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

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          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 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 neural dynamics of human participants with higher fluid intelligence quotient scores are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. The present results are consistent with the notion of “edge-of-chaos” neural computation.

          Abstract

          Ezaki et al. develop a computational tool to analyze neural resting-state dynamics of functional magnetic resonance imaging data. Their data from adult humans suggest that the ability to think logically and find solutions improves with the brain located closer to criticality.

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          Nonlinear dynamical analysis of EEG and MEG: review of an emerging field.

          C. Stam (2005)
          Many complex and interesting phenomena in nature are due to nonlinear phenomena. The theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a stage, where it becomes possible to study self-organization and pattern formation in the complex neuronal networks of the brain. One approach to nonlinear time series analysis consists of reconstructing, from time series of EEG or MEG, an attractor of the underlying dynamical system, and characterizing it in terms of its dimension (an estimate of the degrees of freedom of the system), or its Lyapunov exponents and entropy (reflecting unpredictability of the dynamics due to the sensitive dependence on initial conditions). More recently developed nonlinear measures characterize other features of local brain dynamics (forecasting, time asymmetry, determinism) or the nonlinear synchronization between recordings from different brain regions. Nonlinear time series has been applied to EEG and MEG of healthy subjects during no-task resting states, perceptual processing, performance of cognitive tasks and different sleep stages. Many pathologic states have been examined as well, ranging from toxic states, seizures, and psychiatric disorders to Alzheimer's, Parkinson's and Cre1utzfeldt-Jakob's disease. Interpretation of these results in terms of 'functional sources' and 'functional networks' allows the identification of three basic patterns of brain dynamics: (i) normal, ongoing dynamics during a no-task, resting state in healthy subjects; this state is characterized by a high dimensional complexity and a relatively low and fluctuating level of synchronization of the neuronal networks; (ii) hypersynchronous, highly nonlinear dynamics of epileptic seizures; (iii) dynamics of degenerative encephalopathies with an abnormally low level of between area synchronization. Only intermediate levels of rapidly fluctuating synchronization, possibly due to critical dynamics near a phase transition, are associated with normal information processing, whereas both hyper-as well as hyposynchronous states result in impaired information processing and disturbed consciousness.
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            Computation at the edge of chaos: Phase transitions and emergent computation

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              The neuroscience of human intelligence differences.

              Neuroscience is contributing to an understanding of the biological bases of human intelligence differences. This work is principally being conducted along two empirical fronts: genetics--quantitative and molecular--and brain imaging. Quantitative genetic studies have established that there are additive genetic contributions to different aspects of cognitive ability--especially general intelligence--and how they change through the lifespan. Molecular genetic studies have yet to identify reliably reproducible contributions from individual genes. Structural and functional brain-imaging studies have identified differences in brain pathways, especially parieto-frontal pathways, that contribute to intelligence differences. There is also evidence that brain efficiency correlates positively with intelligence.
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                Author and article information

                Contributors
                naokimas@buffalo.edu
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                3 February 2020
                3 February 2020
                2020
                : 3
                : 52
                Affiliations
                [1 ]ISNI 0000 0004 1754 9200, GRID grid.419082.6, PRESTO, , Japan Science and Technology Agency, ; Kawaguchi, Saitama Japan
                [2 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Research Center for Advanced Science and Technology, , The University of Tokyo, ; Meguro-ku, Tokyo Japan
                [3 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, Department of Engineering Mathematics, , University of Bristol, ; Clifton, Bristol, UK
                [4 ]ISNI 0000000121901201, GRID grid.83440.3b, Institute of Cognitive Neuroscience, , University College London, ; 17 Queen Square, London, WC1N 3AZ UK
                [5 ]GRID grid.474690.8, RIKEN Center for Brain Science, ; Wako, Saitama Japan
                [6 ]ISNI 0000 0004 0457 9566, GRID grid.9435.b, School of Psychology and Clinical Language Sciences, , University of Reading, ; Earley Gate, Whiteknights Road, Reading, UK
                [7 ]GRID grid.440900.9, Research Institute, , Kochi University of Technology, ; Kami, Kochi Japan
                [8 ]ISNI 0000 0004 1936 9887, GRID grid.273335.3, Department of Mathematics, , University at Buffalo, State University of New York, ; Buffalo, New York USA
                [9 ]ISNI 0000 0004 1936 9887, GRID grid.273335.3, Computational and Data-Enabled Science and Engineering Program, , University at Buffalo, State University of New York, ; Buffalo, New York USA
                Author information
                http://orcid.org/0000-0001-9658-380X
                http://orcid.org/0000-0003-1993-5765
                http://orcid.org/0000-0003-1567-801X
                Article
                774
                10.1038/s42003-020-0774-y
                6997374
                32015402
                e08085e0-bda9-469e-b44a-55ee8ac6d9fc
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 July 2019
                : 13 January 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100009023, MEXT | JST | Precursory Research for Embryonic Science and Technology (PRESTO);
                Award ID: JPMJPR16D2
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100009024, MEXT | JST | Exploratory Research for Advanced Technology (ERATO);
                Award ID: JPMJER1201
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000780, European Commission (EC);
                Award ID: CIG618600
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);
                Award ID: 16H02053
                Award ID: 16H05959
                Award ID: 16H06406
                Award ID: 16KT0002
                Award ID: 18H06094
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100009032, MEXT | JST | Development of Advanced Measurement and Analysis Systems (SENTAN);
                Funded by: FundRef https://doi.org/10.13039/501100003382, MEXT | JST | Core Research for Evolutional Science and Technology (CREST);
                Award ID: JPMJCR1304
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000266, RCUK | Engineering and Physical Sciences Research Council (EPSRC);
                Funded by: -Yamaha Sports Challenge Fellowship -Fukuhara Fund for Applied Psychoeducation Research
                Categories
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                © The Author(s) 2020

                computational neuroscience,network models
                computational neuroscience, network models

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