The frustrated brain: from dynamics on motifs to communities and networks

Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems—resonance pairs—promote stable zero-lag synchrony among the small motifs in which they are embedded, and whose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zero-lag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across diverse cognitive processes.     

The large graph limit of a stochastic epidemic model on a dynamic multilayer network

We consider a Markovian SIR-type (Susceptible → Infected → Recovered) stochastic epidemic process with multiple modes of transmission on a contact network. The network is given by a random graph following a multilayer configuration model where edges in different layers correspond to potentially infectious contacts of different types. We assume that the graph structure evolves in response to the epidemic via activation or deactivation of edges of infectious nodes. We derive a large graph limit theorem that gives a system of ordinary differential equations (ODEs) describing the evolution of quantities of interest, such as the proportions of infected and susceptible vertices, as the number of nodes tends to infinity. Analysis of the limiting system elucidates how the coupling of edge activation and deactivation to infection status affects disease dynamics, as illustrated by a two-layer network example with edge types corresponding to community and healthcare contacts. Our theorem extends some earlier results describing the deterministic limit of stochastic SIR processes on static, single-layer configuration model graphs. We also describe precisely the conditions for equivalence between our limiting ODEs and the systems obtained via pair approximation, which are widely used in the epidemiological and ecological literature to approximate disease dynamics on networks. The flexible modeling framework and asymptotic results have potential application to many disease settings including Ebola dynamics in West Africa, which was the original motivation for this study.     

Effect of perinatally supplemented flavonoids on brain structure,circulation, cognition,and metabolism in C57BL/6J mice

Evidence suggests that flavanol consumption can beneficially affect cognition in adults, but little is known about the effect of flavanol intake early in life. The present study aims to assess the effect of dietary flavanol intake during the gestational and postnatal period on brain structure, cerebral blood flow (CBF), cognition, and brain metabolism in C57BL/6J mice.Female wild-type C57BL/6J mice were randomly assigned to either a flavanol supplemented diet or a control diet at gestational day 0. Male offspring remained on the corresponding diets throughout life and performed cognitive and behavioral tests during puberty and adulthood assessing locomotion and exploration (Phenotyper and open field), sensorimotor integration (Rotarod and prepulse inhibition), and spatial learning and memory (Morris water maze, MWM). Magnetic resonance spectroscopy and imaging at 11.7T measured brain metabolism, CBF, and white and gray matter integrity in adult mice. Biochemical and immunohistochemical analyses evaluated inflammation, synaptic plasticity, neurogenesis, and vascular density.Cognitive and behavioral tests demonstrated increased locomotion in Phenotypers during puberty after flavanol supplementation (p = 0.041) but not in adulthood. Rotarod and prepulse inhibition demonstrated no differences in sensorimotor integration. Flavanols altered spatial learning in the MWM in adulthood (p = 0.039), while spatial memory remained unaffected. Additionally, flavanols increased diffusion coherence in the visual cortex (p = 0.014) and possibly the corpus callosum (p = 0.066) in adulthood. Mean diffusion remained unaffected, a finding that corresponds with our immunohistochemical data showing no effect on neurogenesis, synaptic plasticity, and vascular density. However, flavanols decreased CBF in the cortex (p = 0.001) and thalamus (p = 0.009) in adulthood. Brain metabolite levels and neuroinflammation remained unaffected by flavanols.These data suggest that dietary flavanols results in subtle alterations in brain structure, locomotor activity and spatial learning. Comparison of these data to published findings in aging or neurodegeneration suggests that benefits of dietary flavanols may increase with advancing age and in disease.     

The hypothalamus as the primary brain region of metabolic abnormalities in APP/PS1 transgenic mouse model of Alzheimer's disease

