Social Interaction Behavior Research Articles

Bayesian optimization of one-dimensional convolutional neural networks (1D CNN) for early diagnosis of Autistic Spectrum Disorder

Autistic Spectrum Disorder (ASD) is a challenging neurological development disorder, which involves poor social interaction, communication, and repetitive behaviours. If autism is identified early enough it can be treated with better outcomes but present diagnostic tests are dependent on subjective opinion, consume a lot of time, and are vague. This study is aimed at optimizing one-dimensional convolutional neural networks (1D CNN) to improve the precision and speed of early ASD diagnosis. Four ASD datasets representing different age groups — toddlers, children, adolescents, and adults were modelled using one-dimensional convolutional neural networks (1D CNN). These datasets are accessible to the public on the UCI Machine Learning Repository and Kaggle, they consist of behavioural features relevant to ASD diagnosis. Each dataset underwent feature selection, categorical encoding, and missing value handling. Then, baseline 1D CNN with predefined hyperparameters was modelled on each of the datasets. Subsequently, the baseline models were optimized using the Tree-structured Parzen Estimator (TPE). An interactive web-based ASD diagnostic tool was developed, where user inputs are processed through age-specific pre-trained optimized models to determine ASD probability. The optimized 1D CNN models significantly outperformed the baseline models across all age groups and achieved scores of 100% in accuracy, precision, recall, F1-score, MCC, and AUC ROC. This implies that the optimized models can reliably identify people in various age groups who have and do not have ASD. The development of an interactive web-based diagnostic tool extends the practical utility of the models, making them accessible for clinical and at-home use.

The Effect of Observational Learning and Word of Mouth Learning on Consumer Behaviors

This article explores two learning methods of social interaction in social media and e-commerce environments: observational learning and word-of-mouth learning, and their impact on consumer behavior. This article summarize the influence of two learning methods on consumer behavior in social interaction, and their differences. The article found that both observational learning and word-of-mouth learning have a significant impact on consumer behavior, with positive observational learning having a clear effect on information., Negative word-of-mouth learning information has a stronger influence on consumer decision-making. Observational learning enhances external motivation, while word-of-mouth learning stimulates internal motivation. When highly involved in product decision-making, word-of-mouth details are more critical; Low involvement products allow for more effective observation and learning of information. The impact of word-of-mouth learning on long-term decision-making increases over time. Observing and learning information spreads quickly, while word-of-mouth information spreads slowly. Understanding these mechanisms is crucial for e-commerce platforms and sellers to develop marketing strategies.

Psychometric properties and factor structure of triarchic psychopathy measure (TriPM) in portuguese adolescents

Psychopathy is a personality structure characterized by a set of dysfunctional traits, impulsive and irresponsible behavior, a poor and arrogant personal style in social interactions, and a lack of prosocial emotions. Although it cannot be diagnosed in adolescents, there have been references to its presence in its population. There is substantial evidence indicating a correlation between psychopathy and antisocial and criminal behavior in young people. It is therefore important that psychopathic traits be identified as early as possible to predict and control future criminal behavior. However, the specific evaluation instruments for this population are scarce. The aim of the present study is to validate an instrument for the evaluation of psychopathy, the Triarchic Psychopathy Measure (TriPM), for use with this population. The questionnaires were applied a validation sample of 793 young or Portuguese adolescents. A confirmatory factorial analysis (CFA) was performed, and the results found were considered satisfactory, χ2(1350) = 5980.77, p < .001, RMSEA = .067 (95% CI = [.064 - .068]), CFI = .915, TLI = .910, and SRMR = .080.

