Abstract
In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.
Highlights
The brain is a complex nonlinear dynamic system, and the neural oscillations generated by individual neurons or the interaction between neurons define different cognitive and behavioral states [1,2,3,4,5]
We validated the influence of different magnetic flux strengths on them by calculating the test–retest reliability analysis of synchrony and metastability with denoised functional magnetic resonance imaging (fMRI) data divided according to the Destrieux atlas
In summary, we examined the stability of synchrony and metastability in the global network and the resting-state networks
Summary
The brain is a complex nonlinear dynamic system, and the neural oscillations generated by individual neurons or the interaction between neurons define different cognitive and behavioral states [1,2,3,4,5]. There has been increasing research on the measurement of synchrony and metastability in functional magnetic resonance imaging (fMRI) signals [25,27,28,29,30] It is well-verified by a variety of studies that these indexes provide a mechanistic explanation of the origin of functional organization of the brain [28,31,32,33,34], and help us understand the mechanistic causes of diseases [18,25,35], and as a significant predictor of diseases. A recent study by Naik et al assesses the changes in metastability to characterize age-effects on the dynamic repertoire of the functional networks at rest [29] In a word, these two neurodynamic indexes facilitate the exploration of a larger dynamical repertoire of the brain and allow for the all-around visitation of functional states and dynamic responses to the external world [36]
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