Alzheimer's disease (AD) is an amyloid-related neurodegenerative disorder and is also considered to be a metabolic disease. Thus, investigation of metabolic mechanisms of amyloid pathology progression is of substantial importance for the diagnosis, prevention and treatment of AD. In the present study, cognitive function and brain metabolism were explored in the transgenic APP/PS1 mouse model of amyloid pathology at different ages. Using an NMR-based metabolomic approach, we examined metabolic changes in six different brain regions of wild-type and APP/PS1 mice at 1, 5 and 10 months of age. Learning and memory performance in mice was evaluated using the Morris water maze test. Furthermore, a generalized linear mixed model was employed to analyze the interaction effect between the mouse-type and brain region (or age) on metabolic alterations. Brain region-specific changes in energy metabolism occurred prior to a very early-stage of amyloid pathology (1 month of age) in APP/PS1 mice. A hypermetabolic state was identified in the brains of APP/PS1 mice at 5 months of age, and the hypothalamus was identified as the main brain region that underwent significant metabolic alterations. The cognitive function of APP/PS1 mice was impaired at 10 months of age; moreover, the hypermetabolic state identified in various brain regions at 5 months of age was also significantly decreased. In conclusion, our results suggest that a hypothalamic metabolism abnormality may comprise a potential indicator for the early-diagnosis and monitoring of amyloid pathology progression.     

A dynamic water quality index model based on functional data analysis

A water quality index (WQI) incorporates two shortcomings in the dynamic assessment of water quality, namely: (1) the sampling time series must be identical for each indicator and no missing data should occur, and (2) stationary weights cannot represent the changes in the pollutant importance. To solve these problems, the present study introduces the functional data analysis method into WQI research and establishes a dynamic WQI (D-WQI) model. D-WQI is a generalization of the conventional WQI. In the D-WQI model, the changes of water quality and pollutant importance are represented in the form of dynamic functional curves. The generation methods of the concentration curves, sub-index curves, dynamic weight curves, and WQI curves are discussed. As an illustration, the D-WQI model is applied in the water quality assessment of the Changjiang River in Sanjiangying in 2012. Result shows that the river can be classified as II (good) throughout the year, which can satisfy the requirement of the Chinese South-to-North Water Diversion Project.     

The critical period hypothesis of estrogen effects on cognition: Insights from basic research

Background

In addition to its primary role in reproduction estrogen impacts brain areas important for cognition, including the hippocampus and prefrontal cortex. It has been hypothesized that decline in estrogen levels in women following menopause is associated with, or can exacerbate, age-related cognitive decline. However, clinical evidence to support a role for estrogen in preventing cognitive decline in women as they age is equivocal. The critical period hypothesis of estrogen effects on cognition, which proposes that estrogen administration has to be initiated within a critical time period following the loss of ovarian function in order for it to exert positive effects on the central nervous system, is offered as one explanation for inconsistencies across studies.

Scope of review

This review details results from basic research using rodent models investigating the effects of estrogen on cognition in the aging female. Emphasis is placed on work investigating effects of timing of initiation of estrogen administration on its subsequent efficacy.

Major conclusions

Results of basic research provide support for the critical period hypothesis. Furthermore, results of work in rodent models suggest mechanisms by which the response to estrogen is altered if treatment is initiated following long-term ovarian hormone deprivation.

General significance

Understanding if and under what conditions hormone administration following the loss of ovarian function positively affects the brain and behavior could have important implications with regard to female cognitive aging. Results of basic research can contribute to this understanding and provide insight into the complex mechanisms by which estrogen affects cognition.     

A fully nonlinear viscohyperelastic model for the brain tissue applicable to dynamic rates

Understanding the mechanical response of the brain to external loadings is of critical importance in investigating the pathological conditions of this tissue during injurious conditions. Such injurious loadings may occur at high rates, for example among others, during road traffic or sport accidents, falls, or due to explosions. Hence, investigating the injury mechanism and design of protective devices for the brain requires constitutive modeling of this tissue at such rates. Accordingly, this paper is aimed at critically investigating the physical background for viscohyperelastic modeling of the brain tissue with scrutinizing the elastic fields pertinent to large, time dependent deformations, and developing a fully nonlinear multimode Maxwell model that can mathematically explain such deformations. The proposed model can be calibrated using the simple monotonic uniaxial deformation of the sample extracted from the tissue, and does not require additional information from relaxation or creep experiments. The performance of the proposed model is examined using the experimental results of two different studies, which reveals a desirable agreement. The usefulness, limitations, and future developments of the proposed model are discussed in this paper.     

