Rich-club network topology to minimize synchronization cost due to phase difference among frequency-synchronized oscillators

Functions of some networks, such as power grids and large-scale brain networks, rely on not only frequency synchronization, but also phase synchronization. Nevertheless, even after the oscillators reach to frequency-synchronized status, phase difference among oscillators often shows non-zero constant values. Such phase difference potentially results in inefficient transfer of power or information among oscillators, and avoid proper and efficient functioning of the network. In the present study, we newly define synchronization cost by the phase difference among the frequency-synchronized oscillators, and investigate the optimal network structure with the minimum synchronization cost through rewiring-based optimization. By using the Kuramoto model, we demonstrate that the cost is minimized in a network topology with rich-club organization, which comprises the densely-connected center nodes and peripheral nodes connecting with the center module. We also show that the network topology is characterized by its bimodal degree distribution, which is quantified by Wolfson's polarization index. Furthermore, we provide analytical interpretation on why the rich-club network topology is related to the small amount of synchronization cost.Comment: 4 figures + one appendix figur

Energy landscape analysis of neuroimaging data

Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analyzed, and the data length.Comment: 22 pages, 4 figures, 1 tabl

Enhancing the spectral gap of networks by node removal

Dynamics on networks are often characterized by the second smallest eigenvalue of the Laplacian matrix of the network, which is called the spectral gap. Examples include the threshold coupling strength for synchronization and the relaxation time of a random walk. A large spectral gap is usually associated with high network performance, such as facilitated synchronization and rapid convergence. In this study, we seek to enhance the spectral gap of undirected and unweighted networks by removing nodes because, practically, the removal of nodes often costs less than the addition of nodes, addition of links, and rewiring of links. In particular, we develop a perturbative method to achieve this goal. The proposed method realizes better performance than other heuristic methods on various model and real networks. The spectral gap increases as we remove up to half the nodes in most of these networks.Comment: 5 figure

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

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

Network-dependent modulation of brain activity during sleep

AbstractBrain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks

Molecular characteristics of methicillin-resistant Staphylococcus aureus isolated from skin and soft tissue infections collected in the Japanese nationwide surveillance

Skin and soft tissue infections (SSTI) are a common infection among both outpatients and inpatients. The most frequently isolated bacterium in SSTI was Staphylococcus aureus, a quarter of which was methicillin-resistant S. aureus (MRSA). In this study, to investigate molecular epidemiology of the 141 MRSA strains collected in the Japanese nationwide surveillance, we performed multiplex real-time polymerase chain reaction to detect staphylococcal cassette chromosome mec (SCCmec) type and virulence genes. The percentage of SCCmec types I, II, III and IV was 1.4%, 52.5%, 5.7% and 40.4%, respectively. According to the SCCmec type, we classified the strains into health-care-associated (HA)-MRSA (n = 84) and community-associated (CA)-MRSA (n = 57). Among the virulence genes, the percentage of enterotoxin C gene-positive strains was significantly higher in CA-MRSA than in HA-MRSA. No significant differences were detected between the two groups in terms of antibiotic susceptibility and patients’ background information, classification of SSTI or symptoms of SSTI

Hippocampal metabolism of amino acids by L-amino acid oxidase is involved in fear learning and memory

Amino acids participate directly and indirectly in many important biochemical functions in the brain. We focused on one amino acid metabolic enzyme, L-amino acid oxidase (LAO), and investigated the importance of LAO in brain function using LAO1 knockout (KO) mice. Compared to wild-type mice, LAO1 KO mice exhibited impaired fear learning and memory function in a passive avoidance test. This impairment in LAO1 KO mice coincided with significantly reduced hippocampal acetylcholine levels compared to wild-type mice, while treatment with donepezil, a reversible acetylcholine esterase inhibitor, inhibited this reduction. Metabolomic analysis revealed that knocking out LAO1 affected amino acid metabolism (mainly of phenylalanine [Phe]) in the hippocampus. Specifically, Phe levels were elevated in LAO1 KO mice, while phenylpyruvic acid (metabolite of Phe produced largely by LAO) levels were reduced. Moreover, knocking out LAO1 decreased hippocampal mRNA levels of pyruvate kinase, the enzymatic activity of which is known to be inhibited by Phe. Based on our findings, we propose that LAO1 KO mice exhibited impaired fear learning and memory owing to low hippocampal acetylcholine levels. Furthermore, we speculate that hippocampal Phe metabolism is an important physiological mechanism related to glycolysis and may underlie cognitive impairments, including those observed in Alzheimer’s disease

White matter microstructural alterations across four major psychiatric disorders : mega-analysis study in 2937 individuals

Identifying both the commonalities and differences in brain structures among psychiatric disorders is important for understanding the pathophysiology. Recently, the ENIGMA-Schizophrenia DTI Working Group performed a large-scale meta-analysis and reported widespread white matter microstructural alterations in schizophrenia; however, no similar cross-disorder study has been carried out to date. Here, we conducted mega-analyses comparing white matter microstructural differences between healthy comparison subjects (HCS; N = 1506) and patients with schizophrenia (N = 696), bipolar disorder (N = 211), autism spectrum disorder (N = 126), or major depressive disorder (N = 398; total N = 2937 from 12 sites). In comparison with HCS, we found that schizophrenia, bipolar disorder, and autism spectrum disorder share similar white matter microstructural differences in the body of the corpus callosum; schizophrenia and bipolar disorder featured comparable changes in the limbic system, such as the fornix and cingulum. By comparison, alterations in tracts connecting neocortical areas, such as the uncinate fasciculus, were observed only in schizophrenia. No significant difference was found in major depressive disorder. In a direct comparison between schizophrenia and bipolar disorder, there were no significant differences. Significant differences between schizophrenia/bipolar disorder and major depressive disorder were found in the limbic system, which were similar to the differences in schizophrenia and bipolar disorder relative to HCS. While schizophrenia and bipolar disorder may have similar pathological characteristics, the biological characteristics of major depressive disorder may be close to those of HCS. Our findings provide insights into nosology and encourage further investigations of shared and unique pathophysiology of psychiatric disorders