Salubrious effects of proanthocyanidins on behavioral phenotypes and DNA repair deficiency in the BTBR mouse model of autism

Autism is a neurodevelopmental disorder distinguished by impaired social interaction and repetitive behaviors. Global estimates indicate that autism affects approximately 1.6% of children, with the condition progressively becoming more prevalent over time. Despite noteworthy progress in autism research, the condition remains untreatable. This serves as a driving force for scientists to explore new approaches to disease management. Autism is linked to elevated levels of oxidative stress and disturbances in the DNA repair mechanism, which may potentially play a role in its comorbidities development. The current investigation aimed to evaluate the beneficial effect of the naturally occurring flavonoid proanthocyanidins on the behavioral characteristics and repair efficacy of autistic BTBR mice. Moreover, the mechanisms responsible for these effects were clarified. The present findings indicate that repeated administration of proanthocyanidins effectively reduces altered behavior in BTBR animals without altering motor function. Proanthocyanidins decreased oxidative DNA strand breaks and accelerated the rate of DNA repair in autistic animals, as evaluated by the modified comet test. In addition, proanthocyanidins reduced the elevated oxidative stress and recovered the disrupted DNA repair mechanism in the autistic animals by decreasing the expressions of Gadd45a and Parp1 levels and enhancing the expressions of Ogg1, P53, and Xrcc1 genes. This indicates that proanthocyanidins have significant potential as a new therapeutic strategy for alleviating autistic features.

α-Synuclein aggregation decreases cortico-amygdala connectivity and impairs social behavior in mice

Abnormal accumulation of insoluble α-synuclein (α-Syn) inclusions in neurons, neurites, and glial cells is the defining neuropathology of synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy. Accumulation of α-Syn inclusions in the amygdala has been well-documented in post-mortem studies of PD and DLB brains, as well as preclinical animal models of these conditions. Though α-Syn pathology is closely associated with neurodegeneration, there is a poor correlation between neuronal loss in the amygdala and the clinical features of PD and DLB. Moreover, functional interaction between the cerebral cortex and the amygdala is critical to regulating emotion, motivation, and social behaviors. The cortico-amygdala functional interaction is likely to be disrupted by the development of α-Syn pathology in the brain. Thus, we hypothesize that neuronal α-Syn inclusions disrupt cortical modulation of the amygdala circuits and are sufficient to drive social behavioral deficits. In the present work, we designed a series of longitudinal studies to rigorously measure the time courses of neurodegeneration, functional impairment of cortico-amygdala connectivity, and development of amygdala-dependent social behavioral deficits to test this hypothesis. We injected α-Syn preformed fibrils (PFFs) into the dorsal striatum to induce α-Syn aggregation in the amygdala and the medial prefrontal cortex (mPFC) of C57BL6 mice of both sexes, followed by a detailed analysis of temporal development of α-Syn pathology, synaptic deficits, and neuronal loss in the amygdala, as well as behavioral deficits at 3–12 months post injections. Development of α-Syn inclusions caused losses of cortical axon terminals and cell death in the basolateral amygdala (BLA) at 6- and 12-months post injections, respectively. At a relatively early stage of 3 months post injections, the connection strength of the mPFC-BLA synapse was decreased in PFFs-injection mice compared to controls. Meanwhile, the PFFs-injected mice showed impaired social interaction behavior, which was rescued by chemogenetic stimulation of mPFC-BLA connections. Altogether, we presented a series of evidence to delineate circuit events in the amygdala associated with the accumulation of α-Syn inclusions in the mouse brain, highlighting that functional impairment of the amygdala is sufficient to cause social behavior deficits. The present work further suggests that early circuit modulation could be an effective approach to alleviate symptoms associated with α-Syn pathology, necessitating studies of functional consequences of α-Syn aggregation.

