Frontal delta-beta cross-frequency coupling in high and low social anxiety: An index of stress regulation?

Abstract

Cross-frequency coupling (CFC) between frontal delta (1–4 Hz) and beta (14–30 Hz) oscillations has been suggested as a candidate neural correlate of social anxiety disorder, a disorder characterized by fear and avoidance of social and performance situations. Prior studies have used amplitude-amplitude correlation (AAC) as a CFC measure and hypothesized it as a candidate neural mechanism of affective control. However, using this metric has yielded inconsistent results regarding the direction of CFC, and the functional significance of coupling strength is uncertain. To offer a better understanding of CFC in social anxiety, we compared frontal delta-beta AAC with phase-amplitude coupling (PAC) – a mechanism for information transfer through neural circuits. Twenty high socially anxious (HSA) and 32 low socially anxious (LSA) female undergraduates participated in a social performance task (SPT). Delta-beta PAC and AAC were estimated during the resting state, as well as the anticipation and recovery conditions. Results showed significantly more AAC in LSA than HSA participants during early anticipation, as well as significant values during all conditions in LSA participants only. PAC did not distinguish between LSA and HSA participants, and instead was found to correlate with state nervousness during early anticipation, but in LSA participants only. Together, these findings are interpreted to suggest that delta-beta AAC is a plausible neurobiological index of adaptive stress regulation and can distinguish between trait high and low social anxiety during stress, while delta-beta PAC might be sensitive enough to reflect mild state anxiety in LSA participants.Electronic supplementary materialThe online version of this article (10.3758/s13415-018-0603-7) contains supplementary material, which is available to authorized users.

Positive Shyness in the Brain: Frontal Electroencephalogram Alpha Asymmetry and Delta–Beta Correlation in Children

“…Thus, we speculate that a relatively higher delta-beta correlation among the positive shy children may then reflect an adaptive regulatory process. This finding and hypothesis is in line with previous work that has proposed that a relatively higher delta-beta correlation to be an adaptive neural correlate and converges with work finding this neural correlate to be linked with some adaptive psychosocial processes such as attentional control (Morillas-Romero et al, 2015;Putman et al, 2012) and low anxiety which is consistent in studies using a between-subject (Putman, 2011) and within-subjects measure of deltabeta correlation (Poppelaars et al, 2018).…”

“…Another line of work has proposed that heightened delta-beta correlation may actually reflect an adaptive neural regulatory process, which has been supported in studies finding heightened delta-beta correlation in relation to increased attentional control (Morillas-Romero, Tortella-Feliu, Bornas, & Putman, 2015; Putman, Arias-Garcia, Pantazi, & van Schie, 2012) as well as lower levels of anxiety in adults (Poppelaars, Harrewijn, Westenberg, & van der Molen, 2018;Putman, 2011). This hypothesis is further supported by functional MRI studies that have found that the strength of cortico-subcortical coupling of the amygdala and frontal cortex is a correlate of emotion regulation efficiency (Banks, Eddy, Angstadt, Nathan, & Phan, 2007).…”

Frontal brain delta‐beta correlation, salivary cortisol, and social anxiety in children

“…However, some have suggested that a within‐subject approach may provide further information regarding individual differences (e.g. Poppelaars, Harrewijn, Westenberg, & van der Molen, ; Schutter & Knyazev, ). A post hoc exploratory analyses using a within‐subject measure of frontal delta‐beta correlation yielded largely nonsignificant findings, with the exception of a trend ( p = .07) for the right frontal electrode in relation to social anxiety, with results mirroring our between‐subjects approach.…”

Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

“…These predictors include (i) IQR of delta power for the prediction of IQR of FAA; (ii) IQR of beta power for the prediction of IQR of FTP; and (iii) IQR of FRP for the prediction of rFLBP. Although a concrete justification is hard, tight cross-frequency couplings among alpha, delta, and beta frequency bands, such as delta-alpha cross frequency coupling [35,36] and theta-beta coupling [37], might be reflected here.…”