Enhancement of stability in systems with metastable states

Spagnolo, Bernardo; Augello, Giuseppa; Fiasconaro, Alessandro; Pizzolato, Nicola; Valenti, Davide

doi:10.1063/1.2828729

Cited by 1 publication

(1 citation statement)

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“…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 ]. Nonlinear system has two important dynamic characteristics--synchronization and metastability [ 6 , 7 ], which play very important roles [ 8 , 9 , 10 , 11 , 12 ]. Neuronal synchrony plays a role in well-timed coordination and communication between neural populations simultaneously engaged in a cognitive process [ 13 ], and metastability reflects flexible dynamic interactions between neural populations [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI

Yang

Wei

et al. 2021

Brain Sciences

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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.

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