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Neurosciences

开放式协作学习中的脑间同步：fNIRS超扫描研究

Published: July 21, 2021
doi:
10.3791/62777
, ,
1School of Psychology and Cognitive Science,East China Normal University, 2Shanghai Key Laboratory of Mental Health and Crisis Intervention,East China Normal University

Summary

概述了在自然学习环境中对协作学习二元组进行fNIRS超扫描实验的方案。此外，还提出了一种分析含氧血红蛋白（Oxy-Hb）信号的脑间同步（IBS）的管道。

Abstract

fNIRS超扫描被广泛用于检测社会互动的神经生物学基础。通过这种技术，研究人员用一种称为脑间同步（IBS）的新指数（即，神经元或血液动力学信号随时间变化的相位和/或振幅对齐）来限定两个或多个交互式个体的并行大脑活动。本文介绍了在自然学习环境中对协作学习二元组进行fNIRS超扫描实验的协议。此外，还解释了分析含氧血红蛋白（Oxy-Hb）信号的IBS的管道。具体而言，讨论了实验设计、近红外光谱数据记录过程、数据分析方法以及未来的发展方向。总体而言，实施标准化的fNIRS超扫描管道是第二人称神经科学的基本组成部分。此外，这符合开放科学的呼吁，以帮助研究的可重复性。

Introduction

最近，为了揭示交互式二元组或组成员的并行大脑活动，研究人员采用了超扫描方法1，2。具体而言，脑电图（EEG），功能磁共振成像（fMRI）和功能性近红外光谱（fNIRS）用于同时记录两个或多个受试者的神经和大脑活动3，4，5。研究人员基于这种技术提取了需要同时进行脑耦合的神经指数，该技术指的是脑间同步（IBS）（即神经元或血液动力学信号随时间变化的相位和/或振幅对齐）。各种各样的超扫描研究发现，在多个人（例如，玩家 – 观众，教师 – 学习者和领导者 – 追随者）之间的社交互动期间，IBS 6，7，8。此外，IBS具有有效学习和教学的具体含义9，10，11，12，13，14。随着自然学习场景中超扫描研究的激增，建立超扫描实验的标准协议和该领域数据分析的管道是必要的。

因此，本文提供了一种用于对协作学习二元组进行基于fNIRS的超扫描的方案，以及用于分析IBS的管道。fNIRS是一种光学成像工具，它辐射近红外光来间接评估血红蛋白的光谱吸收，然后测量血流动力学/氧合活性15，16，17。与fMRI相比，fNIRS不易出现运动伪影，允许来自正在进行现实生活实验（例如，模仿，说话和非语言交流）的受试者的测量18，7，19。与脑电图相比，fNIRS具有更高的空间分辨率，使研究人员能够检测大脑活动的位置20。因此，fNIRS在空间分辨率、逻辑和可行性方面的这些优势使fNIRS能够进行超扫描测量1。使用这项技术，一个新兴的研究机构检测到一个索引项作为IBS – 两个（或更多）人的大脑活动的神经对齐 – 在不同形式的自然主义社会环境中9，10，11，12，13，14。在这些研究中，应用了各种方法（即相关性分析和小波变换相干性（WTC）分析）来计算该指数;同时，这种分析的标准管道是必要的，但缺乏。因此，在这项工作中提出了一种用于进行基于fNIRS的超扫描的协议以及使用WTC分析来识别IBS的管道。

本研究旨在使用fNIRS超扫描技术评估协作学习二元组中的IBS。首先，在协作学习任务期间，在每个二元组的前额叶和左颞顶区域同时记录血流动力学反应。这些地区已被确定为与互动教学和学习相关的9，10，11，12，13，14。其次，在每个相应的通道上计算IBS。fNIRS数据记录过程由两部分组成：静止状态会话和协作会话。静息状态持续5分钟，在此期间，两个参与者（面对面坐着，彼此分开，在一张桌子（0.8米）旁）需要保持静止和放松。此静息状态会话用作基线。然后，在协作会话中，参与者被告知一起学习整个学习材料，引发理解，总结规则，并确保掌握所有学习材料。这里介绍了进行实验和fNIRS数据分析的具体步骤。

