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Individuals with autism spectrum disorder have deficits in facial emotion recognition and white matter microstructural alterations. Nonetheless, most previous studies were confounded by different variables, such as psychiatric comorbidities and psychotropic medications used by ASD participants. Also, it remains unclear how exactly FER deficits are related to white matter microstructural alterations in ASD. Accordingly, we aimed to investigate the FER functions, white matter microstructure, and their relationship in drug-naive and comorbidity-free ASD individuals. 59 ASD individuals and 59 typically developed individuals were included, where 46 ASD and 50 TD individuals completed FER tasks. Covariance analysis showed scores were lower in both basic and complex FER tasks in the ASD group. Tract-Based Spatial Statistics showed FA values in widespread white matter fibers were lower in the ASD group than in the TD group, including forceps major and forceps minor of the corpus callosum, anterior thalamic radiation, corticospinal tract, cingulum, inferior frontal-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus. Moreover, in the TD group but not the ASD group, the performance in the complex FER task was negatively correlated with the FA value in some white matter fibers, including forceps major of the corpus callosum, ATR, CT, cingulum, IFOF, ILF, SLF. Our study suggests children with ASD may experience deficits in facial emotion recognition and exhibit alterations in white matter microstructure. More importantly, our study indicates that white matter microstructural alterations may be involved in FER deficits in children with ASD.
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There is a pressing need for studies of large sample sizes and variable age ranges to delineate the mechanism underlying reduced visual attention to biological motion in autism. Here we focused on the basic movement of the eyes or mouth in guiding attention. The stimuli face blinked continuously or moved the mouth silently. In a large sample (145 autistic and 132 non-autistic participants) ranging from 3 to 17 years old, we assessed whether autistic participants showed reduced visual attention to basic movement of the eyes or mouth using a free-viewing eye-tracking task. We found that, like non-autistic participants, autistic participants increased their eye-looking time when viewing the blinking face and increased mouth-looking time when viewing the mouth-moving face. Furthermore, these effects were stable across ages, suggesting the presence of a developmentally stable attentional capture by basic face movements in both groups. We also found that autistic participants looked less at basic face movement than non-autistic participants. Our results suggest that autistic children and adolescents could modulate their visual attention to the basic face movements, but their modulation effect is weaker than non-autistic participants. These results further our understanding of the mechanism underlying visual attention-to-face movement in autistic people.
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BACKGROUND: One of features of Visual preference in autism spectrum disorder (ASD) is that they tend to social stimuli to nonsocial stimuli. Though it has been studied widely, until now the results are still not consistent. One of the most important reasons may be that the stimuli of the past visual preference paradigm have different movement patterns. Therefore, the present study aimed to improve the visual preference paradigm to reduce the bias caused by different movement of stimuli and explore the features of visual preference of ASD further. METHODS: Two hundred and seven children who met DSM-5 criteria for ASD and 125 typically developmental (TD) children were enrolled. They were matched in age and gender. Visual preference was measured by eye tracking technology. The subjects presented simultaneously with dynamic object images (DOI) and dynamic social images (DSI) with the same type and same speed of repetitive movement. Social interaction deficits of ASD were assessed by parents with Aberrant Behavior Checklist (ABC). RESULTS: Compared with TD children, the following findings were noted: (I) children with ASD paid less attention to social stimuli and more attention to nonsocial stimuli (t=6.417, P<0.001). (II) Increased preference to DSI was positively correlated with age (R=0.23, P=0.001). (III) No association was noted between visual preference and IQ in children with ASD (R=0.081, P=0.435). (IV) Reduced preference to DSI was negatively correlated with the severity of social interaction impairment rated by parents with ABC in children with ASD (R=0.237, P=0.010). CONCLUSIONS: Children with ASD exhibited abnormal visual preference for social and nonsocial stimuli.
