Participants

Participants were 21 healthy individuals (11 females; mean age ± SD = 25.5 ± 6.1 years). All were right handed, with a mean (±SD) Edinburgh Handedness Inventory45 score of 87.0 (±13.4). All had normal or corrected-to-normal vision, no structural brain abnormality and no past neurological or psychiatric history. All denied use of medication or recreational drugs. Participants were recruited via the University of Sussex psychology subject database and the Brighton Gumtree website. All participants gave written informed consent prior to the experiment. The study was approved by the Brighton and Sussex Medical School Ethics Committee and was carried out in accordance with the approved guidelines.

Experimental procedure

Grip force measurement and feedback

During scanning participants held a purpose-built pressure sensor between the fingers and thumb of their right hand, while resting the right arm over the abdomen. Their left arm was extended down the left side of the body and held relaxed. The pressure sensor was made of an air-filled bottle connected via incompressible plastic tubing to a pressure transducer (Keller PR-21; Keller, Winterthur, Switzerland) capable of converting air pressure into voltage. The pressure device was calibrated and generated a differential voltage signal that was linear across the range 0 to 1 bar, fully covering the pressure ranges produced by participants. Voltage signals were passed to a Cambridge Electronic Design Power1401 data acquisition interface and digitally recorded at 1 kHz on a dedicated computer running Spike2 software. In order to provide participants with real-time visual feedback on their exerted force, a customised Spike2 script averaged the voltage signals every 200 ms and sent this via a serial port to the stimulus presentation computer. Presentation of visual stimuli was programmed with Cogent 2000 (http://www.vislab.ucl.ac.uk/cogent_2000.php) within Matlab 7.8 (MathWorks, Natick, MA, USA) and displayed on a projector screen visible via a mirror positioned on the head coil.

Force calibration and practice sessions

Before starting the main experiment each participant was asked to lightly hold the pressure sensor for 10 s to calibrate the baseline. MVC was obtained by asking the participant to squeeze the bottle as hard as possible for 5 s. MVC was determined as the maximum value obtained during this period. During the task, the force exerted on the pressure sensor was displayed on the projector screen as a blue fluid level moving up and down within a ‘thermometer’ display marked with five scale points, corresponding to baseline, 5, 10, 15 and 20% MVC values, respectively (Fig. 1).

Each participant performed two practice trials to familiarise himself/herself with the stimulus presentation and isometric grip manipulation. In the first trial, the participant was required to adjust and maintain their force level for 20 s to match a red target line positioned at 10% MVC. In the second trial, the participant was asked to match the target force (10% MVC) for the initial 5 s. Visual feedback was then occluded and the participant was instructed to maintain the same level of force over the following 15 s without feedback.

Experimental tasks

Each participant performed two sessions of isometric grip task with or without social evaluation, while undergoing fMRI. Each session lasted ~19 min and consisted of 40 trials, including 10 repetitions of each of the four trial types. The four trial types corresponded to a 2 × 2 factorial design: two social conditions (observed or unobserved) × two target force levels (5% or 10% MVC). A schematic diagram of the isometric grip task is shown in Fig. 1. Random jitter (1.1–4.0 s) was inserted prior to each trial to ensure better sampling of the hemodynamic response and maintain participant engagement.

At trial onset (marked by the appearance of the thermometer), the participant started to squeeze the pressure sensor and adjusted the level of force production to match the observed target force. Following this initial 5-s period, the picture of thermometer was replaced by 15-s video footage showing the faces of two experimenters sitting in the MRI control room. Although the participant was led to believe that the video was in real time, the footage was actually pre-recorded46. Before starting the experimental trials, each participant was told that another participant would be simultaneously performing the same task in the adjacent scanner. On 50% of trials, the video appeared to show the two observers monitoring and discussing the actual participant’s performance (observed condition) and on the other 50% the observers appeared to be monitoring another participant’s performance (unobserved control condition). We produced 20 different videos for the observed and unobserved conditions for each of the four possible pairs of experimenters associated with the study. The videos corresponding to the pair of experimenters present on the study day were used. Before scanning the two experimenters greeted the participant, wearing the same clothes as worn in the video footage to ensure that the participant believed that he/she was looking at the experimenters’ faces in real time through a web camera. A fixed 3-s break was inserted after each trial. The two target force levels and two social conditions were randomly distributed over the session and the videos were presented in a randomised order.

