Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Siyuan Zhou; Yuhang Long; Chunming Lu

doi:10.3791/62927

JoVE Journal > Neuroscience

Please note that all translations are automatically generated. Click here for the English version.

Neurosciences

使用偏小波变换相干法测量 fNIRS-超扫描数据中的定向信息流

Published: September 03, 2021

doi:

10.3791/62927

Siyuan Zhou*¹, Yuhang Long*², Chunming Lu

¹State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research,Beijing Normal University, ²Institute of Developmental Psychology, Faculty of Psychology,Beijing Normal University

Summary

该协议描述了部分小波变换相干性（pWTC），用于计算人际神经同步（INS）的时间滞后模式，以推断社会互动过程中信息流的方向和时间模式。通过两个实验证明了pWTC在消除INS信号自相关混杂方面的有效性。

Abstract

社会互动对人类至关重要。虽然超扫描方法已被广泛用于研究社会互动期间的人际神经同步（INS），但功能性近红外光谱（fNIRS）是超扫描自然主义社会互动的最流行的技术之一，因为它具有相对较高的空间分辨率，声音解剖定位和对运动伪影的极高耐受性。以前基于fNIRS的超扫描研究通常使用小波变换相干性（WTC）来描述个体之间信息流的方向和时间模式来计算时间滞后INS。然而，这种方法的结果可能会被每个个体的fNIRS信号的自相关效应所混淆。为了解决这个问题，引入了一种称为偏小波变换相干性（pWTC）的方法，该方法旨在消除自相关效应并保持fNIRS信号的高时间频谱分辨率。本研究首先进行了模拟实验，验证了pWTC在消除自相关对INS影响方面的有效性。然后，基于社会交互实验中的fNIRS数据集，对pWTC的操作进行了分步指导。此外，还比较了pWTC方法与传统WTC方法以及pWTC方法与格兰杰因果关系（GC）方法之间的比较。结果表明，pWTC可用于确定自然社会互动过程中不同实验条件之间的INS差异以及个体间INS的方向和时间模式。此外，它比传统的WTC提供更好的时间和频率分辨率，比GC方法提供更好的灵活性。因此，pWTC是推断自然主义社会互动期间个体之间信息流动的方向和时间模式的有力候选者。

Introduction

社会互动对人类至关重要¹^，².为了理解社会互动的双脑神经认知机制，超扫描方法最近被广泛使用，表明人际神经同步（INS）的模式可以很好地表征社会互动过程³^，⁴，⁵^，⁶^，⁷^，⁸^，⁹^，¹⁰^，¹¹^，¹²^，¹³^，¹⁴.在最近的研究中，一个有趣的发现是，个体在二元组中的角色差异可能导致INS的时间滞后模式，即当一个个体的大脑活动落后于另一个个体几秒钟时，就会发生INS，例如从听众到说话者⁵^，⁹，从领导者到追随者⁴，从老师到学生^8，从母亲到孩子¹³^，¹⁵，从女性到男性的浪漫夫妇⁶。最重要的是，时间滞后的INS间隔与社会交往行为的间隔之间存在良好的对应关系，例如教师提问与回答⁸的学生之间或母亲的养育行为与儿童¹⁵的顺从行为之间。因此，时间滞后INS可以反映从一个个体到另一个个体的定向信息流，如最近用于人际口头交流的分层模型¹⁶中所提出的那样。

此前，时滞INS主要在研究自然主义社会相互作用时，因其空间分辨率相对较高，声音解剖定位和运动伪像¹⁷的极高容忍度，主要在功能近红外光谱（fNIRS）信号上计算。此外，为了精确表征神经时间滞后与社会互动过程中行为时间滞后之间的对应关系，必须获得每个时间滞后（例如，从无时间滞后到10秒的时间滞后）的INS强度。为此，以前，小波变换相干性（WTC）程序在将一个个体的大脑信号向前或向后移动到另一个个体的大脑信号⁵^，⁶^，¹⁸之后被广泛应用。当对fNIRS信号使用这种传统的WTC程序时，存在一个潜在的挑战，因为观察到的时间滞后INS可能被单个19^，²⁰，²¹的fNIRS信号的自相关效应所混淆。例如，在二元社会互动过程中，参与者A在时间点t的信号可以与参与者B在同一时间点的信号同步。同时，由于自相关效应，参与者A在时间点t的信号可能与参与者A在稍后时间点t+1的信号同步。因此，在时间点t的参与者A信号和时间点t+1的参与者B的信号之间可能会发生虚假的时间滞后INS。

