New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
October 6th, 2023
Wavelet transform coherence (WTC) is a common methodology for assessing the coupling between signals that is used in functional near-infrared spectroscopy (fNIRS) hyperscanning studies. A toolbox for assessing the directionality of the signal interaction is presented in this work.
Many fNIRS studies have focused on coherence to estimate inter-brain synchrony, neglecting phase leg information in the WTC hot plot. Here, a most sensitive approach has been presented that examine coupling directionality, classifying phase angle value as in-phase synchronization, lab-phase synchronization, and anti-phase synchronization. Reanalyzing the previous findings using a new proposed framework offers a comprehensive understanding of the nature of the synchronization between participants.
Differentiating in-phase synchronization and anti-phase synchronization provides a new level of clarity, enabling the precise interpretation of previous findings. This framework can be applied to various scenarios, such as studying the role of interpersonal synchronization in social behavior, communication, and decision making. Emphasizing the application of graph theory in conjunction with the developed phase toolbox for hyper scanning apneas provides a deeper understanding of the various type of directionalities observed in interaction, extending beyond mere coherence.
This approach enables the exploration of the information flow from one brain to another brain in hyperscanning setting. To begin, open MATLAB and navigate to the folder where the raw NIRS files are saved. Select and open the folder.
Type Homer3 into the command window of MATLAB to launch the Homer3 GUI. Homer3 will detect the NIRS files and ask to convert them to snirf format to proceed with the pre-processing of the data. After converting the files, click on the tools option and select edit processing stream.
In the process stream edit GUI, select the pre-processing steps from the registry function column to the current processing stream column by clicking on add. Click on the save option to save the current processing stream, and then exit the process stream edit GUI. Click on the run option to run the pre-processing stream in the main Homer3 GUI.
After Homer3 finishes running the selected processing stream, it will save the pre-processed time series for each participant in a mat file format containing HBO, HBR, and HBT for all the channels and events. A folder named derivatives will be created by Homer3 in the selected folder to store these files. Select the Homer folder located in the derivatives folder.
Choose the mat file for each brain, and export HBO, HBR, HBT. Open the leader follower by phase toolbox to analyze the type of interaction that occurs in a hyperscanning recording. In MATLAB, select the mat files for each brain and load the HBO or HBR data of the specific channel and event into a one dimension vector as signal one and signal two.
In the MATLAB command line, define the parameters of the functions low frequency and high frequency. The default value of low frequency is 0.01 hertz, and high frequency is one hertz. Then define the parameter for phase range, followed by the threshold.
The default value of the threshold is zero. Execute the MATLAB function, leader follower by phase by entering the command shown on the screen. The toolbox generates one figure with four plots.
In MATLAB, inspect the box chart plot, which shows the R squared values for each type of interaction category, in-phase, signal 1 leading, signal 2 leading, and anti-phase. Then inspect the bar graph on the output figure, which displays the maximum values, mean, and median for all interaction types. The scatterplot displays the values of coherence and the types of interaction over time, the division of time according to different types of interactions.
Next, inspect the output table with the statistic values for each type of interaction. The table also presents the percentage of time for which each type of interaction occurred. Finally, examine the output value in the extracted spreadsheet file.
The classification analysis was repeated with a threshold of 0.5 on dyad A to explore the influence of changing the minimum threshold values on the classification of the types of interaction within a dyad. The result shows that when a threshold of 0.5 was used, the distribution of the different types of relative phase relationships changed. The percentage of anti-phase synchronization increased from 35%to 59%And the percentage of in-phase synchronization decreased from 26%to 8%This suggests that anti-phase synchronization may be the type of interaction that is more representative of this dyad.
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Overview
This study introduces a novel approach to assess the coupling directionality between signals in functional near-infrared spectroscopy (fNIRS) hyperscanning studies. By utilizing wavelet transform coherence (WTC), the research differentiates between in-phase and anti-phase synchronization, enhancing the understanding of inter-brain interactions during social behaviors.
Key Study Components
Area of Science
- Neuroimaging
- Neuroscience
- Signal Processing
Background
- Previous fNIRS research primarily focused on coherence without considering phase leg information.
- There is a need to explore the directionality of inter-brain interactions beyond mere coherence measurement.
