Research On Rs-EEG Network Of Children With Tourette Syndrome, And Children With Autism Spectrum Disorder

Tourette syndrome?TS?and Autistic Disorder?ASD?are increasingly common neuropsychiatric disorder in children,but the diagnosis is usually conducted by the description of the parents,which is subjective.There were many research has been made to deepen our knowledge about the etiology and pathogenesis of them.The researchs based on EEG always focus on the frequency of wave,polarity of potential,and spectrum analysis,which hae made some satisfying progress.However,the inherent EEG network is still a challenge.In this study,the discrimination between the brain network of patients and controls are studied on account of brain science,and the main content and research of this paper are as follows:?1?Thirty-six TS patients?3 to 18 years old,average 7.42 years old?and twenty-one healthy controls?13 to 17 years old,average 15.57 years old?from the Zigong First People's Hospital participated in our experiment,and their EEGs were recorded from subjects sitting with eye closed.The functional EEG network was constructed,then the network property and the difference between brain topology were analyzed.The result shows that the TS patients exhibited decreased frontal-temporal,frontal-occipital,frontal-parietal connectivity?p<0.01?.While classifying the two groups by the derived SPN features,our results showed that,compared to network properties,the SPN features showed a much higher prediction accuracy,i.e.,98.25%for SPN features compared to 96.50%for network properties between controls and TS patients.?2?The first batch of subjects included seventeen patients?2 to 6 years old,average3.8 years old,13 male?and eleven controls?3 to 5 years old,average 7.42 years old,8male?from West China Hospital,Sichuan University participated in our experiment.And eleven patients?2 to 7 years old,average 4.82 years old,9 male?from the same place were recruited to make up the second batch of subjects.Their EEGs were recorded from subjects sitting with eye closed.The 1^st batch of data was served as training-set,while the 2^nd batch of data was served as the testing-set.Firstly,the functional EEG network and power spectrum of two type of subjects was analysised,and result shows that the ASD patients indicated significantly activations in frontal-parietal,mainly showing in distant brain regions,inhibition in parietal,central areas compared to the control group?p<0.05?.Then,the power spectrum of them was analysised,and result shows that the ASD patients indicated lower activation in O1,O2channel.Then the ASD prediction model based on the the combination of SPN feature and the PSD feature was constructed considering the result of training-set.The classifying results showed that,compared to other features,the combine of SPN feature and the PSD feature showed a much higher prediction accuracy,i.e.,96.15%for the combine of SPN feature,especially improving the accuracy of training-set,which is vital to simulate the classification in the real world.The prediction accuracy for the combine of SPN feature and the PSD feature in the testing-set is highly 100%.These results suggest that the SPN feature can select modules having obvious spatial differences in the network space than network properties,and this abnormal connection pattern may serve as the sensitive biomarker to differentiate the patients from normal subjects.