Resting state EEG delta–beta coherence in relation to anxiety, behavioral inhibition, and selective attentional processing of threatening stimuli

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Abstract

Variability in human resting state electroencephalography (EEG) may reflect emotion regulation processes (for a review, see Knyazev, 2007). For instance, it has been suggested that correlation between slow (1–3 Hz) and fast (13–30 Hz) activity (or δ–β coherence) may reflect functional synchronization between limbic and cortical brain systems. Indirect support comes from several studies reporting relationships between δ–β coherence and subjectively reported behavioral inhibition and state anxiety. The present study sought to extend this work and tested the prediction that objectively, experimentally, measured threat-selective attention should also be related to δ–β coherence. EEG frequency band power and dot probe task performance were assessed in forty healthy women and results demonstrated a negative association between delta–beta coherence and automatic, anxiety-driven attentional avoidance of threatening pictorial stimuli. These first reported objective measures for cognitive-emotional behavior obtained in relation to delta–beta coherence provide additional support for the hypothesis that this EEG parameter may reflect emotion regulation processes and supports suggestions that δ–β coherence may be a useful tool in the experimental study of affect and psychopathology. In addition, results showed an unexpected negative association between δ–β coherence and self-reported trait anxiety (but no association with behavioral inhibition).

Research Highlights

► EEG δ-β coherence may reflect cortical-subcortical affect regulatory processes. ► Relation with objectively measured emotional-cognitive behavior was still unknown. ► Here δ-β coherence was related to trait anxiety and attentional threat-avoidance. ► Δ-β coherence seems useful for experimental study of affective processes.

Introduction

Neural activity as reflected in resting state encephalographic (EEG) signal can be described as a distribution of signal power across different bands spanning the frequency spectrum. By convention the δ- and θ-frequency bands are considered to represent slow oscillating neural synchronization, or slow wave (SW) activity and the α and β bands represent fast wave (FW) activity. Activity in these frequency bands has been linked to various physiological states and psychological functions (Niedermeyer and Lopes Da Silva, 2004). An increasing number of studies suggest that differential activation across the EEG frequency bands and their topographical distribution can also be linked to affective functioning, such as emotional states and traits or personality factors. For instance, asymmetrical lateralization of α activity has been linked to individual differences in approach-avoidance motivation and affective psychopathology (Harmon-Jones et al., 2010). Relationships between fast and slow spectrum bands have been linked to behavioral inhibition and behavioral activation (Knyazev and Slobodskaya, 2003, Knyazev and Slobodskoy-Plusnin, 2009) and other authors have likewise linked various personality traits to variation of activity in different frequency bands (Chi et al., 2005).

On the basis of comparative physiology and a review of functional activity in various brain structures, Knyazev (2007) argues that relationships between SW and FW activity may represent functional cortical-sub-cortical interactions. Several reports are in line with this view. SW/FW ratios have been linked to affect-related endocrine manipulations (Schutter and van Honk, 2004), motivated decision-making (Schutter and van Honk, 2005a), and personality traits and inhibitory response style for emotional stimuli (Putman et al., 2010a). Other studies have looked at coherence between SW and FW activity. The intriguing suggestion is that increased positive δ–β correlation (δ–β coherence) may reflect stronger functional coherence between the cerebral cortex and sub-cortical limbic structures (cortical-subcortical cross-talk) and may be indicative of emotion-regulation processes. Schutter and van Honk (2005b) reported stronger δ–β coherence in participants with higher levels of the hormone cortisol. Van Peer et al. (2008) reported a positive relationship between trait behavioral inhibition and δ–β coherence. Trait behavioral inhibition is conceptually and empirically closely related to trait anxiety, but the two studies that investigated relationships between anxiety and δ–β coherence only found evidence for relationships with transient state anxiety (Knyazev et al., 2006, Miskovic et al., 2010). One goal for the present study was to assess a possible relationship between δ–β coherence and trait anxiety and replicate evidence for the reported relationship between δ–β coherence and trait behavioral inhibition. However, rather than only study such relationships with self-reported affect, the main goal was to explore relationships between δ–β coherence and more objective implicit experimental measures of cognitive affect regulation.

