Avalanche criticality in individuals, fluid intelligence, and working memory

Abstract

The critical brain hypothesis suggests that efficient neural computation can be achieved through critical brain dynamics. However, the relationship between human cognitive performance and scale‐free brain dynamics remains unclear. In this study, we investigated the whole‐brain avalanche activity and its individual variability in the human resting‐state functional magnetic resonance imaging (fMRI) data. We showed that though the group‐level analysis was inaccurate because of individual variability, the subject wise scale‐free avalanche activity was significantly associated with maximal synchronization entropy of their brain activity. Meanwhile, the complexity of functional connectivity, as well as structure–function coupling, is maximized in subjects with maximal synchronization entropy. We also observed order–disorder phase transitions in resting‐state brain dynamics and found that there were longer times spent in the subcritical regime. These results imply that large‐scale brain dynamics favor the slightly subcritical regime of phase transition. Finally, we showed evidence that the neural dynamics of human participants with higher fluid intelligence and working memory scores are closer to criticality. We identified brain regions whose critical dynamics showed significant positive correlations with fluid intelligence performance and found that these regions were located in the prefrontal cortex and inferior parietal cortex, which were believed to be important nodes of brain networks underlying human intelligence. Our results reveal the possible 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, ranging from subcriticality to supercriticality.

Investigating cognitive neuroscience theories of human intelligence: A connectome‐based predictive modeling approach

“…The use of resting‐state functional connectivity in this study is motivated by a wealth of prior evidence suggesting that resting‐state data enable the prediction of individual differences in general intelligence and further inform the topology and dynamics of information processing (Bolt et al, 2017 ; Schultz & Cole, 2016 ; Thiele et al, 2022 , see also Cole et al, 2013 ). Thus, resting‐state connectivity is well‐established in this context (Dubois et al, 2018 ; Feilong et al, 2021 ; Jiang et al, 2020 ; Saxe et al, 2018 ; Xu et al, 2022 ), and provides a powerful framework for understanding individual differences in high‐level cognition (Dubois & Adolphs, 2016 ).…”

Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture

“…The critical brain hypothesis suggests that efficient neural computation can be achieved through critical dynamics and that the brain operates in close vicinity to a critical point lying between order and disorder [ 23 ]. This hypothesis is supported by a set of observations of power-law scaling in many different neural systems using various approaches [ 31 , 32 ].…”

“…More importantly, assimilating human brain functional data from computing models obtained from structural data is a way to show similarity in the functional connectivity (FC) and time courses of brain imaging, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG) and magnetoencephalography (MEG), between the computing model and its biological counterpart [ 20–22 ]. Statistical physical methods have been used to identify the physical properties of brain dynamics and their relationship to brain functions [ 23 ]. However, most of these works use a dynamical model based on neuron populations or brain areas.…”

Spatiotemporal brain complexity quantifies consciousness outside of perturbation paradigms

“…Simply put, this property allows the brain to be flexible enough to reconfigure and adapt dynamically to a changing environment all while being stable enough to engage in complex sustained activities. Among other things, the critical regime is known to maximize information processing (Beggs 2008) or related to cognitive processes (Xu, Feng, et Yu 2022), and criticality in the brain is also possibly linked to consciousness (Tagliazucchi et al 2016;Tagliazucchi 2017;Toker et al 2022). Dynamical systems theory in neurosciences has also found a strong paradigm with the concept of manifolds.…”