多巴酚丁胺与心得安对家犬脉象图的影响

目的:检测心搏出量对脉象图的影响。方法:选取家犬为实验对象,通过采用静脉推注正心力药多巴酚丁胺、负心力药心得安以观察不同心脏功能状态下的脉象图变化。结果:多巴酚丁胺药后脉搏波主波幅度(h1)、重搏前波幅度(h3)、降中峡幅度(h4)值和脉图收缩期面积(As)、舒张期面积(Ad)、心搏出量(SV)明显高于心得安组;而脉搏波高度之比(h3/h1、h4/h1),后者明显大于前者,差异有统计学意义。结论:脉象图波幅的高低与心脏收缩功能的强弱显著相关,其中尤以主波及收缩期面积的关系更为直接和密切。     

基于HistCite的抗疟药研究相关文献引文编年图和主要路径

采用引文分析可视化软件HistCite对抗疟药研究文献生成可视化引文编年图,将其生成的矩阵导入Pajek软件生成主要路径,以描述抗疟药研究的发展轨迹,为临床用药及新药研发提供参考。     

基于科学计量学与可视化的我国中药及天然药物研究热点分析

目的:对当前我国中药和天然药物领域研究热点进行探测,为学者了解该领域研究现状提供一个全面直观的视角.方法:检索Web of Science核心集中我国学者发表的中药及天然药物相关学术论文,对文献关键词进行共现分析、聚类分析与突发性探测,对施引文献进行共被引分析和突发性探测,并以网络知识图谱的形式进行可视化.结果:最终筛...     

Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm

Water/fat separation is a classical problem for in vivo proton MRI. Although many methods have been proposed to address this problem, robust water/fat separation remains a challenge, especially in the presence of large amplitude of static field inhomogeneities. This problem is challenging because of the nonuniqueness of the solution for an isolated voxel. This paper tackles the problem using a statistically motivated formulation that jointly estimates the complete field map and the entire water/fat images. This formulation results in a difficult optimization problem that is solved effectively using a novel graph cut algorithm, based on an iterative process where all voxels are updated simultaneously. The proposed method has good theoretical properties, as well as an efficient implementation. Simulations and in vivo results are shown to highlight the properties of the proposed method and compare it to previous approaches. Twenty‐five cardiac datasets acquired on a short, wide‐bore scanner with different slice orientations were used to test the proposed method, which produced robust water/fat separation for these challenging datasets. This paper also shows example applications of the proposed method, such as the characterization of intramyocardial fat. Magn Reson Med, 2010. © 2009 Wiley‐Liss, Inc.     

女贞子及其混淆品中五种微量元素的定量分析

采用原子吸收光谱法，测定了四种不同产地女贞子药材及两种混淆品中的必需元素Ｆｅ和四种微量元素Ｃｕ、Ｚｎ、Ｍｎ、Ｎｉ含量。结果表明，四种不同产地女贞子的微量元素含量Ｔ－Ｅ图相似，与混淆品的Ｆｅ元素含量Ｔ－Ｅ图有差异，提示有一定的鉴别意义。     

趺阳脉候胃病及其与胃脘痛虚实辨证关系的初步研究

对胃病组、健康人组、心血管病组及肾病组受检者右侧趺阳脉脉图进行观测分析，并将胃脘痛虚实证型的趺阳脉脉图进行比较。结果表明：胃病组趺阳脉脉图参数主波幅ｈ１，舒张期脉图面积Ｓｄ、脉图总面积Ｓｔ、上升速度Ｖ１均明显低于其余３组（Ｐ＜０．０１），且其余３组之间差异均无显著性意义（Ｐ＞０．０５），提示趺阳脉能候胃病，且具有相对特异性；胃脘痛虚证组趺阳脉脉图参数ｈ１、Ｓｄ、Ｓｔ、Ｖ１均明显低于实证组（Ｐ＜０．０１），表明趺阳脉能辨胃病之虚实，候胃气之强弱     

多焦视网膜电图多位点振幅概率图研究

多焦视网膜电图是用于检测视网膜后极部多个位点尤其是黄斑部功能的一种客观的眼电生理检查技术.本文记录并分析120位不同性别、不同年龄正常人的mfERG的103个刺激位点一阶反应振幅密度,按年龄、眼别统计出振幅密度正常值,自动画出异常概率图,并经过6例临床病人的mfERG数据验证,结果证实所得概率图与眼科医生的分析判断一致.     

