感谢国家自然科学基金（第62207025号）、教育部人文社会科学研究项目（第22YJC190017号）和中央高校基本科研业务费专项资金对潘亚峰的支持。

该研究在佛罗里达大西洋大学（FAU）进行，并得到了FAU机构审查委员会（IRB）的批准。
1. 使用Homer3软件（材料表）对fNIRS超扫描数据进行预处理
注：Homer3 是一个 MATLAB 应用程序，用于分析 fNIRS 数据以获得大脑激活的估计值和图谱29。Homer3 可以从以下链接 （https://openfnirs.org/software/homer/） 下载和安装。
<ol><li>打开 MATLAB，然后导航到保存原始 .nirs 文件的文件夹。选择并打开文件夹。</li>
	<li>在 MATLAB 的命令窗口中键入 Homer3 以启动 Homer3 GUI。荷马3.将检测 .nirs 文件并要求转换为 .snirf 格式（一种通用文件格式，用于独立于任何特定应用特定文件格式（如 MATLAB）存储和共享 NIRS 数据）以继续进行数据预处理。</li>
	<li>在 Homer3 中将 .nirs 文件导入为 .snirf 格式后，单击 Homer3 GUI 中的 “工具 ”选项，然后选择“ 编辑处理流”。</li>
	<li>在 ProcStreamEdit GUI 中，单击“添加”，从“注册表函数”列到“当前处理流”列选择预处理步骤。包含的预处理步骤如下：<ol><li>使用 hmrR_intensity2OD 将强度数据转换为光密度。</li>
		<li>使用 hmrR_MotionCorrectWavelet 通过适当的过滤功能校正运动伪影。</li>
		<li>使用 hmrR_OD2conc 将 OD 数据转换为浓度。</li>
		<li>使用 hmR_BlockAvg 计算浓度数据的区块平均值。 注意：预处理步骤的选择可能因数据集类型而异。</li>
	</ol></li>
	<li>要保存当前处理流，请单击 “保存 ”选项，然后退出 ProcStreamEdit GUI。</li>
	<li>要在 Homer3 主 GUI 中运行预处理流，请单击 RUN 选项。在 Homer3 运行完选定的处理流后，它将以 .mat 文件格式保存每个参与者的预处理时间序列，其中包含所有通道和事件的 Hbo、Hbr 和 Hbt。Homer3 将在当前选定的文件夹中创建一个名为 homer output 的文件夹来存储这些文件。</li>
	<li>Homer3 将在所选文件夹中创建一个名为 derivatives 的文件夹来存储这些文件。选择位于 derivatives 文件夹中的 homer 文件夹。为每个大脑选择 .mat 文件并导出 Hbo、Hbr、Hbt。 注意：Homer3 创建的输出文件夹的名称取决于 Homer3 版本。</li>
</ol>2. LeaderFollowerByPhase 工具箱入门
<ol><li>要分析超扫描记录中发生的交互类型，请使用 LeaderFollowerByPhase 工具箱，如图 3 所示的过程所述。在MATLAB中，为每个大脑选择.mat文件，并将特定通道和特定事件的Hbo（或Hbr）数据加载到一维向量中，作为signal1和signal2。</li>
	<li>在 MATLAB 命令行中，定义参数<ol><li>低频率、高频率： 键入 lowFreq = [low FOI]，highFreq = [high FOI]。默认值为 lowFreq = 0.01 Hz，highFreq = 1 Hz。 注意： 函数 lowFreq 和 highFreq 的参数定义了感兴趣的频率 （FOI） 范围。WTC计算两个大脑在每个时间点和频率的连贯性。相干值通常在特定 FOI 内取平均值。</li>
		<li>定义参数 phaseRange;type phaseRange = [度范围]。 注意： 默认值为 phaseRange = 90°。由于相位的圆形模量性质，相位范围在 0° 到 360° 之间。相位范围根据围绕四个点的范围进行划分。在所介绍的工具箱中，提出了一种对不对称相互作用进行分类的新方法（图 4），方法是根据与信号 1 领先（范围围绕 −90°）或信号 2 领先（范围围绕 90°）、信号 1、信号 2 同相同步（围绕 0 的范围）、 信号 1、信号 2 反相位同步（范围约为 +180° 或 −180°）。</li>
		<li>定义 Threshold 参数。类型阈值 = [阈值 rsq val]。默认值为 Threshold = 0。 注： 该工具箱允许通过指定阈值参数来指定阈值相干性值。这允许研究人员选择具有指定最小相干性值的时间点。因此，仅考虑相干值高于指定阈值的时间点。</li>
	</ol></li>
	<li>从以下链接（https://www.ariel.ac.il/wp/sns/download/ 或 https://github.com/Minisharmaa/Leader-Follower-By-Phase）下载 LeaderFollowerByPhase 工具箱。</li>
	<li>通过在命令行中输入命令 cohervalues = LeaderFollowerByPhase（signal1， signal2， lowFreq， highFreq， phaseRange， threshold） 来执行 MATLAB 函数 LeaderFollowerByPhase 。 注：相干和相位计算分别使用 MATLAB 中的 WTC 和 XWT 函数执行30。</li>
	<li>检查同相、信号 1 超前、信号 2 超前和反相位同步值：<ol><li>在 MATLAB 中检查绘图。该工具箱将生成一个包含四个图的图形。<ol><li>按相互作用类型划分的相干性：检查图左上角的箱形图，该图显示了每种相互作用类型（同相、信号 1 超前、信号 2 超前、反相）的 R 平方 （Rsq）。 注意：有关MATLAB箱形图内置函数的详细说明，请参阅以下链接（https://www.mathworks.com/help/matlab/ref/boxchart.html）。</li>
			<li>按交互类型划分的中心指数：检查输出图右上角的条形图，该条形图根据每种交互类型（同相、信号 1 超前、信号 2 超前、反相位）显示最大均值和中位数。 注意：有关 MATLAB bar 内置函数的详细说明，请参阅以下链接 （https://www.mathworks.com/help/matlab/ref/bar.html）。</li>
			<li>随时间变化的相干性：检查输出图左下角的散点图，该散点图显示随时间变化的相干性值和交互类型。彩色圆点代表不同类型的相互作用（黑点代表同相同步，深灰色圆点代表信号 1 导向，浅灰色圆点代表信号 2 导向，紫色圆点代表反相位同步）。 注：该图显示了交互的动态：在整个全时间序列中四种类型的交互之间的交换。有关 MATLAB 散点函数的详细说明，请参阅以下链接 （https://www.mathworks.com/help/matlab/ref/scatter.html）。</li>
			<li>时间百分比：检查输出图右下角的饼图，该饼图根据不同类型的交互显示时间划分。 注意：有关 MATLAB 饼图函数的详细说明，请参阅以下链接 （https://www.mathworks.com/help/matlab/ref/pie.html）。</li>
		</ol></li>
	</ol></li>
	<li>检查输出表，其中包含每种交互类型（同相同步、信号 1 超前、信号 2 超前、反相位同步）的统计值（即平均值、最大值、中位数和标准差）。该表还显示了每种类型的交互发生的时间百分比。每种类型的交互都显示在不同的列中。</li>
	<li>检查提取的电子表格文件（即位于当前文件夹中的数据表.xlsx）中的输出值。</li>
</ol><img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65347/65347fig03.jpg"> 图 3：工作流概述。 （A）在超扫描fNIRS数据的同时进行自由游戏的母子二人组。（B） 母子时间序列图示。（C） 使用 Homer3 对时间序列进行预处理。（D，E）使用工具箱检查不同类型的相互作用，例如同相同步、反同相同步和滞后同步。<a href="https://www.jove.com/files/ftp_upload/65347/65347fig03large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65347/65347fig04.jpg"> 图 4：基于相位的四种不同类型的相互作用的分类。 这表示 360° 模数中两个神经时间序列的相位差。相位差可以被认为是两个值之间的时间滞后，以度数和弧度或波长的分数来衡量。在这里，360°模量分为四个不同的范围，描述了四个不同的相互作用阶段：（A）信号1前导（范围在90°左右，在45°到135°之间），（B）信号1和信号2之间的反相位同步（范围在180°或-180°之间，在135到-135°之间），（C）信号2前导（在-135°到-45°之间），（D）） 同相同步（范围约为 0，介于 −45° 至 45° 之间）。此分割是默认方法（每个点周围 45°）;但是，该工具箱允许配置不同的分区。虽然其他配置可能无法涵盖所有 360°，但它可能会对每种类型的交互产生更准确的定义。 <a href="https://www.jove.com/files/ftp_upload/65347/65347fig04large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>