- Understanding synchronization types can reveal dynamics involved in social interactions.
- Graph theory applies to the analysis of different interaction types in these settings.
Purpose of Study
- To differentiate between in-phase and anti-phase synchronization in brain interactions.
- To provide a framework for analyzing the directionality of brain signal coupling.
- To broaden understanding of interpersonal synchronization effects in various contexts.
Methods Used
- The study utilized functional near-infrared spectroscopy (fNIRS) hyperscanning to collect data.
- The analysis involved MATLAB for pre-processing NIRS files and applying a new phase toolbox for evaluating interaction types.
- Key steps included converting NIRS files to snirf format and executing the leader-follower phase analysis.
- Statistical analyses were conducted to assess the percentage of in-phase and anti-phase synchronization.
Main Results
- The new toolbox clarified interactions by distinguishing between in-phase and anti-phase synchronization.
- The percentage of anti-phase synchronization increased significantly with different threshold settings.
- Findings contribute to a deeper interpretation of social interactions and communication dynamics between participants.
- Visual representation of results provided insights into the timing and types of interactions.
Conclusions
- This study advances the understanding of how brain signals interact and provide insights into social dynamics.
- Through differentiating synchronization types, the study enables a more precise interpretation of previous findings.
- The developed framework has broad applications in analyzing interpersonal synchronization in real-time interactions.
Frequently Asked Questions
Executive Industry Relevance
Quantifying cross-brain coherence and directionality in fNIRS hyperscanning enables biopharma teams to dissect neural synchronization during social and cognitive tasks. This framework advances predictive confidence in translational neuroscience by distinguishing in-phase, lagged, and anti-phase interactions, supporting mechanistic de-risking at early discovery and preclinical stages. Enhanced signal classification informs target validation and portfolio triage for neuropsychiatric and cognitive disorder programs.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables precise interrogation of neural circuit synchrony underlying social and cognitive phenotypes.
- Supports functional target validation by classifying directionality of inter-brain coupling.
- Improves predictive confidence for mechanistic hypotheses in neurobehavioral models.
- Facilitates portfolio triage by clarifying biological relevance of observed synchrony patterns.
Screening & Assay Development
- Prepares validated neural interaction metrics for downstream compound screening workflows.
- Standardizes quantitative outputs for reproducible assessment of neural synchrony.
- Enables scalable assay development for evaluating pharmacological modulation of brain connectivity.
- Supports reliable comparison of intervention effects on cross-brain coherence.
Translational & Preclinical Research
- Aligns neural synchrony metrics with disease-relevant social and cognitive endpoints.
- Provides continuity from discovery through preclinical validation of neural circuit engagement.
- Enables risk-adjusted advancement decisions based on mechanistic readouts.
- Supports translational biomarker development for social interaction and communication studies.
Pipeline & Workflow Integration
This framework integrates into the discovery-to-preclinical continuum by enabling robust hypothesis testing and mechanistic de-risking of neural synchrony targets.
- Discovery Biology: Supports hypothesis testing on directionality and type of neural coupling in social paradigms.
- Screening: Delivers reproducible, quantitative coherence and phase metrics for compound evaluation.
- Analytics: Provides statistical outputs, including R-squared and interaction percentages, for condition comparison.
- Translational Research: Bridges discovery findings to preclinical models with disease-relevant synchrony endpoints.
- Enterprise Reuse: Offers a reusable analytical toolbox for diverse neurobehavioral and cognitive studies.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in neural synchrony studies.
- Operational Value: Standardizes analysis, enhances reproducibility, and supports scalable workflows.
- Strategic Value: Informs go/no-go decisions and improves capital efficiency by clarifying neural engagement.
- Portfolio Impact: Enables risk-adjusted prioritization of neuropsychiatric and cognitive disorder assets.
Implementation Considerations
- Requires expertise in fNIRS data acquisition and MATLAB-based signal analysis.
- Depends on access to specialized toolboxes and statistical infrastructure for phase classification.
- Necessitates cross-team standardization of preprocessing and threshold parameters.
- Adaptation may be needed for different neural systems or behavioral paradigms.
- Interpretation of synchrony types is sensitive to parameter selection and task design.
