The critical role of the inferior frontal cortex in establishing a prediction model for generating subsequent mismatch negativity (MMN): A TMS-EEG study

BackgroundThe prediction violation account of automatic or pre-attentive change detection assumed that the inferior frontal cortex (IFC) is involved in establishing a prediction model for detecting unexpected changes. Evidence supporting the IFC’s contribution to prediction model is mainly based on the Mismatch Negativity (MMN) to deviants violating predictions that are established based on the frequently presented standard events. However, deviant detection involves processes, such as events comparison, other than prediction model establishment.ObjectiveThe current study investigated the critical role of the IFC in establishing a prediction model during standards processing for subsequent deviant detection.MethodsTranscranial Magnetic Stimulation (TMS) was applied at the IFC to disrupt the processing of the initial 2 or 5 standards of a 3-, 6-, or 9-standard train, while the MMN responses to pitch deviant presented after the standard trains were recorded and compared.ResultsAn abolishment of MMN was only observed when TMS was delivered to the IFC at the initial 2 standards of the 3-standard train, but not at the initial 5 standards, or when TMS at the vertex or TMS sound recording was applied. The MMNs were also preserved when IFC TMS, vertex TMS, or TMS sound recording was applied at the initial 2 or 5 standards of longer trains.ConclusionThe IFC plays a critical role in processing the initial standards of a short standard train for subsequent deviant detection. This result is consistent with the prediction violation account that the IFC is important for establishing the prediction model.     

Putting age-related task activation into large-scale brain networks: A meta-analysis of 114 fMRI studies on healthy aging

Normal aging is associated with cognitive decline and underlying brain dysfunction. Previous studies concentrated less on brain network changes at a systems level. Our goal was to examine these age-related changes of fMRI-derived activation with a common network parcellation of the human brain function, offering a systems-neuroscience perspective of healthy aging. We conducted a series of meta-analyses on a total of 114 studies that included 2035 older adults and 1845 young adults. Voxels showing significant age-related changes in activation were then overlaid onto seven commonly referenced neuronal networks. Older adults present moderate cognitive decline in behavioral performance during fMRI scanning, and hypo-activate the visual network and hyper-activate both the frontoparietal control and default mode networks. The degree of increased activation in frontoparietal network was associated with behavioral performance in older adults. Age-related changes in activation present different network patterns across cognitive domains. The systems neuroscience approach used here may be useful for elucidating the underlying network mechanisms of various brain plasticity processes during healthy aging.     

基于复杂网络技术对电针治疗乳腺增生病处方分析

目的基于复杂网络技术分析电针治疗乳腺增生病的核心穴位及配伍穴相关性,并对电针波型进行探讨。方法检索中国期刊全文数据库(CNKI)、万方数据库、维普网数据库(VIP)中从1954年1月1日到2018年12月31日公开发表的电针治疗乳腺增生病的临床中文文献,利用Excel表格工具建立电针治疗乳腺增生病数据库,利用Matlab2014a软件进行节点中心性分析和聚类分析,利用Gephi0.9.1软件制作复杂网络示意图对分析结果进行描述和展示,利用Excel表格工具制作电针治疗乳腺增生病不同波形比例饼状图并分析其规律。结果选定43条电针处方:电针治疗乳腺增生病主穴核心度前三位依次为膻中穴、足三里、肩井穴;配伍穴位核心度前三位依次为太冲穴、太溪穴、脾俞穴;经络核心度前三位依次为足阳明胃经、任脉、足少阳胆经。配伍穴位相关性分析中,相关性频度前三位组合依次为太冲穴-太溪穴、太冲穴-肾俞穴、太冲穴-三阴交。电针波形中以连续波与疏密波常见,均具有较高的临床应用价值。结论电针治疗乳腺增生病处方以足阳明胃经为最多,其次为任脉;电针波型以连续波为主。     

