Participants

One-hundred-and-four healthy young adults participated in this study. These included 52 male-female pairs in two groups; the “couples” group included cohabitating romantic partners in a long-term relationship and the “strangers” group included unfamiliar man and women. Three dyads were excluded from further analyses since one partner’s inter-electrodes correlation did not reach the threshold of R > 0.5 (see EEG preprocessing). All participants were healthy, with no prior physical or mental illness, completed at least 12 years of education, and had no current psychopathology. The final “couples” group included 24 heterosexual couples (48 participants) who were romantically involved for at least one year. Couples were together on average 2.7 ± 1.7 years (age 25 ± 4.1 years, 13.5 ± 1.9 years of education). The “strangers” group included 25 males and 25 females who did not meet prior to the experiment and the study was their first social encounter (age 24 ± 3.6 years, 13.4 ± 1.7 years of education). To ensure unfamiliarity, strangers were separated by a screen during EEG preparation so that their first encounter occurred during the experiment. Exclusion criteria included medication intake, physical or psychiatric condition, and self-reported health problems. The study was approved by the ethical committee of Bar-Ilan University and all participants signed an informed consent. All procedures were explained to the participants before the study and were performed in accordance with ethical guidelines.

Procedure

Participants were recruited via the internet and by ads posted in a university campus and surrounding area. Prior to arrival at the lab, participants completed self-report measures related to demographic and health information (e.g., weight, height, smoking, medication, and use of conraceptives). Experiments were conducted in a laboratory during the mid-afternoon hours (4:00–7:00 PM). A 32-electrode cap was placed on each participant’s scalp and the forearm of their non-dominant hand was attached to the chair handle to restrict arm and neck movements. The first paradigm included a 3-minute rest with eyes open while the screen was still standing between the participants. Next, participants sat next to each other in a 3-feet distance between their faces while facing each other in a 45-degree angle. This position enabled partners to look at each other during the interaction and for their facial and bodily signals to be captured by a camera on an adjacent wall. Participants were asked to sit comfortably and engage in a positive interaction (“fun day” paradigm) for five minutes. The paradigm involves planning a fun day to spend together and has been previously validated at our lab28,61. Interactions were videotaped for later offline coding. After the interaction, participants completed four questions related to their feeling about the interaction (see below). Participants received 50 USD for participation.

Self-reported Measures

Participants completed questionnaires using the online platform www.qualtrics.com. This included demographics questionnaires and the Revised Experiences in Close Relationships (ECR-R), a self-report measure of romantic attachment. The ECR-R assesses attachment along two orthogonal dimensions; anxiety about the relationship and avoidance of intimacy, and a high score on each reflects insecure attachment62.

Dual-EEG data acquisition

Neuroelectric activity in the two participants was simultaneously and continuously recorded while they were engaged in the interaction. The system was composed of two Acticap helmets with 32 active electrodes arranged according to the international 10/20 system including one electrooculography (EOG) electrode and referenced to the common vertex (Cz), with analog 0.1–500 Hz band-pass filtering. The impedances were maintained below 10 kV. Data acquisition was performed using a 64-channels Brainamp amplifier from the Brain Products Company (Germany) to enable the computation of millisecond-range synchrony between the two EEG recordings63.

Social Interaction Behavior Analysis

Coding was conducted offline by coders trained to reliability who were blind to all other information. We used a microlevel second-by-second coding scheme previously validated in our lab and consistent with prior research that showed correlations between these micro-level behaviors and brain activations64. Coding was conducted for the first 3 minutes of the interaction consistent with our prior research14.

Micro-coding of social synchrony

Gaze and affect, the main non-verbal channels of social communication, were coded using a set of mutually-exclusive codes consistent with our prior brain and behavioral research20,64. Coding for the two partners was conducted in separate passes using a computerized system (Noldus, Waggenigen, The Netherlands) while the system was set to 0.01 s accuracy. The following codes were used for each participant:

Gaze –

Social gaze – Looking at partner’s face

Gaze to object – Looking at an object in the environment (including, for instance, partner’s legs)

Gaze aversion – Looking away from partner’s face but not focusing on any object

Here we use the term Social Gaze to denote looking at the partner’s face and No Gaze, to denote gaze at object or gaze aversion (i.e., no social gaze).

