The relationship between perceived social support and rumination among parents of children with autism: moderating effect of the degree of intervention received by children.

Objective: As the number of children diagnosed with autism rises year by year, the issue of nurturing this particular group becomes increasingly salient. Parents of autistic children, as the nearest and most reliable caregivers for their children, shoulder immense psychological strain and accountability. They are compelled to confront an array of daily life challenges presented by their children, as well as endure multiple pressures such as societal scrutiny and financial burdens. Consequently, the mental health status of the parents is of utmost significance. Methods: In this study, questionnaire survey combined with literature analysis were applied. The rumination thinking scale and the social support scale were used to investigate the relationship between social support perceived by parents of autistic children and rumination. Meanwhile, the moderating effects of intervention on children with autism were also explored. It hopes that our research would provide a basis for alleviating psychological stress and improving the mental health levels of the parents. A total of 303 parents of children with autism were collected (including 160 females and 143 males). Corresponding data analyses were conducted using SPSS 26.0. Results: Parents of autistic children generally exhibited high levels of rumination, with significant gender differences. At the same time, the perceived social support by the parents significantly influenced their level of rumination. It showed that the higher the social support received by parents, the lower the level of rumination. More importantly, the extent of intervention received by the children had a regulating effect on rumination of their parents. Conclusion: The personalized psychological support programs should be developed based on the actual situation of parents, to better manage the challenges presented by raising a child with autism. Our findings would provide important theoretical underpinnings and practical guidance for psychological intervention efforts aimed at families of autistic children. Moreover, these findings offer novel insights for future research, with the potential to advance the field of mental health studies concerning parents of children with autism.

Micro-Expression Recognition Based on Nodal Efficiency in the EEG Functional Networks.

Micro-expression recognition based on ima- ges has made some progress, yet limitations persist. For instance, image-based recognition of micro-expressions is affected by factors such as ambient light, changes in head posture, and facial occlusion. The high temporal resolution of electroencephalogram (EEG) technology can record brain activity associated with micro-expressions and identify them objectively from a neurophysiological standpoint. Accordingly, this study introduces a novel method for recognizing micro-expressions using node efficiency features of brain networks derived from EEG signals. We designed a real-time Supervision and Emotional Expression Suppression (SEES) experimental paradigm to collect video and EEG data reflecting micro- and macro-expression states from 70 participants experiencing positive emotions. By constructing functional brain networks based on graph theory, we analyzed the network efficiencies at both macro- and micro-levels. The participants exhibited lower connection density, global efficiency, and nodal efficiency in the alpha, beta, and gamma networks during micro-expressions compared to macro-expressions. We then selected the optimal subset of nodal efficiency features using a random forest algorithm and applied them to various classifiers, including Support Vector Machine (SVM), Gradient-Boosted Decision Tree (GBDT), Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). These classifiers achieved promising accuracy in micro-expression recognition, with SVM exhibiting the highest accuracy of 92.6% when 15 channels were selected. This study provides a new neuroscientific indicator for recognizing micro-expressions based on EEG signals, thereby broadening the potential applications for micro-expression recognition.

Affection of facial artifacts caused by micro-expressions on electroencephalography signals.

Macro-expressions are widely used in emotion recognition based on electroencephalography (EEG) because of their use as an intuitive external expression. Similarly, micro-expressions, as suppressed and brief emotional expressions, can also reflect a person's genuine emotional state. Therefore, researchers have started to focus on emotion recognition studies based on micro-expressions and EEG. However, compared to the effect of artifacts generated by macro-expressions on the EEG signal, it is not clear how artifacts generated by micro-expressions affect EEG signals. In this study, we investigated the effects of facial muscle activity caused by micro-expressions in positive emotions on EEG signals. We recorded the participants' facial expression images and EEG signals while they watched positive emotion-inducing videos. We then divided the 13 facial regions and extracted the main directional mean optical flow features as facial micro-expression image features, and the power spectral densities of theta, alpha, beta, and gamma frequency bands as EEG features. Multiple linear regression and Granger causality test analyses were used to determine the extent of the effect of facial muscle activity artifacts on EEG signals. The results showed that the average percentage of EEG signals affected by muscle artifacts caused by micro-expressions was 11.5%, with the frontal and temporal regions being significantly affected. After removing the artifacts from the EEG signal, the average percentage of the affected EEG signal dropped to 3.7%. To the best of our knowledge, this is the first study to investigate the affection of facial artifacts caused by micro-expressions on EEG signals.

Responses of functional brain networks in micro-expressions: An EEG study.