Mihanović和他的同事²² 首先引入了一种称为偏小波变换相干性（pWTC）的方法，然后将其应用于海洋科学²³^，²⁴。该方法的最初目的是在估计两个信号的相干性时控制外源混杂噪声。在这里，为了解决fNIRS超扫描数据中的自相关问题，对pWTC方法进行了扩展，以计算fNIRS信号上的时间滞后INS。准确地说，从参与者A到参与者B的时间滞后INS（和定向信息流）可以使用下面的等式（等式1）²³计算。

这里，假设有两个信号，A和B，分别来自参与者A和B。信号B的出现总是先于时间滞后为n的信号A，其中WTC（A_t，B_{t +n}）是传统的时间滞后WTC。WTC（A_t，A_{t + n}）是参与者A中的自相关WTC.WTC（A_t，B_t）是参与者A和B之间时间点t的时间对齐WTC。

图 1：pWTC 概述。 （A） pWTC的逻辑。在二元内有两个信号 A 和 B。A 的出现总是跟在 B 之后，有一个滞后 n。灰色框是位于特定时间点 t 或 t+n 的小波窗口。基于pWTC方程（如图所示），需要计算三个WTC：A_{t + n}和B_t的时间滞后WTC;参与者 A_t 和 A _t+n 的自相关 WTC;以及时间点 t、A_t和 B_t 处的时间对齐 WTC。（二）光电多探头集的布局。CH11被放置在T3，CH25被放置在T4，遵循国际10-20系统²⁷^，²⁸。请点击此处查看此图的大图。

该协议首先引入了一个仿真实验，以证明pWTC解决自相关挑战的能力。然后，它解释了如何基于自然主义社会互动的经验实验，逐步进行pWTC。在这里，使用通信上下文来介绍该方法。这是因为，以前，时间滞后INS通常是在自然主义的通信上下文中计算^的3^，⁴，⁶^，⁸^，¹³^，¹⁵^，¹⁸。此外，还对pWTC与传统WTC进行了比较，并与格兰杰因果关系（GC）测试进行了验证。

Protocol

人体实验方案经北京师范大学认知神经科学与学习国家重点实验室机构评审委员会和伦理委员会批准。所有参与者在实验开始前都给予了书面知情同意。 1. 模拟实验生成两个相互关联的信号时间序列，其中一个信号在 4 s 时间滞后处具有自相关。将两个信号之间的 r 相关系数设置为 0.4。此外，生成两个没有任何相关性但一个信号具有自相关度的信号时间序列?…

Representative Results

仿真结果结果表明，具有自相关的时间滞后INSWTC 显著高于无自相关的时间滞后INSWTC （t（1998）= 4.696， p < 0.001）和时间滞后INSpWTC （t（1998）= 5.098，p < 0.001）。此外，无自相关的时滞INSWTC 与INSpWTC （t（1998）= 1.573， p = 0.114，图2A）之间没有显着差异。这些结果表明，当WTC值设置为接近…

Discussion

在超扫描研究中，通常必须描述个体之间信息流动的方向和时间模式。以前的大多数fNIRS超扫描研究都使用传统的WTC²⁵ 通过计算时间滞后INS来推断这些特征。然而，作为fNIRS信号²⁰^，²¹的固有特征之一，自相关效应可能会混淆时间滞后INS。为了解决这个问题，在本文的协议中，引入了一种称为pWTC的方法²²。该方法估计部…

Divulgations

The authors have nothing to disclose.

