Study On The Neural Mechanism And Regulation Of Autism Based On Large-scale Brain Simulation

Autism spectrum disorder（ASD）is a common neurodevelopmental disorder characterized by deficits in social communication and interaction,as well as repetitive behaviors,interests,and activities.At present,the neural mechanism of ASD brain is still unclear,and there is no unified and effective treatment or regulation method.With the rapid development of large-scale brain dynamic simulation technology,it has become an effective tool for exploring the pathogenesis of brain diseases and the means of regulating brain diseases.The whole brain modeling of autism is not only helpful to understand the pathogenesis of autism,but also hopeful to develop new methods for autism regulation.Therefore,by integrating the multimodal neuroimaging data of ASD patients,we construct a brain-level dynamic computational model of patients,and focuses on exploring the neural mechanisms and regulatory methods of ASD patients from the two dimensions of static and dynamic functional networks.The main results are as follows:Firstly,we used an inversion iterative approach to improve the performance of a large-scale whole-brain model.Through this model,we found an abnormally excitationinhibition ratio of local circuits in autistic patients,and it was caused by a disproportionate decrease in excitation and inhibition.Then we developed a regulation method to modulate the excitation-inhibition ratio by adjusting the synaptic connection strength of local circuits.The results suggest that with the recovery of excitatory-inhibition ratio,the functional connectivity distance between autistic patients and normal individuals gradually decreased,demonstrating that the modulation of excitatory-inhibition ratio can improve the functional network of autistic patients.Secondly,the static functional connectivity analysis revealed significant connectivity deficits in autistic patients,especially in the frontal and temporal lobes.The topological properties of brain networks（clustering coefficient,characteristic path length,local efficiency,and global efficiency）also showed significant abnormalities in autistic patients.This result suggests that brain networks of autistic patients may have disadvantages such as low information efficiency and immature development.Thirdly,the dynamic functional connectivity analysis revealed a large number of edges with significantly enhanced dynamic functional connectivity variability in autistic patients,especially in the frontal and temporal lobes.However,the mean values of dynamic functional connectivity in autistic patients were almost identical to the values of static functional connectivity.In the clustering analysis,under-connectivity in the autistic brain was detected mainly in strong connection state.Also,we detected significant differences in the mean dwell time of states and the probability of state occurrence in patients.Finally,both the static and dynamic functional connectivity analysis results suggest that the regulation of the excitatory-inhibitory ratio can significantly improve the functional connectivity abnormalities in ASD patients,and no obvious overregulation was detected.Our results suggest that there is a disproportionate decrease in excitation-inhibition at the whole-brain level of ASD,and the abnormal increase of the excitatory-inhibitory ratio may be an important reason of autism-related symptoms.The re-establishment of excitation-inhibition balance also contributes to the recovery of functional brain networks in autistic patients.Our research adequately combines autism and large-scale brain models to discuss the regulation method of ASD patients,which provides model support and reference for the study of neural mechanisms and intervention methods of autism.