Name: Getting Started with the LeaderFollowerByPhase Toolbox
Uploaded: 2023-10-06T14:15:00
Description: Watch this Scientific Journal Video about Getting Started with the LeaderFollowerByPhase Toolbox at JoVE.com

getting started with the leaderfollowerbyphase toolbox

fNIRS 연구의 혁명: 신경 결합에 대한 새로운 접근 방식

In recent years, there has been a shift in the types of studies conducted to understand the neural bases of social behavior1,2. Traditionally, studies in social neuroscience have focused on neural activation in one isolated brain during a socially relevant task. However, advances in neuroimaging technology now allow for the examination of neural activation in the brains of one or more individuals during social interaction as it occurs in &#34;real-life&#34; settings3. In &#34;real-life&#34; settings, individuals are able to move freely, and patterns of brain activation are likely to change as information is exchanged and social partners receive feedback from one another4.
Hyperscanning is a method that assesses this bidirectional information exchange by measuring the brain activity from two or more individuals simultaneously5. An emerging body of research has utilized functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging technique that, in comparison to other neuroimaging techniques, is less susceptible to motion artifacts6. Hyperscanning via fNIRS allows for the assessment of inter-brain synchronization (IBS) in real-life settings while the interactive partners move freely and naturally. This is particularly relevant for work with infants and young children, who tend to be quite active. IBS has been reported to reflect mutual understanding between interactive partners, which serves as the foundation for effective social interaction and communication and mediates shared intentionality1,7,8.
Several methods are used to evaluate the IBS of two brains. Such methods include time series correlations, such as cross-correlation and the Pearson correlation coefficient9,10 (see a review by Scholkmann et al.10). Other methods involve evaluating the strength of coupling in the frequency domain. Such methods include phase-locking value (PLV) and phase coherence (see a review by Czeszumski et al.11). One of the most common methods in fNIRS studies uses wavelet transform coherence (WTC)-a measure of the cross-correlation of two time series as a function of frequency and time10.
WTC uses correlational analyses to calculate the coherence and phase lag between two time series in the time-frequency domain. FNIRS hyperscanning studies have used WTC to estimate IBS in many domains of functioning, including action monitoring12, cooperative and competitive behaviour5,13,14,15, imitation16, mother-infant problem solving17, and teaching-learning behavior18,19,20,21. Typically, in hyperscanning studies, cross-brain coherence, as measured by WTC, during an experimental task is compared to cross-brain coherence during a control task. These findings are usually presented with a WTC &#34;hot plot&#34;, which shows the coherence across the two brains at each time point and frequency (see Figure 1).
As suggested by Czesumaski et al.11, WTC has become the standard analytical approach for analyzing fNIRS hyperscanning. WTC analysis is a flexible, &#34;tool-agnostic&#34; method for data visualization and interpretation22. The coherence coefficient heatmap, which provides a narrative form of analysis allowing for the easy identification of periods of synchronous or asynchronous behavior as well as the intensity of brain activity during the completion of a task, is the main advantage of WTC and makes it a strong tool for applied research22. WTC has an advantage over correlation techniques. Correlations are sensitive to the hemodynamic response function&#39;s (HRF) shape, which is thought to differ between individuals (particularly in terms of age) and among different brain areas. In contrast, WTC is unaffected by interregional changes in the (HRF)23. Researchers have used the wavelet approach to study fMRI time series. Zhang et al.24 compared the commonly used functional connectivity metrics, including the Pearson correlation, partial correlation, mutual information, and wavelet coherence transformation (WTC). They carried out classification experiments using large-scale functional connectivity patterns derived from resting-state fMRI data and natural-stimulus fMRI data of video viewing. Their findings indicated that WTC performed best in classification (specificity, sensitivity, and accuracy), implying that WTC is a preferable functional connectivity metric for studying functional brain networks, at least in classification applications24.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65347/65347fig01.jpg" /> 
