Effects of Trait Anxiety on Error Processing and Post-error Adjustments: An Event-Related Potential Study With Stop-Signal Task

The burden of brooding on neural error processing: The role of repetitive negative thinking in major depressive disorder with and without comorbid anxiety disorders

BACKGROUND

Repetitive negative thinking (RNT), particularly its brooding aspect, is a prominent feature in Major Depressive Disorder (MDD) with and without comorbid anxiety. Error processing, an adaptive cognitive operation, seems to be impaired in individuals with exaggerated RNT. This study measured a post-error neural response, error-related negativity (ERN), during an inhibitory task to examine the mechanism underlying the relationship between RNT and faulty error processing.

METHODS

We examined current MDD patients with (n = 61) and without comorbid anxiety disorders (COM; n = 38), propensity-matched into High- or Low-RNT groups according to Ruminative Response Scale Brooding subscale scores. Using 32-channel electroencephalography (EEG) during a stop-signal task, we measured baseline-corrected ERN amplitude at FCz 0-100 ms after an incorrect response. A between-subjects ANOVA was conducted with group (High RNT, Low RNT) and comorbidity (MDD, COM) as factors.

RESULTS

A significant group-by-comorbidity interaction (η2 = 0.07) was found, with MDD participants exhibiting high RNT revealing smaller (more positive) ERN amplitudes compared to their COM counterparts with high RNT (d = 0.77) and MDD participants with low RNT (d = 0.92).

CONCLUSIONS

Non-anxious individuals with MDD and high RNT showed blunted post-error neural responses, potentially indicating a diminished adaptive neural mechanism for recognizing and correcting errors. However, the presence of comorbid anxiety disorders in individuals with high RNT appears to counteract this reduction, potentially through an enhanced neural response to errors, thereby maintaining a higher level of error-processing activity. Further understanding of these relationships is essential for developing targeted interventions for MDD, with particular focus on the detrimental impact of brooding RNT.

Anxiety Symptoms in Young Children Are Associated With a Maladaptive Neurobehavioral Profile of Error Responding

BACKGROUND

Childhood anxiety symptoms have been linked to alterations in cognitive control and error processing, but the diverse findings on neural markers of anxiety in young children, which vary by severity and developmental stage, suggest the need for a wider perspective. Integrating new neural markers with established ones, such as the error-related negativity, the error positivity, and frontal theta, could clarify this association. Error-related alpha suppression (ERAS) is a recently proposed index of post-error attentional engagement that has not yet been explored in children with anxiety.

METHODS

To identify neurobehavioral profiles of anxiety in young children by integrating ERAS with the error-related negativity, error positivity, frontal theta, and post-error performance indicators, we employed K-means clustering as an unsupervised multimetric approach. For this, we first aimed to confirm the presence and scalp distribution of ERAS in young children. We performed event-related potentials and spectral analysis of electroencephalogram data collected during a Go/NoGo task (Zoo Task) completed by 181 children (ages 4-7 years; 103 female) who were sampled from across the clinical-to-nonclinical range of anxiety severity using the Child Behavior Checklist.

RESULTS

Results confirmed ERAS, showing lower post-error alpha power, maximal suppression at occipital sites, and less ERAS in younger children. K-means clustering revealed that high anxiety and younger age were associated with reduction in ERAS and frontal theta, less negative error-related negativity, enlarged error positivity, more post-error slowing, and reduced post-error accuracy.

CONCLUSIONS

Our findings indicate a link between ERAS, maladaptive neural mechanisms of attention elicited by errors, and anxiety in young children, suggesting that anxiety may arise from or interfere with attention and error processing.

Trait Anxiety, Emotion Regulation, and Metacognitive Beliefs: An Observational Study Incorporating Separate Network and Correlation Analyses to Examine Associations with Executive Functions and Academic Achievement

Trait anxiety, emotion regulation strategies, and metacognitive beliefs influence executive functions (EFs) and academic achievement. This study examines their interplay and impact on academic success. In total, 275 adolescents (10-17 years) and parents completed an online questionnaire assessing trait anxiety, emotion regulation strategies, metacognition, parent-reported behaviors related to executive functioning, and overall school average. Preliminary analyses confirmed consistency with the existing literature for each variable and their interaction. Furthermore, we conducted a network analysis among the main variables. This analysis supports the need to pay more attention to reflective variables-maladaptive emotion regulation strategies and metacognitive beliefs about worry-when studying trait anxiety. These variables were linked to problematic executive functioning in adolescents, and the latter was negatively linked to academic achievement. This study offers innovative insights by investigating relationships less explored in the scientific literature. It reveals high and significant correlations between metacognitive beliefs, maladaptive emotion regulation strategies, and trait anxiety (r > 0.500, p < 0.001) but also between these variables and both executive functioning and academic achievement. These findings offer new perspectives for research and underscore the importance of holistically examining the psychological factors related to academic success.

