Background: Individuals with autism spectrum disorder (ASD) often have different social characteristics and particular sensory processing patterns, and these sensory behaviors may affect their social functioning. The objective of our study is to investigate the sensory profiles of children with ASD and their association with social behavior. Specifically, we aim to identify the predictive role of sensory processing in social functioning.Methods: The Short Sensory Profile (SSP) was utilized to analyze sensory differences between ASD children and their peers. The Social Responsiveness Scale (SRS) and other clinical scales were employed to assess the social functioning of children with ASD. Additionally, the predictive ability of sensory perception on social performance was discussed using random forest and support vector machine (SVM) models.Results: The SSP scores of ASD children were lower than those of the control group, and there was a significant negative correlation between SSP scores and clinical scale scores (P < 0.05). The random forest and SVM models, using all the features, showed higher sensitivity, while the random forest model with 7-feature factors had the highest specificity. The area under the receiver operating characteristic (ROC) curve (AUC) for all the models was higher than 0.8.Conclusion: Autistic children in our study have different patterns of sensory processing than their peers, which are significantly related to their patterns of social functioning. Sensory features can serve as a good predictor of social functioning in individuals with ASD.

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2–7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, F_peak = 13.57) and cerebellum/vermis (F_peak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Abnormal lipid metabolism has been suggested to contribute to its pathogenesis. Further exploration of its underlying biochemical mechanisms is needed. In a search for reliable biomarkers for the pathophysiology of ASD, hippocampal tissues from the ASD model BTBR T+ Itpr3tf/J (BTBR) mice and C57BL/6J mice were analyzed, using four-dimensional (4D) label-free proteomic analysis and bioinformatics analysis. Differentially expressed proteins were significantly enriched in lipid metabolic pathways. Among them, apolipoprotein A-I (ApoA-I) is a hub protein and its expression was significantly higher in the BTBR mice. The investigation of protein levels (using Western blotting) also confirmed this observation. Furthermore, expressions of SphK2 and S1P in the ApoA-I pathway both increased. Using the SphK inhibitor (SKI-II), ASD core phenotype and phenotype-related protein levels of P-CREB, P-CaMKII, and GAD1 were improved, as shown via behavioral and molecular biology experiments. Moreover, by using SKI-II, we found proteins related to the development and function of neuron synapses, including ERK, caspase-3, Bax, Bcl-2, CDK5 and KCNQ2 in BTBR mice, whose levels were restored to protein levels comparable to those in the controls. Elucidating the possible mechanism of ApoA-I in ASD-associated phenotypes will provide new ideas for studies on the etiology of ASD.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous clinical phenotypes. Patients often experience abnormal sensory perception, which may further affect the ASD core phenotype, significantly and adversely affecting their quality of life. However, biomarkers for the diagnosis of ASD sensory perception abnormality are currently elusive. We sought to identify potential biomarkers related to ASD sensory perception abnormality to construct a prediction model that could facilitate the early identification of and screening for ASD. Differentially expressed genes in ASD were obtained from the Gene Expression Omnibus database and were screened for genes related to sensory perception abnormality. After enrichment analysis, the random forest method was used to identify disease-characteristic genes. A prediction model was constructed with an artificial neural network. Finally, the results were validated using data from the dorsal root ganglion, cerebral cortex, and striatum of the BTBR T+ Itpr3tf/J (BTBR) ASD mouse model. A total of 1869 differentially expressed genes in ASD were screened, among which 16 genes related to sensory perception abnormality were identified. According to enrichment analysis, these 16 genes were mainly related to actin, cholesterol metabolism, and tight junctions. Using random forest, 15 disease-characteristic genes were screened for model construction. The area under the curve of the training set validation result was 0.999, and for the model function validation, the result was 0.711, indicating high accuracy. The validation of BTBR mice confirmed the reliability of using these disease-characteristic genes for prediction of ASD. In conclusion, we developed a highly accurate model for predicting ASD sensory perception abnormality from 15 disease-characteristic genes. This model provides a new method for the early identification and diagnosis of ASD sensory perception abnormality.

Comparative studies of social interaction in the presence of physical contact (SIPPC) and social interaction in absence of physical contact (SIAPC) enhance our understanding of the neurophysiological mechanisms underlying these activities. We analyzed comparatively the effects of SIPPC and SIAPC on c-fos expression across 100 brain regions in mice, and found that SIPPC activated a broader range of brain regions, particularly those associated with emotion and reward. Subsequent observations of brain activity coordination and network construction highlighted the critical roles of the infralimbic cortex (IL), lateral septal nucleus intermediate part (LSI), and agranular insular cortex ventral part (AIV) in SIPPC. Additionally, we demonstrated through chemogenetic techniques that inhibiting the activity of AIV, LSI brain regions, or AIV-LSI circuit significantly reduces the tactile contact behavior of mice during SIPPC. To sum up, our findings elucidate the similarities and differences in brain activity and network connectivity between SIPPC and SIAPC, while identifying specific brain regions and neural circuit that may mediate tactile contact in social interaction.

Autism spectrum disorder (ASD) is a complex developmental disorder characterized by difficulties in social interaction and communication and repetitive behaviors. Although abnormal brain development has been shown to exist in children with ASD, the link between structural brain abnormalities and resting-state functional connectivity (rsFC) disruptions in children with ASD remains understudied. To address this limitation, we utilized the population-based bundle-to-region connectome, providing a detailed understanding of the connectivity between cortical regions and white matter (WM) tracts. By precisely indexing WM-Gray Matter (GM) interactions, we investigated the rsFC of the cortex-associated ROIs to explore the association between structural and rsFC abnormalities and clinical symptoms in young children with ASD. This MRI study identified significant differences in WM structure and rsFC between children with ASD (n = 34) and typically developing children (TD, n = 43). Our results showed that decreased fractional anisotropy (FA) and increased mean diffusivity (MD) and radial diffusivity (RD) in ASD WM tracts compared to TD, particularly in left hemisphere tracts (anterior thalamic radiation [ATR], cingulum, inferior fronto-occipital fasciculus [IFOF], inferior longitudinal fasciculus [ILF], superior longitudinal fasciculus [SLF], and uncinate fasciculus [UF]). Abnormal rsFC was observed in GM areas connected by ATR, cingulum, IFOF, ILF, and SLF. Furthermore, abnormalities in the structural and functional connectivity index (SFCI) within the SLF and cingulum were identified. An association has been observed between these abnormalities and clinical symptoms. Specifically, SLF structural and functional connectivity appear to be associated with repetitive and restrictive behavior (RRB), while cingulum connectivity is associated with communication abilities. In conclusion, young children with ASD exhibit abnormal WM tract structures and associated rsFC abnormalities. These differences highlight significant disruptions in rsFC mapped from WM tracts to cortical areas in ASD, correlating with the severity of ASD symptoms, and suggest the importance of multi-modal imaging in capturing these variations.