Figure 1: Wavelet transform coherence (WTC). WTC shows the coherence and phase angle between two time series as a function of both time (x-axis) and frequency (y-axis). The coherence increase is depicted by the red color in the graph, and the small arrows in the graph show the phase angle of the two time series. The rightward-pointing arrow represents in-phase synchronization; the downward-pointing and upward-pointing arrows represent lagged synchronization; and the leftward-pointing arrow represents anti-phase synchronization30. This figure was adapted from Pan et al.19. <a href="https://www.jove.com/files/ftp_upload/65347/65347fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Recently, Hamilton25 articulated several limitations to the interpretation of cross-brain coherence data in fNIRS hyperscanning studies. One of Hamilton&#39;s primary concerns was that coherence measures (e.g., WTC) only report effects as symmetrical (i.e., two brains are correlated, showing the same pattern of change). However, many social interactions are asymmetric (e.g., information flow between a speaker and a listener) in that two participants may play different roles, and it is not clear that WTC can capture this information. Here, this concern is addressed by a new framework that allows for a straightforward interpretation of the cross-wavelet power by using the cross-wavelet phase&#160;to detect directionality. This framework will also allow examination of how the dynamics of interactions develop and change throughout a task.
While WTC and correlation methods assess functional connectivity, other methods assess effective connectivity, attempting to extract the causal influences of one neural element over another. Transfer entropy is a measure from the field of information theory that describes the transfer between jointly dependent processes26. Another related method is Granger causality analysis (GCA), which has been described as equivalent to transfer entropy26.
In the existing literature of fNIRS hyperscanning studies, Granger causality analysis (GCA) has been widely used to estimate the coupling directionality between fNIRS time series data obtained during a variety of different tasks, such as cooperation5, teaching19, and imitation16. GCA employs vector autoregressive models to assess the directionality of coupling between time series in brain data. Granger causality is based on prediction and precedence: &#34;a variable X is said to &#39;G-cause&#39; variable Y if the past of X contains the information that helps to predict the future of Y over and above information already in the past of Y&#34;27. Accordingly, the G-causality is analyzed in two directions: 1) from subject A to subject B and 2) from subject B to subject A.
While GCA analysis serves as a complementary analysis aimed at determining whether a high coherence value obtained using a WTC function reflects IBS or lagged synchronization (one signal leading the other), it does not allow for the determination of whether anti-phase synchronization has occurred. In traditional neuroimaging studies, in which only one participant is scanned (i.e., the &#34;single-brain&#34; approach), an anti-phase pattern means that the activity in one brain region is increased while the activity in the other brain region is decreased28. In the hyperscanning literature, the presence of anti-phase synchronization may suggest that neural activation is increased in one subject, and at the same time, neural activation is decreased for the other subject. Therefore, there is a need to provide a comprehensive model that can detect the directionality. More specifically, this model will be able to detect anti-phase synchronization (in which the direction of activity in one individual is opposite to that of their partner) in addition to in-phase synchronization and lagged synchronization.
In an attempt to address the concern that WTC shows only symmetrical effects, where both brains show the same pattern of change25, a new approach to identify the type of interaction by examining the phase of synchronization (i.e., in-phase, lagged, or anti-phase) is presented (see Figure 2). To this end, a toolbox using the WTC method to classify the different types of interactions was developed. The types of interactions are classified by using relative phase data from cross-wavelet transform analysis.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65347/65347fig02.jpg" /> 
Figure 2: Illustration of the different phase relationships of simple sine waves. (A) When the two signals, Signal 1 (blue lines) and Signal 2&#160;(orange lines), reach their respective maximum, minimum, and zero values at the same time point, they are said to be showing in-phase synchronization32. (B) When one signal reaches its maximum value and the other signal reaches zero value at the same time point, they are said to be showing lagged synchronization (one is leading by 90&#176;)32,33,34. (C) When two time series shift in opposite directions, meaning one signal reaches the maximum and the other reaches the minimum value at the same time point, this is referred to as anti-phase synchronization28. (D-P) In all other phase relationships between two time series, one signal is leading the other. In all positive phases, Signal 2&#160;is leading Signal 1 (e.g., panels E, F, M, and N), whereas in all negative phases, Signal 1 is leading Signal 2 (e.g., panels D, G, H, O, and P). Notably, when the absolute value of the phase is higher, it becomes more distinctive which time series is leading the other (e.g., the leadership is more distinctive in panel J than in panel I, and in panel K, the leadership is more distinctive than in panel L). <a href="https://www.jove.com/files/ftp_upload/65347/65347fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