Mild traumatic brain injury gives rise to chronic depression-like behavior and associated alterations in glutamatergic protein expression

Mild traumatic brain injury (mTBI) is known to result in chronic somatic, cognitive, and emotional symptoms. Depression is commonly reported among individuals suffering from persistent concussion symptoms; however, the underlying mechanisms are not understood. The glutamatergic system has recently been linked with mTBI and depression due to reports of similar changes in expression of glutamatergic proteins. Using a closed-head controlled cortical impact (cCCI) model in adult male rats (n = 8/group), this study investigated the emergence of self-care deficits and changes in social interaction behaviors at four, eight and twelve weeks post-injury. Western blotting was used to assess associated changes in expression of glutamate transporters and N-methyl-D-aspartate (NMDA) receptor subunits at twelve weeks. Splash test results revealed deficits in self-care behaviors beginning at eight weeks, which continued through twelve weeks in the injury group. Injured animals also exhibited decreased preference for social novelty at four weeks and loss of desire for social interaction as a whole by twelve weeks. GluN1 was increased in injured animals compared to shams in the frontal cortex and amygdala, while decreased GLT-1 was observed in the hippocampus. Linear regression was performed to evaluate relationships between behavioral and molecular variables; the results suggested that injury affects these relationships in a region-dependent manner. Together, these results suggest that the development of chronic depression-like behavior was associated with changes in glutamatergic protein expression. Deeper investigations into how injury influences glutamatergic synaptic protein expression are needed, as this has the potential to affect circuit-level neurotransmission that drives depression-like behavior following mTBI.

Transforming the future of nursing care: Artificial intelligence in pediatrics autism nursing care

The advent of artificial intelligence (AI) is revolutionizing various fields, including healthcare. AI's impact is increasingly evident in managing and caring for autistic pediatric patients [1]. The integration of AI into pediatrics autism nursing care is revolutionizing the approach to diagnosis, treatment, and ongoing management of children with autism spectrum disorder (ASD). Through their capacity to process and analyze vast amounts of data, AI technologies offer new possibilities for enhancing the precision, efficiency, and personalization of care for children with autism. ASD presents unique challenges that require specialized, often intensive, care strategies. According to Zhao et al., (2024), integrating AI into nursing care for children with autism offers promising opportunities to enhance diagnostics, personalize treatment plans, and improve overall patient outcomes [2]. This commentary explores how AI is transforming pediatric autism nursing care and the vital role nurses play in this evolving landscape. ASD is a developmental disorder characterized by challenges with social interaction, communication, and repetitive behaviors. Symptoms and their severity vary widely among individuals, making ASD a spectrum disorder. Research indicates that early diagnosis and intervention are crucial for improving outcomes, yet these can be difficult due to the complex and varied presentation of the disorder [3].

Assessing sociability using the Three-Chamber Social Interaction Test and the Reciprocal Interaction Test in a genetic mouse model of ASD.

Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders with heterogeneous symptomatology. Arguably, the most pervasive shortfall of ASD are the deficits in sociability and the animal models of the disorder are expected to exhibit such impairments. The most widely utilized behavioral task for assessing sociability in rodents is the Three-Chamber Social Interaction Test (SIT). However, SIT has been yielding inconsistent results in social interaction behavior across different rodent models of ASD, which could be pointing to the suboptimal methodology of the task. Here, we compared social behavior assessed in SIT and in another prominent sociability behavioral assay, Reciprocal Interaction Test (RCI), in a SH3 and multiple ankyrin repeated domains 3 (SHANK3) mouse model of ASD. Head-to-head comparison showed no association (p = 0.15, 0.25, 0.43) and a fixed bias (p = 0.01, < 0.001, < 0.001) in sociability assessment between the behavioral assays in both wild-type (WT) controls and Shank3B(-/-) mice. Adult Shank3B(-/-) mice of both sexes displayed normative sociability in SIT when compared to the WT controls (p = 0.74) but exhibited less than half of social interaction (p < 0.001) and almost three times more social disinterest (p < 0.001) when compared to WT mice in RCI. At least in the Shank3B(-/-) mouse model of ASD, we presume RCI could be a preferable way of assessing social interaction compared to SIT. Considering the variability of animal models of ASD and the wide palette of tools available for the assessment of their behavior, a consensus approach would be needed for observational and interventional analyses.