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BACKGROUND: Health-related risky behaviors generally refer to behaviors that have a negative impact on health and quality of life. Health-related risky behaviors in adolescents with high-functioning autism (HFA) have not been well understood so far. Adolescents with HFA may have more health-related risky behaviors than neurotypical adolescents. AIM: To investigate health-related risky behaviors and their risk factors with HFA. METHODS: This is an observational study. Our study enrolled 110 adolescents aged 12-19-years-old meeting Diagnostic and Statistical Manual of Mental Disorders 4th edition criteria for HFA. They were recruited from Peking University Sixth Hospital. There were also 110 age, sex and nationality matched controls enrolled who came from a public school in Beijing, China. Both groups completed the Adolescents Health-related Risky Behavior Inventory. Nonparametric tests were carried out for comparison of the Adolescents Health-related Risky Behavior Inventory scores between the two groups. Expression recognition, the Inventory of Subjective Life Quality for Child and Adolescent, Chinese Wechsler Intelligence Scale for Children, Wechsler Intelligence Scale for Adult-Chinese Revised, Theory of Mind test and Autism Spectrum Screening Questionnaire were assessed in the autism group to explore factors associated with health-related risky behaviors. Multivariate regression analysis was conducted to explore the risk factors of health-related risky behaviors in the HFA group. RESULTS: The results showed that the total score of the Adolescents Health-related Risky Behavior Inventory and scores of "aggression and violence," "suicide and self-injury," "health compromising behavior" and "unprotected sex" subscales in the HFA group were significantly higher than those in the control group (Z range -4.197 to -2.213, P < 0.05). Among the associated factors, poor emotional experience (B = -0.268, P < 0.001), depression (B = -0.321, P < 0.001), low score of intelligence (B = -0.032, P = 0.042), low score of Theory of Mind test (B = -1.321, P = 0.003) and poor adaptation to school life (B = -0.152, P = 0.006) were risk factors. These risky behaviors may promote the occurrence of health-related risky behaviors in adolescents with HFA. CONCLUSION: This study showed that adolescents with HFA were more likely to be involved in health-related risky behaviors. Different health-related risky behaviors have different reasons.
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BACKGROUND: Health-related risky behaviors (HRB) generally refer to behaviors that have a negative influence on health and quality of life. HRB in adolescents with autism have not been well understood so far. We aim to explore health-related risky behaviors and their risk factors with autistic adolescents. METHODS: In this study, 150 adolescents with autism and 150 neurotypical adolescents were enrolled. Participants in both groups completed the Adolescent Health-Related Risky Behavior Inventory (AHRBI). Autism Spectrum Screening Questionnaire (ASSQ), Wechsler Intelligence Scale, Theory of Mind (ToM) Test, Zung Self-rating Anxiety Scale (SAS), Zung Self-rating Depression Scale (SDS), and Self-Esteem Scale (SES) were also assessed in the autism group to explore risk factors. RESULTS: The results showed that the total score of AHRBI and scores of "aggression and violence (AV)", "suicide or self-injury (SS)", "health-compromising behavior (HCB)", and "unprotected sex (US)" subscales in the autism group were significantly higher than those in the control group (Z value = - 4.58 ~ - 2.26, all P < 0.05). Anxiety, depression, low self-esteem, low IQ score, low ToM test score, increasing age, and communication disorder were found as risk factors for health-related risky behaviors in autistic adolescents. CONCLUSIONS: Adolescents with autism have more health-related risky behaviors than neurotypical adolescents. We should pay attention to the emotional state, self-esteem, cognitive function, and verbal communication levels of autistic adolescent with health-related risky behaviors.
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We explored the social synchronization of gaze-shift behaviors when responding to joint attention in children with autism spectrum disorder (ASD). Forty-one children aged 5 to 8 with ASD and 43 typically developing (TD) children watched a video to complete the response to joint attention (RJA) tasks, during which their gaze data were collected. The synchronization of gaze-shift behaviors between children and the female model in the video was measured with the cross-recurrence quantification analysis (CRQA). Ultimately, we discovered that children with ASD had the ability to synchronize their gaze shifts with the female model in the video during RJA tasks. Compared to the TD children, they displayed lower levels of synchronization and longer latency in this synchronized behavior. These findings provide a new avenue to deepen our understanding of the impairments of social interaction in children with ASD. Notably, the analytic method can be further applied to explore the social synchronization of numerous other social interactive behaviors in ASD. LAY SUMMARY: This study explored how autistic children synchronized their gazed shifts with others' gaze cues during joint attention. We found that compared to typical children, autistic children synchronized their gazed shifts less and needed more time to follow others' gaze. These findings provide a new avenue to deepen our understanding of the impairments of social interaction in children with ASD.