Research On Neural Mechanism And Auxiliary Diagnosis Of Autism Based On EEG

Autism spectrum disorder is a neurodevelopmental disorder in which people have persistent problems with social communication,as well as limited and repetitive behaviors.This study processed resting EEG data of children with autism,explored effective EEG physiological markers,provided effective support for the pathophysiology of autism,and promoted the development of its detection and diagnosis.We first conducted a horizontal research of the severity of autism based on the brain network,and explored the abnormal brain development of children with autism,as well as the brain electrophysiological indicators that are instructive for the condition of children with autism.It was found that children with ASD exhibited overactivated power spectral density,decreased long-range connectivity on the network,increased prefrontal connectivity,and decreased network efficiency,suggesting that the brains of children with ASD are functionally impaired in integrating and processing relevant information.damage.We also found that the severity of symptoms in children with autism was dependent on brain activity.Among them,network indicators were not only significantly correlated with diagnostic scores,but also combined with corresponding spatial network topological features could effectively promote the prediction of ASD symptom severity.Based on the above results,we further conducted a longitudinal study on the age of autistic children based on brain network.The subjects were divided into 3 groups according to their age: 6-8 years old,8-10 years old and 10-12 years old.We found that the brain patterns of children with autism at all groups were similar to the results of horizontal research,and the relative differences in EEG indicators showed a tortuous upward trend.The relative differences in power spectral density and network properties first decreased slightly and then increased rapidly.This may be because a short-term educational intervention in the early school-aged ASD children has a positive effect on their brain development,resulting in a weakened difference in brain development compared with normal children.And because of the aggravation of puberty and academics,the low adaptability of ASD children makes the difference from normal children widen rapidly.To observe short-term brain development in children with autism,we conducted a brain network based short-term longitudinal study of autism.This chapter further studies the development of brain activity and changes in cognitive ability in short-term longitudinal children with high-scoring autism.The sample entropy,network connection matrix,and network properties of children with autism at baseline,6 weeks,24 weeks,and 3 time points were calculated.The differences between ASD chilren and normal children were also culated.The results show that the relative differences of sample entropy and network properties of ASD children have a gradually increasing trend over time.Further integration of the first two network indicators can effectively predict the cognitive score of high-scoring ASD children after 24 weeks,which indicates that the electrophysiological indicators of the brain also have advanced predictive ability for the cognitive ability of ASD children.In conclusion,this study explored the effects of EEG on ASD abnormal neural mechanism,judgment of symptom severity,age changes,short-term disease development and cognitive ability in ASD children.The application of our study provides multidimensional auxiliary research for the clinical diagnosis and treatment of autism.