Behavioural data analyses

Grip task performance

All behavioural data were analysed in Matlab 7.8 using purpose-written routines. Air pressure data sampled at 1 kHz were low-pass filtered at 2 Hz and normalised relative to MVC (% MVC). The normalised force data were segmented into epochs of 16 s (i.e., from 1 s prior to the onset of video to the end of video). The degree of force decay was quantified by subtracting the target force from the mean recorded force during each 1-s period of social video presentation (force error)11. Due to technical problems (i.e., air leakage from the pressure grip), we could calibrate the force data for 19 of the 21 participants. Therefore, the data from these 19 participants were used in the analyses involving force error.

We first conducted a repeated-measures ANOVA on the force error data with three repeated factors: social condition (2 levels: observed or unobserved), target force level (2 levels: 5% or 10% MVC) and time (16 levels: epochs 0–15). Because the social × force × time interaction effect was found to be highly significant (F (15, 270) = 3.96, p < 0.0001), we subsequently performed separate two-way ANOVAs with two repeated factors (i.e., social condition and target force level) for each 1-s epoch.

Anxiety self-ratings

After the fMRI experiment, participants retrospectively rated their level of state anxiety while viewing the social video footage using a visual-analogue scale (VAS), which has been shown to provide a quick, reliable and relatively sensitive measure of emotional state47. The 100-mm VAS ranged from 0 (not anxious at all on the left end) to 100 (extremely anxious on the right end). We asked participants to place a vertical line bisecting the 100-mm VAS to indicate their perceived level of anxiety during the observed and unobserved conditions. Paired t-test was then used to assess the change in VAS score from the unobserved to observed condition. We also asked participants to complete the Brief Fear of Negative Evaluation Scale22, which consisted of 12 items concerning fear of negative evaluation by others (e.g., Sometimes I think I am too concerned with what other people think of me.). Participants rated each item on a scale ranging from 1 (not at all characteristic of me) to 5 (extremely characteristic of me). The relationships between change in force error from the unobserved to observed condition and anxiety ratings were examined using Pearson’s product-moment correlation coefficients.

Scanning and imaging data analyses

MRI acquisition and image preprocessing

Whole brain fMRI data were acquired on a 1.5 T Siemens Magnetom Avanto scanner (Siemens, Erlangen, Germany) at the Clinical Imaging Sciences Centre, Brighton and Sussex Medical School using a 12-channel head coil. We obtained T2*-weighted echo-planar images (EPI) with blood-oxygen level-dependent (BOLD) contrast, each comprising a full volume of 33 slices (slice thickness = 3.0 mm, inter-slice gap = 0.75 mm, in-plane resolution = 3.0 × 3.0 mm, TR = 3300 ms, TE = 50 ms). An average of 345 volume images were acquired for each participant per session (~19 min), with the first five volumes of each session discarded to allow for T1 equilibration effects. During the break between the two sessions, T1-weighted structural images were acquired. At the end of the experiment, gradient field maps were also acquired for each participant to enable subsequent unwarping of functional images with regard to the B0 field.

fMRI data were preprocessed using Statistical Parametric Mapping 8 (SPM8; http://www.fil.ion.ucl.ac.uk/spm) running in Matlab 7.8. Structural images were co-registered with the mean EPI, segmented and normalised to a standard T1 template and averaged across all participants to allow group-level anatomical localization. All fMRI results were overlaid onto this average anatomical image. Functional images were realigned to the first image of each session, unwarped and spatially normalised using parameters from segmentation of the T1 structural images and spatially smoothed with a Gaussian kernel of 8 mm full width half maximum (FWHM).

fMRI data analysis

Statistical analyses were performed using block designs on SPM8. The 5-s periods of isometric grip with visual feedback of force (thermometer phase) and the 15-s periods of social video presentation (video phase) were modelled as blocks of corresponding duration. Participant-specific realignment parameters were modelled as covariates of no interest to correct for motion artefacts. Data were corrected for the effects of serial auto-correlations and high-pass filtered (cutoff = 128 s) to remove low-frequency drifts.