The effects of antidepressant treatment on resting‐state functional brain networks in patients with major depressive disorder

Although most knowledge regarding antidepressant effects is at the receptor level, the neurophysiological correlates of these neurochemical changes remain poorly understood. Such an understanding could benefit from elucidation of antidepressant effects at the level of neural circuits, which would be crucial in identifying biomarkers for monitoring treatment efficacy of antidepressants. In this study, we recruited 20 first‐episode drug‐naive major depressive disorder (MDD) patients and performed resting‐state functional magnetic resonance imaging (MRI) scans before and after 8 weeks of treatment with a selective serotonin reuptake inhibitor—escitalopram. Twenty healthy controls (HCs) were also scanned twice with an 8‐week interval. Whole‐brain connectivity was analyzed using a graph‐theory approach—functional connectivity strength (FCS). The analysis of covariance of FCS was used to determine treatment‐related changes. We observed significant group‐by‐time interaction on FCS in the bilateral dorsomedial prefrontal cortex and bilateral hippocampi. Post hoc analyses revealed that the FCS values in the bilateral dorsomedial prefrontal cortex were significantly higher in the MDD patients compared to HCs at baseline and were significantly reduced after treatment; conversely, the FCS values in the bilateral hippocampi were significantly lower in the patients at baseline and were significantly increased after treatment. Importantly, FCS reduction in the dorsomedial prefrontal cortex was significantly correlated with symptomatic improvement. Together, these findings provided evidence that this commonly used antidepressant can selectively modulate the intrinsic network connectivity associated with the medial prefrontal‐limbic system, thus significantly adding to our understanding of antidepressant effects at a circuit level and suggesting potential imaging‐based biomarkers for treatment evaluation in MDD. Hum Brain Mapp 36:768–778, 2015. © 2014 Wiley Periodicals, Inc.     

Wiring cost and topological participation of the mouse brain connectome

Brain connectomes are topologically complex systems, anatomically embedded in 3D space. Anatomical conservation of “wiring cost” explains many but not all aspects of these networks. Here, we examined the relationship between topology and wiring cost in the mouse connectome by using data from 461 systematically acquired anterograde-tracer injections into the right cortical and subcortical regions of the mouse brain. We estimated brain-wide weights, distances, and wiring costs of axonal projections and performed a multiscale topological and spatial analysis of the resulting weighted and directed mouse brain connectome. Our analysis showed that the mouse connectome has small-world properties, a hierarchical modular structure, and greater-than-minimal wiring costs. High-participation hubs of this connectome mediated communication between functionally specialized and anatomically localized modules, had especially high wiring costs, and closely corresponded to regions of the default mode network. Analyses of independently acquired histological and gene-expression data showed that nodal participation colocalized with low neuronal density and high expression of genes enriched for cognition, learning and memory, and behavior. The mouse connectome contains high-participation hubs, which are not explained by wiring-cost minimization but instead reflect competitive selection pressures for integrated network topology as a basis for higher cognitive and behavioral functions.Network organization of the brain is fundamental to the emergence of complex neuronal dynamics, cognition, learning, and behavior. Modern concepts of anatomical network connectivity originated in the 19th and early 20th century with the ascendancy of the neuron theory: the concept of discrete nerve cells contiguously connected via axonal projections and synaptic junctions (1, 2). In the last decade, the connectome has emerged as a new word to define the complete structural “wiring diagram” of a nervous system or brain (3). At the small scale of synaptically connected neurons, the connectome has only been completely mapped for the 302-neuron nervous system of the roundworm Caenorhabditis elegans, using serial electron microscopy and painstaking visual synaptic reconstruction (4). At the large scale of axonally connected brain regions, draft connectomes have been mapped for the cat and macaque, by collation of primary tract-tracing studies (5–7), and for the human, using in vivo diffusion-weighted magnetic resonance imaging measures of white matter tract organization (8), or interregional covariation measures of cortical thickness or volume (9).Topological analyses of these connectomes have consistently demonstrated a repertoire of complex network properties, including the simultaneous presence of modules and hubs (10). The seemingly ubiquitous appearance of these topological features, e.g., both at the cellular scale of the worm brain and at the areal scale of the human brain, supports scale- and species- invariant organizational principles of nervous systems, consistent with Ramón y Cajal’s seminal “laws of conservation for time, space and material” (1, 11–13). Anatomically localized and functionally specialized modules conserve space and (biological) material by reducing the average length of axonal projections, or wiring cost; anatomically distributed and functionally integrative hubs conserve (conduction) time by reducing the average axonal delay, or speed of interneuronal communication. The simultaneous presence of modules and hubs supports a contemporary reformulation of Ramón y Cajal’s laws as a trade-off between minimization of wiring cost and maximization of topological integration.Magnetic resonance imaging (MRI) allowed for testing such organizational principles in large-scale mammalian connectomes with high throughput whole-brain imaging. However, MRI methods measure anatomical connectivity indirectly and at low (millimeter scale) spatial resolution (14). In contrast, tract tracing methods measure anatomical connectivity directly, by detecting axonally mediated propagation of injected tracer, and at higher (micrometer scale) spatial resolution. Tract-tracing methods represent the current “gold standard” for mapping mammalian connectomes. However, most tract-tracing connectome studies to date have been limited to metaanalyses of primary datasets with limited brain coverage and variable definitions of brain regions and interregional connections (6, 7). Tract-tracing methods for comprehensive and systematic mapping of the connectome did not exist until recently (15–18).The recent step change in the quality and quantity of available tract-tracing measurements in mammalian species, such as the macaque and the mouse, provides a crucial opportunity to test theories of connectome organization more rigorously. Some of the first systematic high-quality tract tracing studies in the macaque have revealed many previously unreported weak and long-range axonal projections (19, 20). These studies have also shown that spatial constraints on wiring cost, modeled by an exponential decay weight–distance relationship, can account for many important aspects of the macaque connectome (21, 22).We therefore considered it important to comprehensively evaluate the design principles of the mouse connectome in a systematically acquired dataset of axonal tract-tracing experiments (17). We measured the topological and spatial properties of this connectome and compared these properties to equivalent properties of reference lattice and random graphs. We hypothesized that the connectome would have a complex topology and include integrative hubs inexplicable by minimization of wiring cost. We also explored the neurobiological substrates of the mouse connectome by correlating topological properties with histological and gene-expression properties quantified from independently acquired datasets.     

