# Distinct social behavior and inter-brain connectivity in Dyads with autistic individuals

Quentin Moreau, Florence Brun, Anaël Ayrolles, Jacqueline Nadel, Guillaume Dumas

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Quentin Moreau, Florence Brun, Anaël Ayrolles, Jacqueline Nadel, Guillaume Dumas. Distinct social behavior and inter-brain connectivity in Dyads with autistic individuals. Social Neuroscience, 2024, 10.1080/17470919.2024.2379917. [hal-04661561](https://hal.science/hal-04661561v1)

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# Distinct social behavior and inter-brain connectivity in Dyads with autistic individuals

Quentin Moreau, Florence Brun, Anaël Ayrolles, Jacqueline Nadel & Guillaume Dumas

**To cite this article:** Quentin Moreau, Florence Brun, Anaël Ayrolles, Jacqueline Nadel & Guillaume Dumas (18 Jul 2024): Distinct social behavior and inter-brain connectivity in Dyads with autistic individuals, Social Neuroscience, DOI: 10.1080/17470919.2024.2379917

**To link to this article:** https://doi.org/10.1080/17470919.2024.2379917

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# Distinct social behavior and inter-brain connectivity in Dyads with autistic individuals

Quentin Moreau<sup>ID a,b</sup>, Florence Brun<sup>c,d</sup>, Anaël Ayrolles<sup>c,d</sup>, Jacqueline Nadel<sup>e</sup> and Guillaume Dumas<sup>a,b,c,d,e,f,g</sup>

## ABSTRACT

Autism Spectrum Disorder (ASD) is defined by distinctive socio-cognitive behaviors that deviate from typical patterns. Notably, social imitation skills appear to be particularly impacted, manifesting early on in development. This paper compared the behavior and inter-brain dynamics of dyads made up of two typically developing (TD) participants with mixed dyads made up of ASD and TD participants during social imitation tasks. By combining kinematics and EEG-hyperscanning, we show that individuals with ASD exhibited a preference for the follower rather than the lead role in imitating scenarios. Moreover, the study revealed inter-brain synchrony differences, with low-alpha inter-brain synchrony differentiating control and mixed dyads. The study's findings suggest the importance of studying interpersonal phenomena in dynamic and ecological settings and using hyperscanning methods to capture inter-brain dynamics during actual social interactions.

## ARTICLE HISTORY

Received 7 November 2023

Revised 1 July 2024

Published online 18 July 2024

## KEYWORDS

Autism spectrum disorder;
hyperscanning; inter-brain synchrony;
social imitation;
social interactions

## Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by atypical social behaviors, ranging from non-verbal interactions to sophisticated social cognition (American Psychiatric Association, 2013; Grzadzinski et al., 2013; Lord et al., 2020). For instance, imitation skills in children with ASD are notably diminished (Ingersoll, 2008) while in typical development (TD), social imitation allows children to learn from others (Heyes, 2011; Ray & Heyes, 2011) but also reflects their search for belongingness (Over, 2016; Over & Carpenter, 2013).

The nature of social imitation differences in ASD remains unclear. Some studies argue for a dysfunction in the “mirror” system (Oberman et al., 2005; Yang & Hofmann, 2016), with specific structural alterations in the angular gyrus (Mengotti et al., 2013). However, recent evidence points against this hypothesis (Dumas et al., 2014; Heyes & Catmur, 2022; Hobson & Bishop, 2016). Although the brain network involved in motor imitation might be under-activated in ASD, imitation abilities do not differ from the typically developed controls (Wadsworth et al., 2017), with even evidence of

imitation learning in low-functionning autistic children (Nadel et al., 2011). On the other hand, others argue that social imitation deficits are due to atypical lower-level social perception such as reduced sensitivity to biological motion in ASD (Mason et al., 2021). However, most findings in imitation research emerged from single participants' experiments examining brain responses to pictures or video clips passively shown. Moreover, several problems usually labeled as general impairment of imitation in ASD, such as a narrow motor repertoire, are likely due to restricted interests and altered attention to others' behaviors (Nadel, 2014b). Indeed, when put in a dyadic context, children with ASD show spontaneous imitation capacities and recognize when they are being imitated (Escalona et al., 2002).

The last two decades have witnessed a “second-person” shift in social neuroscience, with the development of interactive set-ups involving more than one participant at a time (Schilbach et al., 2013). In parallel, hyperscanning (Cui et al., 2012; Czeszumski et al., 2020; Montague, 2002) now allows the simultaneous recording of two or more individuals' brain activities, especially while participants interact with

<footnote><sup>a</sup>Precision Psychiatry and Social Physiology Laboratory (PPSP), CHU Sainte-Justine Research Center, Montréal, Canada; <sup>b</sup>Department of Psychiatry, University of Montréal, Québec, Canada; <sup>c</sup>Child and Adolescent Psychiatry Department, Assistance Publique - Hôpitaux de Paris, Robert Debré Hospital, Paris, France; <sup>d</sup>Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, IUF, Université Paris Cité, Paris, France; <sup>e</sup>CNRS, La Salpêtrière Hospital, Psychiatry Department, Sorbonne University, Paris, France; <sup>f</sup>Mila - Quebec Al Institute, University of Montréal, Montreal, Canada; <sup>g</sup>Human Brain and Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida, USA</footnote>
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each other (Babiloni & Astolfi, 2014). Thanks to this method, inter-cerebral correlation (IBC) and inter-brain synchrony (IBS) have been observed in various social contexts such as mutual gaze (Leong et al., 2017), shared attention (Hirsch et al., 2017), face-to-face deception (Zhang et al., 2017), social connectedness among interacting partners (Kinreich et al., 2017), empathy (Mengotti et al., 2013), verbal interactions (Hirsch et al., 2018), but also coordination (Zamm et al., 2018), interpersonal synchronization (Cui et al., 2012) and collaboration (Matusz et al., 2019). Those inter-brains communications highlight how we can no longer think of the brain as an isolated object (Bottema-Beutel et al., 2019). Consequently, to understand social imitation in ASD, we need to study individuals in bilateral and spontaneous interactions (Nadel & Pezé, 1993). Because many psychiatric disorders, if not all, have a social valence, hyperscanning could represent a valuable tool to identify and/or quantify social misattunement in mental disorders. The inability or difficulty in synchronizing neural activity with others could serve as an objective indicator of atypical interactive social cognition. This measure would be more ecological than assessments of perceptual social skills or Theory of Mind tests (Dumas, 2022). However, few studies have used hyperscanning to study social processes in the ASD population, and they mainly focused on parent-child interaction. These studies have indicated that children with more severe ASD symptoms demonstrate reduced behavioral and neural synchronization with their parents (Wang et al., 2020). However, this was not replicated in adolescents with ASD (Kruppa et al., 2021). In the current study, we asked adult dyads to perform a social imitation task with hand movements, while we recorded their movements and hyperscanning-EEG. Importantly, it has been suggested that the social challenges seen in individuals with ASD may partly stem from typical individuals' inability to accurately interpret the mental states behind the movements of those with ASD (Edey et al., 2016). Here, we contrast the results between ASD-TD and TD-TD pairs of participants, with the main hypothesis that ASD-TD dyads would show distinct behavioral and inter-brain dynamics patterns compared to TD-TD ones. As previously mentioned, the reduction in imitation skills in individuals with ASD is inconsistent and may rather reflect variations in motivation and/or attention. We hypothesized that there would be no behavioral difference in the basic ability to imitate others, but there could be differences in the strategies used by ASD-TD dyads. Consequently, we expected to see

different neural synchrony patterns between control and mixed dyads during tasks requiring imitation and cooperation.

