The human brain is a network that consists of spatial regions, which are functionally linked. These regions share information with each other continually (16). Using the resting-state fMRI, we can explore the functional connections of the brain regions. Functional connectivity of rs-fMRI data is an important issue with an increasing trend of innovations in recent years. An important limitation of most rs-fMRI studies in healthy adults is reliance on functional connectivity indices calculated from an entire scan session (17). In this way, important information about within-scan temporal changes in functional connectivity may be lost.

Therefore, the present study aimed to determine the functional connectivity in a single healthy human using his repeated rs-fMRI data. The current study reveals that whole brain network properties varied within a single resting-state scan session.

Bharat et al have associated the variations of functional connectivity with the intrinsic activities of resting-state networks during a single resting state scan by comparing functional connectivity differences between the situation when a network had higher and lower intrinsic activities (18). Allen et al. have described an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices (19). There are few good review articles about dynamic FC. Hutchison et al have reviewed recent findings, methodological considerations, neural and behavioral correlates, and some directions in the emerging field of dynamic FC studies (10). In addition, Ioannides review FC results from a variety of studies, which suggest that an adequate description of brain organization requires a hierarchy of networks rather than a single one (20). Viviano et al explore the associations between dynamic functional connectivity and age differences, metabolic risk, and cognitive performance in healthy adults (21). Hutchison et al showed that the Resting-state networks have Dynamic FC in awake humans and anesthetized macaques. Their results illustrated that resting-state functional connectivity is not static (22). Marusak et al have explored the Dynamic FC of neurocognitive networks in children in a sample of 146 youth from varied sociodemographic backgrounds. They applied the Independent component analysis, sliding time window correlation, and k-means clustering to rs-fMRI data. Their results showed six dynamic FC networks that re-occur over time (23). Bhattacharya et al have proposed a nonparametric Bayesian approach to model effective connectivity assuming a dynamic non-stationary neuronal system (24).

However a large number of ROIs is possible for the variance model, but we needed to make modifications to the proposed method to accommodate the larger number of ROIs. The reason was that the number of parameters in our model were very large compared with respect to the number of ROIs. To solve this problem we ignored the covariance terms in the between-subject covariance matrix. Because of a small number of sessions, we considered only the scaled identity structure for the between-subject covariance matrix, since by this structure the model has a small number of parameters. Using larger sample sizes, one can consider structures that are more complex.

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