Connectivity Analysis Among Large-scale Functional Brain Networks

The human brain is a complicated system which is engaged in information processing.The collaborations and dynamic interactions of distinct brain networks are preconditions of human higher cognitive functions.To accurately and deeply understand the mechanism of the brain,and explore the pathophysiology of different brain disorders,we need to conduct connectivity analysis among large-scale functional brain networks from the view point of functional integration.In the dissertation,we carried out internetwork connectivity analysis based on resting-state functional magnetic resonance imaging(fMRI).The main contents of the dissertation are as follows:Internetwork static and dynamic functional connectivity analysis and the corresponding application.We studied the methods of static-dynamic-combined internetwork functional connectivity analysis,and proposed the "independent component analysis based brain network extraction—correlation analysis based internetwork static functional connectivity analysis—sliding window analysis and amplitude of low frequency fluctuation based internetwork dynamic functional connectivity analysis" methodology framework.On this basis we explored the functional connectivity between the subnetworks of the default mode network(DMN),attention network(ATN),and frontoparietal control network(FPN)using resting-state fMRI data collected from patients with idiopathic generalized epilepsy(IGE)and matched healthy controls.For the first time,we found that the left FPN exhibited increased connectivity with the anterior DMN and ventral ATN,while the right FPN exhibited increased connectivity with the anterior and posterior DMNs in the patients with IGE relative to healthy controls.Furthermore,the functional connectivity between the anterior DMN and ventral ATN was negative in healthy controls but positive in the patients with IGE.The dynamic functional connectivity analysis also revealed significantly different temporal fluctuations of some relevant connections between the patients and healthy controls.Internetwork time-frequency Granger causal connectivity analysis and the corresponding application.We studied the methods of internetwork effective connectivity analysis based on multivariate Granger causality model,and proposed the methodology framework of time-frequency-combined Granger causal connectivity analysis.The simulation experiments demonstrated that the application of this methodology framework could avoid the indirect Granger causal connections,and guarantee the accuracy and reliability of the final analysis results.On this basis,we also took the idiopathic generalized epilepsy data set to explore the abnormality of effective connectivity among 13 proposed brain networks in patients relative to healthy controls.The time-frequency-combined results showed that the effective connections from the auditory network to anterior DMN,from the motor network to posterior DMN,from the anterior DMN to both ventral ATN and left FPN,and the bidirectional connectivity between the left FPN and ventral ATN exhibited significant group difference.The contribution of the dissertation lies on proposing two methodology frameworks for investigating functional network connectivity,and using IGE data set to demonstrate the methods' potential value in the field of cognitive neuroscience and clinical application.The results showed the patients exhibited internetwork functional and effective connectivity abnormality,and revealed several IGE-related novel findings.The current study suggests that functional network connectivity analysis based on fMRI is an efficient way to explore information interaction and integration relationship among different brain functional modules.The corresponding clinical applications could provide informative evidence for the understanding of the pathophysiology of some brain disorders.