# Methods

## Participants

Forty participants, ten high-functioning adults with autism spectrum disorder (7 males, 3 females; M age ± SD 33.9 ± 6.2 years; range 21–41 years) and thirty typical adults (14 males, 16 females; M age SD 28.7 ± 5.2 years; range 20–39 years), participated in the study, resulting in ten dyads in the Mixed Dyads group (ASD-TD) and ten in the Control Dyads group (TD-TD). Due to technical problems in the recordings of two dyads, the final sample consists of nine dyads in the Mixed Dyads group and nine in the Control Dyads group.

The exclusion criteria were associated with past or present neuropsychiatric and neurological disorders. All participants had normal or corrected-to-normal vision. They were right-handed (except for one individual in the ASD group). All were volunteers and gave their written informed consent according to the Declaration of Helsinki. The institutional ethical review board for Biomedical Research of the Hospital of Pitié-Salpétrière approved the experimental protocol (agreement #104–10).

The diagnosis of high-functioning ASD was established by psychiatrists and neuro-psychologists with the DSM-IV-R (American Psychiatric Association, 2013), the Autism Diagnostic Interview-Revised (Lord et al., 1994), the Autism Diagnostic Observation Schedule-Generic (Lord et al., 2000) module 4 (mean Social-communication score = 10.8, SD = 5.77), and expert clinical evaluation. No ASD participant underwent any drug and/or intervention program or participated in another experiment during the study.

Four ASD participants studied at the university with at least three years of training, and six practiced high-level professions (graphic teacher, archivist, librarian, psychotherapist, engineer, and computer programmer). All TD participants studied at the university with at least three years of training. Academic achievement is, therefore comparable between groups.

## Procedure

Each participant of a dyad sat in a separate room. They faced a 21-inch TV screen, with forearms resting on a table to prevent arms and neck movements (Figure 1a). In each experimental room, a digital video camera filmed participants' hand gestures. These films
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**Figure 1.** a. Experimental setting of the double video system and dual-EEG recording. b. Example of an experimental run, where an observation phase of a prerecorded library of 20 meaningless hand gestures (1'30), a Spontaneous Imitation phase (1'30) where the participants could either produce hand gestures of their own or imitate hand gestures from the other participant transmitted by the video camera, and a video Imitation phase (1'30) where the participants were asked to imitate a prerecorded video. Each run started with a 15s resting-state period with No-view No-movement (NVNM), repeated between each condition. The Spontaneous and video Imitation phases also had a period where participants were asked to produce meaningless hand gestures with no visual feedback from the other participant (i.e., No-view movement, NVM).

were transmitted live to the TV screen of the other room and to the experimenter's recording room. Thus, each participant could see the other's hand gestures in real-time and the experimenter could control if participants followed the requested instructions. The session start was signaled by a LED light manually controlled by the experimenter via a switch.

The experimental protocol was divided into three blocks separated by a 10 min pause (Figure 1b). Each block comprised three runs, where a run of 3 conditions: an *Observation* phase of a prerecorded library of 20 meaningless hand gestures (1'30), a *Spontaneous Imitation* phase (1'30) where the participants could either produce hand gestures of their own or imitate hand gestures from the other participant transmitted by the video camera, and a *Video Imitation* phase (1'30) where the participants were asked to imitate a prerecorded video. The prerecorded videos featured two experimenters adhering to the guidelines of the

*Spontaneous Imitation* condition. Both experimenters were wearing black T-shirts and they were positioned against a black background, ensuring that only their forearms and hands were distinctly visible. The movements executed during these interactions were designed to be simple and easily replicable. Each run started with a 15s resting-state period with No-View No-Movement (NVNM), repeated between each condition. The Spontaneous and Video Imitation phases also had a period where participants were asked to produce meaningless hand gestures with no visual feedback from the other participant (i.e., No-View Movement, NVM). At the end, a short block of calibration comprised periods of blinks, jaws contraction, and head movements of 30 seconds each. All conditions were presented in a fixed order for group comparison. For further information about the design, please look at previous papers (Dumas et al., 2010, 2014).
<figure><img src="image_17.png" /></figure>

## Hyperscanning-EEG acquisition

The neural activities of the two participants were simultaneously recorded with a dual-EEG recording system. It was composed of two Acticap helmets (Brain Products, Germany) with 64 active electrodes arranged according to the international 10/20 system. The helmets were aligned to nasion, inion, and left and right pre-auricular points. A three-dimensional Polhemus digitizer (Polhemus Inc., Colchester, VT, USA) was used to record the position of all electrodes and fiducial landmarks (nasion and pre-auricular points). The ground electrode was placed on the right shoulder of the participants and the reference was fixed on the nasion. The impedances were maintained below 10 kΩ. Data acquisition was performed using two 64-channel Brainamp MR amplifiers (Brain Products, Germany). Signals were analog filtered between 0.16 Hz and 250 Hz, amplified, and digitized at 500 Hz with a 16-bit vertical resolution in the range of ±3.2 mV. Note that both subjects were connected to the same amplifier that guaranteed millisecond-range synchrony between the two EEG recordings.

## Data analysis

### Behavioral data analyses

We analyzed the video recordings of hand movements during Spontaneous Imitation and Video Imitation to

define periods of time during which participants were really imitating, in contrast to non-imitative periods (based on the morphology and direction of the hand movement, see previous work for more details (Delaherche et al., 2014)). Through homemade Matlab codes, we extracted several variables. We first computed the total duration of imitative periods (Overall Imitation, see Figure 2a) to assess whether the task instructions were followed (i.e., how long each dyad correctly performed the task by imitating each other). Additionally, we analyzed the video recordings of hand movements during Spontaneous Imitation: we extracted measures of interactional synchrony, measured when the hands of the two participants started and ended a movement simultaneously, thus showing a coordinated rhythm (Synchrony, Figure 2b). We also distinguished Role Symmetry, to explore whether dyads had a balanced repartition of roles (Figure 2c), using the formula:

$$ \text{Symmetry Index} = \frac{(S1dr - S2dr)}{(S1dr + S2dr)} $$

where S1dr and S2dr represent the time spent as a model by subject 1 and subject 2 respectively, b = 0 indicating a perfect symmetry of the two roles (Dumas et al., 2010).