革新 fNIRS 研究：一种神经耦合的新方法

作者声明，该研究是在没有任何可被解释为潜在利益冲突的商业或财务关系的情况下进行的。

fNIRS研究中最常用的方法之一是小波变换相干性（WTC），它是两个时间序列作为频率和时间10函数的互相关性的度量。WTC使用相关分析（补充文件1）计算两个时间序列之间的相干性和相位滞后。FNIRS 超扫描研究使用 WTC 来估计许多功能领域的 IBS，包括行动监测 12、合作和竞争行为5、13、14、15、模仿16、母婴问题解...

近年来，为理解社会行为的神经基础而进行的研究类型发生了变化 1,2.传统上，社会神经科学的研究集中在社会相关任务中一个孤立大脑的神经激活。然而，神经影像技术的进步现在允许在社交互动期间检查一个或多个人大脑中的神经激活，因为它发生在“现实生活”环境中3.在“现实生活”环境中，个人能够自由移动，随着信息交换和社会伙伴相互接收反馈，大脑激活的模式可能会发生变化4.
超扫描是一种通过同时测量两个或多个个体的大脑活动来评估这种双向信息交换的方法5.一项新兴的研究利用了功能性近红外光谱 （fNIRS），这是一种非侵入性神经成像技术，与其他神经成像技术相比，它不太容易受到运动伪影的影响6。通过fNIRS进行的超扫描允许在现实生活中评估脑间同步（IBS），同时互动伙伴可以自由自然地移动。这与婴儿和幼儿的工作尤其相关，他们往往非常活跃。据报道，IBS 反映了互动伙伴之间的相互理解，这是有效社会互动和沟通的基础，并调解了共同的意向性 1,7,8。
有几种方法用于评估两个大脑的IBS。这些方法包括时间序列相关性，例如互相关和皮尔逊相关系数 9,10（参见 Scholkmann 等人的评论 10）。其他方法包括评估频域中的耦合强度。这些方法包括锁相值（PLV）和相干性（参见Czeszumski等人的评论11）。fNIRS研究中最常见的方法之一是使用小波变换相干性（WTC），这是两个时间序列的互相关性作为频率和时间10的函数的度量。
WTC使用相关分析来计算时频域中两个时间序列之间的相干性和相位滞后。FNIRS 超扫描研究使用 WTC 来估计许多功能领域的 IBS，包括行动监测 12、合作和竞争行为5、13、14、15、模仿16、母婴问题解决 17 和教学行为18、19、20、21.通常，在超扫描研究中，将实验任务期间由WTC测量的跨脑相干性与控制任务期间的跨脑相干性进行比较。这些发现通常以WTC“热图”呈现，该图显示了每个时间点和频率下两个大脑的连贯性（见图1）。
正如 Czesumaski 等人 11 所建议的，WTC 已成为分析 fNIRS 超扫描的标准分析方法。WTC 分析是一种灵活的、“与工具无关”的数据可视化和解释方法22.相干系数热图提供了一种叙述性的分析形式，可以轻松识别同步或异步行为的时期以及完成任务期间的大脑活动强度，这是WTC的主要优势，使其成为应用研究的有力工具22。与相关技术相比，WTC具有优势。相关性对血流动力学反应函数 （HRF） 的形状很敏感，这被认为在个体之间（特别是在年龄方面）和不同大脑区域之间有所不同。相比之下，世贸中心不受（HRF）23区域间变化的影响。研究人员使用小波方法研究了fMRI时间序列。Zhang等[24 ]比较了常用的功能连通性指标，包括Pearson相关、偏相关、互信息和小波相干变换（WTC）。他们使用源自静息态fMRI数据和视频观看的自然刺激fMRI数据的大规模功能连接模式进行了分类实验。他们的研究结果表明，WTC在分类（特异性、灵敏度和准确性）方面表现最好，这意味着WTC是研究功能性脑网络的首选功能连接指标，至少在分类应用中是这样24。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65347/65347fig01.jpg"> 图 1：小波变换相干性 （WTC）。 WTC 将两个时间序列之间的相干性和相位角显示为时间（x 轴）和频率（y 轴）的函数。相干性增加用图中的红色表示，图中的小箭头表示两个时间序列的相位角。向右箭头表示同相同步;向下和向上的箭头表示滞后同步;向左箭头表示反相位同步30。该图改编自 Pan et al.19。 <a href="https://www.jove.com/files/ftp_upload/65347/65347fig01large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
最近，Hamilton25 阐明了fNIRS超扫描研究中跨脑相干数据解释的几个局限性。汉密尔顿的主要担忧之一是，连贯性测量（例如，WTC）仅将效应报告为对称（即，两个大脑是相关的，显示出相同的变化模式）。然而，许多社交互动是不对称的（例如，说话者和听众之间的信息流），因为两个参与者可能扮演不同的角色，目前尚不清楚WTC是否可以捕获这些信息。在这里，这个问题通过一个新的框架来解决，该框架允许通过使用交叉小波相位来检测方向性来直接解释交叉小波功率。该框架还将允许检查交互的动态如何在整个任务中发展和变化。