Unveiling the role of IGF1R in autism spectrum disorder: a multi-omics approach to decipher common pathogenic mechanisms in the IGF signaling pathway.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by impairments in social interaction, communication, and repetitive behaviors. Emerging evidence suggests that the insulin-like growth factor (IGF) signaling pathway plays a critical role in ASD pathogenesis; however, the precise pathogenic mechanisms remain elusive. This study utilizes multi-omics approaches to investigate the pathogenic mechanisms of ASD susceptibility genes within the IGF pathway. Whole-exome sequencing (WES) revealed a significant enrichment of rare variants in key IGF signaling components, particularly the IGF receptor 1 (IGF1R), in a cohort of Chinese Han individuals diagnosed with ASD, as well as in ASD patients from the SFARI SPARK WES database. Subsequent single-cell RNA sequencing (scRNA-seq) of cortical tissues from children with ASD demonstrated elevated expression of IGF receptors in parvalbumin (PV) interneurons, suggesting a substantial impact on their development. Notably, IGF1R appears to mediate the effects of IGF2R on these neurons. Additionally, transcriptomic analysis of brain organoids derived from ASD patients indicated a significant association between IGF1R and ASD. Protein-protein interaction (PPI) and gene regulatory network (GRN) analyses further identified ASD susceptibility genes that interact with and regulate IGF1R expression. In conclusion, IGF1R emerges as a central node within the IGF signaling pathway, representing a potential common pathogenic mechanism and therapeutic target for ASD. These findings highlight the need for further investigation into the modulation of this pathway as a strategy for ASD intervention.

Understanding Depression in Autism: The Role of Subjective Perception and Anterior Cingulate Cortex Volume.

The prevalence of depression is elevated in individuals with autism spectrum disorder (ASD) compared to the general population, yet the reasons for this disparity remain unclear. While social deficits central to ASD may contribute to depression, it is uncertain whether social interaction behavior themselves or individuals' introspection about their social behaviors are more impactful. Although the anterior cingulate cortex (ACC) and amygdala are frequently implicated in ASD, depression, and social functioning, it is unknown if these regions explain differences between ASD adults with and without co-occurring depression. The present study contrasted observed vs. subjective perception of autism symptoms and social performances assessed with both standardized measures and a lab task, in 65 sex-balanced (52.24% male) autistic young adults. We also quantified ACC and amygdala volume with 7-Tesla structural neuroimaging to examine correlations with depression and social functioning. We found that ASD individuals with depression exhibited differences in subjective evaluations including heightened self-awareness of ASD symptoms, lower subjective satisfaction with social relations, and less perceived affiliation during the social interaction task, yet no differences in corresponding observed measures, compared to those without depression. Larger ACC volume was related to depression, greater self-awareness of ASD symptoms, and worse subjective satisfaction with social interactions. In contrast, amygdala volume, despite its association with clinician-rated ASD symptoms, was not related to depression. Due to the cross-sectional nature of our study, we cannot determine the directionality of the observed relationships. Additionally, we included only individuals with an IQ over 60 to ensure participants could complete the social task, which excluded many on the autism spectrum. We also utilized self-reported depression indices instead of clinically diagnosed depression, which may limit the comprehensiveness of the findings. Our approach highlights the unique role of subjective perception of autism symptoms and social interactions, beyond the observable manifestation of social interaction in ASD, in contributing to depression, with the ACC playing a crucial role. These findings imply possible heterogeneity of ASD concerning co-occurring depression. Using neuroimaging, we were able to demarcate depressive phenotypes co-occurring alongside autistic phenotypes.

Autism-Related Cc2d1a Heterozygous Mice: Increased Levels of miRNAs Retained in DNA/RNA Hybrid Profiles (R-Loop).