Finally, we broke down the Symmetry Index values between each member of the dyad to better highlight the driving and following roles between the participants

<figure><img src="image_18.png" /><figcaption>Figure 2. Behavioral results comparing control and mixed dyads for a. overall Imitation; b. Synchrony; c. role symmetry and d. imitation symmetry variables - * shows significant differences (p < 0.05).</figcaption></figure>
across time (*Imitation Symmetry*, reflecting the time spent imitating, see Figure 2d) within each dyad.

Due to the violation of two-sample t-test assumptions, *Overall Imitation, Synchrony, and Role Symmetry* variables were analyzed using non-parametric Mann-Whitney tests between Control Dyads (TD-TD) and Mixed Dyads (ASD-TD). *Imitation Symmetry* was analyzed using a mixed repeated-measure ANOVA with Group (Control, Mixed) as a between-subjects factor, and Subjects (S1/ASD, S2/TD) as a within-subjects factor.

## EEG-Hyperscanning data preprocessing

The EEG data were initially pre-processed in Matlab, where blink, muscles, and head movement artifacts were filtered by optimal projection (FOP) methodology (Boudet et al., 2007). Visual control allowed to reject the remaining artifacts (<0.1% of the data, no difference between the two groups), and noisy EEG channels that were marked as bad (for ASD subjects: average = 1.80 ± 2.82, min = 0, max = 10; for TD subjects: average = 1.06 ± 1.19, min = 0, max = 6). We then converted the EEG data from Matlab to Python and used the MNE-Python library (Gramfort et al., 2013) for further analyses and statistics on the EEG data. We also used the open-source library Hyperscanning Python Pipeline (HyPyP), based on MNE-Python, that our team implemented (https://github.com/GHFC/HyPyP, Ayrolles et al., 2021) to analyze inter-brain dynamics.

For each phase of a run (Resting-States, *Observation Phase, Spontaneous Imitation, or Video Imitation*), we converted the hyperscanning-EEG data to the MNE-Python Raw data format. Then, we low-pass filtered Raw data at 2 Hz with a finite impulse response filter and created 1s epochs around fixed events. The epochs were concatenated across the blocks for each condition (*Observation Phase, Spontaneous Imitation, Video Imitation*). Epochs were cleaned for each dyad using the preprocessing HyPyP functions adapted from Autoreject (Jas et al., 2017). The process involved the rejection of all epochs marked bad for at least one participant, the rejection or interpolation of partially bad sensors per participant, and the removal of the irreparable bad sensors across participants. Thus, only sensors and epochs that were deemed "good" for the two participants were preserved.

## Neurodynamical analyses

We defined 4 frequency-bands-of-interest, whose ranges have been previously associated with increased synchrony in previous studies: Theta [4–8 Hz;

(Lindenberger et al., 2009)], Alpha Low [8–10 Hz, (Kawasaki et al., 2013)], Alpha High [11–13 Hz, (Tognoli et al., 2007)], Beta [14–31 Hz, (Astolfi et al., 2010)]. For each frequency-bands-of-interest, we estimated the analytic signal by a multitaper and calculated the circular correlation coefficient between all inter-brain sensor pairs of a dyad. Circular Correlation (CCorr) measures the covariance of phase variance between two data streams and is more robust to coincidental synchrony (Burgess, 2013) compared to phase-locking value or phase-locking index. CCorr has seen increasing popularity and has been successfully implemented in studies investigating touch (Goldstein et al., 2018), learning (Davidesco et al., 2019), and language (Perez Repetto et al., 2017). We averaged circular correlation coefficient values across epochs and applied the Log ratio normalization mentioned above.

## EEG-behavior preprocessing

In order to match the EEG along *Imitation Symmetry* behavioral values (i.e., distinguishing leaders and followers within the dyad), for the *Spontaneous Imitation* condition, we cropped the filtered Raw data corresponding to the task to differentiate periods of time during which: a) participant 1 was driving and participant 2 was following the hand movement b) participant 2 was driving and participant 1 was following the hand movement c) they do not really imitate each other. We epoched the raw data for each period of time with different event identities and concatenated all of them across the blocks. Concatenated Epochs were cleaned for each dyad as described above. Then, we were able to split cleaned Epochs between the periods of time we mentioned thanks to event identity. Cleaned Epochs corresponding to periods of time 1/and 2/were used for further analyses, and realigned alongside the same axis so that all leader and follower epochs were ordered alongside the same dimension (i.e., instead of divided by participant 1 and participant 2). We show the results of these analyses in Figure 5.

## Statistics

We used a cluster-level statistical permutation test to contrast CCorr values between dyads and conditions. When comparing within dyads, the statistical test and threshold used in the cluster-level statistical permutation test were provided by dependent t-test (p-value = 0.025) and when comparing dyads, we used cluster-level statistics provided by one-way repeated measure ANOVA (p-value 0.025). The
<figure><img src="image_19.png" /></figure>

cluster-level statistical permutation test reduces family-wise error due to multiple comparisons by clustering neighboring quantities that exhibit the same effect. The neighborhood is corrected by space (adjacent sensors over the scalp) and frequencies (adjacent frequency bins). We assumed no sensors' connectivity between the brains of the two participants in each dyad – however, we took into account intra-participant neighboring (inter-brain sensors' pairs including common or neighboring sensors in one of the two participants). The sum of *t* values in a given cluster was used for cluster-statistic. Clusters' p-value was estimated through the distribution of cluster-statistics from randomizations of the dataset (Gramfort et al., 2013; Maris et al., 2007) 5000 permutations, p-value set at 0.025).

# Results

## Behavioral measures

### Overall imitation

The Mann-Whitney test revealed no significant difference between Control Dyads (M = 70.959, SD = 18.405) and Mixed Dyads (M = 78.111, SD = 12.386) for Overall Imitation along trials (W = 30.00, *p* = 0.387, rrb = 0.259, see Figure 2a).

### Synchrony

The Mann-Whitney test revealed no significant difference between Control Dyads (M = 57.173, SD = 15.895) and Mixed Dyads (M = 55.913, SD = 15.022) for Synchrony measure (W = 43.00, *p* = 0.863, rrb = 0.062, see Figure 2b).

### Role symmetry

The Mann-Whitney test revealed no significant difference between Control Dyads (M = 0.102, SD = 0.644) and Mixed Dyads (M = 0.419, SD = 0.663) for the Symmetry Index (W = 27.00, *p* = 0.258, rrb = 0.333, see Figure 2c).

