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Assessing Computational Methods to Quantify Mother-Child Brain Synchrony in Naturalistic Settings Based on fNIRS Signals

Neuroinformatics (2021)Cite this article

Abstract

Mother-child brain-to-brain synchrony captures the temporal similarities in brain signals between dyadic partners, and has been shown to emerge during the display of joint behaviours. Despite the rise in the number of studies that investigate synchrony in naturalistic contexts, the use of varying methodological approaches to compute synchrony remains a central problem. When dyads engage in unstructured social interactions, the wide range of behavioural cues they display contribute to the use of varying lengths of signals to compute synchrony. The present functional Near-infrared Spectroscopy (fNIRS) study investigates how different methods to quantify brain signals during joint and non-joint portions of dyadic play affect the outcome of brain-to-brain synchrony. Three strategies to cope with unstructured data are tested and different signal lengths of 15, 20, 25, 30, 35, 40, 45s were used to determine the optimal method to sensitively capture synchrony. Results showed that using all available portions of the signals generated a greater number of less conservative results compared to the other two strategies, which were to compute the average synchrony for the joint and non-joint signals portions and to compute the difference between the average synchrony of joint and non-joint portions. From the different signal durations, only length portions of 25s to 35s generated significant results. These findings demonstrate that differences in computational approaches and signal lengths affect synchrony measurements and should be considered in naturalistic synchrony studies.

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References

  • Andras, P. (2017). Solomon Coder (Version Beta: 17.03.22): A Simple Solution for Behaviour Coding. Accessed 28 Sept 2021.

  • Atzil, S., & Gendron, M. (2017). Bio-behavioral synchrony promotes the development of conceptualized emotions. Current Opinion in Psychology, 17, 162–169.

    PubMed  PubMed Central  Google Scholar 

  • Atzil, S., Hendler, T., & Feldman, R. (2014). The brain basis of social synchrony. Social cognitive and affective neuroscience, 9(8), 1193–1202.

    PubMed  Google Scholar 

  • Azhari, A., Leck, W. Q., Gabrieli, G., Bizzego, A., Rigo, P., Setoh, P., et al. (2019). Parenting stress undermines mother-child brain-to-brain synchrony: A hyperscanning study. Scientific Reports, 9, 1.

    CAS  Google Scholar 

  • Azhari, A., Gabrieli, G., Bizzego, A., Bornstein, M. H., & Esposito, G. (2020a). Probing the association between maternal anxious attachment style and mother-child brain-to-brain coupling during passive co-viewing of visual stimuli. Attachment & Human Development, 1–16.

  • Azhari, A., Lim, M., Bizzego, A., Gabrieli, G., Bornstein, M. H., & Esposito, G. (2020b). Physical presence of spouse enhances brain-to-brain synchrony in co-parenting couples. Scientific Reports, 10(1), 1–11.

    Google Scholar 

  • Azhari, A., Wong, A. W. T., Lim, M., Balagtas, J. P. M., Gabrieli, G., Setoh, P., & Esposito, G. (2020c). Parents’ past bonding experience with their parents interacts with current parenting stress to influence the quality of interaction with their child. Behavioral Sciences, 10(7), 114.

    PubMed Central  Google Scholar 

  • Azhari, A., Bizzego, A., & Esposito, G. (2021). Father-child dyads exhibit unique inter-subject synchronisation during co-viewing of animation video stimuli. Social Neuroscience, 16.

  • Babiloni, F., & Astolfi, L. (2014). Social neuroscience and hyperscanning techniques: past, present and future. Neuroscience & Biobehavioral Reviews, 44, 76–93.

    Google Scholar 

  • Bell, M. A. (2020). Chapter Six - Mother-child behavioral and physiological synchrony. vol. 58 of Advances in Child Development and Behavior. JAI, pp. 163–188.

  • Bilek, E., Ruf, M., Schäfer, A., Akdeniz, C., Calhoun, V. D., Schmahl, C., et al. (2015). Information flow between interacting human brains: Identification, validation, and relationship to social expertise. Proceedings of the National Academy of Sciences, 112(16), 5207–5212.

    CAS  Google Scholar 

  • Bizzego, A., Azhari, A., Campostrini, N., Truzzi, A., Ng, L. Y., Gabrieli, G., et al. (2020). Strangers, friends, and lovers show different physiological synchrony in different emotional states. Behavioral Sciences, 10(1), 11.

    Google Scholar 

  • Bizzego, A., Azhari, A., & Esposito, G. (2021). Reproducible inter-personal brain coupling measurements in hyperscanning settings with functional near infra-red spectroscopy. Neuroinformatics, 1–11.

  • Bizzego, A., Gabrieli, G., Azhari, A., Setoh, P., & Esposito, G. (2021). Computational methods for the assessment of empathic synchrony. In Progresses in Artificial Intelligence and Neural Systems. Springer, pp. 555–564.

  • Bornstein, M. H., Haynes, O. M., O’Reilly, A. W., & Painter, K. M. (1996). Solitary and collaborative pretense play in early childhood: Sources of individual variation in the development of representational competence. Child development, 67(6), 2910–2929.

    CAS  PubMed  Google Scholar 

  • Bzdok, D., Schilbach, L., Vogeley, K., Schneider, K., Laird, A. R., Langner, R., & Eickhoff, S. B. (2012). Parsing the neural correlates of moral cognition: Ale meta-analysis on morality, theory of mind, and empathy. Brain Structure and Function, 217(4), 783–796.

    PubMed  Google Scholar 

  • Carretié, L., Hinojosa, J. A., Martín-Loeches, M., Mercado, F., & Tapia, M. (2004). Automatic attention to emotional stimuli: Neural correlates. Human Brain Mapping, 22(4), 290–299.

    PubMed  PubMed Central  Google Scholar 

  • Cui, X., Bryant, D. M., & Reiss, A. L. (2012). NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. Neuroimage, 59(3), 2430–2437.

    PubMed  Google Scholar 

  • Czeszumski, A., Eustergerling, S., Lang, A., Menrath, D., Gerstenberger, M., Schuberth, S., et al. (2020). Hyperscanning: A valid method to study neural inter-brain underpinnings of social interaction. Frontiers in Human Neuroscience, 14, 39.

    PubMed  PubMed Central  Google Scholar 

  • Dai, B., Chen, C., Long, Y., Zheng, L., Zhao, H., Bai, X., et al. (2018). Neural mechanisms for selectively tuning in to the target speaker in a naturalistic noisy situation. Nature Communications, 9(1), 1–12.

    Google Scholar 

  • Davis, M., West, K., Bilms, J., Morelen, D., & Suveg, C. (2018). A systematic review of parent-child synchrony: It is more than skin deep. Developmental Psychobiology, 60(6), 674–691.

    PubMed  Google Scholar 

  • Delaherche, E., Chetouani, M., Mahdhaoui, A., Saint-Georges, C., Viaux, S., & Cohen, D. (2012). Interpersonal synchrony: A survey of evaluation methods across disciplines. IEEE Transactions on Affective Computing, 3(3), 349–365.

    Google Scholar 

  • Djalovski, A., Dumas, G., Kinreich, S., & Feldman, R. (2021). Human attachments shape interbrain synchrony toward efficient performance of social goals. Neuroimage, 226, 117600.

    PubMed  Google Scholar 

  • Durnford, J. R., Balagtas, J. P. M., Azhari, A., Lim, M., Gabrieli, G., Bizzego, A., & Esposito, G. (2020). Presence of parent, gender and emotional valence influences preschoolers’ pfc processing of video stimuli. Early Child Development and Care, 1–11.

  • Ekman, I., Chanel, G., Järvelä, S., Kivikangas, J. M., Salminen, M., & Ravaja, N. (2012). Social interaction in games: Measuring physiological linkage and social presence. Simulation & Gaming, 43(3), 321–338.

    Google Scholar 

  • Feldman, R. (2012a). Bio-behavioral synchrony: A model for integrating biological and microsocial behavioral processes in the study of parenting. Parenting, 12(2–3), 154–164.

    Google Scholar 

  • Feldman, R. (2012b). Interactive synchrony: A biobehavioral model of mutual influences in the formation of affiliative bonds in healthy and pathological development. Neuropsychiatrie de l’Enfance et de l’Adolescence, 5(60), S2.

    Google Scholar 

  • Feldman, R. (2014). Synchrony and the neurobiological basis of social affiliation (pp. 145–166). In Mechanisms of social connection: From brain to group. American Psychological Association.

    Google Scholar 

  • Fishburn, F. A., Murty, V. P., Hlutkowsky, C. O., MacGillivray, C. E., Bemis, L. M., Murphy, M. E., et al. (2018). Putting our heads together: Interpersonal neural synchronization as a biological mechanism for shared intentionality. Social Cognitive and Affective Neuroscience, 13(8), 841–849.

    PubMed  PubMed Central  Google Scholar 

  • Gamer, M., Lemon, J., Fellows, I., & Singh, P. (2012). Various Coefficients of Interrater Reliability and Agreement. Accessed 28 Sept 2021.

  • Gilbert, S. J., & Burgess, P. W. (2008). Executive function. Current Biology, 18(3), R110–R114.

    CAS  PubMed  Google Scholar 

  • Giraud, A.-L., & Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational principles and operations. Nature Neuroscience, 15(4), 511–517.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Goldstein, P., Weissman-Fogel, I., Dumas, G., & Shamay-Tsoory, S. G. (2018). Brain-to-brain coupling during handholding is associated with pain reduction. Proceedings of the National Academy of Sciences, 115(11), E2528–E2537.

    CAS  Google Scholar 

  • Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S., & Keysers, C. (2012). Brain-to-brain coupling: A mechanism for creating and sharing a social world. Trends in Cognitive Sciences, 16(2), 114–121.

    PubMed  PubMed Central  Google Scholar 

  • Hentschke, H., & Stüttgen, M. C. (2011). Computation of measures of effect size for neuroscience data sets. European Journal of Neuroscience, 34(12), 1887–1894.

    Google Scholar 

  • Hove, M. J., & Risen, J. L. (2009). It’s all in the timing: Interpersonal synchrony increases affiliation. Social Cognition, 27(6), 949–960.

    Google Scholar 

  • Hu, Y., Hu, Y., Li, X., Pan, Y., & Cheng, X. (2017). Brain-to-brain synchronization across two persons predicts mutual prosociality. Social Cognitive and Affective Neuroscience, 12(12), 1835–1844.

    PubMed  PubMed Central  Google Scholar 

  • Huppert, T. J., Diamond, S. G., Franceschini, M. A., & Boas, D. A. (2009). Homer: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Applied Optics, 48(10), D280–D298.

    PubMed  PubMed Central  Google Scholar 

  • Jiang, J., Dai, B., Peng, D., Zhu, C., Liu, L., & Lu, C. (2012). Neural synchronization during face-to-face communication. Journal of Neuroscience, 32(45), 16064–16069.

    CAS  PubMed  Google Scholar 

  • Kerby, D. S. (2014). The simple difference formula: An approach to teaching nonparametric correlation. Comprehensive Psychology 3, 11–IT.

  • Kinreich, S., Djalovski, A., Kraus, L., Louzoun, Y., & Feldman, R. (2017). Brain-to-brain synchrony during naturalistic social interactions. Scientific Reports, 7(1), 1–12.

    CAS  Google Scholar 

  • Konvalinka, I., & Roepstorff, A. (2012). The two-brain approach: how can mutually interacting brains teach us something about social interaction? Frontiers in Human Neuroscience, 6, 215.

    PubMed  PubMed Central  Google Scholar 

  • Leclère, C., Viaux, S., Avril, M., Achard, C., Chetouani, M., Missonnier, S., & Cohen, D. (2014). Why synchrony matters during mother-child interactions: A systematic review. PloS one, 9(12), e113571.

    PubMed  PubMed Central  Google Scholar 

  • Lee, T.-H., Miernicki, M. E., & Telzer, E. H. (2017). Families that fire together smile together: Resting state connectome similarity and daily emotional synchrony in parent-child dyads. Neuroimage, 152, 31–37.

    PubMed  Google Scholar 

  • Li, R., Mayseless, N., Balters, S., & Reiss, A. L. (2021). Dynamic inter-brain synchrony in real-life inter-personal cooperation: A functional near-infrared spectroscopy hyperscanning study. NeuroImage, 118263.

  • Lillard, A. S. (2017). Why do the children (pretend) play? Trends in Cognitive Sciences, 21(11), 826–834.

    PubMed  Google Scholar 

  • Markova, G., Nguyen, T., & Hoehl, S. (2019). Neurobehavioral interpersonal synchrony in early development: The role of interactional rhythms. Frontiers in Psychology, 10, 2078.

    PubMed  PubMed Central  Google Scholar 

  • Marks, T. D., & Goard, M. J. (2021). Stimulus-dependent representational drift in primary visual cortex. Nature Communications, 12(1), 1–16.

    Google Scholar 

  • McDonald, K. R., Pearson, J. M., & Huettel, S. A. (2020). Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior. Social Cognitive and Affective Neuroscience, 15(4), 383–393.

    PubMed  PubMed Central  Google Scholar 

  • McGraw, K. O., & Wong, S. P. (1992). A common language effect size statistic. Psychological Bulletin, 111(2), 361.

    Google Scholar 

  • Miller, J. G., Vrtička, P., Cui, X., Shrestha, S., Hosseini, S. H., Baker, J. M., & Reiss, A. L. (2019). Inter-brain synchrony in mother-child dyads during cooperation: an fNIRS hyperscanning study. Neuropsychologia, 124, 117–124.

    PubMed  Google Scholar 

  • Morais, G. A. Z., Scholkmann, F., Balardin, J. B., Furucho, R. A., de Paula, R. C. V., Biazoli, C. E., & Sato, J. R. (2017). Non-neuronal evoked and spontaneous hemodynamic changes in the anterior temporal region of the human head may lead to misinterpretations of functional near-infrared spectroscopy signals. Neurophotonics, 5(1), 011002.

    Google Scholar 

  • Nguyen, T., Schleihauf, H., Kayhan, E., Matthes, D., Vrtička, P., & Hoehl, S. (2020). The effects of interaction quality on neural synchrony during mother-child problem solving. Cortex, 124, 235–249.

    PubMed  Google Scholar 

  • Nguyen, T., Schleihauf, H., Kayhan, E., Matthes, D., Vrtička, P., & Hoehl, S. (2021). Neural synchrony in mother-child conversation: Exploring the role of conversation patterns. Social Cognitive and Affective Neuroscience, 16(1–2), 93–102.

    PubMed  Google Scholar 

  • Pollonini, L., Bortfeld, H., & Oghalai, J. S. (2016). Phoebe: A method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy. Biomedical Optics Express, 7(12), 5104–5119.

    PubMed  PubMed Central  Google Scholar 

  • Provenzi, L., Scotto di Minico, G., Giusti, L., Guida, E., & Müller, M. (2018). Disentangling the dyadic dance: Theoretical, methodological and outcomes systematic review of mother-infant dyadic processes. Frontiers in Psychology 9, 348.

  • Reindl, V., Gerloff, C., Scharke, W., & Konrad, K. (2018). Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning. Neuroimage, 178, 493–502.

