The incidence of autism spectrum disorders is increasing, about 1 / 36. Neural cadherin (NCAD) is closely related to synaptic structure and function. The cdh2 gene encoding NCAD has been shown to be associated with a variety of neurodevelopmental diseases and is one of the six risk genes for ASD. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technology that uses the principle of near-infrared spectroscopy to detect the functional activation of human cortex. The application of fNIRS to explore the neural mechanism of children with ASD has developed rapidly. It has the advantages of non-invasive, portable, relatively low cost and unlimited physical activity. And for ASD children, their command compliance and self-control are relatively low, so fNIRS is more widely used in ASD children. According to dsm-5 standard, we recruited ASD children and TD children, collected their peripheral blood, and detected the plasma n-cad concentration. At the same time, the resting state of fNIRS of the two groups of children was collected, and the ultra scan monitoring was carried out simultaneously during Gesell assessment. The purpose of this study was to explore the difference of peripheral blood NCAD concentration between preschool ASD children and TD children and the difference of brain function in resting state and social interaction state. And the relationship between the characteristics of brain function and the level of cognitive development in children with ASD.

1. Procedure. Children in ASD group and TD group will complete free physical measurement, blood routine and serum nutrient tests, neuropsychological assessment, and near-infrared brain functional imaging after signing up and giving informed consent. 3ml of peripheral blood was collected from the subjects, and the plasma was rapidly separated and frozen in a -80 ℃ refrigerator. The resting state of ASD and TD children was collected using fNIRS, and the ultra scan monitoring was performed simultaneously during Gesell assessment
2. Demographic questionnaires and clinical data. The demographic questionnaire was completed by the child's primary caregiver, specifying the child's name, sex, and date of birth. Clinical data will be determined from medical records, including relevant dsm-5 diagnosis, disease classification, etc. The
3. Sample size. The sample size was calculated by g*power 3.1. The paired t-test was used to set the effect size d=0.35, α =0.05 (two-sided) Power=0.9， Software calculation showed that 44 cases were needed in each group. Considering the group variability of children and the 10% dropout rate, 49 cases in each group (49 cases in ASD group and 49 cases in TD group) were finally included.
4. Statistical analysis. ① NCAD: the R language 4.5.1 statistical software is used for data analysis. The measurement data conforming to the normal distribution adopts the mean ± standard deviation, and the paired sample t test is used for comparison between groups; The median (quartile) was used for non normally distributed measurement data, and Wilcoxon signed rank test was used for comparison between groups. ② Near infrared: nirspark software was used for data preprocessing, R language 4.5.1 statistical software was used for data analysis, Pearson correlation was used for correlation between channels, and generalized linear model (GLM) or mixed effects model was used for comparison between groups. Multiple linear regression model (adjusting for covariates such as age and gender) was used to explore the association between brain activation and DQ level in children with ASD.
5. ethical issues and data protection. Patients participating in the study will sign an informed consent form. This study was approved by the local ethics committee. . Parents' authorization of patients' health information remained valid until the study was completed. After that, the researcher will delete the private information from the research record.

Inclusion Criteria:

1. Age at enrollment ≥ 3 years and <5 years;
2. The diagnosis was made by 2 developmental behavioral pediatricians or child psychiatrists with vice senior titles or above, and the history and clinical manifestations were in line with ASD and ICD-11 diagnostic criteria for ASD; .

Exclusion Criteria:

1. Diagnosis or manifestation of genetic diseases or syndromes related to ASD (such as Rett syndrome);
2. Medical or nervous system diseases affecting growth, development or cognition (such as central nervous system infection, epilepsy, congenital heart disease, etc.);
3. Sensory impairment such as vision or hearing loss;
4. Low birth weight (birth weight <2000 g) or preterm birth (gestational age <37 weeks);
5. Adopted children;
6. Parents refused to sign the informed consent form;
7. Other researchers consider it inappropriate to participate in the study.

ASD

Data is confidential during the study.