Affect –

Positive - Clear expressions of high positive arousal or energy indicated by laugh, giggle, excited talk, or positive excitement

Neutral – No clear expression of any specific affect. Facial expression is pleasant/neural and arousal is low

Negative: Withdrawn – Clear expression of negative affect. Facial expression is sad or withdrawn, facial expression is flat, body position/muscle tone express disengagement.

Negative: Angry - Negative arousal is clearly indicated by angry voice, screams, scolding, scary or angry body movement or looming.

Speech – speech, no speech.

Inter-rater reliability was computed on 20 interactions and inter-rater reliability exceeded 90% on all codes (kappa = 0.87, range = 0.81–95).

Speech Content: The task of the conversation was to plan a fun day to spend together. Using the well-validated CIB coding scheme for adult-adult interactions65, we coded interaction content into three groups (a) Practical - Partners were dealing with the task in a rational and practical manner (e.g., “we should do x and then y”, if we do x we won’t have time for y”). (b) Emotional – Partners mainly focused on expressing emotions (e.g., “I really like to do that”, “this gets me very excited”, “this is really disappointing”). and (c) Reminiscent – partners mainly shared memories of past positive experiences, places, or activities they did and wish to do again.

Coding was conducted by two individuals who trained to use the CIB coding system and reliability on 20 interactions averaged 93% (intraclass r = 0.93).

EEG preprocessing

Matlab (Mathworks Inc, Natick, MA), EEGLAB66 and Fieldtrip toolbox for MATLAB67, were used for all calculations. The continuous EEG data was low-pass filtered with a cutoff of 60 Hz to reduce motor artifacts, and Spatial Independent Component Analysis (ICA) was applied in order to clean eye movements and blinks. A digital notch filter was applied at 50 Hz and its harmonics to remove artifacts caused by alternating current line noise. Data from three couples were omitted from analysis due to low inter-correlation among relevant electrodes and the final sample comprised 98 individuals (couples = 24 dyads, strangers = 25 dyads).

Extended gamma related EMG artifacts removal

A possible confounding effect for cortically induced gamma power is electromyographic (EMG) activity from scalp and neck muscles68. In addition to the common source of noise in EEG experiments, the free conversation setup can naturally impose ‘electrode drift’, the physical movement of the electrode relative to the brain or different levels of electrodes detachment from the head. In this study, we applied three methods to reduce EMG noise from the signal. First, we visually inspected the raw data, extracting artifact-free epochs, reducing large muscle artifacts69 (see EEG Frequency Analysis). Second, in each hemisphere, we used only electrodes that at least moderately correlated with each other (R > 0.5) to create a coherent and stable mutual signal, reducing large and small muscle artifacts. Averaging the electrodes and applying the threshold improve signal quality and reduce noise from non-cerebral sources. The threshold of R = 0.5 was chosen since Spearman Correlation ranks with magnitudes higher than 0.5 are considered moderate and above70 We set the lower threshold of gaze to 6 seconds of gaze during the entire interaction based on previous finding showing that a single gaze during typical conversation lasts on average 4 and 7 seconds for males and females respectively71. When the cumulative amount of gaze during an entire interaction does not reach this amount, which characterizes a single gaze, this may point to atypical interaction72. We coded episodes of speech versus non-speech during the interaction to control for large and small muscle artifacts.

EEG Frequency Analysis

We conducted two separate frequency calculations; (1) continuous: for the rest and the social interaction paradigms and (2) epochs-based analyses: only for the social interaction paradigm. The continuous calculation was performed on the 3 minutes of rest and the 5 minutes of the conversation and used to estimate synchronous neuro-electrical activity between dyads. The epoch-based frequency calculation was performed on the first 3 minutes of the experiment, consistent with prior micro-analytic studies14, and was used to anchor the neural synchrony in episodes of social gaze and positive affect.

