Common and distinct neural bases of multiple positive emotion regulation strategies: A functional magnetic resonance imaging study

Event-related prefrontal activations during online video game playing are modulated by game mechanics, physiological arousal and the amount of daily playing

There is a trend to study human brain functions in ecological contexts and in relation to human factors. In this study, functional near-infrared spectroscopy (fNIRS) was used to record real-time prefrontal activities in 42 male university student habitual video game players when they played a round of multiplayer online battle arena game, League of Legends. A content-based event coding approach was used to analyze regional activations in relation to event type, physiological arousal indexed by heart rate (HR) change, and individual characteristics of the player. Game events Slay and Slain were found to be associated with similar HR and prefrontal responses before the event onset, but differential responses after the event onset. Ventrolateral prefrontal cortex (VLPFC) activation preceding the Slay onset correlated positively with HR change, whereas activations in dorsolateral prefrontal cortex (DLPFC) and rostral frontal pole area (FPAr) preceding the Slain onset were predicted by self-reported hours of weekly playing (HoWP). Together, these results provide empirical evidence to support the notion that event-related regional prefrontal activations during online video game playing are shaped by game mechanics, in-game dynamics of physiological arousal and individual characteristics the players.

Enhancing ventrolateral prefrontal cortex activation mitigates social pain and modifies subsequent social attitudes: Insights from TMS and fMRI

Social pain, a multifaceted emotional response triggered by interpersonal rejection or criticism, profoundly impacts mental well-being and social interactions. While prior research has implicated the right ventrolateral prefrontal cortex (rVLPFC) in mitigating social pain, the precise neural mechanisms and downstream effects on subsequent social attitudes remain elusive. This study employed transcranial magnetic stimulation (TMS) integrated with fMRI recordings during a social pain task to elucidate these aspects. Eighty participants underwent either active TMS targeting the rVLPFC (n = 41) or control stimulation at the vertex (n = 39). Our results revealed that TMS-induced rVLPFC facilitation significantly reduced self-reported social pain, confirming the causal role of the rVLPFC in social pain relief. Functional connectivity analyses demonstrated enhanced interactions between the rVLPFC and the dorsolateral prefrontal cortex, emphasizing the collaborative engagement of prefrontal regions in emotion regulation. Significantly, we observed that negative social feedback led to negative social attitudes, whereas rVLPFC activation countered this detrimental effect, showcasing the potential of the rVLPFC as a protective buffer against adverse social interactions. Moreover, our study uncovered the impact role of the hippocampus in subsequent social attitudes, a relationship particularly pronounced during excitatory TMS over the rVLPFC. These findings offer promising avenues for improving mental health within the intricate dynamics of social interactions. By advancing our comprehension of the neural mechanisms underlying social pain relief, this research introduces novel intervention strategies for individuals grappling with social distress. Empowering individuals to modulate rVLPFC activation may facilitate reshaping social attitudes and successful reintegration into communal life.

The influence of induced moods on aging of phonological encoding in spoken word production: an ERP study

This study investigated the influence of induced mood on the phonological encoding involved in Chinese spoken word production with a picture-word inference task while concurrently recorded electrophysiological signals. In the experiment, young and older participants watched videos for inducing positive, negative, or neutral mood, and then they were instructed to name target picture while ignoring phonologically related or unrelated distractor words. A phonological facilitation effect was observed in young adults but not in older adults, suggesting an age-related decline of phonological encoding. Both groups showed an inhibition effect in negative mood but not in positive mood, suggesting that speakers have different processing styles in different moods. ERP data revealed a phonological effect around the time window of 250-350 ms in both groups. Meanwhile, young adults showed a phonological effect around 350-450 ms in negative mood and positive mood which may reflect self-monitoring in speech production. We suggest that the former effect may reflect phonological encoding while the latter reflects self-monitoring of internal syllables or phonemes. Furthermore, induced moods influence the phonological effect in older and young adults differently. Behavioral and ERP results provide consistent evidence for the aging decline of phonological encoding in spoken word production.

EEG-Based Emotion Classification Using Improved Cross-Connected Convolutional Neural Network

The use of electroencephalography to recognize human emotions is a key technology for advancing human–computer interactions. This study proposes an improved deep convolutional neural network model for emotion classification using a non-end-to-end training method that combines bottom-, middle-, and top-layer convolution features. Four sets of experiments using 4500 samples were conducted to verify model performance. Simultaneously, feature visualization technology was used to extract the three-layer features obtained by the model, and a scatterplot analysis was performed. The proposed model achieved a very high accuracy of 93.7%, and the extracted features exhibited the best separability among the tested models. We found that adding redundant layers did not improve model performance, and removing the data of specific channels did not significantly reduce the classification effect of the model. These results indicate that the proposed model allows for emotion recognition with a higher accuracy and speed than the previously reported models. We believe that our approach can be implemented in various applications that require the quick and accurate identification of human emotions.