### Imitation symmetry

Distinguishing between TD in the Controls Dyads and TD and ASD members within Mixed Dyads, we see a significant difference in time spent imitating (F(3, 32) = 3.387, *p* = 0.030, ηₚ² = 0.241). Post-hoc tests only revealed a significant difference between ASD and TD (*p*bonferonni = 0.030, see Figure 2d) participants within Mixed Dyads; no other comparison was significant (*ps* > 0.118).

## EEG hyperscanning results – within dyads comparisons

### Spontaneous Imitation vs Observation Phase within Control dyads

The cluster-based analysis over the 5 frequency bands of interest (Theta, Low-Alpha, High-Alpha, Beta, and Gamma) revealed a significant positive cluster in the Low-Alpha band, highlighting increased inter-brain synchrony during *Spontaneous Imitation* compared to the *Observation Phase* (*p* = 0.012, see Figure 3a) and a significant negative cluster in the High-Alpha band (*p* = 0.015, see Figure 3b) showing reduced inter-brain synchrony during *Spontaneous Imitation* compared to the *Observation Phase* within Control dyads.

No other cluster-based analysis (i.e., Video Imitation vs Spontaneous Imitation within Control dyads, Spontaneous Imitation vs Observation Phase within Mixed dyads, and Video Imitation vs Spontaneous Imitation within Mixed dyads) over the 5 frequency bands of interest (Theta, Low-Alpha, High-Alpha, Beta, and Gamma) revealed any difference in inter-brain synchrony.

## EEG hyperscanning results - between dyads comparisons

### Control dyads vs mixed dyads in the observation phase

No cluster-based analysis over the 5 frequency bands of interest (Theta, Low-Alpha, High-Alpha, Beta, and Gamma) revealed any difference in inter-brain synchrony between Control and Mixed Dyads in the *Observation Phase*.

### Control dyads vs mixed dyads in the video imitation.

The cluster-based analysis over the 5 frequency bands of interest (Theta, Low-Alpha, High-Alpha, Beta, and Gamma) revealed a significant positive cluster in the Low-Alpha band, highlighting increased inter-brain synchrony for Control Dyads compared to Mixed Dyads in the *Video Imitation* condition (*p* = 0.035, see Figure 4a)

### Control dyads vs mixed dyads in the Spontaneous Imitation

The cluster-based analysis over the 5 frequency bands of interest (Theta, Low-Alpha, High-Alpha, Beta, and Gamma) revealed a significant positive cluster in the Low-Alpha band, highlighting increased inter-brain synchrony for Control Dyads compared to Mixed Dyads in the *Spontaneous Imitation* condition (*p* = 0.034, see Figure 4b)
<figure><img src="image_20.png" /></figure>

## Spontaneous Imitation vs Observation Phase in Control Dyads

<figure><img src="image_21.png" /><figcaption>Figure 3. Significant inter-brain synchrony differences between *spontaneous imitation* and *observation phase* in control dyads revealed by cluster-based analyses in the a. low-alpha and b. high-alpha frequency bands.</figcaption></figure>

### EEG hyperscanning results - between dyads comparisons with leader-follower distinction

The cluster-based analysis over the 5 frequency bands of interest (Theta, Low-Alpha, High-Alpha, Beta, and Gamma) revealed two significant positive clusters in the Low-Alpha and the Beta bands, high-lighting increased inter-brain synchrony for Control Dyads compared to Mixed Dyads (p = 0.003 and p = 0.026, see Figure 5a,c), as well as a cluster in the High-Alpha band showing larger IBS for Mixed Dyads compared to Control Dyads (p = 0.019, see Figure 5b).

### Discussion

In this paper, we conducted dyadic social imitation experiments while recording movement kinematics and EEG-hyperscanning. Our aim was to compare the results between dyads made of two TD participants, and mixed dyads comprising ASD and TD people, with the primary hypothesis that the ASD-TD dyads would exhibit different patterns in both behavioral and inter-brain dynamics compared to TD-TD dyads.

### Behavioral differences in ecological imitating scenarios between mixed and control dyads

First, our research sheds new light on the behavior of individuals with ASD in dyadic social scenarios. Our findings indicate that, while there were no significant differences in overall dyadic performance between Mixed and Control dyads (i.e., Overall Imitation and Synchrony, see Figure 2a, 2b), there were notable differences in within-dyad dynamics. Specifically, individuals with ASD exhibited a preference for not taking the lead role in imitating scenarios. First, although not significant, we observe an imbalance in Role Symmetry (Figure 2C) that is further confirmed by breaking down the role of each individual within the dyad (Figure 2d), where we see that in Mixed dyads, participants with ASD were significantly more likely to be followers than leaders during Spontaneous imitation phases. This suggests that individuals with ASD may have intact lower-level skills for interpersonal imitation (Wadsworth et al., 2017), but they may be less inclined to assume leadership roles in social interactions involving TD participants. Furthermore, our results support the view that social challenges
<figure><img src="image_22.png" /></figure>

Control vs Mixed Dyads in Video Imitation

a

<figure><img src="image_23.png" /></figure>

Control vs Mixed Dyads in Spontaneous Imitation

b

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

**Figure 4.** Significant inter-brain synchrony differences in the low-alpha band between control and mixed dyads during a. *video imitation* and b. *spontaneous imitation* conditions.

experienced by individuals with ASD may partly stem
from the difficulty that typical individuals have in
accurately interpreting the mental states behind the
behavior of those with ASD. This aligns with the sug-
gestion by Edey et al. (2016) that typical individuals'
misinterpretations contribute to the perceived social
barriers in ASD, emphasizing the need for a more
nuanced understanding of these social dynamics.
This also resonates with the so-called "double-
empathy" problem (Milton, 2012) stating that indivi-
duals with ASD have difficulty fitting into society not
just because they misunderstand others but also
because they are misunderstood by others (Mitchell
et al., 2021). Overall, our study underscores the impor-
tance of studying interpersonal phenomena in
dynamic and ecological settings. Indeed, our findings
suggest an atypical pattern at the dynamic interactive
level. Previous research has often focused on pseudo-
social paradigms, and these scenarios may not fully
capture the nuances of social interactions. By consid-
ering the ecological context in which social interac-
tions occur, we can better understand the behavior of
individuals with ASD (Dumas, 2022; Schilbach et al.,
2013; Nadel, 2014a).