Protocol

所有招募的参与者（40对偶，平均年龄22.1±1.2岁;100%右撇子;正常或矫正到正常视力）都是健康的。在实验之前，参与者给予了知情同意。参与者因其参与而获得经济补偿。该研究获得华东师范大学人类研究保护委员会（HR-0053-2021）的批准。 1. 采用数据前的准备步骤 自制近红外瓶盖 采用弹性泳帽放置光电支架网格。注意：考虑到参与者的头部尺寸不同，使用两…

Representative Results

图1所示为实验方案和探针位置。fNIRS数据记录过程由两部分组成：静息状态会话（5分钟）和协作会话（15-20分钟）。协作学习二元组需要放松并保持静止状态。之后，参与者被告知共同学习学习材料（图1A）。它们的前额叶和左颞顶区域被相应的探针组覆盖（图1B）。 图 2说明了 fNIRS 数?…

Discussion

首先，在本协议中，阐述了在协作学习场景中进行fNIRS超扫描实验的具体步骤。其次，还介绍了评估协同学习二元组中血流动力学信号IBS的数据分析管道。进行fNIRS超扫描实验的详细操作将促进开放科学的发展。此外，这里提供了分析管道，以提高超扫描研究的再现性。在下文中，重点介绍了实验设计、进行实验、（fNIRS）超扫描实验中的数据分析等关键问题。此外，还讨论了当前限制的可能解决?…

Divulgations

The authors have nothing to disclose.

Acknowledgements

这项工作得到了华东师范大学优秀博士生学术创新促进计划（YBNLTS2019-025）和国家自然科学基金（31872783和71942001）的支持。

Materials

EEG caps Compumedics Neuroscan,Charlotte,USA 64-channel Quik-Cap We choose two sizes of cap(i.e.medium and large).
NIRS measurement system with probe sets and probe holder grids Hitachi Medical Corporation, Tokyo, Japan ETG-7100 Optical Topography System The current study protocol requires an optional second adult probe set for 92 channels of measurement in total.
Numeric computing platform The MathWorks, Inc., Natick, MA MATLAB R2020a Serves as base for Psychophysics Toolbox extensions (stimulus presentation), Homer2  (fNIRS preprocess analysis), and "wtc" function(WTC computation).
Psychology software psychology software tools,Sharpsburg, PA,USA E-prime 2.0 we apply E-prime to start the fNIRS measurement system and send triggers which marking the rest phase and collaborative learning phase for fNIRS recording data
Swimming caps Zoke corporation,Shanghai,China 611503314 We first placed the standard 10-20 EEG cap on the head mold, and placed the swimming cap on the EEG cap. Second, we marked (inion, Cz, T3, T4, PFC and P5) with chalk.
Three-dimensional (3-D) digitizer Polhemus, Colchester, VT, USA; Three-dimensional (3-D) digitizer Anatomical locations of optodes in relation to standard head landmarks were determined for each participant using a Patriot 3D Digitizer
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References

