Dynamic Analysis Of Resting State Functional Connectivity Of Functional Magnetic Resonance Imaging

The mystery of brain promotes people to probe its functional mechanism.,and the researches of brain science will promote us to understand the brain functions and brain diseases.In brain imaging technologies,the functional magnetic resonance imaging(fMRI)technology has been widely used because of its good temporal and spatial resolution.During the researches of resting-state brain functional networks,people found that functional network shows spontaneous temporal dynamics and has the connectivity states.In present study,we propose a method of dynamics analysis on fMRI-based resting-state functional connectivity(FC).The method contains dynamics analysis and connectivity state analysis.Specially,the main steps of the method are data scanning and preprocessing,capture of dynamic functional networks and connectivity states,features selection and extraction,pattern classification and regression,and the investigations of reliability and significance.The method combines the brain network analysis and pattern recognition method,introduces an index to measure the fluctuations of connectivity,and provides a normal method to select and capture features.The validity of the method has been demonstrated by the following analysis.Furthermore,we use the method to investigate how the maturation of human brain and long-term driving behavior impact on dynamic FC.The study mainly contains the following parts:Investigating that dynamic functional network decodes individual brain maturity.Neuroimaging based FC analyses have revealed significant developmental trend in specific intrinsic connectivity networks linked to cognitive and behavior maturation.However,there is very limited knowledge about how brain functional maturation is associated with the FC dynamics at rest.Based on sliding window analysis and partial least square regression,we examined age-related differences in temporal variability of FC dynamics with heath subjects(n=183,ages 7-30)and showed that inter-region dynamic interaction can be used to accurately predict individual brain maturity across development.Furthermore,we identified a significant age-dependent trend underlying dynamic inter-network FC,including the connections within the cerebellum and default mode network(DMN),as well as between the cerebellum,DMN and cingulo-opercular network.Overall,the results suggested significant developmental changes in functional network dynamics,which may shed new lights on dynamic functional connectivity.Investigating that long-term driving behavior can alter the dwell time of FC states.Based on the proposed method,the present study investigated the potential influence of driving behavior on the temporally dynamic properties of resting-sate FC.We found that the significant differences between drivers and nondrivers in temporal property of resting-state FC associated with the vigilance network,and the dwell time of vigilance-related FC states.Furthermore,as the driving behavior could be regarded as a long-term behavior in this study,our findings may also provide new insight into how extensive training or experiences influences the functional network dynamics in healthy human brains.