Hyperscanning reveals inter-brain synchrony
differences within control and mixed dyads

Hyperscanning provides a novel perspective on ASD, by
capturing the neural activity during actual social inter-
actions rather than in isolated contexts. Our results here
show that dyads involving ASD individuals exhibit dis-
tinct patterns of brain activity compared to neurotypical
dyads. First, when comparing *Spontaneous Imitation*
data with the *Observation Phase*, we show a significant
increase of IBS in the low-alpha band and an IBS
decrease in the high-alpha band in Control dyads
(Figure 3a,3b). Although a lack of an effect does not
constitute sufficient proof, we note that no such pattern
has been detected in Mixed dyads. Regarding the inter-
pretation of the results, we observe a dissociation
between low- and high-alpha bands (Dumas et al.,
2014; Naeem et al., 2012; Tognoli et al., 2007). We show
an increase of inter-brain synchrony during a more
demanding social task in the lower band, in phase with
previous results (Dumas et al., 2010; Konvalinka et al.,
2014), and more IBS during the simultaneous passive
viewing of videos in the high-alpha band. While an
increase in high-alpha power at the intra-brain level is
<figure><img src="image_27.png" /></figure>

Control vs Mixed Dyads in Spontaneous Imitation
with leader-follower distinction

<figure><img src="image_28.png" /><figcaption>**Figure 5.** Significant inter-brain synchrony differences in the a. low-Alpha; b. high-alpha and c. beta bands between control and mixed dyads the *Spontaneous Imitation* with leader-follower distinction.</figcaption></figure>

in line with increased visual attention accounts (Lobier et al., 2018; Peylo et al., 2021), the observation of increased IBS might be the byproduct of simultaneously processing similar stimuli (Hasson et al., 2004; Haxby et al., 2020). Furthermore, recent findings have demonstrated that non-verbal social and affective gestures influence both intra- and inter-brain dynamics, including activity in the alpha band in typical adults (Balconi & Fronda, 2020). The differences observed in our study suggest that the presence of individuals with ASD in Mixed dyads, who exhibit distinct patterns of non-verbal communication, may disrupt the typical shared representations found in Control dyads. Additionally, it has been suggested that the formation of inter-brain dynamics relies on shared representations (Koike et al.,

2016) that are supported by neuroanatomical similarity (Dumas et al., 2012). Dual fMRI studies have also shown synchronization in the right inferior frontal gyrus during joint attention (Saito et al., 2010), and our results, especially the IBS topography, align with these findings.

Low-alpha IBS differentiates control and mixed dyads

The comparisons between Control and Mixed dyads in both *Video* and *Spontaneous* imitations show both larger inter-brain synchrony in the low-alpha band for TD-TD vs ASD-TD dyads (Figure 4a,4b). These results are further confirmed in the comparisons with leader-follower distinction (Figure 5a), with larger low-alpha IBS for Controls compared to Mixed dyads. The leader-follower analysis also revealed patterns that were not detected in the sole
<figure><img src="image_29.png" /></figure>

condition comparison: higher IBS in the beta band for Controls (Figure 5c), in line with previous results (Dumas et al., 2010), but crucially higher IBS in the high-alpha band for Mixed dyads (Figure 5b). These distinct patterns of IBS, rather than simply reflecting a "lower" IBS in ASD-TD dyads, suggest that different interactive experiences – such as the variations in interpersonal dynamics indicated by our behavioral results – lead to unique inter-brain neurophysiological patterns. In line with previous statements, the increased IBS in the high-alpha band for Mixed dyads may indicate atypical strategies and a bilateral struggle to adapt to each other, thereby recruiting more cognitive resources and necessitating heightened visual attention (Lobier et al., 2018; Peylo et al., 2021).

## Limitations

While our study provides novel insights, there are some limitations to our findings. Firstly, the sample size of our study was relatively small and not fully gendered-balanced, consisting solely of high-functioning ASD in the Mixed Dyads. Therefore, caution should be exercised when generalizing our results to a larger population. Secondly, the sample of individuals with ASD in our study inherently leads to heterogeneity in terms of their clinical presentation, which could have influenced our results. Therefore, the presented findings should be viewed in the context of a proof-of-concept perspective. Finally, while our study utilized hyperscanning to capture inter-brain dynamics during social interaction, this method has some inherent limitations such as the difficulty in establishing the causality or directionality of the observed neural patterns (Moreau & Dumas, 2021).

## Conclusion

In conclusion, our study provides new insights into the behavioral and neural differences between people with and without ASD in social imitation tasks. Our findings highlight the importance of considering the dynamic and ecological nature of social interactions and using hyperscanning methods to capture inter-brain dynamics during actual social interactions. We also show that individuals with ASD may have intact abilities for inter-personal imitation, but still rather not take the lead in social situations involving TD participants. These differences are also highlighted at the inter-brain level, with consistent differences in inter-brain synchrony between Mixed and Control dyads in the low-alpha band. These differences suggest distinct interactive experiences and neural strategies, with Control dyads showing more efficient neural coupling and Mixed dyads potentially

requiring heightened cognitive resources and visual attention. The increased high-alpha IBS in Mixed dyads emphasizes the need for a nuanced understanding of these interactions. Overall, our study contributes to the growing body of literature aimed at better understanding the social and cognitive processes as well as the neural mechanisms underlying social interaction in individuals with and without ASD and provides potential markers for interpersonal approaches to psychiatric conditions with specific social misattunement (Bolis et al., 2022; Dumas, 2022). Our study also provides insights into our understanding of ASD beyond isolated behavioral observations, giving a richer, more dynamic picture of social cognition through the integration of behavioral and neurophysiological data. Future research should continue to explore these dynamics, potentially informing targeted interventions that leverage these compensatory strategies to improve social outcomes for individuals with ASD. Future research will also aim to study ASD-ASD interactions and their neural markers. By focusing on interactions between ASD people, we can gain a deeper understanding of their social dynamics and communication strategies that may emerge within this diverse population. A previous study analyzed both individual movements (head, hand, and leg) and inter-personal interactions (mutual gaze and synchrony of head, hand, and leg movements) from videos to train classifiers on three types of dyads (TD-TD, ASD-TD, TD-TD). The results revealed that their machine learning algorithms could detect ASD with 70% accuracy and recognize dyad type with 72% accuracy (Celiktutan et al., in press). This new perspective is crucial for developing more inclusive and effective interventions and support systems that acknowledge and leverage the inherent capabilities of individuals with ASD in their social interactions.

## Acknowledgements

We thank Robert Soussignan and Emeline Mercier for their help in the manual indexing, Laurent Hugueville for his assistance in the setting of the hyperscanning system, and Florence Bouchet for her generous help in the EEG preparation.