Micro-expressions (MEs) can reflect an individual's subjective emotions and true mental state, and they are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, one of the major challenges of working with MEs is that their neural mechanism is not entirely understood. To the best of our knowledge, the present study is the first to use electroencephalography (EEG) to investigate the reorganizations of functional brain networks involved in MEs. We aimed to reveal the underlying neural mechanisms that can provide electrophysiological indicators for ME recognition. A real-time supervision and emotional expression suppression experimental paradigm was designed to collect video and EEG data of MEs and no expressions (NEs) of 70 participants expressing positive emotions. Based on the graph theory, we analyzed the efficiency of functional brain network at the scalp level on both macro and micro scales. The results revealed that in the presence of MEs compared with NEs, the participants exhibited higher global efficiency and nodal efficiency in the frontal, occipital, and temporal regions. Additionally, using the random forest algorithm to select a subset of functional connectivity features as input, the support vector machine classifier achieved a classification accuracy for MEs and NEs of 0.81, with an area under the curve of 0.85. This finding demonstrates the possibility of using EEG to recognize MEs, with a wide range of application scenarios, such as persons wearing face masks or patients with expression disorders.

Electrophysiological evidence for inhibition hypothesis of micro-expressions based on tensor component analysis and Physarum network algorithm.

The inhibition hypothesis advocated by Ekman (1985) states when an emotion is concealed or masked, the true emotion is manifested as a micro-expression (ME) which is a fleeting expression lasting for 40 to 500 ms. However, research about the inhibition hypothesis of ME from the perspective of electrophysiology is lacking. Here, we report the electrophysiological evidence obtained from an electroencephalography (EEG) data analysis method. Specifically, we designed an ME elicitation paradigm to collect data of MEs of positive emotions and EEG from 70 subjects, and proposed a method based on tensor component analysis (TCA) combined with the Physarum network (PN) algorithm to characterize the spatial, temporal, and spectral signatures of dynamic EEG data of MEs. The proposed TCA-PN methods revealed two pathways involving dorsal and ventral streams in functional brain networks of MEs, which reflected the inhibition processing and emotion arousal of MEs. The results provide evidence for the inhibition hypothesis from an electrophysiological standpoint, which allows us to better understand the neural mechanism of MEs.

Differences in brain activations between micro- and macro-expressions based on electroencephalography.

Micro-expressions can reflect an individual's subjective emotions and true mental state and are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, the current approach based on image and expert assessment-based micro-expression recognition technology has limitations such as limited application scenarios and time consumption. Therefore, to overcome these limitations, this study is the first to explore the brain mechanisms of micro-expressions and their differences from macro-expressions from a neuroscientific perspective. This can be a foundation for micro-expression recognition based on EEG signals. We designed a real-time supervision and emotional expression suppression (SEES) experimental paradigm to synchronously collect facial expressions and electroencephalograms. Electroencephalogram signals were analyzed at the scalp and source levels to determine the temporal and spatial neural patterns of micro- and macro-expressions. We found that micro-expressions were more strongly activated in the premotor cortex, supplementary motor cortex, and middle frontal gyrus in frontal regions under positive emotions than macro-expressions. Under negative emotions, micro-expressions were more weakly activated in the somatosensory cortex and corneal gyrus regions than macro-expressions. The activation of the right temporoparietal junction (rTPJ) was stronger in micro-expressions under positive than negative emotions. The reason for this difference is that the pathways of facial control are different; the production of micro-expressions under positive emotion is dependent on the control of the face, while micro-expressions under negative emotions are more dependent on the intensity of the emotion.

The Influence of Music Tempo on Inhibitory Control: An ERP Study.

The purpose of the present study is to investigate the influence of music tempo on inhibition control. An electroencephalogram (EEG) was recorded when participants performed a Go/No-go task while listening to slow (54 bpm), medium-paced (104 bpm), fast (154 bpm), or no music. The behavioral results showed that the accuracies for the No-go trials were lower in the fast than in the slow tempo music conditions, while the accuracies for the Go trials were also lower in the fast tempo than in no music conditions. The event-related potential (ERP) study results showed that larger N2 and P3 amplitudes were elicited by No-go than by Go conditions. Moreover, the difference N2 (N2d) amplitudes observed by No-go vs. Go condition were larger in fast music than in medium-paced, slow, and no music conditions, indicating more consumption of cognitive resources in the process of conflict monitoring under the fast music condition. However, no such differences were observed among medium-paced, slow, and no music conditions. In addition, the difference P3 (P3d) amplitudes, an index of response inhibition, were not significant among these four music conditions. The present study showed a detrimental influence of music tempo on inhibition control. More specifically, listening to fast music might impair an individual's ability to monitor conflict when performing the inhibitory control task.