Acknowledgements

这项工作得到了国家自然科学基金（61977008）和“青年顶尖人才万人计划”的支持。

Materials

fNIRS topography system	Shimadzu Corporation	Shimadzu LABNIRS systen	LABNIRS system contains 40 emitters and 40 detectors for fNIRS signals measurement. In this protocol we used these emitters and detectors created two customized 26-channels probe sets and attached to two caps accroding to 10-20 system. Further, LABNIRS system also contains built-in GUI softwares for data quality check, data convert and data export.
MATLAB	The MathWorks, Inc.	MATLAB 2019a	In this protocol, several toolboxs and functions bulit in MATLAB were used: SPM12 toolbox was used to normalize the valided MRI data through its GUI. NIRS_SPM toolbox was used to project the MNI coordinates of the probes to the AAL template through its GUI. Homer3 toolbox was used to remove motion artifacts through its function hmrMotionCorrectWavelet with default parameters. Wavelet toolbox was used to compute WTC and pWTC through its function wcoherence.
MRI scanner	Siemens Healthineers	TRIO 3-Tesla scanner	In this protocol, the MRI scanner was used to obtain MNI coordinates of each channel and optpde. Scan parameters are described in main text.
customized caps			In this protocol, we first marked two nylon caps with 10-20 system. Then, we made two 26-channels customized optode probes sets. Finally, we attached probes sets to caps aligned with landmarks.