저자 스포트라이트: fNIRS 연구에서 새로운 통찰력 확보 - 뇌 간 동기화 분석을 위한 새로운 프레임워크 

기능적 근적외선 분광법(fNIRS) 하이퍼스캐닝 연구에서 교차 뇌 일관성을 이해하기 위한 새로운 프레임워크

Many fNIRS studies have focused on coherence to estimate interbrain synchrony, neglecting phase lag information in the WBC hot plot. Here, a more sensitive approach has been presented that estimates coupling directionality, classifying phase angle value as in-phase synchronization, lag 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 craft theory in conjunction with the developed phase toolbox for hyperscanning 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.

Watch this Scientific Journal Video about Getting Started with the LeaderFollowerByPhase Toolbox at JoVE.com

Getting Started with the LeaderFollowerByPhase Toolbox  

LeaderFollowerByPhase Toolbox 시작하기

단계별 리더 팔로워"도구 상자를 열어 하이퍼 스캐닝 기록에서 발생하는 상호 작용 유형을 분석합니다. MATLAB에서 각 브레인에 대한 mat 파일을 선택하고 특정 채널과 이벤트의 HBO 또는 HBR 데이터를 신호 1과 신호 2로 구성된 1차원 벡터로 불러옵니다. MATLAB 명령줄에서 함수 low frequency와 high frequency의 파라미터를 정의합니다.저주파의 디폴트 값은 0.01Hz이고 고주파는 1Hz입니다. 그런 다음 위상 범위에 대한 매개변수를 정의하고 임계값을 정의합니다. 임계값의 기본값은 0입니다.화면에 표시된 명령을 입력하여 MATLAB 함수 LeaderFollowerByPhase"를 실행합니다. 이 툴박스는 4개의 플롯이 있는 Figure 1개를 생성합니다. MATLAB에서 위상 신호 1, 선행 신호 2, 역위상의 각 상호 작용 범주 유형에 대한 R 제곱 값을 보여주는 상자 차트 플롯을 살펴봅니다.그런 다음 출력 Figure의 막대 그래프를 검사하여 모든 상호 작용 유형에 대한 최댓값, 평균, 중앙값을 표시합니다. 산점도는 일관성 값과 시간 경과에 따른 교호작용 유형을 표시합니다. 다양한 유형의 상호 작용에 따른 시간 분할.다음으로, 각 교호작용 유형에 대한 통계 값이 있는 출력 테이블을 검사합니다. 이 표에는 각 유형의 상호 작용이 발생한 시간의 백분율도 표시됩니다. 마지막으로 추출된 스프레드시트 파일의 출력 값을 검사합니다.다이어드 A에 대해 임계값 0.5로 분류 분석을 반복하여 최소 임계값 변경이 다이아드 내 교호작용 유형의 분류에 미치는 영향을 조사했습니다. 결과는 임계값 0.5를 사용했을 때 서로 다른 유형의 상대 위상 관계의 분포가 변경되었음을 보여줍니다. 반위상 동기화의 비율은 35%에서 59%로 증가했고 말단 동기화의 비율은 26%에서 8%로 감소했으며, 이는 반위상 동기화가 이 다이아드를 더 잘 대표하는 상호 작용 유형일 수 있음을 시사합니다.

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Getting Started with the LeaderFollowerByPhase Toolbox

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals ...