Neural synchrony

This article is about the correlation of brain activity across two or more people. For the general concept in a single nervous system, see Neural oscillation.

Neural synchrony is the correlation of brain activity across two or more people over time. In social and affective neuroscience, neural synchrony specifically refers to the degree of similarity between the spatio-temporal neural fluctuations of multiple people. This phenomenon represents the convergence and coupling of different people's neurocognitive systems, and it is thought to be the neural substrate for many forms of interpersonal dynamics and shared experiences. Some research also refers to neural synchrony as inter-brain synchrony, brain-to-brain coupling, inter-subject correlation, between-brain connectivity, or neural coupling. In the current literature, neural synchrony is notably distinct from intra-brain synchrony—sometimes also called neural synchrony—which denotes the coupling of activity across regions of a single individual's brain.

Neural synchrony approaches represent an important theoretical and methodological contribution to the field. Since its conception, studies of neural synchrony have helped elucidate the mechanisms underlying social phenomena, including communication, narrative processing, coordination, and cooperation. By emphasizing the social dynamics of the brain, this area of research has played a critical role in making neuroscience more attuned to people's social proclivities—a perspective that is often lost on individual-level approaches to understanding the brain.

History

Motivation

Driven by the desire to understand the social nature of the human brain, the study of neural synchrony stems from social cognition, a subfield of psychology that explores how we understand and interact with other people through processes like mentalization or theory of mind.^[1] Given that it relies on measuring brain activity, neural synchrony also has its roots in cognitive neuroscience.^[2]

Despite the growth of social cognition and cognitive neuroscience prior to the early 2000s, research into the brain neglected interpersonal processes, focusing mostly on the neural mechanisms of individuals' behaviors.^[2] Furthermore, neuroscience research that did probe social questions only investigated how social processes affect neural dynamics in a single brain.^[3] Considering that researchers clearly recognized how interpersonal interaction was fundamental to human cognition, the paucity of social and multi-brain neuroscience research represented a tension in the field. In response to the discrepancy between the complexity of social interaction and the single-brain focus of cognitive neuroscience, researchers called for a multi-person, interaction-oriented approach to understanding the brain.^{[1][2][4][5][6]}

Early history

In 2002, the American neuroscientist P. Read Montague^[4] articulated the need to examine the neural activity of multiple individuals at one time. To this point, Montague and his colleagues wrote, "Studying social interactions by scanning the brain of just one person is analogous to studying synapses while observing either the presynaptic neuron or the postsynaptic neuron, but never both simultaneously."^[7] They performed the first brain scan of more than one person by using functional magnetic resonance imaging (fMRI) to take simultaneous recordings of two people engaged in a simple deception game. While this study marked the first example of multi-brain neuroimaging, in 2005, King-Casas and others^[8] combined neuroimaging with an economic exchange game to conduct the first study that directly compared neural activity between pairs of subjects.^[3] Since then, multi-brain imaging studies have grown in popularity, leading to the formation of preliminary neural synchrony frameworks.^[2]

Early conceptualizations of neural synchrony, mostly from the Hasson Lab at Princeton University, were motivated by models of stimulus-to-brain coupling. In these models, aspects of the physical environment emit mechanical, chemical, and electromagnetic signals, which the brain receives and translates into electrical impulses that guide our actions and allow us to understand the world.^[2] Researchers presumed that the synchronization of neural activity between two brains should leverage the same system that binds one's neural activity to environmental stimuli. If the stimulus is another person, then the perceptual system of one brain may couple with the behaviors or emotions of the other person, causing "vicarious activations"^[9] that manifest as synchronized neural responses across perceiver and agent.^[2] According to the theory, this process also occurs through more complex, synergistic interactions, especially when people communicate and convey meaning.^[10]

Further development

Over the last two decades, neural synchrony has become an increasingly common topic of study in social and affective neuroscience research, spurring conceptual and methodological development. Along with an emphasis on ecologically valid, naturalistic experimental designs, the focus on multi-brain neuroscience studies has increased researchers' ability to explore neural synchrony in social contexts. As a result, conceptualizations of neural synchrony have been expanded to incorporate a wider range of ideas, though it is often viewed as a neural correlate for two or more people's shared experiences. Studies now involve a variety of social processes, with applications spanning simple motor synchronization to classroom learning.^[3]

Notable methodological advancements have come from the evolution of multi-brain imaging techniques beyond fMRI, especially magnetoencephalography/electroencephalography (MEG/EEG) and functional near-infrared spectroscopy (fNIRS)—methods which afford more socially interactive experimental designs.^[3][11] These technologies are also complemented by comprehensive data processing techniques that are useful in multi-brain analyses,^[12][13] such as Granger causality^[14] or Phase Locking Value (PLV).^[15]

As a progressively paradigmatic approach in social and affective neuroscience, neural synchrony undergirds the field's search for the brain basis of social interaction.^[3]

A 2022 study by the University of Helsinki measured brain synchronization among players during cooperative online video gaming.^[16]

Methods

Hyperscanning

See also: hyperscanning

The study of neural synchrony is predicated on advanced neuroimaging methods, particularly hyperscanning. Coined in 2002 by Montague et al.,^[4] hyperscanning refers to the method of simultaneously measuring the hemodynamic or neuroelectric responses of two or more brains as they engage with the same task or stimulus.^[17][18][19] The ability to record time-locked activity from multiple brains makes hyperscanning conducive to exploring the variation in activity across brains. It also allows experimenters to examine various aspects of neural recordings in naturalistic scenarios, from low-level stimulus processing to high-level social cognition.^[13] For these reasons, hyperscanning has helped foster a systematic investigation of interpersonal dynamics at the level of the brain.^[19][20]

Though hyperscanning has become the most common imaging technique for studying neural synchrony, researchers do not necessarily need to scan brains simultaneously. Sometimes referred to as off-line measurement, or "pseudo-hyperscanning";^[20] this alternative approach follows the same basic premise as hyperscanning, except that participants' brain activity is recorded one at a time. Data from different scans of isolated participants are then analyzed to compare functional similarities during identical tasks or stimuli.^[18][19]

Imaging techniques

Hyperscanning and off-line scanning methods can be achieved through common noninvasive hemodynamic or neuroelectric brain imaging techniques. A review of neural synchrony hyperscanning studies showed that the most prevalent methods are EEG, fNIRS, and fMRI, which account for 47%, 35%, and 17% of studies, respectively.^[3] Each technique offers unique contributions to the understanding of neural synchrony given their relative advantages and limitations.^[18]

EEG measures the brain's electrical activity through the scalp. It is widely used to study neural synchrony because of its superior millisecond-range temporal resolution.^[21] Though susceptible to head movements, EEG still allows for exploring neural synchrony through naturalistic designs where people can interact socially.^[11] The downside to EEG is its relatively poor spatial resolution, which makes it difficult to elucidate spatial qualities of brain activation in social contexts.^[18]  

fNIRS uses near infrared waves to measure the blood-oxygen-level-dependent (BOLD) response in the brain. It is an increasingly popular imaging method for neural synchrony studies because of its portability and motion tolerance, which makes it ideal for testing real-world social stimuli.^[22] fNIRS only measures the cortical regions of the brain, and its temporal resolution is not as fine as EEG. However, the balance between spatial and temporal properties, combined with subjects' ability to move around and interact with relative freedom during scanning, qualify fNIRS as a versatile option for exploring neural synchrony.^[3]

fMRI uses magnetic resonance to measure the brain's BOLD response. The major advantage of fMRI is the precise spatial resolution. fMRI allows researchers to examine in-depth neurocognitive processes that occur across brains. However, fMRI has low temporal resolution, is highly sensitive to motion, and requires that subjects lie flat in a loud MRI machine while interacting with a screen. These factors pose limitations to the study of neural synchrony, which often calls for naturalistic environments and tasks that are representative of real-world social contexts.^[3][6]

Analysis

A standard approach to investigating neural synchrony, especially with data from naturalistic experimental designs, is inter-subject correlation (ISC).^[23][24] Often, ISC is the Pearson correlation, or robust regression, of spatio-temporal patterns of neural activity in multiple subjects. In ISC, an individual's brain responses are either correlated across the average of the other subjects in a leave-one-out analysis, or all pairs of subjects are correlated in a pairwise analysis.^[13] This method leverages time-locked stimuli in order to understand how brain activity across participants relates to different parts of the task. Rather than focusing on the strength of activation in brain areas, ISC explores the variability in neural activity across subjects,^[25] allowing researchers to probe the level of similarity or idiosyncrasy in people's brain responses.^[26] Shared variance in neural activity is assumed to be indicative of similar processing of identical stimuli or tasks. Similar to the general linear model, it is important to compare ISC values to a null, which can be derived from recordings of resting states or irrelevant stimuli. Because it depends on extended designs that allow for activity recording over time, ISC is especially conducive to social interaction studies, which makes it a powerful approach for exploring neural synchrony in social contexts. However, ISC depends on stimulus-driven responses, which poses difficulties for researchers interested in resting-state activity.^[27]

Recently, inter-subject representational similarity analysis (IS-RSA) has been put forward as a way to detect the individual differences, or “idiosynchrony,” across people experiencing naturalistic experimental stimuli. This analysis takes the neural synchrony of each subject to the other subjects and relates it to known individual behavioral measures, allowing researchers to compare multi-person-level brain data with individual-level traits and behaviors.^[13][28]

Best practices

Neural synchrony is a relatively new area of study that affords a variety of approaches, and no prevailing paradigm exists to collect, analyze, and interpret the data. Many decisions, such as imaging techniques or analysis methods, depend on researchers’ goals. However, there are some generally agreed upon best practices when designing these experiments. For example, sample sizes of about 30 are necessary to acquire reliable and reproducible statistical ISC maps.^[27] Furthermore, when studying shared responses, researchers typically prefer a strong stimulus that is able to generate significant brain responses, allowing researchers to detect greater levels of neural synchrony across participants. The exception to this preference is when researchers are more interested in the individual differences that drive synchrony. In these cases, researchers should employ stimuli that are strong enough to evoke neural synchrony, yet modest enough to maintain sufficient neural variability that researchers can later relate to the variability in behavioral measures.^[29][13]

One of the biggest considerations for conducting neural synchrony studies concerns the ecological validity of the design. As an inherently social phenomenon, neural synchrony calls for multidimensional stimuli that emulate the richness of the social world.^[17][30] Furthermore, by nature of how it is measured—through computing the variance in multiple brains' responses to a task over time—neural synchrony is particularly amenable to extended social stimuli. Ecological designs are notably difficult in most neuroimaging studies, yet they are especially important for capturing social processes, and they also play to the strengths and affordances of neural synchrony approaches.^[17]

Experimental evidence and implications

Communication

Examining neural synchrony through multi-brain studies has offered insight into the shared and idiosyncratic aspects of human communication. As a potential neural mechanism for the effective transfer of information across brains, neural synchrony has shown how brain activity temporally and spatially couples when people communicate. Synchrony during communication occurs in a number of brain frequencies and regions, notably alpha and gamma bands, the temporal parietal junction, and inferior frontal areas.^[18]

In a seminal study, Stephens et al.^[31] demonstrated this inter-brain link through an fMRI analysis of speakers and listeners. Using the speaker's spatial and temporal neural responses to model the listener's responses during natural verbal communication, they found that brain activity synchronized in dyads in both a delayed and anticipatory manner, but this synchrony failed to occur when subjects did not communicate (e.g., speaking in a language the listener does not understand). Greater synchrony across brains, especially in the predictive anticipatory responses, indicated better scores on comprehension measures. Building on this work, other research has sought to pinpoint communicative factors associated with neural synchrony. By manipulating conversation modality and instruction, research has found that neural synchrony is strongest during face-to-face conversations that incorporate turn-taking behavior and multi-sensory verbal and nonverbal interaction.^[32][33] Network structure dynamics also play a role in neural synchrony, such that central figures, like conversation leaders, tend to show greater neural synchrony than non-leaders with other discussion partners.^[34]

Neural synchrony is also found in nonverbal communication, such as hand gestures and facial expressions. An early study found synchronization across participants playing a game of charades. Using fMRI to record brain activity as people gestured or watched the gestures, researchers found synchronized temporal variation in brain activity in mirror neuron and mentalizing systems.^[14] Another study showed that communicative behaviors like shared gaze and positive affect expression generated neural synchrony in romantic partners, though not in strangers.^[35] As a whole, neural synchrony studies surrounding verbal, multi-sensory, and nonverbal communication demonstrate its potential as a tool for exploring the underlying mechanisms of interpersonal communication.^[2]

Narrative processing

Another focus of neural synchrony studies involves narrative processing. This direction of research has some crossover with neural synchrony studies of communication, but there remains sufficient interest in the similarities and differences in how people specifically process multimodal narrative information, such as watching movies, hearing stories, or reading passages. Importantly, narrative processing studies of neural synchrony observe hierarchical levels of processing that unfold over time,^[36][37] starting in areas responsible for low-level processing of auditory or visual stimuli. As semantic information becomes more salient in the narrative, synchronized processing moves to more integrative networks, such as the inferior parietal lobe or temporal parietal junction.^[36]

Research shows that neural synchrony is indicative of the similarity in people's narrative recall and understanding, even for ambiguous narratives. One study demonstrated this phenomenon using Heider and Simmel's^[38] classic paradigm, where simple shapes move around the screen in a way that causes people to imbue the shapes with stories and social meaning.^[39] Participants who interpreted the movement of shapes in similar ways showed greater neural synchrony in cortical brain regions. This connection between neural synchrony and similarity in comprehension reliably occurs across other types of narratives, including listening to stories and free viewing of visual content,^[40][41][23] and it persists throughout different stages of the narrative, such as consuming the story, recalling the story, and listening to another person recall the story. Together, these findings highlight neural synchrony as a reliable neural mechanism for the convergence of people's hierarchical narrative processing, suggesting that synchrony plays a critical role in how, if, and why we see meaning in the world similarly.^[42][43]

Coordination

The pursuit of complex goals for individuals or groups depends on successful coordination, and neural synchrony provides a window into the underlying mechanisms of these processes as well. A review of hyperscanning research shows that neural synchrony approaches have explored coordination through a range of paradigms, including joint attention, movements, ideas, and tasks.^[18] These findings also demonstrate synchronization across a variety of brain areas associated with sharing actions and mentalizing, namely the inferior and temporal parietal areas, as well as alpha band and other frequencies. Furthermore, converging evidence suggests that inter-brain models (i.e., neural synchrony) are more effective than intra-brain models at predicting performance for tasks requiring social coordination.^[18]

Understanding how coordination via joint attention relates to neural synchrony, and how this relationship drives performance, is of particular interest to researchers. Research shows that even simple social interactions, like attention convergence, can induce synchrony. For example, in a task where one participant must direct another participant to a target location through eye gazing only, which requires that both participants eventually coordinate eye movements, researchers found significant neural synchrony in mentalizing regions of interacting pairs.^[44] Other studies show strong neural synchrony during simple coordinated events like hand and finger movement imitation,^[45][46] humming,^[47] and even eye-blinking.^[48]

Coordination studies also find neural synchrony in more complex social coordinations. A set of studies has demonstrated the prevalence of neural synchrony in music production while people coordinate rhythms and movements. Early studies showed that dyads of guitarists generate greater low frequency band neural synchrony when playing together than when playing solo.^[49] Also, people who performed distinct roles in an intricate musical piece showed synchrony between brains during periods of coordination.^[50] Another series of studies examined pilots and copilots in a flight simulator, finding that synchrony was strongest when the situation demanded more social coordination, such as during stressful scenarios or takeoff and landing.^[51][52] These findings implicate neural synchrony as a reliable correlate of social coordination, even when interactions call for coordination of various forms and complexities.^[53]

Cooperation

As measured through tasks that involve interactive decision-making and games, results from the field suggest a close association between neural synchrony and cooperation. Decision-making contexts and games that demand greater levels of social, high-level, and goal-directed engagement with other people are typically more conducive to neural synchrony.^[54] In this domain, researchers are particularly interested in how neural synchrony levels vary depending on whether people collaborate, compete, or play alone.^[3][11]

For example, one study that employed a computer video game found high levels of neural synchrony - and better performance - across subjects when they played on the same team, but this effect disappeared when people played against each other or by themselves.^[55] Similarly, researchers that administered a puzzle solving task found neural synchrony for people when they are working as a team, yet synchrony decreased for the same people when they worked separately or watched others solve the puzzle.^[56] Another study using a classic prisoner's dilemma game showed that participants experienced higher neural synchrony with each other in the high-cooperation-context conditions than they did in the low-cooperation-context conditions or when they interacted with the computer.^[57] Subjective measures of perceived cooperativeness mediated this effect. Critically, the idea that neural synchrony is robust during cooperation, that more interactive and demanding cooperative tasks recruit greater neural synchrony, and that better cooperation often links to better performance is corroborated throughout the neural synchrony literature.^[11][17]

Individual-level differences

Much of the neural synchrony literature examines how stimuli drive responses across multiple brains. Because these responses are often task-dependent, it becomes hard to disentangle state-level factors from individual-level factors (e.g., traits). However, creative experimental designs, access to certain populations, and advances in analysis methods, like IS-RSA, have offered some recent insight into how individual-level differences affect neural synchrony.^[13]

Using an ambiguous social narrative, Finn et al.^[58] report that individuals with high-trait paranoia showed stronger neural synchrony with each other in socially-motivated cortical regions than they did with low-trait paranoia subjects - a finding that also scales when examining the semantic and syntactic similarities of their narrative recall. Similarly, research shows that people's cognitive styles affect their level of synchrony with each other. In response to viewing a film, Bacha-Trams et al. demonstrated that holistic thinkers showed greater neural synchrony with each other, and presumably understood the film more similarly, than analytic thinkers did with each other. The two groups also exhibited within-group synchrony in different brain regions.^[59]

The idea that individual-level differences affect neural synchrony extends to clinical areas as well. Some research indicates that people who manage autism spectrum disorder exhibit distinct and diminished patterns of neural synchrony compared to people without autism spectrum disorder.^[60][61] Clinically driven discrepancies in neural synchrony have also been shown to increase along with symptom severity.^[62]

The brain-as-predictor approach

Neural synchrony has major implications for the brain-as-predictor approach, which encourages the use of neuroimaging data to predict robust, ecologically valid behavioral outcomes. The brain-as-predictor approach has been effective in predicting outcomes across a variety of domains, including health and consumer choices. Given its social nature, neural synchrony has the potential to build on brain-as-predictor models by allowing for predictions about real-world social processes. Some researchers have started to employ this approach.^[63]

In one study, members of a bounded social network watched a battery of short audiovisual movies in an MRI scanner. Hypothesizing that similarity in neural responses tracks with social closeness, the researchers used the strength of neural synchrony measures across participants to reliably predict real-world social network proximity and friendship. Another example of how neural synchrony can be leveraged to predict outcomes involves the use of neural reference groups, which can predict behaviors like partisan stance on controversial topics at above-chance levels. This approach requires identifying groups of people that perceive and respond to the world in similar ways, measuring their brain activity and dispositional attitudes related to any stimuli of interest, and then using a synchrony-based classification method to predict whether new individuals see the world similarly or differently depending on their synchrony with the reference group. Together, these findings illustrate the power and potential for neural synchrony to contribute to brain-as-predictor models, ultimately framing neural synchrony as a tool for understanding real-world outcomes above and beyond behavioral measures alone.^[64]

See also

• Social cognition
• Social neuroscience
• Cognitive neuroscience
• Behavioural synchrony
• Brainwave entrainment
• Group cohesiveness
• Electroencephalography
• Functional near-infrared spectroscopy
• Functional magnetic resonance imaging

References

1. Schilbach, Leonhard; Timmermans, Bert; Reddy, Vasudevi; Costall, Alan; Bente, Gary; Schlicht, Tobias; Vogeley, Kai (2013). "Toward a second-person neuroscience". The Behavioral and Brain Sciences. 36 (4): 393–414. CiteSeerX 10.1.1.476.2200. doi:10.1017/S0140525X12000660. ISSN 1469-1825. PMID 23883742. S2CID 54587375.
2. Hasson, Uri; Ghazanfar, Asif A.; Galantucci, Bruno; Garrod, Simon; Keysers, Christian (2012-02-01). "Brain-to-brain coupling: a mechanism for creating and sharing a social world". Trends in Cognitive Sciences. 16 (2): 114–121. doi:10.1016/j.tics.2011.12.007. ISSN 1364-6613. PMC 3269540. PMID 22221820.
3. Nam, Chang; Choo, Sanghyun; Huang, Jiali; Park, Jiyoung (2020-09-24). "Brain-to-Brain Neural Synchrony During Social Interactions: A Systematic Review on Hyperscanning Studies". Applied Sciences. 10 (19): 6669. doi:10.3390/app10196669.
4. Montague, P. Read; Berns, Gregory S.; Cohen, Jonathan D.; McClure, Samuel M.; Pagnoni, Giuseppe; Dhamala, Mukesh; Wiest, Michael C.; Karpov, Igor; King, Richard D.; Apple, Nathan; Fisher, Ronald E. (2002). "Hyperscanning: simultaneous fMRI during linked social interactions". NeuroImage. 16 (4): 1159–1164. doi:10.1006/nimg.2002.1150. ISSN 1053-8119. PMID 12202103. S2CID 15988039.
5. Sänger, Johanna; Lindenberger, Ulman; Müller, Viktor (2011-11-01). "Interactive brains, social minds". Communicative & Integrative Biology. 4 (6): 655–663. doi:10.4161/cib.17934. ISSN 1942-0889. PMC 3306325. PMID 22448303.
6. Balconi, Michela; Vanutelli, Maria E. (2017). "Cooperation and Competition with Hyperscanning Methods: Review and Future Application to Emotion Domain". Frontiers in Computational Neuroscience. 11: 86. doi:10.3389/fncom.2017.00086. ISSN 1662-5188. PMC 5627061. PMID 29033810.
7. Montague, P. Read; Berns, Gregory S.; Cohen, Jonathan D.; McClure, Samuel M.; Pagnoni, Giuseppe; Dhamala, Mukesh; Wiest, Michael C.; Karpov, Igor; King, Richard D.; Apple, Nathan; Fisher, Ronald E. (2002). "Hyperscanning: simultaneous fMRI during linked social interactions". NeuroImage. 16 (4): 1160. doi:10.1006/nimg.2002.1150. ISSN 1053-8119. PMID 12202103. S2CID 15988039.
8. King-Casas, Brooks; Tomlin, Damon; Anen, Cedric; Camerer, Colin F.; Quartz, Steven R.; Montague, P. Read (2005-04-01). "Getting to know you: reputation and trust in a two-person economic exchange". Science. 308 (5718): 78–83. Bibcode:2005Sci...308...78K. CiteSeerX 10.1.1.215.598. doi:10.1126/science.1108062. ISSN 1095-9203. PMID 15802598. S2CID 8068031.
9. Keysers, Christian; Gazzola, Valeria (2009-12-01). "Expanding the mirror: vicarious activity for actions, emotions, and sensations". Current Opinion in Neurobiology. Motor systems • Neurology of behaviour. 19 (6): 666–671. doi:10.1016/j.conb.2009.10.006. ISSN 0959-4388. PMID 19880311. S2CID 2692907.
10. Riley, Michael; Richardson, Michael; Shockley, Kevin; Ramenzoni, Verónica (2011). "Interpersonal Synergies". Frontiers in Psychology. 2: 38. doi:10.3389/fpsyg.2011.00038. ISSN 1664-1078. PMC 3110940. PMID 21716606.
11. Liu, Difei; Liu, Shen; Liu, Xiaoming; Zhang, Chong; Li, Aosika; Jin, Chenggong; Chen, Yijun; Wang, Hangwei; Zhang, Xiaochu (2018). "Interactive Brain Activity: Review and Progress on EEG-Based Hyperscanning in Social Interactions". Frontiers in Psychology. 9: 1862. doi:10.3389/fpsyg.2018.01862. ISSN 1664-1078. PMC 6186988. PMID 30349495.
12. Zhao, Yang; Dai, Rui-Na; Xiao, Xiang; Zhang, Zong; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe (2017). "Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies". Journal of Biomedical Optics. 22 (2): 027004. Bibcode:2017JBO....22b7004Z. doi:10.1117/1.JBO.22.2.027004. ISSN 1083-3668. PMID 28301653. S2CID 206440646.
13. Finn, Emily S.; Glerean, Enrico; Khojandi, Arman Y.; Nielson, Dylan; Molfese, Peter J.; Handwerker, Daniel A.; Bandettini, Peter A. (2020-07-15). "Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging". NeuroImage. 215: 116828. doi:10.1016/j.neuroimage.2020.116828. ISSN 1053-8119. PMC 7298885. PMID 32276065.
14. Schippers, Marleen B.; Roebroeck, Alard; Renken, Remco; Nanetti, Luca; Keysers, Christian (2010-05-18). "Mapping the information flow from one brain to another during gestural communication". Proceedings of the National Academy of Sciences. 107 (20): 9388–9393. Bibcode:2010PNAS..107.9388S. doi:10.1073/pnas.1001791107. ISSN 0027-8424. PMC 2889063. PMID 20439736.
15. Tognoli, Emmanuelle; Lagarde, Julien; DeGuzman, Gonzalo C.; Kelso, J. A. Scott (2007-05-08). "The phi complex as a neuromarker of human social coordination". Proceedings of the National Academy of Sciences. 104 (19): 8190–8195. doi:10.1073/pnas.0611453104. ISSN 0027-8424. PMC 1859993. PMID 17470821.
16. "Interacting Brains Sync Without Physical Presence". August 30, 2022.
17. Babiloni, Fabio; Astolfi, Laura (2014-07-01). "Social neuroscience and hyperscanning techniques: Past, present and future". Neuroscience & Biobehavioral Reviews. Applied Neuroscience: Models, methods, theories, reviews. A Society of Applied Neuroscience (SAN) special issue. 44: 76–93. doi:10.1016/j.neubiorev.2012.07.006. ISSN 0149-7634. PMC 3522775. PMID 22917915.
18. Mu, Yan; Cerritos, Cindy; Khan, Fatima (2018-10-23). "Neural mechanisms underlying interpersonal coordination: A review of hyperscanning research". Social and Personality Psychology Compass. 12 (11). doi:10.1111/spc3.12421. ISSN 1751-9004. S2CID 150259098.
19. Dumas, G.; Lachat, F.; Martinerie, J.; Nadel, J.; George, N. (2011-02-01). "From social behaviour to brain synchronization: Review and perspectives in hyperscanning". IRBM. NUMÉRO SPÉCIAL : LE CERVEAU DANS TOUS SES ÉTATS. 32 (1): 48–53. doi:10.1016/j.irbm.2011.01.002. ISSN 1959-0318. S2CID 5856141.
20. Schoot, Lotte; Hagoort, Peter; Segaert, Katrien (2016-09-01). "What can we learn from a two-brain approach to verbal interaction?". Neuroscience & Biobehavioral Reviews. 68: 454–459. doi:10.1016/j.neubiorev.2016.06.009. hdl:11858/00-001M-0000-002A-E28D-F. ISSN 0149-7634. PMID 27311632. S2CID 17906867.
21. Burgess, Adrian P. (2013-12-24). "On the interpretation of synchronization in EEG hyperscanning studies: a cautionary note". Frontiers in Human Neuroscience. 7: 881. doi:10.3389/fnhum.2013.00881. ISSN 1662-5161. PMC 3870947. PMID 24399948.
22. Scholkmann, Felix; Holper, Lisa; Wolf, Ursula; Wolf, Martin (2013). "A new methodical approach in neuroscience: assessing inter-personal brain coupling using functional near-infrared imaging (fNIRI) hyperscanning". Frontiers in Human Neuroscience. 7: 813. doi:10.3389/fnhum.2013.00813. ISSN 1662-5161. PMC 3841755. PMID 24348362.
23. Hasson, Uri; Nir, Yuval; Levy, Ifat; Fuhrmann, Galit; Malach, Rafael (2004-03-12). "Intersubject Synchronization of Cortical Activity During Natural Vision". Science. 303 (5664): 1634–1640. Bibcode:2004Sci...303.1634H. doi:10.1126/science.1089506. PMID 15016991. S2CID 12688628.
24. Nastase, Samuel A; Gazzola, Valeria; Hasson, Uri; Keysers, Christian (2019-08-07). "Measuring shared responses across subjects using intersubject correlation". Social Cognitive and Affective Neuroscience. 14 (6): 667–685. doi:10.1093/scan/nsz037. ISSN 1749-5016. PMC 6688448. PMID 31099394.
25. Garrett, Douglas D.; Samanez-Larkin, Gregory R.; MacDonald, Stuart W. S.; Lindenberger, Ulman; McIntosh, Anthony R.; Grady, Cheryl L. (2013). "Moment-to-moment brain signal variability: a next frontier in human brain mapping?". Neuroscience and Biobehavioral Reviews. 37 (4): 610–624. doi:10.1016/j.neubiorev.2013.02.015. ISSN 1873-7528. PMC 3732213. PMID 23458776.
26. Pajula, Juha; Kauppi, Jukka-Pekka; Tohka, Jussi (2012-08-08). "Inter-Subject Correlation in fMRI: Method Validation against Stimulus-Model Based Analysis". PLOS ONE. 7 (8): e41196. Bibcode:2012PLoSO...841196P. doi:10.1371/journal.pone.0041196. ISSN 1932-6203. PMC 3414505. PMID 22924089.
27. Pajula, Juha; Tohka, Jussi (2016). "How Many Is Enough? Effect of Sample Size in Inter-Subject Correlation Analysis of fMRI". Computational Intelligence and Neuroscience. 2016: 2094601. doi:10.1155/2016/2094601. ISSN 1687-5265. PMC 4738700. PMID 26884746.
28. Chen, Pin-Hao A.; Jolly, Eshin; Cheong, Jin Hyun; Chang, Luke J. (2020-08-01). "Intersubject representational similarity analysis reveals individual variations in affective experience when watching erotic movies". NeuroImage. 216: 116851. doi:10.1016/j.neuroimage.2020.116851. ISSN 1053-8119. PMC 7955800. PMID 32294538.
29. Hedge, Craig; Powell, Georgina; Sumner, Petroc (2018-06-01). "The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences". Behavior Research Methods. 50 (3): 1166–1186. doi:10.3758/s13428-017-0935-1. ISSN 1554-3528. PMC 5990556. PMID 28726177.
30. Hari, Riitta; Kujala, Miiamaaria V. (2009-04-01). "Brain Basis of Human Social Interaction: From Concepts to Brain Imaging". Physiological Reviews. 89 (2): 453–479. doi:10.1152/physrev.00041.2007. ISSN 0031-9333. PMID 19342612.
31. Stephens, Greg J.; Silbert, Lauren J.; Hasson, Uri (2010-08-10). "Speaker–listener neural coupling underlies successful communication". Proceedings of the National Academy of Sciences. 107 (32): 14425–14430. Bibcode:2010PNAS..10714425S. doi:10.1073/pnas.1008662107. ISSN 0027-8424. PMC 2922522. PMID 20660768.
32. Jiang, Jing; Dai, Bohan; Peng, Danling; Zhu, Chaozhe; Liu, Li; Lu, Chunming (2012-11-07). "Neural Synchronization during Face-to-Face Communication". Journal of Neuroscience. 32 (45): 16064–16069. doi:10.1523/JNEUROSCI.2926-12.2012. ISSN 0270-6474. PMC 6621612. PMID 23136442.
33. Tang, Honghong; Mai, Xiaoqin; Wang, Shun; Zhu, Chaozhe; Krueger, Frank; Liu, Chao (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. doi:10.1093/scan/nsv092. ISSN 1749-5024. PMC 4692317. PMID 26211014.
34. Jiang, Jing; Chen, Chuansheng; Dai, Bohan; Shi, Guang; Ding, Guosheng; Liu, Li; Lu, Chunming (2015-04-07). "Leader emergence through interpersonal neural synchronization". Proceedings of the National Academy of Sciences. 112 (14): 4274–4279. Bibcode:2015PNAS..112.4274J. doi:10.1073/pnas.1422930112. ISSN 0027-8424. PMC 4394311. PMID 25831535.
35. Kinreich, Sivan; Djalovski, Amir; Kraus, Lior; Louzoun, Yoram; Feldman, Ruth (2017-12-06). "Brain-to-Brain Synchrony during Naturalistic Social Interactions". Scientific Reports. 7 (1): 17060. Bibcode:2017NatSR...717060K. doi:10.1038/s41598-017-17339-5. ISSN 2045-2322. PMC 5719019. PMID 29213107.
36. Hasson, Uri; Yang, Eunice; Vallines, Ignacio; Heeger, David J.; Rubin, Nava (2008-03-05). "A Hierarchy of Temporal Receptive Windows in Human Cortex". Journal of Neuroscience. 28 (10): 2539–2550. doi:10.1523/JNEUROSCI.5487-07.2008. ISSN 0270-6474. PMC 2556707. PMID 18322098.
37. Lerner, Yulia; Honey, Christopher J.; Silbert, Lauren J.; Hasson, Uri (2011-02-23). "Topographic Mapping of a Hierarchy of Temporal Receptive Windows Using a Narrated Story". Journal of Neuroscience. 31 (8): 2906–2915. doi:10.1523/JNEUROSCI.3684-10.2011. ISSN 0270-6474. PMC 3089381. PMID 21414912.
38. Heider, Fritz; Simmel, Marianne (1944). "An Experimental Study of Apparent Behavior". The American Journal of Psychology. 57 (2): 243–259. doi:10.2307/1416950. ISSN 0002-9556. JSTOR 1416950. S2CID 143057281.
39. Nguyen, Mai; Vanderwal, Tamara; Hasson, Uri (2019-01-01). "Shared understanding of narratives is correlated with shared neural responses". NeuroImage. 184: 161–170. doi:10.1016/j.neuroimage.2018.09.010. ISSN 1053-8119. PMC 6287615. PMID 30217543.
40. Saalasti, Satu; Alho, Jussi; Bar, Moshe; Glerean, Enrico; Honkela, Timo; Kauppila, Minna; Sams, Mikko; Jääskeläinen, Iiro P. (2019-04-11). "Inferior parietal lobule and early visual areas support elicitation of individualized meanings during narrative listening". Brain and Behavior. 9 (5): e01288. doi:10.1002/brb3.1288. ISSN 2162-3279. PMC 6520291. PMID 30977309.
41. Wilson, Stephen M.; Molnar-Szakacs, Istvan; Iacoboni, Marco (2008-01-01). "Beyond Superior Temporal Cortex: Intersubject Correlations in Narrative Speech Comprehension". Cerebral Cortex. 18 (1): 230–242. doi:10.1093/cercor/bhm049. ISSN 1047-3211. PMID 17504783.
42. Zadbood, A.; Chen, J.; Leong, Y.C.; Norman, K.A.; Hasson, U. (2017-10-01). "How We Transmit Memories to Other Brains: Constructing Shared Neural Representations Via Communication". Cerebral Cortex. 27 (10): 4988–5000. doi:10.1093/cercor/bhx202. ISSN 1047-3211. PMC 6057550. PMID 28922834.
43. Chen, Janice; Leong, Yuan Chang; Honey, Christopher J.; Yong, Chung H.; Norman, Kenneth A.; Hasson, Uri (2017). "Shared memories reveal shared structure in neural activity across individuals". Nature Neuroscience. 20 (1): 115–125. doi:10.1038/nn.4450. ISSN 1546-1726. PMC 5191958. PMID 27918531.
44. Bilek, Edda; Ruf, Matthias; Schäfer, Axel; Akdeniz, Ceren; Calhoun, Vince D.; Schmahl, Christian; Demanuele, Charmaine; Tost, Heike; Kirsch, Peter; Meyer-Lindenberg, Andreas (2015-04-21). "Information flow between interacting human brains: Identification, validation, and relationship to social expertise". Proceedings of the National Academy of Sciences. 112 (16): 5207–5212. Bibcode:2015PNAS..112.5207B. doi:10.1073/pnas.1421831112. ISSN 0027-8424. PMC 4413334. PMID 25848050.
45. Dumas, Guillaume; Nadel, Jacqueline; Soussignan, Robert; Martinerie, Jacques; Garnero, Line (2010-08-17). "Inter-Brain Synchronization during Social Interaction". PLOS ONE. 5 (8): e12166. Bibcode:2010PLoSO...512166D. doi:10.1371/journal.pone.0012166. ISSN 1932-6203. PMC 2923151. PMID 20808907.
46. Holper, Lisa; Scholkmann, Felix; Wolf, Martin (2012-10-15). "Between-brain connectivity during imitation measured by fNIRS". NeuroImage. 63 (1): 212–222. doi:10.1016/j.neuroimage.2012.06.028. ISSN 1095-9572. PMID 22732563. S2CID 5475091.
47. Osaka, Naoyuki; Minamoto, Takehiro; Yaoi, Ken; Azuma, Miyuki; Shimada, Yohko Minamoto; Osaka, Mariko (2015). "How Two Brains Make One Synchronized Mind in the Inferior Frontal Cortex: fNIRS-Based Hyperscanning During Cooperative Singing". Frontiers in Psychology. 6: 1811. doi:10.3389/fpsyg.2015.01811. ISSN 1664-1078. PMC 4659897. PMID 26635703.
48. Koike, Takahiko; Tanabe, Hiroki C.; Okazaki, Shuntaro; Nakagawa, Eri; Sasaki, Akihiro T.; Shimada, Koji; Sugawara, Sho K.; Takahashi, Haruka K.; Yoshihara, Kazufumi; Bosch-Bayard, Jorge; Sadato, Norihiro (2016-01-15). "Neural substrates of shared attention as social memory: A hyperscanning functional magnetic resonance imaging study". NeuroImage. 125: 401–412. doi:10.1016/j.neuroimage.2015.09.076. ISSN 1095-9572. PMID 26514295. S2CID 17214984.
49. Lindenberger, Ulman; Li, Shu-Chen; Gruber, Walter; Müller, Viktor (2009-03-17). "Brains swinging in concert: cortical phase synchronization while playing guitar". BMC Neuroscience. 10 (1): 22. doi:10.1186/1471-2202-10-22. ISSN 1471-2202. PMC 2662862. PMID 19292892.
50. Sänger, Johanna; Müller, Viktor; Lindenberger, Ulman (2012). "Intra- and interbrain synchronization and network properties when playing guitar in duets". Frontiers in Human Neuroscience. 6: 312. doi:10.3389/fnhum.2012.00312. ISSN 1662-5161. PMC 3509332. PMID 23226120.
51. Astolfi, L.; Toppi, J.; Borghini, G.; Vecchiato, G.; Isabella, R.; Fallani, F. D. V.; Cincotti, F.; Salinari, S.; Mattia, D.; He, B.; Caltagirone, C. (2011). "Study of the functional hyperconnectivity between couples of pilots during flight simulation: An EEG hyperscanning study". 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 2011. pp. 2338–2341. doi:10.1109/IEMBS.2011.6090654. ISBN 978-1-4577-1589-1. PMID 22254810. S2CID 4902837.
52. Toppi, Jlenia; Borghini, Gianluca; Petti, Manuela; He, Eric J.; Giusti, Vittorio De; He, Bin; Astolfi, Laura; Babiloni, Fabio (2016-04-28). "Investigating Cooperative Behavior in Ecological Settings: An EEG Hyperscanning Study". PLOS ONE. 11 (4): e0154236. Bibcode:2016PLoSO..1154236T. doi:10.1371/journal.pone.0154236. ISSN 1932-6203. PMC 4849782. PMID 27124558.
53. Baldassno, Christopher; Chen, Janice; Zadbood, Asieh; Pillow, Jonathan W.; Hasson, Uri; Norman, Kenneth A. (2017-08-02). "Discovering Event Structure in Continuous Narrative Perception and Memory". Neuron. 95 (3): 709–721.e5. doi:10.1016/j.neuron.2017.06.041. PMC 5558154. PMID 28772125.
54. Valencia, Ana Lucía; Froese, Tom (2020-06-18). "What binds us? Inter-brain neural synchronization and its implications for theories of human consciousness". Neuroscience of Consciousness. 2020 (1): niaa010. doi:10.1093/nc/niaa010. PMC 7288734. PMID 32547787.
55. Cui, Xu; Bryant, Daniel M.; Reiss, Allan L. (2012-02-01). "NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation". NeuroImage. 59 (3): 2430–2437. doi:10.1016/j.neuroimage.2011.09.003. ISSN 1053-8119. PMC 3254802. PMID 21933717.
56. Fishburn, Frank A.; Murty, Vishnu P.; Hlutkowsky, Christina O.; MacGillivray, Caroline E.; Bemis, Lisa M.; Murphy, Meghan E.; Huppert, Theodore J.; Perlman, Susan B. (2018-09-05). "Putting our heads together: interpersonal neural synchronization as a biological mechanism for shared intentionality". Social Cognitive and Affective Neuroscience. 13 (8): 841–849. doi:10.1093/scan/nsy060. ISSN 1749-5024. PMC 6123517. PMID 30060130.
57. Hu, Yi; Pan, Yafeng; Shi, Xinwei; Cai, Qing; Li, Xianchun; Cheng, Xiaojun (2018). "Inter-brain synchrony and cooperation context in interactive decision making". Biological Psychology. 133: 54–62. doi:10.1016/j.biopsycho.2017.12.005. ISSN 1873-6246. PMID 29292232. S2CID 46859640.
58. Finn, Emily S.; Corlett, Philip R.; Chen, Gang; Bandettini, Peter A.; Constable, R. Todd (2018-05-23). "Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative". Nature Communications. 9 (1): 2043. Bibcode:2018NatCo...9.2043F. doi:10.1038/s41467-018-04387-2. ISSN 2041-1723. PMC 5966466. PMID 29795116.
59. Bacha-Trams, M.; Alexandrov, Y.; Broman, Emilia; Glerean, E.; Kauppila, Minna; Kauttonen, J.; Ryyppö, Elisa; Sams, M.; Jääskeläinen, I. (2018). "A drama movie activates brains of holistic and analytical thinkers differentially". Social Cognitive and Affective Neuroscience. 13 (12): 1293–1304. doi:10.1093/scan/nsy099. PMC 6277741. PMID 30418656. S2CID 53280358.
60. Salmi, J.; Roine, U.; Glerean, E.; Lahnakoski, J.; Nieminen-von Wendt, T.; Tani, P.; Leppämäki, S.; Nummenmaa, L.; Jääskeläinen, I. P.; Carlson, S.; Rintahaka, P. (2013-01-01). "The brains of high functioning autistic individuals do not synchronize with those of others". NeuroImage: Clinical. 3: 489–497. doi:10.1016/j.nicl.2013.10.011. ISSN 2213-1582. PMC 3830058. PMID 24273731.
61. Tanabe, Hiroki; Kosaka, Hirotaka; Saito, Daisuke; Koike, Takahiko; Hayashi, Masamichi; Izuma, Keise; Komeda, Hidetsugu; Ishitobi, Makoto; Omori, Masao; Munesue, Toshio; Okazawa, Hidehiko (2012). "Hard to "tune in": neural mechanisms of live face-to-face interaction with high-functioning autistic spectrum disorder". Frontiers in Human Neuroscience. 6: 268. doi:10.3389/fnhum.2012.00268. ISSN 1662-5161. PMC 3459004. PMID 23060772.
62. Guo, Christine C.; Nguyen, Vinh T.; Hyett, Matthew P.; Parker, Gordon B.; Breakspear, Michael J. (2015-06-26). "Out-of-sync: disrupted neural activity in emotional circuitry during film viewing in melancholic depression". Scientific Reports. 5 (1): 11605. Bibcode:2015NatSR...511605G. doi:10.1038/srep11605. ISSN 2045-2322. PMC 4481375. PMID 26112251.
63. Berkman, Elliot T.; Falk, Emily B. (2013). "Beyond Brain Mapping: Using Neural Measures to Predict Real-World Outcomes". Current Directions in Psychological Science. 22 (1): 45–50. doi:10.1177/0963721412469394. ISSN 0963-7214. PMC 3903296. PMID 24478540.
64. Parkinson, Carolyn; Kleinbaum, Adam M.; Wheatley, Thalia (2018-01-30). "Similar neural responses predict friendship". Nature Communications. 9 (1): 332. Bibcode:2018NatCo...9..332P. doi:10.1038/s41467-017-02722-7. ISSN 2041-1723. PMC 5790806. PMID 29382820.