From birth to adulthood, we often align our behaviors, attitudes, and opinions with a majority, a phenomenon known as social conformity. A seminal framework has proposed that conformity behaviors are mainly driven by three fundamental motives: a desire to gain more information to be accurate, to obtain social approval from others, and to maintain a favorable self-concept. Despite extensive interest in neuroimaging investigation of social conformity, the relationship between brain systems and these fundamental motivations has yet to be established. Here, we reviewed brain imaging findings of social conformity with a componential framework, aiming to reveal the neuropsychological substrates underlying different conformity motivations. First, information-seeking engages the evaluation of social information, information integration, and modification of task-related activity, corresponding to brain networks implicated in reward, cognitive control, and tasks at hand. Second, social acceptance involves the anticipation of social acceptance or rejection and mental state attribution, mediated by networks of reward, punishment, and mentalizing. Third, self-enhancement entails the excessive representation of positive self-related information and suppression of negative self-related information, ingroup favoritism and/or outgroup derogation, and elaborated mentalizing processes to the ingroup, supported by brain systems of reward, punishment, and mentalizing. Therefore, recent brain imaging studies have provided important insights into the fundamental motivations of social conformity in terms of component processes and brain mechanisms.
Humans ; Social Conformity ; Motivation ; Brain ; Social Behavior ; Brain Mapping

OBJECTIVES: To investigate the efficacy and required indicators of Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) in the differential diagnosis of autism spectrum disorder (ASD) and global developmental delay (GDD). METHODS: A total of 277 children with ASD and 415 children with GDD, aged 18-48 months, were enrolled as subjects. CNBS-R2016 was used to assess the developmental levels of six domains, i.e., gross motor, fine motor, adaptive ability, language, social behavior, and warning behavior, and a total of 13 indicators on intelligence age and developmental quotient (DQ) were obtained as the input features. Five commonly used machine learning classifiers were used for training to calculate the classification accuracy, sensitivity, and specificity of each classifier. RESULTS: DQ of warning behavior was selected as the first feature in all five classifiers, and the use of this indicator alone had a classification accuracy of 78.90%. When the DQ of warning behavior was used in combination with the intelligence age of warning behavior, gross motor, and language, it had the highest classification accuracy of 86.71%. CONCLUSIONS Machine learning combined with CNBS-R2016 can effectively distinguish children with ASD from those with GDD. The DQ of warning behavior plays an important role in machine learning, and its combination with other features can improve classification accuracy, providing a basis for the efficient and accurate differential diagnosis of ASD and GDD in clinical practice.
Child ; Humans ; Autism Spectrum Disorder/psychology* ; Diagnosis, Differential ; Machine Learning ; Social Behavior

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions. Traditional tracking methods (e.g., marking each animal with dye or surgically implanting microchips) can be invasive and may have an impact on the social behavior being measured. To overcome these shortcomings, video-based methods for tracking unmarked animals, such as fruit flies and zebrafish, have been developed. However, tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction patterns. In this study, we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network (R-CNN) deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion technology. The system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing behavior. The proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.
Animals ; Mice ; Deep Learning ; Zebrafish ; Algorithms ; Neural Networks, Computer ; Social Behavior

OBJECTIVE: To investigate the underlying molecular mechanisms by which silence information regulator (SIRT) 2 and glutaminase (GLS) in the amygdala regulate social behaviors in autistic rats. METHODS: Rat models of autism were established by maternal sodium valproic acid (VPA) exposure in wild-type rats and SIRT2-knockout ( SIRT2 ^-/-) rats. Glutamate (Glu) content, brain weight, and expression levels of SIRT2, GLS proteins and apoptosis-associated proteins in rat amygdala at different developmental stages were examined, and the social behaviors of VPA rats were assessed by a three-chamber test. Then, lentiviral overexpression or interference vectors of GLS were injected into the amygdala of VPA rats. Brain weight, Glu content and expression level of GLS protein were measured, and the social behaviors assessed. RESULTS: Brain weight, amygdala Glu content and the levels of SIRT2, GLS protein and pro-apoptotic protein caspase-3 in the amygdala were increased in VPA rats, while the level of anti-apoptotic protein Bcl-2 was decreased (all P<0.01). Compared with the wild-type rats, SIRT2 ^-/- rats displayed decreased expression of SIRT2 and GLS proteins in the amygdala, reduced Glu content, and improved social dysfunction (all P<0.01). Overexpression of GLS increased brain weight and Glu content, and aggravated social dysfunction in VPA rats (all P<0.01). Knockdown of GLS decreased brain weight and Glu content, and improved social dysfunction in VPA rats (all P<0.01). CONCLUSIONS The glutamate circulatory system in the amygdala of VPA induced autistic rats is abnormal. This is associated with the upregulation of SIRT2 expression and its induced increase of GLS production; knocking out SIRT2 gene or inhibiting the expression of GLS is helpful in maintaining the balanced glutamate cycle and in improving the social behavior disorder of rats.
Animals ; Rats ; Amygdala/metabolism* ; Autistic Disorder/metabolism* ; Behavior, Animal ; Disease Models, Animal ; Glutamates/metabolism* ; Glutaminase/metabolism* ; Sirtuin 2/metabolism* ; Social Behavior