The fossil evidence of anthropoid brain evolution

The endocast of Aegyptopithecus, a 27 million year old ape, reveals that its brain was advanced over that of prosimians and comparable to that of modern anthropoids in relative size and in having expanded visual cortex, reduced olfactory bulbs, and a central sulcus separating primary somatic sensory and motor cortex. The early appearance of those features suggests that they may have been among the adaptations responsible for the evolution of anthropoids from prosimian ancestors. The frontal lobe was relatively smaller in Aegyptopithecus than in modern anthropoids. An endocast of Dolichocebus, one of the oldest known New World monkeys (25–30 million years old), reveals visual cortex expanded as in modern anthropoids. The 19 million year old Napak frontal bone displays a hominoid rather than cercopithecoid sulcal pattern. An 18 million year old endocast of the ape Dryopithecus (Proconsul) was neither monkey-like nor primitive, as originally described, but rather apelike and essentially modern in all observable features. The oldest undoubted Old World monkey endocast, from nine million year old Mesopithecus, reveals that the brain was modern in sulcal pattern and proportions. The sulcal pattern was like that of modern colobines, but that appears to be the more primitive condition, from which features characteristic of modern cercopithecine brains have evolved. The brain of six million year old Libypithecus was similar to that of Mesopithecus. A two million year old endocast of “Dolichopithecus” arvernensis displays a modern cercopithecine sulcal pattern.     

基于最小费用模型的景观连接度评价

作为物种扩散的重要影响因子,景观连接度反映了景观促进或阻碍生物体在资源斑块间运动的程度,它也是区域土地可持续利用和生物保护领域的主要指标.最小费用模型起源于图论,其结合了景观中的详细地理信息和生物体的行为特征,通过费用距离分析可直观形象地描绘出物种在异质景观中的连接度,且可在GIS程序包中实现简便运算和适度的数据需求量,使其在大尺度景观连接度评价中受到广泛关注.本文结合当前的景观连接度研究,详细阐述了最小费用模型评价景观连接度的意义、原理及运算过程,并探讨了该模型应用中存在的问题,以期为进一步开展相关研究及生物多样性保护提供依据.     

分子信标DNA计算模型的研究进展与展望

由于分子信标具有结构简单,灵敏度高及反应迅速等优点,因此,利用分子信标进行数学问题的求解将成为可能.通过对分子信标的计算模型进行详细的介绍,并对分子信标的计算模型的研究思路进行了展望,据此思路,可以建立多种组合优化问题及逻辑门的分子信标计算模型.     

Brain functional network modeling and analysis based on fMRI: a systematic review

In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.     

The brain of the lesser panda Ailurus fulgens: A quantitative approach

A quantitative analysis of the brain of the lesser panda Ailurus fulgens was carried out, using both ratios and progression indices. In a general way, the lesser panda appears to be an intermediate form between the giant panda and the raccoon. Relations between brain size or brain component size, and life-habits, suggest the same conclusion. The traditionally assumed closeness of both pandas implied in the vernacular names, is questioned. A comprehensive neuroethological work on the comparative organization of the brain in all ursoid species is desirable, as a good case in fundamental functional morphology.     

脑电磁场及脑内源定位的研究进展

本文从物理学的电磁场理论阐述脑电磁场的产生原理;从源的等效偶极子定位和皮层成像方法两个方面回顾了脑内源定位的研究进展;作为脑电的基础性研究,它对临床医学的发展具有积极的推动作用。     

Hypofunction of directed brain network within alpha frequency band in depressive patients: a graph-theoretic analysis

Directed brain networks may provide new insights into exploring physiological mechanism and neuromarkers for depression. This study aims to investigate the abnormalities of directed brain networks in depressive patients. We constructed the directed brain network based on resting electroencephalogram for 19 depressive patients and 20 healthy controls with eyes closed and eyes open. The weighted directed brain connectivity was measured by partial directed coherence for α, β, γ frequency band. Furthermore, topological parameters (clustering coefficient, characteristic path length, and et al.) were computed based on graph theory. The correlation between network metrics and clinical symptom was also examined. Depressive patients had a significantly weaker value of partial directed coherence at alpha frequency band in eyes-closed state. Clustering coefficient and characteristic path length were significantly lower in depressive patients (both p < .01). More importantly, in depressive patients, disruption of directed connectivity was noted in left-to-left (p < .05), right-to-left (p < .01) hemispheres and frontal-to-central (p < .01), parietal-to-central (p < .05), occipital-to-central (p < .05) regions. Furthermore, connectivity in LL and RL hemispheres was negatively correlated with depression scale scores (both p < .05). Depressive patients showed a more randomized network structure, disturbed directed interaction of left-to-left, right-to-left hemispheric information and between different cerebral regions. Specifically, left-to-left, right-to-left hemispheric connectivity was negatively correlated with the severity of depression. Our analysis may serve as a potential neuromarker of depression.     

