Approximately one percent of people are diagnosed with Autism Spectrum Disorder (ASD) in the world , and the prevalence of this disease is similar across different countries. This disorder defined a group of neurological signs that are usually recognized by deficiencies in interactions and social communications. Due to the similarity of autism disorder with a number of cognitive disorders in some symptoms, diagnosis of this disease, especially in children, is usually delayed by several years. The use of functional images, especially resting-state functional magnetic resonance imaging (rs-fMRI), is a good procedure to find accurate biomarkers for the diagnosis of this disease due to its non-invasive nature and its independence of task performance during imaging. To better understand the function of brain activity in this disease, many studies have used functional connectivity; but these studies have reported inconsistent results. The default mode network (DMN) is an important brain network in ASD that has the most disruption among functional networks. Furthermore, brain asymmetry is one of the fundamental aspects of the human brain that changes in many psychiatric disorders. In this study, to reduce the effect of factors affecting the non-convergence of results in brain connectivity studies in ASD, effective connectivities in the core DMN were calculated using spectral dynamic causal modeling (spDCM) method. These connectivity parameters have used for investigation of brain asymmetry in ASD, and align="right" dir="RTL" Hemispheric asymmetry- Machine Learning- resting state fMRI (rs-fMRI)- dynamic causal modeling (DCM)- Autism Spectrum Disorder (ASD)- brain connectivity- Default mode network (DMN)