An additional noise removal process was conducted separately for each frequency calculation. For the epoch-based frequency calculation noisy epochs were visually inspected and manually extracted as described above and for the continuous frequency calculation EMG artifacts were identified and removed using independent component analysis (ICA) based on their characteristic topographies, time courses, and frequency distributions73. For both calculations and for each electrode, the absolute spectral power was grouped into frequency bands: theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz) and gamma (30–60 Hz), with no overlap between frequencies during the analysis. Electrodes were collapsed into four Regions of Interest (ROI): frontal (F3,F4,F7,F8), parietal (P3,P4,CP1,CP2), temporal/parietal (T7,T8,P7,P8,CP5,CP6), and occipital (O1, O2) and the spectral power was calculated for each ROI separately.

EEG Continuous frequency calculation

Time frequency representation of the continuous EEG (over the full 3 minutes of rest and 5 minutes of the social conversation) was calculated using the Stockwell transform74 with a time resolution of 0.002 sec and a frequency resolution of 0.3 Hz. Two analyses were performed: (a) Dyadic continuous spectral EEG synchronous calculation; and (b) Behavioral and temporal/parietal gamma correlation.

(a) Dyadic continuous spectral EEG Synchronous calculation. The brain-to-brain neural synchrony was quantified using the Spearman correlation between the two partners’ spectral power and power was computed over the entire social interaction (300 Sec). The Spearman correlation was computed over time signal of the Stockwell transform frequency spectrum (for each frequency bins in the range of 4–60 HZ), averaged over each ROI electrodes (frontal, parietal, temporoparietal, occipital) in the two partners. Dyadic correlation values per bin and per ROI were averaged across groups (couples, strangers). Significance of the results was evaluated using t-test over the averaged of the correlation values grouped into the four main frequencies bands (theta, alpha, beta, and gamma). FDR correction was applied to the resulting correlation p values of all comparisons and tested at 0.05 level75. (b) Behavioral and temporal/parietal gamma correlation analysis. The dyadic gamma temporal/parietal correlation value was correlated with the corresponding behavioral data, including questionnaires, gaze, and affect variables. First, we performed an ANOVA with gamma synchrony as the dependent variable and group (couples vs strangers), and condition (gaze, affect, none) as the between-subject factors. Following, t-tests explored differences between groups’ averaged values (see Results section for data on group and Fig. 2 for data on condition). FDR correction was applied to the resulting correlation p values of comparisons and tested at 0.05 level75.

EEG Epoch’s frequency calculation

Data were segmented into 1000 ms. epochs. A Hamming window was used to control for artifacts resulting from data splicing. Trials containing power jumps and/or muscle artifacts were visually rejected. The frequency calculation was done on each of the remaining trials, down-sampled to 5HZ for each trial separately.

Dyadic Segmented Behavioral and EEG Synchronous calculation

The behavioral and brain synchronization between partners were evaluated in terms of the correlation of EEG oscillatory amplitudes in specific frequency bands and times of relevant behavior, respectively. Specifically, behavioral measures of specific gaze and affect patterns were defined and Spearman correlation was computed between each dyad’s temporoparietal gamma power during these time segments. Next, t-tests were applied to assess the significance of the differences between the groups’ gamma correlation scores during times of gaze and positive affect.

Rest Analysis

To compare the dyadic temporal-parietal gamma synchrony during the social interaction and the rest experiment, the same steps of dyadic continuous calculation were repeated over the rest signal of the participants limited to the temporoparietal ROI and the gamma frequency. Next, t-tests were applied to assess the differences between gamma correlation scores during rest and during social interaction in each group.

Post-Interaction Questions

Following the interaction male and female participants answered the following questions on a five-point Likert scale from 1 (low) to 5 (high)