@article {Ezaki688655, author = {Takahiro Ezaki and Elohim Fonseca dos Reis and Takamitsu Watanabe and Michiko Sakaki and Naoki Masuda}, title = {Critical brain dynamics and human intelligence}, elocation-id = {688655}, year = {2019}, doi = {10.1101/688655}, publisher = {Cold Spring Harbor Laboratory}, abstract = {According to the critical brain hypothesis, the brain is considered to operate near criticality and realize efficient neural computations. Despite the prior theoretical and empirical evidence in favor of the hypothesis, no direct link has been provided between human cognitive performance and the neural criticality. Here we provide such a key link by analyzing resting-state dynamics of functional magnetic resonance imaging (fMRI) networks at a whole-brain level. We develop a novel data-driven analysis method, inspired from statistical physics theory of spin systems, to map out the whole-brain neural dynamics onto a phase diagram. Using this tool, we show evidence that dynamics of more intelligent human participants are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. This result was specific to fluid intelligence as opposed to crystalized intelligence. The present results are also consistent with the notion of {\textquotedblleft}edge-of-chaos{\textquotedblright} neural computation.Author summary According to the critical brain hypothesis, the brain should be operating near criticality, i.e., a boundary between different states showing qualitatively different dynamical behaviors. Such a critical brain dynamics has been considered to realize efficient neural computations. Here we provide direct neural evidence in favor of this hypothesis by showing that the brain dynamics in more intelligent individuals are closer to the criticality. We reached this conclusion by deploying a novel data-analysis method based on statistical-physics theory of Ising spin systems to functional magnetic resonance imaging (fMRI) data obtained from human participants. Specifically, our method maps multivariate fMRI data obtained from each participant to a point in a phase diagram akin to that of the Sherrington-Kirkpatrick model of spin-glass systems. The brain dynamics for the participants having high fluid intelligence scores tended to be close to the phase boundary, which marks criticality, between the paramagnetic phase and the spin-glass phase, but not to the boundary between the paramagnetic and ferromagnetic phases.}, URL = {https://www.biorxiv.org/content/early/2019/07/01/688655}, eprint = {https://www.biorxiv.org/content/early/2019/07/01/688655.full.pdf}, journal = {bioRxiv} }