repeat experiments and further possible perspectives. In the future, more suitable and comprehensive algorithms should be proposed to refine the quality of the signal, calculate the IBS, and explore the directionality of IBS. In addition, a broader application area should be developed, such as the field of psychiatry, the married couple, a family system, or even an organizational system. Furthermore, researchers could combine fNIRS with other imaging techniques like EEG or MRI to provide richer, more comprehensive insights into brain activity and interactions. The real-time analysis of fNIRS data should also be implemented to provide immediate feedback in clinical, educational, or managing settings, enhancing therapeutic learning and managing outcomes.

The authors have nothing to disclose.

This research was supported by the National Natural Science Foundation of China (31900767), the Research Project of Shanghai Science and Technology Commission (20dz2260300), and The Fundamental Research Funds for the Central Universities.