Capturing the non-stationarity of whole-brain dynamics underlying human brain states

Galadí, Javier A.; Pereira, Silvana Silva; Perl, Yonatan Sanz; Kringelbach, Morten L.; Gayte, Inmaculada; Laufs, Helmut; Tagliazucchi, Enzo; Langa, José A.; Deco, Gustavo

doi:10.1016/j.neuroimage.2021.118551

Cited by 20 publications

(29 citation statements)

References 38 publications

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“…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 ]. Specific to the brain network, synchrony in the oscillatory activity of network regions is considered to underpin information exchange [ 15 ], whereas metastability represents the variability in the synchronization of network regions over time that is considered important for adaptive information processing [ 16 , 17 , 18 ], and can be estimated by calculating the well-defined order parameter [ 19 , 20 , 21 , 22 , 23 ]. Existent theories suggest that synchrony is considered as the core mechanism for sculpting communication and plasticity of the entire brain network that underpins human cognition [ 24 ], and metastability can reconcile the competing demands of integration and segregation of brain regions interact [ 17 , 25 , 26 ].…”

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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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 this regard, three types of connections are commonly recognized: (1) structural or anatomic connectivity; (2) functional connectivity, defined as statistical associations or dependencies between neurophysiological events recorded in distant brain regions; and (3) effective connectivity, defined as directed or causal relationships between brain regions ( Bullmore and Sporns, 2009 ; Friston, 2011 ). Connectivity also evolves over time on multiple time scales ( Hansen et al, 2015 ; Galadí et al, 2021 ) and establishes a functional connectivity dynamics predictive of aging ( Battaglia et al, 2020 ; Escrichs et al, 2021 ), cognitive processes ( Lombardo et al, 2020 ), and brain disease ( Courtiol et al, 2020 ).…”

Section: Brain Complexitymentioning

confidence: 99%

Linking Brain Structure, Activity, and Cognitive Function through Computation

Amunts

DeFelipe

Pennartz

et al. 2022

eNeuro

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“…While the mathematical research behind DST has a long history, nonlinear dynamical systems exhibit behavior difficult to analyse without simulation. Advances in computational power have rendered DST much more tractable as a tool for neuroimaging (Breakspear, 2017;Cabral et al, 2014;Deco et al, 2009Deco et al, , 2009Deco et al, , 2011Deco, Jirsa, et al, 2013;Deco, Ponce-Alvarez, et al, 2013;Deco et al, 2015Deco et al, , 2015Deco et al, , 2021Deco & Jirsa, 2012;Ghosh et al, 2008;Gollo et al, 2015;Hlinka & Coombes, 2012;Pillai & Jirsa, 2017;Sanz Perl et al, 2021;Shine, Breakspear, et al, 2019). Further, the DST modeling framework has enabled simulations of neural dynamics that are predictive and generative: simulated trajectories can be used to fit specific datasets (beim Graben et al, 2019;Golos et al, 2015;Hansen et al, 2015;Koppe et al, 2019;Vyas et al, 2020), but can also point 5 researchers beyond data, for example by contributing to experimental design, and facilitating integration of findings from different paradigms and species.…”

Section: Introductionmentioning

confidence: 99%

It’s about time: Linking dynamical systems with human neuroimaging to understand the brain

John

Sawyer

Srinivasan

et al. 2022

Network Neuroscience

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Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain’s time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective; by focusing on dynamics instead of static snapshots of neural activity; and by embracing modeling approaches that map neural dynamics using “forward” models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

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Capturing the non-stationarity of whole-brain dynamics underlying human brain states

Cited by 20 publications

References 38 publications

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

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

Linking Brain Structure, Activity, and Cognitive Function through Computation

It’s about time: Linking dynamical systems with human neuroimaging to understand the brain

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