  1. Babiloni, F., Astolfi, L. Social neuroscience and hyperscanning techniques: past, present and future. Neuroscience & Biobehavioral Reviews. 44, 76-93 (2014).
  2. Schilbach, L., et al. Toward a second-person neuroscience. Behavior Brain Science. 36, 393-414 (2013).
  3. Montague, P. Hyperscanning: simultaneous fMRI during linked social interactions. NeuroImage. 16, 1159-1164 (2002).
  4. 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).
  5. Dikker, S., et al. Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Current Biology. 27 (9), 1375-1380 (2017).
  6. Abrams, D. A., et al. Inter-subject synchronization of brain responses during natural music listening. European Journal of Neuroscience. 37 (9), 1458-1469 (2013).
  7. Pan, Y., et al. Instructor-learner brain coupling discriminates between instructional approaches and predicts learning. NeuroImage. 211, 116657 (2020).
  8. 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).
  9. Bevilacqua, D., et al. Brain-to-brain synchrony and learning outcomes vary by student-teacher dynamics: Evidence from a real-world classroom electroencephalography study. Journal of Cognitive Neuroscience. 31 (3), 401-411 (2019).
  10. Dikker, S., et al. Morning brain: real-world neural evidence that high school class times matter. Social Cognitive and Affective Neuroscience. 15 (11), 1193-1202 (2020).
  11. Pan, Y., Guyon, C., Borragán, G., Hu, Y., Peigneux, P. Interpersonal brain synchronization with instructor compensates for learner’s sleep deprivation in interactive learning. Biochemical Pharmacology. , 114111 (2020).
  12. Pan, Y., Novembre, G., Song, B., Li, X., Hu, Y. Interpersonal synchronization of inferior frontal cortices tracks social interactive learning of a song. NeuroImage. 183, 280-290 (2018).
  13. Zheng, L., et al. Enhancement of teaching outcome through neural prediction of the students’ knowledge state. Human Brain Mapping. 39 (7), 3046-3057 (2018).
  14. 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).
  15. Kleinschmidt, A., et al. Simultaneous recording of cerebral blood oxygenation changes during human brain activation by magnetic resonance imaging and near-infrared spectroscopy. Journal of Cerebral Blood Flow & Metabolism. 16 (5), 817-826 (1996).
  16. Strangman, G., Culver, J. P., Thompson, J. H., Boas, D. A. A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. NeuroImage. 17 (2), 719-731 (2002).
  17. Huppert, T. J., Hoge, R. D., Diamond, S. G., Franceschini, M. A., Boas, D. A. A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. NeuroImage. 29 (2), 368-382 (2006).
  18. Holper, L., Scholkmann, F., Wolf, M. Between-brain connectivity during imitation measured by fNIRS. NeuroImage. 63, 212-222 (2012).
  19. Hirsch, J., Zhang, X., Noah, J. A., Ono, Y. Frontal temporal and parietal systems synchronize within and across brains during live eye-to-eye contact. NeuroImage. 157, 314-330 (2017).
  20. Wilcox, T., Biondi, M. fNIRS in the developmental sciences. Wiley Interdisciplinary Reviews: Cognitive Science. 6 (3), 263-283 (2015).
  21. Ye, J. C., Tak, S., Jang, K. E., Jung, J., Jang, J. NIRS-SPM: statistical parametric mapping for nearinfrared spectroscopy. NeuroImage. 44 (2), 428-447 (2009).
  22. Huppert, T. J., Diamond, S. G., Franceschini, M. A., Boas, D. A. HomER: A review of time-series analysis methods for near-infrared spectroscopy of the brain. Applied Optics. 48 (10), 280-298 (2009).
  23. Santosa, H., Zhai, X., Fishburn, F., Huppert, T. The NIRS Brain AnalyzIR toolbox. Algorithms. 11 (5), 73 (2018).
  24. Xu, Y., Graber, H. L., Barbour, R. L. nirsLAB: a computing environment for fNIRS neuroimaging data analysis. Biomedical Optics. , (2014).
  25. Cope, M., Delpy, D. T. System for long-term measurement of cerebral blood and tissue oxygenation on newborn infants by near infra-red transillumination. Medical and Biological Engineering and Computing. 26 (3), 289-294 (1988).
  26. Hoshi, Y. Functional near-infrared spectroscopy: current status and future prospects. Journal of Biomedical Optics. 12 (6), 062106 (2007).
  27. Molavi, B., Dumont, G. A. Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiological Measurement. 33 (2), 259 (2012).
  28. Cooper, R., et al. A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Frontiers in Neuroscience. 6, 147 (2012).
  29. Zhang, X., Noah, J. A., Hirsch, J. Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering. Neurophotonics. 3 (1), 015004 (2016).