A dynamic model of darkness tolerance for phytoplankton: model description

To analyze various effects of prolonged darkness on phytoplankton population dynamics, we developed a dynamic model of darkness tolerance for phytoplankton and investigated its characteristics. To construct the basic concepts of the model, we categorized various changes in abundance of phytoplankton both during prolonged darkness and after reillumination into several patterns, and then considered the physiological processes producing these patterns. The nature of darkness tolerance was considered to incorporate previously experienced light conditions, including darkness, as a physiological activity, and members of the same phytoplankton species exhibit different dynamics even in identical light conditions due to such career effects. Taking this into consideration, we developed a cell quota model in relation to darkness tolerance. State variables for abundance were indicated by cell numbers, and physiological condition by three intracellular carbon pools with different physiological functions. Using our model, we analyzed the various changes in abundance of phytoplankton in relation to exposure to prolonged darkness. Various responses in terms of phytoplankton abundance to prolonged darkness and after reillumination were successfully reproduced by the model that simply assumed that deterioration of physiological mechanics, such as photosynthetic functions, was due to a prolonged dark condition. On the basis of the results of calculation and assumptions for the model, we discuss the limitations, problems, and effectiveness of the model. Handling editor: Luigi Naselli-Flores     

基于图论的两栖类生物栖息地网络规划——以黑斑侧褶蛙为例

生物多样性保护和生物栖息地网络建设是目前我国国土空间规划的重要内容,提升生物栖息地网络的景观功能连接度对生物多样性保护具有重要作用.目前,已有研究对生物栖息地网络规划进行了探索,但在实际规划层面仍缺乏可操作性强的技术方法支撑.本研究采用图论方法,聚焦国土空间规划中生物多样性保护和生态网络建设涉及的进行生物栖息地斑块重要...     

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron connections and functions, previous research has strived to dig out the relations between neurons and brain regions. Verbal fluency test (VFT) is a general neuropsychological test, which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections.     

基于MSPA和MCR模型的秦岭(陕西段)山地生态网络构建

快速的城市化发展破坏了生境斑块的连通性,而在应对此环境变化中,从斑块层构建区域生态网络的研究不足.本研究以秦岭(陕西段)为对象,采用形态学空间格局分析(MSPA)辨别生态源地,利用最小阻力模型(MCR)提取潜在生态廊道,构建秦岭生态网络;基于重力模型对生态网络内部斑块的重要性进行分级,并分析了网络的结构特征及景观构成....     

A stochastic model of brain cell differentiation in tissue culture

 The timing of cell differentiation can be controlled both by cell-intrinsic mechanisms and by cell-extrinsic signals. Oligodendrocyte type-2 astrocyte progenitor cells are known to be the precursor cells that give rise to oligodendrocytes. When stimulated to divide by purifed cortical astrocytes or by platelet-derived growth factor, these progenitor cells generate oligodendrocytes in vitro with a timing like that observed in vivo. The most widely accepted model of this process assumes a cell-intrinsic biological clock that resides in the progenitor cell. The intrinsic clock model originally proposed in 1986 remains as the dominant theoretical concept for the analysis of timed differentiation in this cell lineage. However, the results of a recent experimental study (Ibarrola et al., Developmental Biology, vol. 180, 1–21, 1996) are most consistent with the hypothesis that the propensity of a clone of dividing O-2A progenitor cells initially to generate at least one oligodendrocyte may be regulated by cell-intrinsic mechanisms, but that environmental signals regulate the extent of further oligodendrocyte generation. We propose a stochastic model of cell differentiation in culture to accommodate the most recent experimental findings. Our model is an age-dependent branching stochastic process with two types of cells. The model makes it possible to derive analytical expressions for the expected number of progenitor cells and of oligodendrocytes as functions of time. The model parameters were estimated by fitting these functions through data on the average (sample mean) number of both types of cells per colony at different time intervals from start of experiment. Using this method we provide a biologically meaningful interpretation of the observed pattern of oligodendrocyte generation in vitro and its modification in the presence of thyroid hormone. Received: 18 April 1997 / Revised version: 30 November 1997