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a highly variable expression of phenotypes (restricted interest or activity and repetitive behavior in communication and social interactions), genes (mutation), markers (alteration of transcription) and pathways. Loss of function of the CC2D1A gene appears to primarily affect the brain, leading to a range of behavioral problems in humans. In our study published in 2020, we found that the expressions of miR-19a-3p, miR-361-5p, miR-150-5p, miR-3613-3p, miR-126-3p and miR-499a-5p were downregulated in the serum samples of autistic patients, their families and mouse models (Cc2d1a +/- and valproic acid treated males). Here, acquired non-Mendelian hereditary character in a genetically defined mouse model of autism (Cc2d1a +/-) correlates with the transcriptional alteration of five miRNAs. We seek to test the hypothesis that miRNA levels vary by changes in RNA/DNA structure during development, thereby creating transcription alteration and cell memory. Behavioral tests were conducted on the offspring of Cc2d1a (+/-) mutant and control mice, such as novel object, social interaction, marble burying and tail suspension behavior. Two RNA fractions were isolated from mouse hippocampal tissues and sperm cells via standard TRIzol extraction: free RNA and the fraction of RNA bound to DNA in the form of a DNA/RNA hybrid (R-loop). The expression levels of miR-19a-3p, miR-361-5p, miR-150-5p, miR-126-3p and miR-499a-5p were investigated by quantitative real-time RT-PCR. We report differences in the distribution of five miRNAs in the hippocampus between male and female mice, particularly in colonies of Cc2d1a (+/-) mice. Furthermore, the number of miRNAs engaged in the DNA/RNA hybrid fraction is generally higher in the mutant pedigree than in the control group. On the other hand, in sperm, both fractions are at lower levels than in controls. R-loops contribute to the physiology and pathology of organisms including human disease. Here, we report a variation in five miRNA levels between gender and tissue. Our results suggest that the transcription levels of these five miRNAs are directly regulated by their RNA.

EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts

Understanding emotional states is pivotal for the development of next-generation human-machine interfaces. Human behaviors in social interactions have resulted in psycho-physiological processes influenced by perceptual inputs. Therefore, efforts to comprehend brain functions and human behavior could potentially catalyze the development of AI models with human-like attributes. In this study, we introduce a multimodal emotion dataset comprising data from 30-channel electroencephalography (EEG), audio, and video recordings from 42 participants. Each participant engaged in a cue-based conversation scenario, eliciting five distinct emotions: neutral, anger, happiness, sadness, and calmness. Throughout the experiment, each participant contributed 200 interactions, which encompassed both listening and speaking. This resulted in a cumulative total of 8,400 interactions across all participants. We evaluated the baseline performance of emotion recognition for each modality using established deep neural network (DNN) methods. The Emotion in EEG-Audio-Visual (EAV) dataset represents the first public dataset to incorporate three primary modalities for emotion recognition within a conversational context. We anticipate that this dataset will make significant contributions to the modeling of the human emotional process, encompassing both fundamental neuroscience and machine learning viewpoints.

Harmonizing Insights: LR-FFT Feature Extraction for Alzheimer’s and Autism Spectrum Disorder Detection in EEG Signals

Alzheimer’s Disease (AD) and Autism Spectrum Disorder (ASD) represent two distinct but equally impactful challenges in the field of neurology and cognitive health. AD is a degenerative neurological condition characterized by its progressive nature, typically affecting individuals in later stages of life. The hallmark features include cognitive impairment, mem- ory deterioration, and alterations in behavior. In contrast, ASD is a developmental disorder typically diagnosed in childhood, marked by difficulties in social interaction, communication, and repetitive behaviors. This paper explores the potential of the time-frequency feature extraction model known as the Left-Right Fast Fourier Transform (LR-FFT) in the context of these two disorders. While AD and ASD differ significantly in their onset, presentation, and demographic affected, both necessitate early and accurate diagnosis to enable timely intervention and tailored treatment strategies. Our research yields promising results, with classification accuracies reaching 88.26% for AD and 99.86% for ASD, demonstrating the LR-FFT’s potential to enhance diagnostic accuracy. By contributing to improved differentiation between these complex neurological conditions, this work aims to advance our understanding and management of AD and ASD, ultimately benefiting patients, their families, and healthcare practitioners.

