Abstract:Abstract:Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying functional connectivity patterns of the developing and aging brain. Normal brain development is characterized by continuous and significant network evolution through infancy, childhood, and adolescence, following specific maturational patterns. Normal aging is related to some resting state brain networks disruption, which are associated with certain cognitive decline. It is a big challen… Show more
“…Very recently, the concept of eigen-entropy was used in studying financial markets [21]. It is computed from eigencentrality of the network obtained from the short time series correlation matrices [21,46]. In order to capture the global feature of the network, every node is ranked by its eigen-centrality (for mathematical details and schematic diagram, see Supplementary Material).…”
In this mini-review, we critically examine the recent work done on correlation-based networks in financial systems. The structure of empirical correlation matrices constructed from the financial market data changes as the individual stock prices fluctuate with time, showing interesting evolutionary patterns, especially during critical events such as market crashes, bubbles, etc. We show that the study of correlation-based networks and their evolution with time is useful for extracting important information of the underlying market dynamics. Also, we present our perspective on the use of recently-developed entropy measures, such as structural entropy and eigen-entropy, for continuous monitoring of correlation-based networks.
“…Very recently, the concept of eigen-entropy was used in studying financial markets [21]. It is computed from eigencentrality of the network obtained from the short time series correlation matrices [21,46]. In order to capture the global feature of the network, every node is ranked by its eigen-centrality (for mathematical details and schematic diagram, see Supplementary Material).…”
In this mini-review, we critically examine the recent work done on correlation-based networks in financial systems. The structure of empirical correlation matrices constructed from the financial market data changes as the individual stock prices fluctuate with time, showing interesting evolutionary patterns, especially during critical events such as market crashes, bubbles, etc. We show that the study of correlation-based networks and their evolution with time is useful for extracting important information of the underlying market dynamics. Also, we present our perspective on the use of recently-developed entropy measures, such as structural entropy and eigen-entropy, for continuous monitoring of correlation-based networks.
“…Our results revealed that the orderness of the gene expression networks in the brain regions presented a downwards trend from the subcortex to the archicortex to the neocortex, consistent in all six brains. Previous studies have reported that the eigen entropy of the whole-brain network in the human connectome is associated with the neurodevelopment and ageing of individuals 30 . Considering the theory of evolution of life systems that the development of organisms and a highly complex brain is a process of diminishing entropy 30 , 31 , such brain organization may be the result of evolution.…”
Section: Resultsmentioning
confidence: 99%
“…Previous studies have reported that the eigen entropy of the whole-brain network in the human connectome is associated with the neurodevelopment and ageing of individuals 30 . Considering the theory of evolution of life systems that the development of organisms and a highly complex brain is a process of diminishing entropy 30 , 31 , such brain organization may be the result of evolution. Fig.…”
Section: Resultsmentioning
confidence: 99%
“…The closer the local clustering coefficient is to 1, the more likely that the network will form clusters. The eigen entropy of networks was defined as the entropy of the normalized largest eigenvector of an adjacency matrix 30 . For a network with n genes, its adjacency matrix can be represented as , which describes the interacting relationships of the genes in the networks.…”
The human cerebral cortex is vastly expanded relative to nonhuman primates and rodents, leading to a functional orderly topography of brain networks. Here, we show that functional topography may be associated with gene expression heterogeneity. The neocortex exhibits greater heterogeneity in gene expression, with a lower expression of housekeeping genes, a longer mean path length, fewer clusters, and a lower degree of ordering in networks than archicortical and subcortical areas in human, rhesus macaque, and mouse brains. In particular, the cerebellar cortex displays greater heterogeneity in gene expression than cerebellar deep nuclei in the human brain, but not in the mouse brain, corresponding to the emergence of novel functions in the human cerebellar cortex. Moreover, the cortical areas with greater heterogeneity, primarily located in the multimodal association cortex, tend to express genes with higher evolutionary rates and exhibit a higher degree of functional connectivity measured by resting-state fMRI, implying that such a spatial distribution of gene expression may be shaped by evolution and is favourable for the specialization of higher cognitive functions. Together, the cross-species imaging and genetic findings may provide convergent evidence to support the association between the orderly topography of brain function networks and gene expression.
“…The entropy of a single discrete random variable is a measure of its uncertainty, which can characterise the degree of randomness of the random variable. Based on the entropy parameter, it can reflect the orderly changes of complex systems and point out the development trend of the system, thus being used in the assessment of brain function status [20]. The approximate entropy was proposed so that its algorithm can be calculated with a small amount of data and applied to the analysis of physiological signals [21].…”
Cognitive workload is an internal factor that can be influenced by external factors. When drivers are distracted by these internal and external influences, their ability to drive safely can be compromised. Electroencephalogram (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain. and it can provide information about brain activity related to various cognitive and motor functions. In this study, on-road driving experiments were conducted to investigate the EEG characteristics of drivers under different levels of cognitive workload generated through mathematical calculations with varying difficulties. The collected EEG signals were processed using continuous wavelet transform to decompose them into delta, theta, alpha, beta, and gamma waveforms. Numerical values and power spectral characteristics of each waveband in the left and right frontal regions of the driver were then analyzed. Furthermore, the study utilized an improved multiscale sample entropy algorithm to analyze the EEG entropy characteristics of drivers in different states. The results indicate that under different degrees of cognitive workload, the amplitude of delta, theta, and alpha waves decreased, while beta and gamma waves showed significant changes. The improved multiscale sample entropy algorithm provided a more objective assessment of changes in entropy across different wavebands and stages, with smaller entropy fluctuation ranges and more accurate complexity changes. Overall, this research provides valuable insights into the characteristics of drivers under cognitive workload, contributing to the analysis of human factors in driving safety. These findings can inform the development of strategies to mitigate the adverse effects of cognitive workload on driving performance and enhance overall road safety.
INDEX TERMSCognitive workload, EEG characteristics, continuous wavelet transform, power spectrum characteristics, improved multiscale sample entropy.
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