  30. 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).
  31. Maris, E., Oostenveld, R. Nonparametric statistical testing of EEG-and MEG-data. Journal of Neuroscience Methods. 164 (1), 177-190 (2007).
  32. Nozawa, T., Sasaki, Y., Sakaki, K., Yokoyama, R., Kawashima, R. Interpersonal frontopolar neural synchronization in group communication: an exploration toward fNIRS hyperscanning of natural interactions. NeuroImage. 133, 484-497 (2016).
  33. Reindl, V., Gerloff, C., Scharke, W., Konrad, K. Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning. NeuroImage. 178, 493-502 (2018).
  34. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., Farmer, J. D. Testing for nonlinearity in time series: the method of surrogate data. Physica D: Nonlinear Phenomena. 58 (1-4), 77-94 (1992).
  35. Genovese, C. R., Lazar, N. A., Nichols, T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage. 15 (4), 870-878 (2002).
  36. Nichols, T., Hayasaka, S. Controlling the familywise error rate in functional neuroimaging: a comparative review. Statistical Methods in Medical Research. 12 (5), 419-446 (2003).
  37. Tsuzuki, D., et al. Virtual spatial registration of stand-alone fNIRS data to MNI space. NeuroImage. 34 (4), 1506-1518 (2007).
  38. Singh, A. K., Okamoto, M., Dan, H., Jurcak, V., Dan, I. Spatial registration of multi-channel multi-subject fNIRS data to MNI space without MRI. NeuroImage. 27 (4), 842-851 (2005).
  39. Noah, J. A., et al. Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals. Neurophotonics. 8 (1), 015004 (2021).
  40. Noah, J. A., et al. Real-time eye-to-eye contact is associated with cross-brain neural coupling in angular gyrus. Frontiers in Human Neuroscience. 14 (19), (2020).
  41. Torrence, C., Compo, G. P. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society. 79 (1), 61-78 (1998).
  42. Osaka, N., Minamoto, T., Yaoi, K., Azuma, M., Osaka, M. Neural synchronization during cooperated humming: a hyperscanning study using fNIRS. Procedia-Social and Behavioral Sciences. 126, 241-243 (2014).
  43. Dommer, L., Jäger, N., Scholkmann, F., Wolf, M., Holper, L. Between-brain coherence during joint n-back task performance: a two-person functional near-infrared spectroscopy study. Behavioural Brain Research. 234 (2), 212-222 (2012).
  44. Holper, L., Scholkmann, F., Wolf, M. Between-brain connectivity during imitation measured by fNIRS. Neuroimage. 63, 212-222 (2012).
  45. Nguyen, T., et al. The effects of interaction quality on neural synchrony during mother-child problem solving. Cortex. 124, 235-249 (2020).
  46. Seth, A. K., Barrett, A. B., Barnett, L. Granger causality analysis in neuroscience and neuroimaging. Journal of Neuroscience. 35 (8), 3293-3297 (2015).
  47. Funane, T., et al. Synchronous activity of two people’s prefrontal cortices during a cooperative task measured by simultaneous near-infrared spectroscopy. Journal of Biomedical Optics. 16 (7), 077011 (2011).
  48. Liu, T., Saito, H., Oi, M. Role of the right inferior frontal gyrus in turn-based cooperation and competition: a near-infrared spectroscopy study. Brain and Cognition. 99, 17-23 (2015).
  49. Lachaux, J. P., Rodriguez, E., Martinerie, J., Varela, F. J. Measuring phase synchrony in brain signals. Human Brain Mapping. 8 (4), 194-208 (1999).
  50. Burgess, A. P. On the interpretation of synchronization in EEG hyperscanning studies: a cautionary note. Frontiers in Human Neuroscience. 7, 881 (2013).
  51. Burgos-Robles, A., et al. Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment. Nature Neuroscience. 20 (6), 824-835 (2017).
  52. Mende, S., Proske, A., Narciss, S. Individual preparation for collaborative learning: Systematic review and synthesis. Educational Psychologist. , 1-25 (2020).
  53. Hamilton, A. F. D. C. Hyperscanning: Beyond the hype. Neuron. 109 (3), 404-407 (2021).
  54. Novembre, G., Iannetti, G. D. Hyperscanning alone cannot prove causality. Multibrain stimulation can. Trends in Cognitive Sciences. 25 (2), 96-99 (2021).

Tags

FNIRS HyperscanningInter-brain SynchronyOpen-ended Collaborative LearningNaturalistic EnvironmentNear Infrared SpectroscopyProbe PlacementSignal QualityData Export3D DigitizerMNI Coordinates
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Zhao, N., Zhu, Y., Hu, Y. Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study. J. Vis. Exp. (173), e62777, doi:10.3791/62777 (2021).
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