Introducing yourself to strangers: does conversational self-presentation matter in peer-to-peer accommodation

PurposeSelf-presentation has emerged as a pivotal marketing strategy for service providers seeking to craft virtual images in the peer-to-peer (P2P) accommodation sector. However, the literature lacks an understanding of conversational self-presentation, which offers more informal and personal communication.Design/methodology/approachDrawing upon social interaction theory and uncertainty reduction theory, this study explores the impact of conversational self-presentation and its interplay with space sharing and review ratings on customer purchases.FindingsUtilizing the fixed effects model on a panel dataset comprising 498,817 observations from Airbnb, we discover a positive association between conversational self-presentation and customer purchases. Furthermore, our analysis reveals that space sharing reinforces this positive effect, while review ratings tend to diminish it. A deeper mechanism analysis indicates that perceived authenticity plays a mediating role in the relationship between conversational self-presentation and customer purchases.Research limitations/implicationsThis study highlights the importance of conversational communication in enhancing host–guest relationships under information asymmetry. However, the proposed model can be extended to other accommodation-sharing platforms in various regions, identifying whether cultural differences exist in explaining the importance of conversational self-presentation.Practical implicationsMarketers are suggested to build a harmonious customer–brand relationship and achieve superior performance through effective online communication.Originality/valueThis study adds valuable insights to the literature about self-presentation, social interaction and customer behavior by revealing the significance of conversational self-presentation in driving the growth of P2P accommodation businesses.

A state-space relational event modeling approach for learning dynamic social interaction behavior

Relational event models (REMs) are the primary choice for the analysis of relational-event network data. However, the standard REM assumes static parameters, which hinders the modeling of time-varying dynamics. This assumption might be too restrictive in real-life scenarios, making a model that allows for time-varying parameters more valuable. We introduce a state-space extension of the relational event model as a way to tackle this problem. The model has three main attributes. First, it provides a statistical framework of the temporal change of the parameters. Second, it enables the forecasting of future parameter values (which can be utilized to simulate new networks that can account for temporal dynamics in out-of-sample predictions). Third, it requires smaller data structures to be loaded into computer memory compared to the standard REM; this makes the model easily scalable to large networks. We conduct empirical analyses on bike-sharing data, corporate communications, and interactions among socio-political actors to illustrate model usage and applicability.

The Psychological and Social Effects of Social Media on Adolescents

This study examines the psychological and social effects of social media on adolescents, using a sample of 300 participants to analyze their social media interaction behaviors. Through descriptive statistics, correlation analysis, regression models, and independent samples tests, variables such as social media usage duration, frequency of checks, notification responses, and online interaction preferences were examined. The regression analysis revealed an R^2 value of 0.031, indicating a poor model fit and suggesting that the predictors did not significantly explain the variance in the psychological outcomes measured. The findings indicate a minimal influence of specific measured social media behaviors, pointing to the potential impact of unmeasured factors such as personality traits and offline social environments. The study highlights the need for future research to incorporate a broader range of variables and employ longitudinal designs to better understand the complex effects of social media on adolescent well-being.

Predictors of mother-infant interaction quality in women at risk of postpartum psychosis: The role of emotion recognition

BackgroundLimited research exists on mother-infant interaction in women at-risk-of postpartum psychosis (PP). This study aimed to investigate potential predictors of mother-infant interaction quality in women at-risk-of-PP during the first postnatal year. Potential predictors investigated were: maternal ability to recognize emotions, childhood maltreatment, parenting stress, and infant social-interactive behaviour at birth. Methods98 women (and their offspring) were included, 40 at-risk-of-PP because of a diagnosis of Bipolar Disorder, Schizoaffective Disorder or previous PP, and 58 with no current/previous mental illness or family history of PP. Mother-infant interaction was assessed using the CARE-Index at 8 weeks and 12 months postpartum. Maternal ability to recognize emotions was assessed with the VERT-K, maternal experience of childhood maltreatment with the CECA-Q, maternal parenting stress with the PSI-SF and infant social-interactive behaviour with the NBAS. ResultsWomen at-risk-of-PP were less able to recognize fear than healthy controls and this predicted the quality of the mother-infant interaction at 8 weeks' and 12 months' post partum, over and above the effect of maternal Group (respectively, β = 0.33, p = .015; β = 0.40, p = .006). Infant social-interactive behaviour at birth was a significant predictor for mother-infant interaction at 12 months (β = 0.32, p = .031), although this did not differ significantly between the groups. LimitationsA relatively small sample size precluded a more in-depth investigation of indirect pathways and other potential predictors. ConclusionsThese results are important as they suggest that preventive interventions targeting emotion recognition may be implemented in women at-risk-of-PP, with the aim of improving mother-infant interaction and potentially also the infant long-term development.

