Criticality And Dynamic Functional Connectivity In Human Brain And Application In Moyamoya Disease

Functional magnetic resonance imaging(fMRI)has become one of the primary tools to dissect human brain function and behavior due to its recent advances in scanning techniques and data analysis.It also provides a powerful method to characterize brain disorders such as Alzheimer’s disease,schizophrenia in a non-invasive way.The exploration of time varying functional connectivity(FC)through human neuroimaging technique provides important new insights in the spatio-temporal organization of functional communication in the brain network and its alterations in diseased brains.However,little is known about the underlying dynamic mechanism with which such dynamical FC is flexibly organized under constraint of structural connections.We are trying to explore the relationship between criticality dynamics and FC flexibility based on both functional magnetic resonance imaging data and computer model.First,we proposed the connectivity number entropy(CNE),which is an entropy measure for the flexibility of FC.And through analysis of resting state fMRI(rs-fMRI)data from 95 healthy subjects,we explored the correlation between CNE and long-range temporal correlations(LRTCs)which can represent the criticality dynamics.Then we also employed a whole brain computer model based on diffusion tensor imaging(DTI)to further prove this relationship.We demonstrated that the most flexible FC is endowed when the brain is operating close to the critical point of a phase transition.And around this point,our model could yield best prediction for the regional distribution of CNE,owing to the nature that structure information is reflected the most by the CNE through critical dynamics.Our results not only revealed the underlying dynamic mechanism for the organization of time-dependent FC,but also provided a possible pathway to model the flexible functional organization in brains,and may have potential application in analysis of altered dynamic FC in diseased brains.Treatment of vascular cognitive impairment(VCI)in adult moyamoya disease(MMD)is still unclear because of its unveiled neural synchronization.Fifty-one patients with MMD were recruited(27 with VCI and 24 with intact cognition),as well as 26 normal controls(NC).Static network properties were first examined to confirm its aberrance in MMD with VCI.Then,the dynamic measurement of CNE was used to detect the deteriorated flexibility of MMD with VCI at global,regional,and network levels.Finally,dynamic reconfiguration of flexible and specialized regions was traced across the three groups.Graph theory analysis indicated that MMD exhibited “smallworld” network topology but presented with a deviating pattern from NC as the disease progressed in all topologic metrics of integration,segregation,and small-worldness.Subsequent dynamic analysis showed significant CNE differences among the three groups at both global(p < 0.001)and network levels(default mode network,p = 0.004;executive control network,p = 0.001).Specifically,brain regions related to key aspects of cognition exhibited significant CNE changes across the three groups.Furthermore,CNE values of both flexible and specialized regions changed with impaired cognition.These results indicate that moyamoya disease could make human brain departing from critical point.This part of work not only sheds light on both the static and dynamic organizational principles behind network changes in adult MMD for the first time,but also provides a new methodologic viewpoint to acquire more knowledge of its pathophysiology and treatment direction.