WTC和相关方法评估功能连接，而其他方法评估有效连接，试图提取一个神经元素对另一个神经元素的因果影响。传递熵是信息论领域的一种度量，它描述了共同依赖过程之间的传递26.另一种相关方法是格兰杰因果关系分析（GCA），它被描述为等同于转移熵26。
在现有的fNIRS超扫描研究文献中，Granger因果关系分析（GCA）已被广泛用于估计在各种不同任务中获得的fNIRS时间序列数据之间的耦合方向性，例如合作5、教学19和模仿16。GCA 采用向量自回归模型来评估大脑数据中时间序列之间耦合的方向性。格兰杰因果关系基于预测和优先性：“如果 X 的过去包含有助于预测 Y 未来的信息，而不是 Y 过去已经存在的信息，则称变量 X 为变量 Y'G-cause'”27。因此，从两个方向分析G因果关系：1）从受试者A到受试者B，2）从受试者B到受试者A。
虽然GCA分析是一种补充分析，旨在确定使用WTC函数获得的高相干值是否反映了IBS或滞后同步（一个信号领先另一个信号），但它无法确定是否发生了反相位同步。在传统的神经影像学研究中，只扫描一名参与者（即“单脑”方法），反相模式意味着一个大脑区域的活动增加，而另一个大脑区域的活动减少28。在超扫描文献中，反相位同步的存在可能表明一个受试者的神经激活增加，同时，另一个受试者的神经激活减少。因此，需要提供一个可以检测方向性的综合模型。更具体地说，除了同相同步和滞后同步之外，该模型还能够检测反相位同步（其中一个人的活动方向与其伴侣的活动方向相反）。
为了解决WTC仅显示对称效应（两个大脑都显示出相同的变化模式）25的问题，提出了一种通过检查同步相位（即同相、滞后或反相位）来识别相互作用类型的新方法（见 图2）。为此，开发了一个使用WTC方法对不同类型的相互作用进行分类的工具箱。通过使用来自交叉小波变换分析的相对相位数据对相互作用类型进行分类。
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65347/65347fig02.jpg"> 图 2：简单正弦波的不同相位关系图示。 （A） 当信号 1（蓝线 s）和信号 2（橙线s）这两个信号在同一时间点达到各自的最大值、最小值和零值时，称它们显示同相同步32。（B） 当一个信号在同一时间点达到其最大值而另一个信号达到零值时，称它们表现出滞后同步（一个信号领先 90°）32,33,34。（C）当两个时间序列向相反方向移动时，即一个信号在同一时间点达到最大值，另一个信号达到最小值，这称为反相位同步28。（D-P）在两个时间序列之间的所有其他相位关系中，一个信号领先于另一个信号。在所有正相中，信号 2 领先信号 1（例如，面板 E、F、M 和 N），而在所有负相中，信号 1 领先信号 2（例如，面板 D、G、H、O 和 P）。值得注意的是，当阶段的绝对值较高时，哪个时间序列领先于另一个时间序列变得更加明显（例如，面板 J 中的领导力比面板 I 中的领导力更独特，而在面板 K 中，领导力比面板 L 更独特）。<a href="https://www.jove.com/files/ftp_upload/65347/65347fig02large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>NIRScout&#160;&#160;</td>
			<td>NIRx Medical Technologies, LLC</td>
			<td>n.a.</td>
			<td>8 sources, 8 detectors</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>The Mathworks, Inc.</td>
			<td>Matlab 2022a</td>
			<td>In this protocol, several toolboxes and buit in MATLAB functions were used: HOMER3 toolbox was used to convert Intensity to OD, to remove motion artifacts through its function hmrMotionCorrectWavelet with default parameters and to convert OD to Conc. Wavelet Toolbox was used to compute WTC.</td>
		</tr>
	</tbody>
</table>

