Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence

Ezaki, Takahiro; Reis, Elohim Fonseca dos; Watanabe, Takamitsu; Sakaki, Michiko; Masuda, Naoki

doi:10.1038/s42003-020-0774-y

Cited by 55 publications

(55 citation statements)

References 69 publications

(133 reference statements)

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“…A recent resting-state fMRI study of neurotypical adults with a range of IQ scores found that high fluid intelligence is associated with proximity to a critical phase transition in a spin-glass model ( Ezaki et al, 2020 ). This finding was consistent with previous work suggesting near-criticality as perhaps optimal for learning ( Gisiger et al, 2014 ).…”

Section: Clinical Applications Of Brain Criticalitymentioning

confidence: 99%

Why Brain Criticality Is Clinically Relevant: A Scoping Review

Zimmern

2020

Front. Neural Circuits

124

118

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The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains-including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry-are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered.

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Section: Clinical Applications Of Brain Criticalitymentioning

confidence: 99%

Why Brain Criticality Is Clinically Relevant: A Scoping Review

Zimmern

2020

Front. Neural Circuits

124

118

View full text Add to dashboard Cite

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“…Recently, Ezaki et al used the Ising model to map BOLD signals on a two-dimensional phase space and found that human fMRI data were in the paramagnetic phase and were close to the boundary with the spin-glass phase but not to the boundary with the ferromagnetic phase 79 . Since the spin-glass phase usually yields chaotic dynamics whereas the ferromagnetic phase is obviously nonchaotic, their results suggested that the brain is around the "edge of chaos criticality" instead of "avalanche criticality".…”

Section: Discussionmentioning

confidence: 99%

Avalanche criticality in individuals, fluid intelligence and working memory

Xu¹,

Feng

2020

Preprint

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The critical brain hypothesis suggests that efficient neural computation could be realized by critical brain dynamics hallmarked by scale-free avalanche activity. However, its further application requires not only accurately identifying the critical point but also depicting the phase transition in brains so that different cognitive states could be mapped on a spectrum. In this work, we mapped individuals' brains onto an inverted-U curve between the mean synchronization and synchronization entropy of blood oxygenation level-dependent (BOLD) signals from resting-state fMRI scans. We found that the critical point lies at the tipping point (i.e., moderate synchrony and maximal variability in synchrony) of this curve, which is consistent with previous findings. We then verified that the complexity of functional connectivity, as well as the similarity between structural and functional networks, was maximized near the critical point, whereas reduction in complexity and structure-function decoupling were found both in the sub- and supercritical regimes. We then observed phase transitions in resting-state brain dynamics and found that the brains showed longer dwell times in the subcritical regime. These results provided strong evidence that the large-scale brain networks were hovering around the critical point. Finally, we found that critical dynamics were associated with high scores in fluid intelligence and working memory tests but not crystallized intelligence scores. Our results revealed the role that avalanche criticality plays in cognitive performance and provide a simple method to identify the critical point and map cortical states on a spectrum of neural dynamics, with a critical point in the domain.

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“…Recent data-driven studies support the notion that the brain acts near criticality. Ezaki et al (2020) have used functional magnetic resonance imaging (fMRI) of networks at a whole-brain level to posit that individuals with higher fluid intelligence have brains operating closer to criticality, as compared with others [12]. They address patterns of transition between discrete states.…”

Section: Free Energy Minimization In the Brainmentioning

confidence: 99%

The 2-D Cluster Variation Method: Initial Findings and Topography Illustrations

Maren¹

2021

Preprint

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One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D Cluster Variation Method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy and the interaction enthalpy. Initial investigations with two different representative topographies (``scale-free-like'' and ``rich club-like'') produce interesting results when brought to a CVM free energy minimum. Additional phase space investigations, where one of these two parameters has been set to zero, identify useful parameter ranges. Careful comparison of the analytically-predicted configuration variables versus those obtained when performing computational free energy minimization on a 2-D grid show that the computational results differ significantly from the analytic solution. The 2-D CVM can potentially function as a secondary free energy minimization within the hidden layer of a neural network, providing a basis for extending node activations over time and allowing temporal correlation of patterns.

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Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence

Cited by 55 publications

References 69 publications

Why Brain Criticality Is Clinically Relevant: A Scoping Review

Why Brain Criticality Is Clinically Relevant: A Scoping Review

Avalanche criticality in individuals, fluid intelligence and working memory

The 2-D Cluster Variation Method: Initial Findings and Topography Illustrations

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