    PubMed  Google Scholar 

  • Rennung, M., & Göritz, A. S. (2016). Prosocial consequences of interpersonal synchrony. Zeitschrift für Psychologie.

  • Schilbach, L., Timmermans, B., Reddy, V., Costall, A., Bente, G., Schlicht, T., & Vogeley, K. (2013). Toward a second-person neuroscience. Behavioral and Brain Sciences, 36(4), 393–414.

    Google Scholar 

  • Schippers, M. B., Roebroeck, A., Renken, R., Nanetti, L., & Keysers, C. (2010). Mapping the information flow from one brain to another during gestural communication. Proceedings of the National Academy of Sciences, 107(20), 9388–9393.

    CAS  Google Scholar 

  • Scholkmann, F., & Wolf, M. (2013). General equation for the differential pathlength factor of the frontal human head depending on wavelength and age. Journal of Biomedical Optics, 18(10), 105004.

    PubMed  Google Scholar 

  • Scott Kelso, J. A. (1995). Dynamic Patterns: The self-organization of Brain and Behavior. MIT Press.

  • Starr, J. M., Farrall, A. J., Armitage, P., McGurn, B., & Wardlaw, J. (2009). Blood-brain barrier permeability in alzheimer’s disease: A case-control mri study. Psychiatry Research: Neuroimaging, 171(3), 232–241.

    CAS  PubMed  Google Scholar 

  • Tang, H., Mai, X., Wang, S., Zhu, C., Krueger, F., & Liu, C. (2016). Interpersonal brain synchronization in the right temporo-parietal junction during face-to-face economic exchange. Social Cognitive and Affective Neuroscience, 11(1), 23–32.

    PubMed  Google Scholar 

  • Tomasello, M., & Carpenter, M. (2007). Shared intentionality. Developmental Science, 10(1), 121–125.

    PubMed  Google Scholar 

  • Voss, M. W. (2016). The chronic exercise-cognition interaction: fmri research (pp. 187–209). In Exercise-cognition interaction: Neuroscience perspectives. Elsevier Academic Press.

    Google Scholar 

  • Wass, S. V., Whitehorn, M., Haresign, I. M., Phillips, E., & Leong, V. (2020). Interpersonal neural entrainment during early social interaction. Trends in Cognitive Sciences, 24(4), 329–342.

    PubMed  Google Scholar 

  • White, R. E., Thibodeau-Nielsen, R. B., Palermo, F., & Mikulski, A. M. (2021). Engagement in social pretend play predicts preschoolers’ executive function gains across the school year. Early Childhood Research Quarterly, 56, 103–113.

    Google Scholar 

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Funding

A.B. was supported by a Post-doctoral Fellowship within MIUR programme framework “Dipartimenti di Eccellenza” (DiPSCO, University of Trento). G.E. was supported by NAP SUG 2015, Singapore Ministry of Education ACR Tier 1 (RG149/16 and RT10/19).

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Authors and Affiliations

  1. Department of Psychology and Cognitive Science, University of Trento, Rovereto, 38068, TN, Italy

    Andrea Bizzego & Gianluca Esposito

  2. Psychology Program, Nanyang Technological University, Singapore, 639818, Singapore

    Atiqah Azhari & Gianluca Esposito

  3. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore

    Gianluca Esposito

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  1. Andrea Bizzego
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  2. Atiqah Azhari
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Correspondence to Gianluca Esposito.

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Bizzego, A., Azhari, A. & Esposito, G. Assessing Computational Methods to Quantify Mother-Child Brain Synchrony in Naturalistic Settings Based on fNIRS Signals. Neuroinform (2021). https://doi.org/10.1007/s12021-021-09558-z

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Keywords

  • Synchrony
  • Hyperscanning
  • Mother-child
  • Parenting
  • Computational neuroscience
  • Social neuroscience