2022
DOI: 10.1098/rsif.2022.0214
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May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data

Abstract: Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of th… Show more

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Cited by 15 publications
(17 citation statements)
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“…Second, from the perspective of alternative dFC approaches and pipelines, we did not perform global signal, white matter or cerebral spinal fluid regression. From a complexity science perspective [34], one cannot explore any subsystem of a complex system such as the brain in isolation, and accumulating evidence points to contributions other than neuronal to the fMRI signal [72][73][74]. As in [42], we defined communities of oscillators directly from the phase-locking data and not from intrinsic connectivity networks [36] nor predefined templates [61].…”
Section: Discussionmentioning
confidence: 99%
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“…Second, from the perspective of alternative dFC approaches and pipelines, we did not perform global signal, white matter or cerebral spinal fluid regression. From a complexity science perspective [34], one cannot explore any subsystem of a complex system such as the brain in isolation, and accumulating evidence points to contributions other than neuronal to the fMRI signal [72][73][74]. As in [42], we defined communities of oscillators directly from the phase-locking data and not from intrinsic connectivity networks [36] nor predefined templates [61].…”
Section: Discussionmentioning
confidence: 99%
“…In a neuroscientific context this reflects a tension established by the competition between trends for functional specialization and functional integration within and between brain regions [32]. Metastability is nowadays an ubiquitous concept across diverse models of brain functioning including coordination dynamics [33] and complex systems [34], while its metrics have found application in both empirical studies and computational modeling [35][36][37][38][39][40][41][42][43][44]. Furthermore, a proxy measure of metastability was recently found to be stable and representative across multiple fMRI scans of healthy young adults, highlighting its potential as a grouplevel biomarker of psychiatric disorders [42].…”
Section: Introductionmentioning
confidence: 99%
“…To address this question, we first need to understand that META and VAR are based on two different order parameters. In dynamical systems theory, an order parameter captures the collective behavior of an underlying high-dimensional non-linear system [ 39 ]. META is based on the Kuramoto order parameter [ 75 ] which is the mean phase in a system of weakly coupled oscillators.…”
Section: Resultsmentioning
confidence: 99%
“…Second, from the perspective of alternative dFC approaches and pipelines, we did not perform global signal, white matter or cerebral spinal fluid regression. From a complexity science perspective [39], one cannot explore any subsystem of a complex system such as the brain in isolation, and accumulating evidence points to contributions other than neuronal to the fMRI signal [89][90][91]. As in [47], we defined communities of oscillators directly from the phase-locking data and not from intrinsic connectivity networks [41] nor predefined templates [68].…”
Section: Plos Onementioning
confidence: 99%
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