Global Brain Dynamics during Social Exclusion Predict Subsequent Behavioral Conformity

Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences

Linking Emotional Reactivity Between Laboratory Tasks and Immersive Environments Using Behavior and Physiology

An event or experience can induce different emotional responses between individuals, including strong variability based on task parameters or environmental context. Physiological correlates of emotional reactivity, as well as related constructs of stress and anxiety, have been found across many physiological metrics, including heart rate and brain activity. However, the interdependances and interactions across contexts and between physiological systems are not well understood. Here, we recruited military and law enforcement to complete two experimental sessions across two different days. In the laboratory session, participants viewed high-arousal negative images while brain activity electroencephalogram (EEG) was recorded from the scalp, and functional connectivity was computed during the task and used as a predictor of emotional response during the other experimental session. In an immersive simulation session, participants performed a shoot-don’t-shoot scenario while heart rate electrocardiography (ECG) was recorded. Our analysis examined the relationship between the sessions, including behavioral responses (emotional intensity ratings, task performance, and self-report anxiety) and physiology from different modalities [brain connectivity and heart rate variability (HRV)]. Results replicated previous research and found that behavioral performance was modulated within-session based on varying levels of emotional intensity in the laboratory session (t(24) = 4.062, p < 0.0005) and stress level in the simulation session (Z = 2.45, corrected p-value = 0.0142). Both behavior and physiology demonstrated cross-session relationships. Behaviorally, higher intensity ratings in the laboratory was related to higher self-report anxiety in the immersive simulation during low-stress (r = 0.465, N = 25, p = 0.019) and high-stress (r = 0.400, N = 25, p = 0.047) conditions. Physiologically, brain connectivity in the theta band during the laboratory session significantly predicted low-frequency HRV in the simulation session (p < 0.05); furthermore, a frontoparietal connection accounted for emotional intensity ratings during the attend laboratory condition (r = 0.486, p = 0.011) and self-report anxiety after the high-stress simulation condition (r = 0.389, p = 0.035). Interestingly, the predictive power of the brain activity occurred only for the conditions where participants had higher levels of emotional reactivity, stress, or anxiety. Taken together, our findings describe an integrated behavioral and physiological characterization of emotional reactivity

Individual differences in compliance and agreement for sleep logs and wrist actigraphy: A longitudinal study of naturalistic sleep in healthy adults

<div><p>There is extensive laboratory research studying the effects of acute sleep deprivation on biological and cognitive functions, yet much less is known about naturalistic patterns of sleep loss and the potential impact on daily or weekly functioning of an individual. Longitudinal studies are needed to advance our understanding of relationships between naturalistic sleep and fluctuations in human health and performance, but it is first necessary to understand the efficacy of current tools for long-term sleep monitoring. The present study used wrist actigraphy and sleep log diaries to obtain daily measurements of sleep from 30 healthy adults for up to 16 consecutive weeks. We used non-parametric Bland-Altman analysis and correlation coefficients to calculate agreement between subjectively and objectively measured variables including sleep onset time, sleep offset time, sleep onset latency, number of awakenings, the amount of wake time after sleep onset, and total sleep time. We also examined compliance data on the submission of daily sleep logs according to the experimental protocol. Overall, we found strong agreement for sleep onset and sleep offset times, but relatively poor agreement for variables related to wakefulness including sleep onset latency, awakenings, and wake after sleep onset. Compliance tended to decrease significantly over time according to a linear function, but there were substantial individual differences in overall compliance rates. There were also individual differences in agreement that could be explained, in part, by differences in compliance. Individuals who were consistently more compliant over time also tended to show the best agreement and lower scores on behavioral avoidance scale (BIS). Our results provide evidence for convergent validity in measuring sleep onset and sleep offset with wrist actigraphy and sleep logs, and we conclude by proposing an analysis method to mitigate the impact of non-compliance and measurement errors when the two methods provide discrepant estimates.</p></div

Time series for an example participant showing actigraphy, sleep logs, and model output.

<p>Green dots represent the combined estimate for sleep onset time, which was computed as a weighted sum of the actigraphy and sleep log measurements. The text with arrows highlight days exemplifying good agreement, poor agreement, and non-compliance due to a missing sleep log. The upper violin-style plots represent the empirical distribution of all sleep onset measurements from actigraphy and sleep logs. The model was designed for robustness in cases where actigraphy and sleep logs have discrepant measurements (upper left) by producing a composite estimate closer to the more likely value considering the individual’s sleep history. When measurements have similar probability (upper right), the model produces a value that is near the average of the two measurements.</p

Narrowing of difference distribution and outlier reduction for combined estimates.

<p>Bivariate scatter plots for (a) sleep onset time, and (b) sleep offset time, showing actigraphy measurements (red) and combined estimates (green) with reference to sorted daily sleep log measurements (blue) across the entire data set. The difference distributions (right) are much sharper for combined estimates, with corresponding increase in agreement (e.g. a higher percentage of differences contained within ±1.0 hour).</p

The relationship between daily sleep log and wrist actigraphy measurements.

<p>Bivariate histograms for (a) sleep onset (SON), (b) sleep offset (SOFF), and (c) total sleep time (TST). The white dotted line represents the equality line (perfect agreement); therefore, deviations from the line indicate a lack of agreement. The empirical distributions (right panels) show the distribution of differences (actigraphy minus sleep log values), with the black dotted line representing the bias, and boxes demarcating the three reference intervals included in our non-parametric Bland-Altman analysis (±0.5, ±1.0, ±1.5 hours).</p

Example data comparing daily sleep onset times from actigraphy and sleep logs.

<p>The time series’ in (a) show a participant with strong agreement, and (b) show a participant with relatively weak agreement. Green bars represent the difference between the measures (actigraphy–sleep log). The right panels illustrate difference distributions across all measurements for that participant.</p

Individual differences in bias, agreement, compliance and demographic variables.

<p>Individual differences in bias, agreement, compliance and demographic variables.</p