The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each other, mostly using simultaneous-recording signals of brains (namely hyperscanning) with fNIRS. In fNIRS hyperscanning studies, IBS has been commonly assessed through the wavelet transform coherence (WTC) method because of its advantage on expanding time series into time-frequency space where oscillations can be seen in a highly intuitive way. The observed IBS can be further validated via the permutation-based random pairing of the trial, partner, and condition. Here, a protocol is presented to describe how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation in a hyperscanning study. Further, we discuss the critical issues when using the above methods, including the choice of fNIRS signals, methods of data preprocessing, and optional parameters of computations. In summary, using the WTC method and permutation is a potentially standard pipeline for analyzing IBS in fNIRS hyperscanning studies, contributing to both the reproducibility and reliability of IBS.
This protocol provides a potentially standard pipeline of analyzing Inter-Brain synchronization in fNIRS Hyper-scanning Study. The main advantage of this technique is that it allows calculating Inter-Brain synchronization law which will transform coherent method and validate Inter-Brain synchronization ways, permutation based toward empowering of chest conditions and participates. Perform all data analysis using the MATLAB software with appropriate toolboxes.
During data pre-processing, specifically using the principle component analysis and correlation based signal improvement method, remove the F N I R S global physiological noise using the NPCA filter. And the head motion artifacts using the HMR motion correct underscore CBSI of Homer two. Next, for calculating Inter-Brain synchronization or IBS, adopt the wavelet transform coherent function of cross wavelet and wavelet coherent toolbox with default parameters, and compute the coherent values at each time in frequency point to obtain a two axis matrix of coherent values.
For the default parameters, use the Morlet mother wavelet to transform each time series into the time and frequency domain by the continuous wavelet transformation. Next, to choose the frequency band of interest, select an average the coherent values of the frequency band between 0.5 to one Hertz. According to the frequency band used in the finger motion task of the F N I R S Hyper-scanning study.
Obtain one column of coherent values for each pair. Then, select an average the coherent values of the time window during the resting stage. And for each experimental condition, using the information of mark, obtain five coherent values for each dyad for the task session, only select the duration during which participants tapped to reproduce the auditory stimulus, which is about 12 seconds for each trial resulting in a total of 180 seconds for each experimental condition.
Then, subtract the resting coherent value from the task related coherent value. Since the coherence value during the resting state is used as a baseline in this experiment. Next, using the paired samples permutation T test with the malt underscore comp underscore perm T1 function of drops work.
Compare the subtracted coherent values with zero at each channel for each experimental condition. Then, using the a FDR function of the MATLAB toolbox, correct the P values by the false discovery rate method. Next, using the paired samples permutation T test with the malt underscore comp underscore perm underscore T1 function of drops work, compare the coherence values between different task conditions at the channel where IBS existed.
For validating the IBS, use the rant perm function a MATLAB to randomize the label of trials in the meter coordination condition for one dyad at each channel. Follow the demonstrated pipeline for calculating the IBS and statistics for the randomized trial label, excluding the sensitivity analysis for the frequency band of interest. Conduct the permutation 1000 times, then plot the distribution of statistical Z values generated within dyad permutation.
Next, follow the pipeline to calculate the IBS and statistics after randomizing the pairing of the participants of the same trial, in the meter coordination condition. Finally, follow the pipeline again after randomizing the condition labels for the same members of one dyad in the same trial. The results showed the presence of IBS at channel five in the meter coordination condition.
Whereas no IBS was detected in the other conditions. At channel five, the IBS and the meter coordination condition was significantly higher than the coherent values in the non meter coordination and meter independence condition. Permutation analysis showed that the observed IBS probably presented in two individuals of one dyad who tried to synchronize with each other in the matched time, but not in the time partner or condition of random pairing.
Ensure that, the IBS is specific for rare interaction. So, calculating IBS in the task, correlated frequency band and per mutating trust conditions and interviews. Computing the IBS of all channels within the simple reach, or across different channels and the regions, would enrich the IBS calculation partner.
Analyzing the Inter-Brain network for large groups of participates in natural interactions will benefit is the qualification of social interaction.