new framework for understanding cross-brain coherence in functional near-infrared spectroscopy (fnirs) hyperscanning studies

尽管功能性近红外光谱（fNIRS）超扫描研究越来越多，但使用小波变换相干性（WTC）评估两个神经信号之间的耦合似乎忽略了相互作用的方向性。该领域目前缺乏一个框架，使研究人员能够确定使用WTC函数获得的高相干值是否反映了同相同步（即，神经激活同时出现在二元组的两个成员中），滞后同步（即神经激活在二元组的一个成员之前在另一个成员之前看到）， 或反相位同步（即，神经激活在二元组的一个成员中增加，在另一个成员中减少）。为了满足这一需求，本文提出了一种互补且更灵敏的方法来分析两个神经信号的相位相干性。该工具箱允许研究人员通过将使用传统WTC获得的相位角值分类为同相同步、滞后同步和反相位同步来估计耦合方向性。该工具箱还允许研究人员评估整个任务中交互的动态如何发展和变化。使用这种新颖的WTC方法和工具箱，将通过它们在fNIRS超扫描研究中的使用来促进我们对复杂社会互动的理解。

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>

作者聚焦：解锁 fNIRS 研究的新见解 - 脑间同步分析的新框架 

理解功能性近红外光谱 （fNIRS） 超扫描研究中跨脑相干性的新框架

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.