References

Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S., Keysers, C. Brain-to-brain coupling: A mechanism for creating and sharing a social world. Trends in Cognitive Sciences. 16 (2), 114-121 (2012).
Hasson, U., Frith, C. D. Mirroring and beyond: Coupled dynamics as a generalized framework for modelling social interactions. Philosophical Transactions of the Royal Society B: Biological Sciences. 371, 20150366 (2016).
Jiang, J., et al. Neural synchronization during face-to-face communication. Journal of Neuroscience. 32 (45), 16064-16069 (2012).
Jiang, J., et al. Leader emergence through interpersonal neural synchronization. Proceedings of the National Academy of Sciences of the United States of America. 112 (14), 4274-4279 (2015).
Dai, B., et al. Neural mechanisms for selectively tuning in to the target speaker in a naturalistic noisy situation. Nature Communications. 9 (1), 1-12 (2018).
Long, Y., et al. Interpersonal neural synchronization during interpersonal touch underlies affiliative pair bonding between romantic couples. Cerebral Cortex. 31 (3), 1647-1659 (2021).
Liu, W., et al. Shared neural representations of syntax during online dyadic communication. NeuroImage. 198, 63-72 (2019).
Zheng, L., et al. Enhancement of teaching outcome through neural prediction of the students’ knowledge state. Human Brain Mapping. 39 (7), 3046-3057 (2018).
Stephens, G. J., Silbert, L. J., Hasson, U. Speaker-listener neural coupling underlies successful communication. Proceedings of the National Academy of Sciences of the United States of America. 107 (32), 14425-14430 (2010).
Hirsch, J., et al. Interpersonal agreement and disagreement during face-to-face dialogue: An fNIRS investigation. Frontiers in Human Neuroscience. 14, 606397 (2021).
Pan, Y., Cheng, X., Zhang, Z., Li, X., Hu, Y. Cooperation in lovers: An fNIRS-based hyperscanning study. Human Brain Mapping. 38 (2), 831-841 (2017).
Cui, X., Bryant, D. M., Reiss, A. L. NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. NeuroImage. 59 (3), 2430-2437 (2012).
Piazza, E. A., Hasenfratz, L., Hasson, U., Lew-Williams, C. Infant and Adult Brains Are Coupled to the Dynamics of Natural Communication. Psychological Science. 31 (1), 6-17 (2020).
Djalovski, A., Dumas, G., Kinreich, S., Feldman, R. Human attachments shape interbrain synchrony toward efficient performance of social goals. NeuroImage. 226, 117600 (2021).
Zhao, H., et al. How mother-child interactions are associated with a child’s compliance. Cerebral Cortex. 31 (9), 4398-4410 (2021).
Jiang, J., Zheng, L., Lu, C. A hierarchical model for interpersonal verbal communication. Social Cognitive and Affective Neuroscience. 16 (1-2), 246-255 (2021).
Yan, W., et al. Bibliometric evaluation of 2000-2019 publications on functional near-infrared spectroscopy. NeuroImage. 220, 117121 (2020).
Zheng, L., et al. Affiliative bonding between teachers and students through interpersonal synchronisation in brain activity. Social Cognitive and Affective Neuroscience. 15 (1), 97-109 (2020).
Dean, R. T., Dunsmuir, W. T. M. Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models. Behavior Research Methods. 48 (2), 783-802 (2016).
Barker, J. W., Rosso, A. L., Sparto, P. J., Huppert, T. J. Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy. Neurophotonics. 3 (3), 031410 (2016).
Huppert, T. J. Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy. Neurophotonics. 3 (1), 010401 (2016).
Mihanović, H., Orlić, M., Pasarić, Z. Diurnal thermocline oscillations driven by tidal flow around an island in the Middle Adriatic. Journal of Marine Systems. 78, 157-168 (2009).
Ng, E. K. W., Chan, J. C. L. Geophysical applications of partial wavelet coherence and multiple wavelet coherence. Journal of Atmospheric and Oceanic Technology. 29 (12), 1845-1853 (2012).
Ng, E. K. W., Chan, J. C. L. Interannual variations of tropical cyclone activity over the north Indian Ocean. International Journal of Climatology. 32 (6), 819-830 (2012).
Grinsted, A., Moore, J. C., Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics. 11, 561-566 (2004).
Jurcak, V., Tsuzuki, D., Dan, I. 10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems. NeuroImage. 34 (4), 1600-1611 (2007).
Homan, R. W., Herman, J., Purdy, P. Cerebral location of international 10-20 system electrode placement. Electroencephalography and Clinical Neurophysiology. 66 (4), 376-382 (1987).
Koessler, L., et al. Automated cortical projection of EEG sensors: Anatomical correlation via the international 10-10 system. NeuroImage. 46 (1), 64-72 (2009).
Penny, W., Friston, K., Ashburner, J., Kiebel, S., Nichols, T. . Statistical Parametric Mapping: The Analysis of Functional Brain Images. , (2007).
Shalinsky, M. H., Kovelman, I., Berens, M. S., Petitto, L. A. Exploring cognitive functions in babies, children & adults with near infrared spectroscopy. Journal of Visualized Experiments: JoVE. (29), e1268 (2009).
Xu, S. Y., Cheong, L. I., Zhuang, Y., Couto, T. A. P., Yuan, Z. Conducting concurrent electroencephalography and functional near-infrared spectroscopy recordings with a flanker task. Journal of Visualized Experiments: JoVE. (159), e6069 (2020).
Noah, J. A., et al. fMRI validation of fNIRS measurements during a naturalistic task. Journal of Visualized Experiments: JoVE. (100), e52116 (2015).
Tong, Y., Lindsey, K. P., Frederick, B. D. Partitioning of physiological noise signals in the brain with concurrent near-infrared spectroscopy and fMRI. Journal of Cerebral Blood Flow and Metabolism. 31 (12), 2352-2362 (2011).
Buck, R., Miller, R. E., Caul, W. F. Sex, personality, and physiological variables in the communication of affect via facial expression. Journal of Personality and Social Psychology. 30 (4), 587-596 (1974).
Walen, H. R., Lachman, M. E. Social support and strain from partner, family, and friends: Costs and benefits for men and women in adulthood. Journal of Social and Personal Relationships. 17 (1), 5-30 (2000).
Chang, C., Glover, G. H. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage. 50 (1), 81-98 (2010).
Liu, H., et al. Inferring subsurface preferential flow features from a wavelet analysis of hydrological signals in the shale hills catchment. Water Resources Research. 56 (11), 0 (2020).
Rhif, M., Abbes, A. B., Farah, I. R., Martínez, B., Sang, Y. Wavelet transform application for/in non-stationary time-series analysis: A review. Applied Sciences. 9 (7), 1345 (2019).
Molavi, B., Dumont, G. A. Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiological Measurement. 33 (2), 259-270 (2012).
Scholkmann, F., Spichtig, S., Muehlemann, T., Wolf, M. How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiological Measurement. 31 (5), 649-662 (2010).
Zhang, Y., Brooks, D. H., Franceschini, M. A., Boas, D. A. Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging. Journal of Biomedical Optics. 10 (1), 011014 (2005).
Cui, X., Bray, S., Reiss, A. L. Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics. NeuroImage. 49 (4), 3039-3046 (2010).
Cooper, R. J., et al. A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Frontiers in Neuroscience. , 147 (2012).
Brigadoi, S., et al. Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data. NeuroImage. 85, 181-191 (2014).
Liu, Y., et al. Measuring speaker-listener neural coupling with functional near infrared spectroscopy. Scientific Reports. 7, 1-13 (2017).
Li, Z., et al. Speaker-listener neural coupling reveals an adaptive mechanism for speech comprehension in a noisy environment. Cerebral Cortex. , (2021).

Play Video

PDF

DOI

DOWNLOAD MATERIALS LIST

Citer Cet Article

Zhou, S., Long, Y., Lu, C. Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method. J. Vis. Exp. (175), e62927, doi:10.3791/62927 (2021).