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Funding

This study was partly enabled by support provided by the Canadian Institute for Health Research (CIHR 192031; SCALE), the Natural Sciences and Engineering Research Council of
<figure><img src="image_30.png" /></figure>

Canada (NSERC; DGECR-2023-00089), as well as Calcul Québec (www.calculquebec.ca) and the Digital Research Alliance of Canada (www.alliancecan.ca). G.D. was supported by the Orange Foundation for Autism Spectrum Disorders, the Institute for Data Valorization, Montreal (IVADO; CF00137433), the Fonds de recherche du Québec (FRQ; 285289), the Brain Canada Foundation (2022 Future Leaders in Canadian Brain Research program), and the Azrieli Global Scholars Fellowship from the Canadian Institute for Advanced Research (CIFAR) in the Brain, Mind, & Consciousness program. Q.M. was supported by the Unifying Neuroscience and Artificial Intelligence - Québec (UNIQUE) postdoctoral excellence fellowship. The author has no conflicts of interest to disclose. Author Contribution: GD and JN designed the study. GD recorded the data. QM, AA, FB and GD analyzed the data. QM and GD wrote the manuscript. QM prepared the Figures. All authors edited and reviewed the manuscript

## ORCID

Quentin Moreau http://orcid.org/0000-0003-2824-3373

## Data availability statement

The raw data cannot be shared for consent reasons, but the codes and non-identifying aggregate data will be shared on a dedicated GitHub depo.

## References

* American Psychiatric Association. (2013). *Diagnostic and statistical manual of mental disorders (DSM-5®)*. American Psychiatric Pub.

* Astolfi, L., Toppi, J., De Vico Fallani, F., Vecchiato, G., Cincotti, F., Wilke, C. T., Yuan, H., Mattia, D., Salinari, S., He, B., & Babiloni, F. (2010). Imaging the social brain: Multi-subjects EEG recordings during the chicken's game. *Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires* (pp. 1734–1737). IEEE.

* Ayrolles, A., Brun, F., Chen, P., Djalovski, A., Beauxis, Y., Delorme, R., Bourgeron, T., Dikker, S., & Dumas, G. (2021). HyPyP: A hyperscanning python pipeline for inter-brain connectivity analysis. *Social Cognitive and Affective Neuroscience, 16*(1–2), 72–83. https://doi.org/10.1093/scan/nsaa141

* Babiloni, F., & Astolfi, L. (2014). Social neuroscience and hyperscanning techniques: Past, present and future. *Neuroscience & Biobehavioral Reviews, 44*, 76–93. https://doi.org/10.1016/j.neubiorev.2012.07.006

* Balconi, M., & Fronda, G. (2020). The use of hyperscanning to investigate the role of social, affective, and informative gestures in non-verbal communication. Electrophysiological (EEG) and inter-brain connectivity evidence. *Brain Sciences, 10*(1), 29. https://doi.org/10.3390/brainsci10010029

* Bolis, D., Dumas, G., & Schilbach, L. (2022). Interpersonal attention in social interactions: From collective psychophysiology to inter-personalized psychiatry and beyond. *Philosophical Transactions of the Royal Society B: Biological Sciences, 378*(1870), 20210365. Retrieved April 4, 2023.

https://royalsocietypublishing.org/doi/full/10.1098/rstb. 2021.0365

* Bottema-Beutel, K., Kim, S. Y., & Crowley, S. (2019). A systematic review and meta-regression analysis of social functioning correlates in autism and typical development. *Autism Research, 12*(2), 152–175. https://doi.org/10.1002/aur.2055

* Boudet, S., Peyrodie, L., Gallois, P., & Vasseur, C. (2007). Filtering by optimal projection and application to automatic artifact removal from EEG. *Signal Processing, 87*(8), 1978–1992. https://doi.org/10.1016/j sigpro.2007.01.026

* Burgess, A. P. (2013). On the interpretation of synchronization in EEG hyperscanning studies: A cautionary note. *Frontiers in Human Neuroscience, 7*, 881. https://doi.org/10.3389/fnhum. 2013.00881

* Celiktutan, O., Wu, W., Vogeley, K., & Georgescu, A. (in press). A computational approach for analysing autistic behaviour during dyadic interactions: A computational approach for analysing autistic behaviour. *Proceedings of the 12th International Workshop on Human Behavior Understanding IEEE*.

* Cui, X., Bryant, D. M., & Reiss, A. L. (2012). NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. *Neuroimage: Reports, 59*(3), 2430–2437. https://doi.org/10.1016/j.neuroimage. 2011.09.003

* Czeszumski, A., Eustergerling, S., Lang, A., Menrath, D., Gerstenberger, M., Schuberth, S., Schreiber, F., Rendon, Z. Z., & König, P. (2020). Hyperscanning: A valid method to study neural inter-brain underpinnings of social interaction. *Frontiers in Human Neuroscience, 14*, 39. https:// www.frontiersin.org/article/10.3389/fnhum.2020.00039/full

* Davidesco, I., Laurent, E., Valk, H., West, T., Dikker, S., Milne, C., & Poeppel, D. (2019). *Brain-to-brain synchrony predicts long-term memory retention more accurately than individual brain measures*. bioRxiv:644047. Available at: Retrieved October 4, 2019, from. https://www.biorxiv.org/content/10. 1101/644047v1

* Delaherche, E., Dumas, G., Nadel, J., & Chetouani, M. (2014). Automatic measure of imitation during social interaction: A behavioral and hyperscanning-EEG benchmark. *Pattern Recognition Letters, 66*, 118–126. https://doi.org/10.1016/j. patrec.2014.09.002

* Dumas, G. (2022). From inter-brain connectivity to inter-personal psychiatry. *World Psychiatry, 21*(2), 214–215. Retrieved May 26, 2022. https://doi.org/10.1002/wps.20987

* Dumas, G., Chavez, M., Nadel, J., Martinerie, J., & Boccaletti, S. (2012) Anatomical connectivity influences both intra- and inter-brain synchronizations. *PLOS ONE, 7*(5), e36414. https:// doi.org/10.1371/journal.pone.0036414

* Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., Garnero, L., & Lauwereyns, J. (2010). Inter-brain synchronization during social interaction. *PLOS ONE, 5*(8), e12166. https://doi.org/ 10.1371/journal.pone.0012166

* Dumas, G., Soussignan, R., Hugueville, L., Martinerie, J., & Nadel, J. (2014). Revisiting mu suppression in autism spectrum disorder. *Brain Research, 1585*, 108–119. https://doi. org/10.1016/j.brainres.2014.08.035

* Edey, R., Cook, J., Brewer, R., Johnson, M. H., Bird, G., & Press, C. (2016, October). Interaction takes two: Typical adults exhibit mind-blindness towards those with autism spectrum disorder. *Journal of Abnormal Psychology, 125*(7), 879–885.
<figure><img src="image_31.png" /></figure>

https://doi.org/10.1037/abn0000199. Epub 2016 Sep 1.
PMID: 27583766.

