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.