In the first categorical analysis, 6 separate regressors were created for the 2 stimuli conditions of the thermometer phase (2 target force levels) and the 4 stimuli conditions of the video phase (2 social conditions × 2 target force levels) and convolved with a canonical hemodynamic response function to model corresponding changes in BOLD contrast signal. The general linear model was used to generate parameter estimates of block-related activity for each voxel, for each trial type and participant. Group-level random-effect analyses were performed using repeated-measures ANOVAs on the contrast images obtained for each of the 2 × 2 factor combinations of the video phase for each participant. We first examined BOLD responses to the presence of observers (observed >unobserved and observed <unobserved) separately for the 5% and 10% MVC tasks. To elucidate neural mechanisms commonly underlying the effecs of social evaluation in both tasks, we then conducted a conjunction analysis48 to identify the intersection of the regions activated or deactivated to the 5% and 10% MVC contrasts. To this end, we applied inclusive masking of the 5% MVC contrasts (p < 0.05, uncorrected) to the 10% MVC contrasts.

Next, to examine more in detail brain activity associated with motor effects of social evaluation, we performed independent parametric analyses by using 2 separate models. In both models, 2 separate regressors were created for all the conditions of the thermometer phase and all the conditions of the video phase. For the thermometer phase, the maximum force produced by each participant during this 5-s period was included as a parametric modulator. In the first model, we computed the parametric modulator for the video phase as follows: we first determined the normal level of force decay (normal force error) separately for the 5% and 10% MVC tasks for each participant by averaging the force error for the last 1-s period of video phase across all 10 unobserved control trials of each session. We then subtracted the normal force error of the corresponding force level (5% or 10% MVC) from the actual force error of the last 1-s period of video phase for each trial for each participant (force increase index). This force increase index allowed us to test for brain regions whose activity proportionally changed with socially-induced increases in isometric grip force on a trial-by-trial basis. Linear contrasts of regression coefficients were computed at the individual level and taken to a group-level random-effect analysis (one-sample t-test) to investigate brain activity correlating with this force increase index. These linear contrasts might also contain clusters that simply correlate with the level of exerted force. To exclude such brain regions, we produced a second model where we included absolute force value (in % MVC) of the last 1-s period of video phase as a parametric modulator. The brain regions emerging from the second model should be related to the memory-based control of isometric grip force in general. Therefore, to isolate brain regions involved in motor audience effects, we removed voxels with significant correlation (p < 0.005, uncorrected) in the second model from those in the first model with exclusive masking.

Since the parametric analyses indicated that the left IPC played a key role in mediating the social effect on isometric force production, we further tested if the left IPC activity also accounted for individual differences in motor responses to social evaluation. To this end, we computed differences in parameter estimates between the observed and unobserved conditions for the 5% and 10% MVC tasks separately, from a 4-mm radius sphere placed around the group peak within the left IPC for each participant. We then investigated whether there is a significant relationship between the changes in force error over the last 3-s period of video presentation and those in IPC activity from the unobserved to observed condition by using a repeated-measures linear mixed-effects model with the target force level (5% or 10% MVC) as an independent variable, the force increase as a covariate and the IPC deactivation as a dependent variable. A preliminary analysis suggested that the relationship between the covariate and the dependent variable did not significantly differ as a function of the independent variable (F (1, 31) = 0.18, p = 0.672). Since the homogeneity-of-slopes assumption was met, we subsequently performed the linear mixed-effects analysis.

We also examined whether social evaluation changed functional connectivity between the two key regions identified in the categorical and parametric analyses, namely the right pSTS and the left IPC, by performing a PPI analysis23. We first extracted the time series of data from a 4-mm radius sphere placed around the peak within the right pSTS for each participant. The interaction term was computed by multiplying a task vector reflecting the social condition (1 for the observed and −1 for the unobserved condition; convolved with a canonical hemodynamic response function before multiplication) with the source regressor. The general linear model was fitted to the PPI regressor, the source regressor and the task vector for each participant. The group-level random-effect analysis was then performed using a one-sample t-test on the contrast images obtained for the PPI regressor. Finally, we examined whether there was any overlap between the left IPC found in the PPI analysis and that found in the parametric analysis. We tested for brain regions that decreased connectivity with pSTS in the observed condition and also showed deactivation in proportion to the force increase index by applying inclusive masking of the PPI contrasts (p < 0.05, uncorrected) to the contrasts derived from the parametric analysis.

To determine a cluster extent threshold that corresponds to a threshold of p < 0.05 after correction for multiple comparisons, we conducted 1,000 simulations of whole-brain fMRI activation assuming a type I error voxel activation probability of 0.00149,50. Based on these simulations, we reported only clusters containing a minimum of 120 resampled voxels.