Escalona, A., Field, T., Nadel, J., & Lundy, B. (2002). Brief report: Imitation effects on children with autism. *Journal of Autism & Developmental Disorders, 32*(2), 141–144. https://doi.org/10.1023/A:1014896707002

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. https://doi.org/10.1073/pnas.1703643115

Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., & Hämäläinen, M. (2013). MEG and EEG data analysis with MNE-Python. *Frontiers in Neuroscience, 7*, 267. https://doi.org/10.3389/fnins.2013.00267

Grzadzinski, R., Huerta, M., & Lord, C. (2013). DSM-5 and autism spectrum disorders (ASDs): An opportunity for identifying ASD subtypes. *Molecular Autism, 4*(1), 12 Retrieved May 16, 2018, from. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3671160/

Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. *Science, 303*(5664), 1634–1640. https://doi.org/10.1126/science.1089506

Haxby, J. V., Guntupalli, J. S., Nastase, S. A., & Feilong, M. (2020). Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies Baker CI, de Lange FP. *eLife, 9*, e56601. https://doi.org/10.7554/eLife.56601

Heyes, C. (2011). Automatic imitation. *Psychological Bulletin, 137*(3), 463–483. https://doi.org/10.1037/a0022288

Heyes, C., & Catmur, C. (2022). What happened to mirror neurons? *Perspectives on Psychological Science, 17*(1), 153–168. https://doi.org/10.1177/1745691621990638

Hirsch, J., Adam Noah, J., Zhang, X., Dravida, S., & Ono, Y. (2018). A cross-brain neural mechanism for human-to-human verbal communication. *Social Cognitive and Affective Neuroscience, 13*(9), 907–920. https://academic.oup.com/scan/article/13/9/907/5077585

Hirsch, J., Zhang, X., Noah, J. A., & Ono, Y. (2017). Frontal temporal and parietal systems synchronize within and across brains during live eye-to-eye contact. *Neuroimage: Reports, 157*, 314–330 Retrieved June 19, 2017, from. http://linkinghub.elsevier.com/retrieve/pii/S1053811917304871

Hobson, H. M., & Bishop, D. V. M. (2016). Mu suppression – a good measure of the human mirror neuron system? *Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 82*, 290–310 http://www.sciencedirect.com/science/article/pii/S0010945216300570

Ingersoll, B. (2008). The social role of imitation in autism: Implications for the treatment of imitation deficits. *Infants & Young Children, 21*(2), 107–119 Retrieved February 22, 2016. https://doi.org/10.1097/01.IYC.0000314482.24087.14Ingersoll, B.

Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F., & Gramfort, A. (2017). Autoreject: Automated artifact rejection for MEG and EEG data. *Neuroimage: Reports, 159*, 417–429 Retrieved May 26, 2022, from. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7243972/

Kawasaki, M., Yamada, Y., Ushiku, Y., Miyauchi, E., & Yamaguchi, Y. (2013). Inter-brain synchronization during coordination of speech rhythm in human-to-human social

interaction. *Scientific Reports*, 3(1), 1692. https://doi.org/10.1038/srep01692

Kinreich, S., Djalovski, A., Kraus, L., Louzoun, Y., & Feldman, R. (2017). Brain-to-brain synchrony during naturalistic social interactions. *Scientific Reports*, 7(1), 17060 Retrieved December 8, 2017. https://doi.org/10.1038/s41598-017-17339-5

Koike, T., Tanabe, H. C., Okazaki, S., Nakagawa, E., Sasaki, A. T., Shimada, K., Sugawara, S. K., Takahashi, H. K., Yoshihara, K., Bosch-Bayard, J., & Sadato, N. (2016) Neural substrates of shared attention as social memory: A hyperscanning functional magnetic resonance imaging study. *Neuroimage: Reports*, 125, 401–412. Jan 15. https://doi.org/10.1016/j.neuimage.2015.09.076. Advance online publication.

Konvalinka, I., Bauer, M., Stahlhut, C., Hansen, L. K., Roepstorff, A., & Frith, C. D. (2014). Frontal alpha oscillations distinguish leaders from followers: Multivariate decoding of mutually interacting brains. *Neuroimage: Reports*, 94, 79–88. https://doi.org/10.1016/j.neuroimage.2014.03.003

Kruppa, J. A., Reindl, V., Gerloff, C., Oberwelland Weiss, E., Prinz, J., Herpertz-Dahlmann, B., Konrad, K., & Schulte-Rüther, M. (2021). Brain and motor synchrony in children and adolescents with ASD—a fNIRS hyperscanning study. *Social Cognitive and Affective Neuroscience, 16*(1–2), 103–116. https://doi.org/10.1093/scan/nsaa092

Leong, V., Byrne, E., Clackson, K., Georgieva, S., Lam, S., & Wass, S. (2017). Speaker gaze increases information coupling between infant and adult brains. *Proceedings of the National Academy of Sciences*, 114(50), 13290–13295. https://doi.org/10.1073/pnas.1702493114

Lindenberger, U., Li, S. C., Gruber, W., & Müller, V. (2009). Brains swinging in concert: Cortical phase synchronization while playing guitar. *BMC Neuroscience*, 10(1). https://doi.org/10.1186/1471-2202-10-22

Lobier, M., Palva, J. M., & Palva, S. (2018). High-alpha band synchronization across frontal, parietal and visual cortex mediates behavioral and neuronal effects of visuospatial attention. *Neuroimage: Reports*, 165, 222–237 Retrieved November 13, 2017, from. http://www.sciencedirect.com/science/article/pii/S1053811917308716

Lord, C., Brugha, T. S., Charman, T., Cusack, J., Dumas, G., Frazier, T., Jones, E. J. H., Jones, R. M., Pickles, A., State, M. W., Taylor, J. L., & Veenstra-VanderWeele, J. (2020). Autism spectrum disorder. *Nature Reviews Disease Primers*, 6(1), 1–23 Retrieved November 3, 2021, from. https://www.nature.com/articles/s41572-019-0138-4

Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., Pickles, A., & Rutter, M. (2000). The autism diagnostic observation schedule—generic: A standard measure of social and communication deficits associated with the spectrum of autism. *Journal of Autism & Developmental Disorders*, 30, 205–223.

Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. *Journal of Autism & Developmental Disorders*, 24(5), 659–685.

Maris, E., Schoffelen, J.-M., & Fries, P. (2007). Nonparametric statistical testing of coherence differences. *Journal of Neuroscience Methods*, 163(1), 161–175. https://doi.org/10.1016/j.jneumeth.2007.02.011
<figure><img src="image_32.png" /></figure>

Mason, L., Shic, F., Falck-Ytter, T., Chakrabarti, B., Charman, T., Loth, E., Tillmann, J., Banaschewski, T., Baron-Cohen, S., Bölte, S., Buitelaar, J., Durston, S., Oranje, B., Persico, A. M., Beckmann, C., Bougeron, T., Dell'acqua, F., Ecker, C. ... Wooldridge, C. (2021). Preference for biological motion is reduced in ASD: Implications for clinical trials and the search for biomarkers. *Molecular Autism*, 12(1), 74. https://doi.org/10.1186/s13229-021-00476-0

Matusz, P. J., Dikker, S., Huth, A. G., & Perrodin, C. (2019). *Are we ready for real-world neuroscience?*. MIT Press.

