| The brain may undergo a series of changes such as structural atrophy and functional decline,which may likely cause neurodegenerative diseases.Age-related cognitive decline significantly reduces life quality of the elderly,and brings heavy mental and economic burden to patients’ families and society.The decline of brain function is an inevitable trend in life,and studying this normal decline trajectory is important for the study of aging related diseases and for revealing the mechanism of brain aging.The study of brain aging based on resting state functional magnetic resonance imaging(fMRI)shows that the decline of brain cognitive function is closely related to the change of functional connectivity of different brain networks.Studies have shown that the whole brain functional connectivity network has a series of fixed patterns which repeat across time and individuals,that is micro states.However,the study of micro states about age-related brain dynamic functional connectivity has not been reported.We select 31 young subjects,28 middle-aged subjects and 27 elderly subjects who were selected from resting state fMRI public database.The dynamic functional connectivity was constructed by sliding window technology,and K_means clustering algorithm was used to cluster the dynamic functional connectivity matrix.5 micro states of dynamic functional connectivity network repeated across time and individuals were extracted.And the age-related change of functional connectivity was analyzed in the 5 micro states.Firstly,the distance and the number of transition between micro states were used to quantify influence of different sliding window length on the clustering results.The result shows that the variability of the micro state is stronger when the window length is shorter and when the window length is larger than 60 s,the distance between the micro states and the number of transition tend to be so stable that the dynamic characteristics of the functional connection can’t be reflected.Therefore,the window length of 50 s is optimized for further analysis.Then,the dwell time,the number of transition and the stability of micro states were compared among different groups.It is found that the dwell time and the transition time of the micro states don’t change significantly with age,but the state transition becomes more concentrated.And compared with the youth group,the stability of state 4 is significantly lower in the elderly group.Finally,based on the variability of functional connectivity,zones of instability of each micro state were extracted,and it was found that the zones of instability of each state were different,and all of them involved different intrinsic networks.Therefore,we assumed that the state transition is to balance the different degrees of decline between the intrinsic networks and alleviate the effect of local functional deterioration on brain functional output.Then the intra-network and inter-network functional connectivity was compared between different groups.The result shows that the functional connectivity of the brain network assumes an inverted U shape with age which suggests that micro state appears to be generally weakened in functional connectivity in the elderly.The results of this study show that the micro states of dynamic functional connectivity can reflect more information of brain network than resting state,which proves that the sensitivity and specificity of dynamic functional connectivity to analyze brain functional networks,and provides a new idea for the study of brain aging,and lays a foundation for the study of the brain functional network and the mechanism of brain aging. |
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