Research On Resting State Networks Baesd On Independent Component Analysis

The incidence of neurological diseases is increasing at an alarming rate.The research results of brain science can provide new ways for the diagnosis,prevention,and treatment of neurological diseases.The countries around the world have begun to give priority to brain science research.Functional magnetic resonance imaging(fMRI)is widely used in the research of brain science.Researchers use functional connectivity to detect the operational mechanism of brain functional activity.In this paper,independent component analysis and resting state functional magnetic resonance imaging techniques are used to study the resting state networks of autism spectrum disorder patients and mild cognitive impairment patients.The main contents are as follows:(1)The resting state networks of autism spectrum disorder patients are studied by employing linear independent component analysis and the voxel based functional connectivity technique.The resting state fMRI data from 50 autism spectrum disorder patients and 42 healthy controls select in the ABIDE database are studied.Firstly,the resting state networks of autism spectrum disorder patients and healthy controls are extracted by using linear independent component analysis and the principle of maximum spatial correlation.Secondly,the voxel based functional connectivity of resting state network is calculated.Finally,the corresponding functional connectivity of resting state network between autism spectrum disorder patients and healthy controls is compared.It is found that the functional connectivity of the auditory network and somato-motor network in autism spectrum disorder patients is specifically increased specifically.The functional connectivity of the dorsal attention network is specifically reduced.The default mode network,central executive network,core network,visual network,and self-referential network have both reduced and increased functional connectivity.This result shows that the abnormality of resting state networks in autism spectrum disorder patients is extensive,which provides a new way for the diagnosis of autism spectrum disorder patients.(2)Post-nonlinear independent component analysis is introduced into the study of resting state fMRI data.The resting state networks of mild cognitive impairment patients are studied using post-nonlinear independent component analysis and voxel based functional connectivity technique.The resting state fMRI data of 38 mild cognitive impairment patients and 32 healthy controls select in the ADNI database are studied.Firstly,the resting state networks of mild cognitive impairment patients and healthy controls are extracted using the post-linear independent component analysis and the principle of maximum spatial correlation.Secondly,the voxel based functional connectivity of resting state network is calculated.Finally,the corresponding functional connectivity of resting state network between mild cognitive impairment patients and healthy controls is compared.It is found that the default mode network,central executive network,dorsal attention network,somato-motor network,visual network have both reduced and increased functional connectivity in mild cognitive impairment patients.The functional connectivity of the auditory network,self-referential network is specifically reduced.The core network does not reveal significant group difference.