Convolutional neural networks for classifying healthy individuals practicing or not practicing meditation according to the EEG data

The development of objective methods for assessing stress levels is an important task of applied neuroscience. Analysis of EEG recorded as part of a behavioral self-control program can serve as the basis for the development of test methods that allow classifying people by stress level. It is well known that participation in meditation practices leads to the development of skills of voluntary self-control over the individual's mental state due to an increased concentration of attention to themselves. As a consequence of meditation practices, participants can reduce overall anxiety and stress levels. The aim of our study was to develop, train and test a convolutional neural network capable of classifying individuals into groups of practitioners and non-practitioners of meditation by analysis of eventrelated brain potentials recorded during stop-signal paradigm. Four non-deep convolutional network architectures were developed, trained and tested on samples of 100 people (51 meditators and 49 non-meditators). Subsequently, all structures were additionally tested on an independent sample of 25 people. It was found that a structure using a one-dimensional convolutional layer combining the layer and a two-layer fully connected network showed the best performance in simulation tests. However, this model was often subject to overfitting due to the limitation of the display size of the data set. The phenomenon of overfitting was mitigated by changing the structure and scale of the model, initialization network parameters, regularization, random deactivation (dropout) and hyperparameters of cross-validation screening. The resulting model showed 82 % accuracy in classifying people into subgroups. The use of such models can be expected to be effective in assessing stress levels and inclination to anxiety and depression disorders in other groups of subjects.

Anxiety disrupts performance monitoring: integrating behavioral, event-related potential, EEG microstate, and sLORETA evidence

Anxiety impacts performance monitoring, though theory and past research are split on how and for whom. However, past research has often examined either trait anxiety in isolation or task-dependent state anxiety and has indexed event-related potential components, such as the error-related negativity or post-error positivity (Pe), calculated at a single node during a limited window of time. We introduced 2 key novelties to this electroencephalography research to examine the link between anxiety and performance monitoring: (i) we manipulated antecedent, task-independent, state anxiety to better establish the causal effect; (ii) we conducted moderation analyses to determine how state and trait anxiety interact to impact performance monitoring processes. Additionally, we extended upon previous work by using a microstate analysis approach to isolate and sequence the neural networks and rapid mental processes in response to error commission. Results showed that state anxiety disrupts response accuracy in the Stroop task and error-related neural processes, primarily during a Pe-related microstate. Source localization shows that this disruption involves reduced activation in the dorsal anterior cingulate cortex and compensatory activation in the right lateral prefrontal cortex, particularly among people high in trait anxiety. We conclude that antecedent anxiety is largely disruptive to performance monitoring.

Acute Sleep Deprivation Impairs Motor Inhibition in Table Tennis Athletes: An ERP Study

Excellent response inhibition is the basis for outstanding competitive athletic performance, and sleep may be an important factor affecting athletes’ response inhibition. This study investigates the effect of sleep deprivation on athletes’ response inhibition, and its differentiating effect on non-athlete controls’ performance, with the aim of helping athletes effectively improve their response inhibition ability through sleep pattern manipulation. Behavioral and event-related potential (ERP) data were collected from 36 participants (16 table tennis athletes and 20 general college students) after 36 h of sleep deprivation using ERP techniques and a stop-signal task. Sleep deprivation’s different effects on response inhibition in the two groups were explored through repeated-measures ANOVA. Behavioral data showed that in a baseline state, stop-signal response time was significantly faster in table tennis athletes than in non-athlete controls, and appeared significantly longer after sleep deprivation in both groups. ERP results showed that at baseline state, N2, ERN, and P3 amplitudes were lower in table tennis athletes than in non-athlete controls, and corresponding significant decreases were observed in non-athlete controls after 36 h of sleep deprivation. Table tennis athletes showed a decrease in P3 amplitude and no significant difference in N2 and ERN amplitudes, after 36 h of sleep deprivation compared to the baseline state. Compared to non-athlete controls, table tennis athletes had better response inhibition, and the adverse effects of sleep deprivation on response inhibition occurred mainly in the later top-down motor inhibition process rather than in earlier automated conflict detection and monitoring.