Emergence of system roles in normative neurodevelopment

Adult human cognition is supported by systems of brain regions, or modules, that are functionally coherent at rest and collectively activated by distinct task requirements. However, an understanding of how the formation of these modules supports evolving cognitive capabilities has not been delineated. Here, we quantify the formation of network modules in a sample of 780 youth (aged 8–22 y) who were studied as part of the Philadelphia Neurodevelopmental Cohort. We demonstrate that the brain’s functional network organization changes in youth through a process of modular evolution that is governed by the specific cognitive roles of each system, as defined by the balance of within- vs. between-module connectivity. Moreover, individual variability in these roles is correlated with cognitive performance. Collectively, these results suggest that dynamic maturation of network modules in youth may be a critical driver for the development of cognition.The human brain is composed of large-scale functional networks that are coherent at rest, forming identifiable modules that support specific cognitive functions (1–3). These modules include well-known subsystems, such as the default-mode, visual, motor, auditory, attention, salience, and cognitive control systems. Prior research has shown that this modular structure evolves considerably during development in youth (4, 5) and across the life span (6, 7). Network modularity, a measure of the segregation between modules, is high during young adulthood and decreases across the latter life span (6, 7). Other features of network reorganization accompany development (8), including a growing preference for interactions between hubs and nonhubs (9), and between regions separated by large physical distances (10).Although prior research has explored such changes in gross network features, it remains unknown how the relationships between specific types of cognitive systems evolve during adolescent development. Ongoing developmental changes in connectivity between cognitive systems are suggested by known differences in how these systems are organized in the adult brain: Primary motor and sensory systems display a high degree of segregation with limited connections to other modules, whereas higher order cognitive systems have more between-module connectivity (1). Moreover, the disparate connectivity profiles of such systems may be critical for optimal cognitive functioning (11). Differentiation of specific network modules may thus support the burgeoning cognitive, emotional, and motor capabilities seen during adolescence (12). Furthermore, abnormalities in functional network organization are a ubiquitous finding in major neuropsychiatric conditions (11), which are increasingly considered disorders of neurodevelopment (13). Thus, a quantitative characterization of the modular maturation of functional networks in youth is critical to understanding the development of both normal and abnormal brain function.Here, we tested the hypothesis that the brain’s functional network organization changes in youth through a process of modular evolution that is governed by the specific cognitive roles of each system. Specifically, we predicted that the development of the functional organization of the brain is driven, in part, by changes in the balance of within- vs. between-module (henceforth “system”) connectivity. To address this hypothesis, we quantify the formation of putative functional network systems (1) in a sample of 780 youth (aged 8–22 y) who were studied as part of the Philadelphia Neurodevelopmental Cohort. Critically, we adapt a previously defined approach to role determinations used in other complex systems, such as airline transportation networks and the Internet (14). This approach allows network systems roles to be defined based on their position in a 2D plane mapped out by their within- and between-system connectivity. In this framework, modules with high between-system connectivity are designated connector systems, whereas modules with low between-system connectivity are provincial systems. Similarly, modules with high within-system connectivity are cohesive systems, whereas modules with low within-system connectivity are incohesive systems. Using this approach, we define intuitive network roles for network modules in the early life span and delineate changes in these roles over development.As described below, our results demonstrate that network modules, initially less disparately sized and highly integrated, become increasingly differentiated in a manner that matches the organization of the adult brain. Moreover, we observe that the within- vs. between-network connectivity profile of each network module falls into one of four categories that correspond to their functional role in the brain: Roles are defined as functional hub (connectors) vs. nonhub (provincial) systems and as functionally cohesive vs. incohesive systems. Finally, we find that individual variability in the between-network connectivity of the sensorimotor and default mode networks is correlated with cognitive performance. Collectively, these results suggest that dynamic maturation of network modules in youth may be a critical driver for the development of cognition and provides an important context for understanding psychopathology.