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.
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 "edge-of-chaos" neural computation. Author summaryAccording 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 dataanalysis 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.
Intervals between discrete events representing human activities, as well as other types of events, often obey heavy-tailed distributions, and their impacts on collective dynamics on networks such as contagion processes have been intensively studied. The literature supports that such heavy-tailed distributions are present for inter-event times associated with both individual nodes and individual edges in networks. However, the simultaneous presence of heavy-tailed distributions of inter-event times for nodes and edges is a non-trivial phenomenon, and its origin has been elusive. In the present study, we propose a generative model and its variants to explain this phenomenon. We assume that each node independently transits between a high-activity and low-activity state according to a continuous-time two-state Markov process and that, for the main model, events on an edge occur at a high rate if and only if both end nodes of the edge are in the high-activity state. In other words, two nodes interact frequently only when both nodes prefer to interact with others. The model produces distributions of inter-event times for both individual nodes and edges that resemble heavy-tailed distributions across some scales. It also produces positive correlation in consecutive inter-event times, which is another stylized observation for empirical data of human activity. We expect that our modeling framework provides a useful benchmark for investigating dynamics on temporal networks driven by non-Poissonian event sequences.
The mammalian immune system protects individuals from infection and disease. It is a complex system of interacting cells and molecules, which has been studied extensively to investigate its detailed function, principally using laboratory mice. Despite the complexity of the immune system, it is often analysed using a restricted set of immunological parameters. Here we have sought to generate a system-wide view of the murine immune response, which we have done by undertaking a network analysis of 120 immune measures. To date, there has only been limited network analyses of the immune system. Our network analysis identified a relatively low number of communities of immune measure nodes. Some of these communities recapitulate the well-known T helper 1 vs. T helper 2 cytokine polarisation (where ordination analyses failed to do so), which validates the utility of our approach. Other communities we detected show apparently novel juxtapositions of immune nodes. We suggest that the structure of these other communities might represent functional immunological units, which may require further empirical investigation. These results show the utility of network analysis in understanding the functioning of the mammalian immune system.
A study of the damages caused by gallium focused ion beam (FIB) is presented. Potential damages caused by local heating, ion implantation, and selective sputtering are presented. Preliminary analysis shows that local heating is negligible. Gallium implantation is shown to occur over areas tens of nanometers thick. Gallium accumulation as well as selective sputtering during III-V compound milling is expected. Particularly, for GaAs, this effect leads to gallium segregation and the formation of metallic clusters. Microdisks resonators are fabricated using FIB milling of different emission currents. It is shown that for higher emission current, thus higher implantation doses, the cavity quality factor rapidly decreases.
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