本节演示了可以使用工具箱（可在 https://www.ariel.ac.il/wp/sns/download/ 或 https://github.com/Minisharmaa/Leader-Follower-By-Phase 下载）执行的分析类型。对于这些分析，使用了用少量婴儿-父母二元组样本收集的fNIRS数据。使用经过验证的行为任务（自由游戏任务31）测试了六对母婴二人组，该任务尽可能接近现实生活中的婴儿与母亲的互动。在实验之前，婴儿和父母被配备了一套定制的光电设?...

Watch this Scientific Journal Video about Unlocking New Insights in fNIRS Studies -  A Novel Framework for Inter-Brain Synchrony Analysis at JoVE.com

小波变换相干性 （WTC） 是评估信号之间耦合的常用方法，用于功能性近红外光谱 （fNIRS） 超扫描研究。本文提出了一个用于评估信号相互作用方向性的工具箱。

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

许多fNIRS研究都集中在估计脑间同步的相干性上，而忽略了WBC热图中的相位滞后信息。在这里，提出了一种更灵敏的方法，用于估计耦合方向性，将相位角值分类为同相同步、滞后相位同步和反相位同步。使用新提出的框架重新分析以前的发现，可以全面了解参与者之间同步的性质。区分同相同步和反同相同步提供了新的清晰度，能够精确解释以前的发现。该框架可以应用于各种场景，例如研究人际同步在社会行为、沟通和决策中的作用。强调工艺理论的应用与开发的超扫描呼吸暂停相位工具箱相结合，可以更深入地理解在相互作用中观察到的各种类型的方向性，而不仅仅是相干性。这种方法可以在超扫描环境中探索从一个大脑到另一个大脑的信息流。

new-framework-for-understanding-cross-brain-coherence-functional-near

Research

JoVE Journal

Neuroscience

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

1.6K 次观看.  Ariel University. 许多fNIRS研究都集中在估计脑间同步的相干性上，而忽略了WBC热图中的相位滞后信息。在这里，提出了一种更灵敏的方法，用于估计耦合方向性，将相位角值分类为同相同步、滞后相位同步和反相位同步。使用新提出的框架重新分析以前的发现，可以全面了解参与者之间同步的性质。区分同相同步和反同相同步提供了新的清晰度，能够精确解释以前的发现。该框架可以?...

视频: 作者聚焦：解锁 fNIRS 研究的新见解 - 脑间同步分析的新框架 

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals using wavelet transform coherence (WTC) seems to ignore the directionality of the interaction. The field is currently lacking a framework that allows researchers to determine whether a high coherence value obtained using a WTC function reflects in-phase synchronization (i.e., neural activation is seen in both members of the dyad at the same time), lagged synchronization (i.e., neural activation is seen in one member of the dyad prior to the other member), or anti-phase synchronization (i.e., neural activation is increased in one member of the dyad and decreased in the other). To address this need, a complementary and more sensitive approach for analyzing the phase coherence of two neural signals is proposed in this work. The toolbox allows investigators to estimate the coupling directionality by classifying the phase angle values obtained using traditional WTC into in-phase synchronization, lagged synchronization, and anti-phase synchronization. The toolbox also allows researchers to assess how the dynamics of interactions develop and change throughout the task. Using this novel WTC approach and the toolbox will advance our understanding of complex social interactions through their uses in fNIRS hyperscanning studies.

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.

科研

神经科学

Using Homer3 Software to Perform the Pre-Processing of the fNIRS Hyperscanning Data

To begin open MATLAB, then navigate to the folder where the raw and irs 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.

使用 Homer3 软件对 fNIRS 超扫描数据进行预处理

Getting Started with the LeaderFollowerByPhase Toolbox

Open the leader follower by phase"toolbox to analyze the type of interaction that occurs in a hyper scanning 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 LeaderFollowerByPhase"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 one, leading signal two leading, and antiphase.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 antiphase synchronization increased from 35%to 59%and the percentage of endphase synchronization decreased from 26%to 8%This suggests that antiphase synchronization may be the type of interaction that is more representative of this dyad.