经食管实时三维超声心动图评价房性心律失常对二尖瓣结构和功能的影响

目的:应用经食管实时三维超声心动图(RT-3D-TEE)技术,探讨房性心律失常对二尖瓣结构和功能的影响.方法:选取2018年6月~2019年6月本院收治的房性心律失常拟行射频消融的患者49例纳入观察组,另外选取21例正常窦性心律者作为对照组,所有患者均行经胸超声心电图(TTE)和经食管实时三维超声心动图(RT-3D-TEE)检查,将两组患者的瓣环投影面积(A2D)、瓣环周长(C3D)、瓣环高度(H)、瓣环前后径(DAP)、瓣环前外侧至后内侧直径(DAIPm)、左室射血分数(LVEF)、左心房前后径(Lad)等参数进行比较,并分别计算对合面积和对合指数,之后采取二项Logistic回归分析或逐步线性回归对各参数及临床因素与对合指数的相关性进行分析.结果:两组患者在LVEF和对合面积方面的比较无明显差异(P>0.05),在A2D、C3D、H、DA、PDAIPm、Lad方面的比较,房性心律失常组患者明显高于正常窦性心律组患者(P<0.05),且观察组患者的对合指数明显降低(P<0.05).将49例观察组患者以不同心律失常类型分为持续性房颤、阵发性房颤、房扑、混合型房性心律失常4个亚组,采用单因素方差分析,发现在LVEF、C3D、Lad、对合指数方面的比较,各亚组间无明显差异(P>0.05).采用二项Logistic回归分析发现,女性是导致对合指数低的危险因素,另外对合指数较低、房性心律失常是导致二尖瓣返流的重要因素.结论:房性心律失常对患者二尖瓣的A2D、C3D、H、DA、PDAIPm、Lad等都会造成较大影响,从而降低对合指数,影响二尖瓣的正常功能,导致二尖瓣返流的发生.     

中药复方配伍的研究方法及其进展

中药复方是由2味或2味以上中药遵循中医理论组合而成的方剂。多味中药在合适的剂量配比之下,协同发挥作用,实现中医的整体调节治疗。研究中药复方的配伍对推动中药现代化发展、新药开发以及临床应用有着重要意义。近年来,研究者们在传统的"七情和合"与"君臣佐使"的基础上,运用新技术和新方法对中药复方的成分、药效活性和药代动力学性质等进行了研究,从不同角度探讨了中药复方配伍的科学内涵。同时,多种数理方法和模型的建立、网络药理学和数据挖掘方法的发展与应用,也对中药复方配伍研究提供了很大帮助。研究方法的发展虽促进了中药复方配伍的科学研究,但还需进一步建立适合中药复方配伍复杂关系的研究方法,以阐明中药复方及其成分/组分配伍的内在规律,进而构建新的现代中药复方,这也是目前中药复方配伍研究的重点任务。     

基于网络药理学的济脉通片降压机制研究

目的 采用网络药理学阐明济脉通片多成分-多靶点-多途径的作用理念，为进一步研究济脉通片降压药效物质基础和机制提供一定理论参考。方法 通过TCMSP数据库，结合口服利用度（≥ 30%）和类药性分析（≥ 0.18）参数，筛选济脉通片的活性成分；通过Drugbank和TCMSP数据库进行靶点预测分析；通过GENCARD数据库筛选出高血压疾病相关基因；结合DAVID和KEGG数据库进行GO分析和通路分析；使用Cystoscope软件构建"化合物-靶点-作用通路"网络图。结果 经筛选后得到济脉通片的33个化合物，148个潜在靶基因并映射到了223条信号通路，其中31条信号通路与高血压的发生发展密切相关，其中AGE-RAGE signaling pathway in diabetic complications、PI3K-Akt signaling pathway、TNFsignaling pathway、Adrenergic signaling in cardiomyocytes和Focal adhesion为重要的通路枢纽。结论 济脉通片主要通过多成分、多靶点、多通路调节血管内皮功能、炎症反应、钙钠离子转运、糖脂代谢等参与血管舒张、改善炎症、调节机体代谢、调节离子转运等而产生降压作用。     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.