Chronic stress leads to many psychiatric disorders, including social and anxiety disorders that are associated with over-activation of neurons in the basolateral amygdala (BLA). However, not all individuals develop psychiatric diseases, many showing considerable resilience against stress exposure. Whether BLA neuronal activity is involved in regulating an individual's vulnerability to stress remains elusive. In this study, using a mouse model of chronic social defeat stress (CSDS), we divided the mice into susceptible and resilient subgroups based on their social interaction behavior. Using in vivo fiber photometry and in vitro patch-clamp recording, we showed that CSDS persistently (after 20 days of recovery from stress) increased BLA neuronal activity in all the mice regardless of their susceptible or resilient nature, although impaired social interaction behavior was only observed in susceptible mice. Increased anxiety-like behavior, on the other hand, was evident in both groups. Notably, the CSDS-induced increase of BLA neuronal activity correlated well with the heightened anxiety-like but not the social avoidance behavior in mice. These findings provide new insight to our understanding of the role of neuronal activity in the amygdala in mediating stress-related psychiatric disorders.
Amygdala ; Animals ; Anxiety/etiology* ; Anxiety Disorders ; Avoidance Learning ; Mice ; Mice, Inbred C57BL ; Social Behavior ; Stress, Psychological/complications*

Objective: To investigate the prevalence of suicidal ideation among MSM, and to explore the influence of perceived social support and depression on suicidal ideation and the possible pathway. Methods: MSM who are ≥18 years, HIV-negative, and ever had sex with men (oral sex/anal sex) in the last year participated in is study. Cross-sectional survey and convenient sampling method were used to recruit MSM through a male social interaction platform Blued 7.5 software from December 2020 to March 2021, with a questionnaire survey. Demographic questionnaire, Perceived Social Support Questionnaire and Center for Epidemiologic Studies Depression Scale were employed to collect data. Mplus 8.3 was used for data analysis. Results: Among 1 394 eligible MSM participants, 25.7% (358/1 394) had suicidal ideation. Perceived social support could have a direct effect on suicidal ideation (β=-0.11, P=0.009), and an indirect effect on suicidal ideation through depression (β=-0.18, P<0.001). Conclusions: Suicidal ideation was found to be lower in MSM with a higher level of perceived social support and a lower level of depression. Using social media to improve MSM's perceived social support and reduce depressive symptoms might be an effective means to prevent suicidal ideation.
Cross-Sectional Studies ; Depression/epidemiology* ; Homosexuality, Male ; Humans ; Male ; Sexual Behavior ; Sexual and Gender Minorities ; Social Support ; Suicidal Ideation

Autism Spectrum Disorder ; Autistic Disorder ; Humans ; Serotonin ; Social Behavior

Medical disputes are one of the common problems concerned by the whole world. All countries and regions have established their own medical dispute resolution mechanisms, in accordance with their own national conditions. Medical dispute identification opinions, as one of the important bases for identifying the responsibilities of both doctors and patients, play a pivotal role in the process of dispute settlement. A reasonable medical dispute resolution mechanism and standardized medical dispute identification model can help resolve disputes flexibly and reduce the conflict between doctors and patients. This paper briefly compares the medical dispute resolution mechanism and identification mode of China and several other representative countries (the United States, Britain, France, Germany, Italy, Japan, etc.), and discusses their respective characteristics and shortcomings, to bring some enlightenment to the medical dispute resolution and identification in our country.
China ; Dissent and Disputes ; Humans ; Social Behavior

BACKGROUND: The study aimed to explore the factors influencing protective behavior and its association with factors during the post-COVID-19 period in China based on the risk perception emotion model and the protective action decision model (PADM). METHODS: A total of 2830 valid questionnaires were collected as data for empirical analysis via network sampling in China. Structural equation modeling (SEM) was performed to explore the relationships between the latent variables. RESULTS: SEM indicated that social emotion significantly positively affected protective behavior and intention. Protective behavioral intention had significant direct effects on protective behavior, and the direct effects were also the largest. Government trust did not have a significant effect on protective behavior but did have a significant indirect effect. Moreover, it was found that government trust had the greatest direct effect on social emotion. In addition, we found that excessive risk perception level may directly reduce people's intention and frequency of engaging in protective behavior, which was not conducive to positive, protective behavior. CONCLUSION In the post-COVID-19 period, theoretical framework constructed in this study can be used to evaluate people's protective behavior. The government should strengthen its information-sharing and interaction with the public, enhance people's trust in the government, create a positive social mood, appropriately regulate people's risk perception, and, finally, maintain a positive attitude and intent of protection.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; COVID-19/prevention & control* ; China/epidemiology* ; Communicable Disease Control ; Cross-Sectional Studies ; Emotions ; Female ; Health Behavior ; Humans ; Intention ; Latent Class Analysis ; Male ; Middle Aged ; SARS-CoV-2 ; Social Behavior ; Surveys and Questionnaires ; Trust ; Young Adult

Animals ; Anxiety ; Fear ; Mice ; Mice, Inbred C57BL ; Social Behavior