Brief historical account of autism in Peru.

We present a historical account of autism in Peru. Currently, the term "autism spectrum conditions", from the neurodiversity paradigm, is used to describe neurodevelopmental disorders characterized by persistent difficulties in communication, social interaction, and restricted and repetitive behaviors and interests. In Peru, the scientific study of nervous and mental diseases began around 1920 and although the diagnosis of "childhood autism" was proposed in 1959, it only began to spread in the 1980s. Although significant advances were made in the 21st century, Peru still faces many challenges in addressing autism.

Safety behaviors and positive emotions in social anxiety disorder

The present study examined contextual factors that affect safety behavior use as well as positive emotions when using safety behaviors among individuals with SAD. Eighty-eight participants took part in the study, half (n = 44) met diagnostic criteria for SAD and half (n = 44) did not meet criteria for SAD. Participants completed a 21-day experience sampling methodology (ESM) measurement in which they reported on daily social interactions, safety behavior use, and emotions. Using multilevel linear modeling we found that both individuals with and without SAD used more safety behaviors when interacting with distant others compared to close others, but this effect was greater for individuals with SAD compared to individuals without SAD. We also found that social anxiety significantly moderated the relationship between safety behaviors in social interactions and positive emotions. Specifically, our findings indicated that individuals with higher levels of social anxiety reported lower levels of positive emotions when using safety behaviors. Implications of our findings for models of psychopathology and for treatment of SAD are discussed.

Selective deletion of Tsc1 from mouse cerebellar Purkinje neurons drives sex-specific behavioral impairments linked to autism.

There is a striking sex bias in the prevalence and severity of autism spectrum disorder (ASD) with 80% of diagnoses occurring in males. Because the molecular etiology of ASD is likely combinatorial, including interactions across multiple genetic and environmental factors, it is difficult to investigate the physiological mechanisms driving sex-specific differences. Loss of function mutations in TSC1 result in dysregulated mTORC1 signaling and underlie a multi-system disorder known as tuberous sclerosis (TSC). Interestingly, more than 50% of individuals diagnosed with TSC are also diagnosed with ASD, making TSC mutations one of the most prevalent monogenic causes of ASD. Mice harboring targeted deletion of Tsc1 selectively in cerebellar Purkinje neurons, referred to here as Tsc1 mut/mut , have multiple ASD-linked behavioral impairments, including deficits in social interactions, motor coordination, and vocalizations. However, these ASD-linked behavioral deficits have only been investigated using male Tsc1 mut/mut animals. Here, we used cohorts of male and female Tsc1 mut/mut animals to determine if behavioral impairments, previously identified in this model, are similar across sex. Specifically, we measured balance and motor coordination and social interaction behaviors in two age groups across sex. W e determined balance and motor coordination deficits are similar in male and female Tsc1 mut/mut mice, and that deficits in the firing of Tsc1 mut/mut Purkinje neurons located in the cerebellar vermis are also similar across sex. However, impairments in social approach behavior were found to be significantly more severe in Tsc1 mut/mut males compared to females. These results indicate the selective deletion of Tsc1 in Purkinje neurons differentially impairs cerebellar circuits based on sex.