Mengotti, P., Corradi-Dell'acqua, C., Negri, G. A. L., Ukmar, M., Pesavento, V., & Rumiati, R. I. (2013). Selective imitation impairments differentially interact with language processing. *Brain A Journal of Neurology*, 136(8), 2602–2618. https://doi.org/10.1093/brain/awt194

Milton, D. E. (2012). On the ontological status of autism: The ‘double empathy problem’. *Disability & Society*, 27(6), 883–887. https://doi.org/10.1080/09687599.2012.710008

Mitchell, P., Sheppard, E., & Cassidy, S. (2021, March). Autism and the double empathy problem: Implications for development and mental health. *British Journal of Developmental Psychology*, 39(1), 1–8. https://doi.org/10.1111/bjdp.12350

Montague, P. R. (2002). Hyperscanning: Simultaneous fMRI during linked social interactions. *Neuroimage: Reports*, 16 (4), 1159–1164. https://doi.org/10.1006/nimg.2002.1150

Moreau, Q., & Dumas, G. (2021). Beyond correlation versus causation: Multi-brain neuroscience needs explanation. *Trends in Cognitive Sciences*, 25, 542–543. https://doi.org/10.1016/j.tics.2021.02.011

Nadel, J. (2014a). *How imitation boots development in infancy and autism spectrum disorder*. Oxford University Press.

Nadel, J. (2014b). Perception-action coupling and imitation in autism spectrum disorder. *Developmental Medicine & Child Neurology*, 57(s2), 55–58. https://doi.org/10.1111/dmcn.12689

Nadel, J., Aouka, N., Coulon, N., Gras-Vincendon, A., Canet, P., Fagard, J., & Bursztejn, C. (2011). Yes they can! An approach to observational learning in low-functioning children with autism. *Autism*, 15(4), 421–435. https://doi.org/10.1177/1362361310386508

Nadel, J., & Pezé, A. (2017). What makes immediate imitation communicative in toddlers and autistic children?. In *New perspectives in early communicative development* (pp. 139–156). Routledge.

Naeem, M., Prasad, G., Watson, D. R., & Kelso, J. A. S. (2012). Functional dissociation of brain rhythms in social coordination. *Clinical Neurophysiology*, 123(9), 1789–1797. https://doi.org/10.1016/j.clinph.2012.02.065

Oberman, L. M., Hubbard, E. M., Jp, M., Altschuler, E. L., Ramachandran, V. S., & Pineda, J. A. (2005). EEG evidence for mirror neuron dysfunction in autism spectrum disorders. *Cognitive Brain Research*, 24(2), 190–198. Retrieved July 28, 2015, from. http://linkinghub.elsevier.com/retrieve/pii/S0926641005000224

Over, H. (2016). The origins of belonging: Social motivation in infants and young children. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 371(1686), 20150072. Retrieved November 3, 2021, from. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4685518/

Over, H., & Carpenter, M. (2013). The social side of imitation. *Child Development Perspectives*, 7(1), 6–11. https://doi.org/10.1111/cdep.12006

Perez Repetto, L., Jasmin, E., Fombonne, E., Gisel, E., & Couture, M. (2017). *Longitudinal study of sensory features in children with autism spectrum disorder. Autism res treat* Retrieved February 19, 2019, from. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5592014/

Peylo, C., Hilla, Y., & Sauseng, P. (2021). Cause or consequence? Alpha oscillations in visuospatial attention. *Trends in Neurosciences*, 44(9), 705–713. https://doi.org/10.1016/j.tins.2021.05.004

Ray, E., & Heyes, C. (2011). Imitation in infancy: The wealth of the stimulus. *Developmental Science*, 14(1), 92–105. https://doi.org/10.1111/j.1467-7687.2010.00961.x

Saito, D. N., Tanabe, H. C., Izuma, K., Hayashi, M. J., Morito, Y., Komeda, H., Uchiyama, H., Kosaka, H., Okazawa, H., Fujibayashi, Y., & Sadato, N. (2010). “Stay tuned”: Individual neural synchronization during mutual gaze and joint attention. *Frontiers in Integrative Neuroscience*, 4, 127. https://doi.org/10.3389/fnint.2010.00127. PMID: 21119770; PMCID: PMC2990457.

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. Retrieved July 24, 2015. https://doi.org/10.1017/S0140525X12000660

Tognoli, E., Lagarde, J., Gc, D., & Kelso, J. S. (2007). The phi complex as a neuromarker of human social coordination. *Proceedings of the National Academy of Sciences (Vol. 104. pp. 8190–8195 Retrieved September 24, 2015, from. http://www.pnas.org/content/104/19/8190.short*

Wadsworth, H. M., Maximo, J. O., Lemelman, A. R., Clayton, K., Sivaraman, S., Deshpande, H. D., Ver Hoef, L., & Kana, R. K. (2017). *The action imitation network and motor imitation in children and adolescents with autism*. Neuroscience Available at: Retrieved December 17, 2016, from. http://www.sciencedirect.com/science/article/pii/S0306452216306893

Wang, Q., Han, Z., Hu, X., Feng, S., Wang, H., Liu, T., & Yi, L. (2020). Autism symptoms modulate interpersonal neural synchronization in children with autism spectrum disorder in cooperative interactions. *Brain Topography*, 33(1), 112–122. https://doi.org/10.1007/s10548-019-00731-x

Yang, J., & Hofmann, J. (2016). Action observation and imitation in autism spectrum disorders: An ALE meta-analysis of fMRI studies. *Brain Imaging and Behavior*, 10(4), 960–969. https://doi.org/10.1007/s11682-015-9456-7

Zamm, A., Debener, S., Bauer, A.-K., Bleichner, M. G., Demos, A. P., & Palmer, C. (2018). Amplitude envelope correlations measure synchronous cortical oscillations in performing musicians: Amplitude envelopes measure inter-brain synchrony. *Annals of the New York Academy of Sciences*, 1423(1), 251–263. https://doi.org/10.1111/nyas.13738

Zhang, M., Liu, T., Pelowski, M., & Yu, D. (2017). Gender difference in spontaneous deception: A hyperscanning study using functional near-infrared spectroscopy. *Scientific Reports*, 7(1), 1–13. Retrieved April 1, 2020, from. https://www.nature.com/articles/s41598-017-06764-1