An important cognitive correlate to emotional states and traits is threat-related selective attention. Threat-selective attention, resulting from interactions between cortical and sub-cortical processes of emotion regulation (Eysenck et al., 2007, Monk et al., 2006, Monk et al., 2008) is thought to be an important factor in the aetiology and maintenance of affective disorders (mainly for anxiety disorders; Mogg and Bradley, 1998). Reduced prefrontal control over attentional reflexes during anxious states is thought to allow more automatic influence of task irrelevant threatening stimuli on attention (Eysenck et al., 2007). The vigilance–avoidance hypothesis states that in anxious people a primary involuntary shift toward threatening stimuli is followed by active attentional avoidance of those threatening stimuli, reflecting attempted counter-regulation of the fear activation that resulted from this initially enhanced threat-processing (Mogg and Bradley, 1998). The most widely used method for assessing automatic selective attention to emotional stimuli is the dot probe task which measures response times to target probes appearing in locations that were previously occupied by neutral or emotional cue stimuli. In spatial attentional tasks such as this, one can vary intervals between cues and targets which allows one to study the direction of attentional selectivity as it develops over time (Koster et al., 2005).

In summary, it was expected that attentional avoidance of threat as measured with a pictorial dot probe task, specifically after longer cue-target delays, should be related to δ–β coherence. Also, possible relationships between trait behavioral inhibition and δ–β coherence and trait anxiety and δ–β coherence were investigated. Although some studies suggest that a frontal midline electrode position should be the primary target to test such hypotheses (Schutter and van Honk, 2004, Schutter and van Honk, 2005a, Schutter and van Honk, 2005b), this is not altogether clear (Knyazev et al., 2006, van Peer et al., 2008) and it was chosen to study lateral as well as midline frontal positions. These predictions were tested by comparing frontal δ–β coherences between healthy young women who scored either low or high on the various predictor variables (behavioral inhibition, trait anxiety, and dot probe attentional bias). Only women were tested for better comparison with the majority of previous reports that have tested associations between affect or cognitive-affective processing and slow and fast EEG power relationships.

Section snippets

Participants

Forty right-handed and healthy women aged between 18 and 27 years (M = 20.6, SD = 2.1) participated in the study against a small financial compensation or for course credits. Behavioral inhibition (BIS) scores ranged between 16 and 27 (M = 21.9, SD = 3.0, median = 22). Trait anxiety scores (STAI-t scores) ranged between 35 and 59 (M = 41.0, SD = 6.4, median = 39). Written informed consent was obtained and the study was approved by the local review board.

Questionnaires

The BIS/BAS scale was used to assess self-reported

Dot probe task

See Table 1. In the 500 ms condition, the sample as a whole demonstrated significant vigilance toward threat-pictures; t(39) = 2.47; p = .018. In the 150 ms and 1500 ms conditions, dot probe congruency scores did not deviate significantly from zero (respectively t = −.351 and t = 1.446). Median congruency scores for the 150, 500, and 1500 ms conditions were respectively −3, 18, and 4 ms. In the 500 ms condition (but not in the 150 and 1500 ms conditions), the expected negative correlation between the dot

Discussion and conclusions

This study investigated relationships between δ–β coherence and selective attention and results demonstrated that stronger δ–β coherence was related to reduced attentional avoidance of threat (which was mildly related to self-reported anxiety) after a 500 and most noticeably a 1500 ms cue-target delay, but not after a short 150 ms delay. In addition, this study revisited previously reported relationships between δ–β coherence and self-reported behavioral inhibition or anxiety. Results showed no

Acknowledgements

This work was supported by a grant for innovative research from the Netherlands Organization for Scientific Research (NWO; # 451.07.028). The author is grateful to Steven van der Werff for his assistance with data-collection and Maartje Schoorl for her assistance with design of the dot probe task.

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