Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.

Age-related hearing loss accelerates the decline in fast speech comprehension and the decompensation of cortical network connections

Patients with age-related hearing loss face hearing difficulties in daily life.The causes of age-related hearing loss are complex and include changes in peripheral hearing,central processing,and cognitive-related abilities.Furthermore,the factors by which aging relates to hearing loss via changes in audito ry processing ability are still unclear.In this cross-sectional study,we evaluated 27 older adults(over 60 years old) with age-related hearing loss,21 older adults(over 60years old) with normal hearing,and 30 younger subjects(18-30 years old) with normal hearing.We used the outcome of the uppe r-threshold test,including the time-compressed thres h old and the speech recognition threshold in noisy conditions,as a behavioral indicator of auditory processing ability.We also used electroencephalogra p hy to identify presbycusis-related abnormalities in the brain while the participants were in a spontaneous resting state.The timecompressed threshold and speech recognition threshold data indicated significant diffe rences among the groups.In patients with age-related hearing loss,information masking(babble noise) had a greater effect than energy masking(speech-shaped noise) on processing difficulties.In terms of resting-state electroencephalography signals,we observed enhanced fro ntal lobe(Brodmann’s area,BA11) activation in the older adults with normal hearing compared with the younger participants with normal hearing,and greater activation in the parietal(BA7) and occipital(BA19) lobes in the individuals with age-related hearing loss compared with the younger adults.Our functional connection analysis suggested that compared with younger people,the older adults with normal hearing exhibited enhanced connections among networks,including the default mode network,sensorimotor network,cingulo-opercular network,occipital network,and frontoparietal network.These results suggest that both normal aging and the development of age-related hearing loss have a negative effect on advanced audito ry processing capabilities and that hearing loss accele rates the decline in speech comprehension,especially in speech competition situations.Older adults with normal hearing may have increased compensatory attentional resource recruitment represented by the to p-down active listening mechanism,while those with age-related hearing loss exhibit decompensation of network connections involving multisensory integration.

Extracting Multiple Nodes in a Brain Region of Interest for Brain Functional Network Estimation and Classification

Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.

Statistical Analysis of Network Based Issues and Their Impact on Social Computing Practices in Pakistan

Social computing and online groups have accompanied in a new age of the network, where information, networking and communication technologies are enabling systematized human efforts in primarily innovative ways. The social network communities working on various social network domains face different hurdles, including various new research studies and challenges in social computing. The researcher should try to expand the scope and establish new ideas and methods even from other disciplines to address the various challenges. This idea has diverse academic association, social links and technical characteristics. Thus it offers an ultimate opportunity for researchers to find out the issues in social computing and provide innovative solutions for conveying the information between social online groups on network computing. In this research paper we investigate the different issues in social media like users’ privacy and security, network reliabilities, and desire data availability on these social media, users’ awareness about the social networks and problems faced by academic domains. A huge number of users operated the social networks for retrieving and disseminating their real time and offline information to various places. The information may be transmitted on local networks or may be on global networks. The main concerns of users on social media are secure and fast communication channels. Facebook and YouTube both claimed for efficient security mechanism and fast communication channels for multimedia data. In this research a survey has been conducted in the most populated cities where a large number of Facebook and YouTube users have been found. During the survey several regular users indicate the certain potential issues continuously occurred on these social web sites interfaces, for example unwanted advertisement, fake IDS, uncensored videos and unknown friend request which cause the poor speed of channel communication, poor uploading and downloading data speed, channel interferences, security of data, privacy of users, integrity and reliability of user communication on these social sites. The major issues faced by active users of Facebook and YouTube have been highlighted in this research.

Brain organoids are new tool for drug screening of neurological diseases

At the level of in vitro drug screening,the development of a phenotypic analysis system with highcontent screening at the core provides a strong platform to support high-throughput drug screening.There are few systematic reports on brain organoids,as a new three-dimensional in vitro model,in terms of model stability,key phenotypic fingerprint,and drug screening schemes,and particula rly rega rding the development of screening strategies for massive numbers of traditional Chinese medicine monomers.This paper reviews the development of brain organoids and the advantages of brain organoids over induced neurons or cells in simulated diseases.The paper also highlights the prospects from model stability,induction criteria of brain organoids,and the screening schemes of brain organoids based on the characteristics of brain organoids and the application and development of a high-content screening system.

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome

Maintaining sustained attention during a prolonged cognitive task often comes at a cost: high levels of mental fatigue. Heuristically, mental fatigue refers to a feeling of tiredness or exhaustion, and a disengagement from the task at hand;it manifests as impaired cognitive and behavioral performance. In order to effectively reduce the undesirable yet preventable consequences of mental fatigue in many real-world workspaces, a better understanding of the underlying neural mechanisms is needed, and continuous efforts have been devoted to this topic. In comparison with conventional univariate approaches, which are widely utilized in fatigue studies, convergent evidence has shown that multivariate functional connectivity analysis may lead to richer information about mental fatigue. In fact, mental fatigue is increasingly thought to be related to the deviated reorganization of functional connectivity among brain regions in recent studies. In addition, graph theoretical analysis has shed new light on quantitatively assessing the reorganization of the brain functional networks that are modulated by mental fatigue. This review article begins with a brief introduction to neuroimaging studies on mental fatigue and the brain connectome, followed by a thorough overview of connectome studies on mental fatigue. Although only a limited number of studies have been published thus far, it is believed that the brain connectome can be a useful approach not only for the elucidation of underlying neural mechanisms in the nascent field of neuroergonomics, but also for the automatic detection and classification of mental fatigue in order to address the prevention of fatigue-related human error in the near future.

Changes in brain functional network connectivity after stroke

Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

Chronic antiepileptic drug use and functional network efficiency: A functional magnetic resonance imaging study

AIM To increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug(AED) treatment.METHODS The relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A "low risk" category(lamotrigine or levetiracetam, n=16), an "intermediate risk" category(carbamazepine, oxcarbazepine, phenytoin, or valproate, n=34) and a "high risk" category(topiramate, n=5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment. RESULTS Central information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant(P < 0.05, ANCOVA), with a significantly higher global efficiency for patient from the low category compared with the high risk category(P < 0.05, post-hoc test). Risk category had no significant effect on the clustering coefficient(ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient(linear regression analysis, P > 0.15) were observed. CONCLUSION Only the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.

Identification of protein targets for the antidepressant effects of Kai-Xin-San in Chinese medicine using isobaric tags for relative and absolute quantitation

Kai-Xin-San consists of Ginseng Radix, Polygalae Radix, Acori Tatarinowii Rhizoma, and Poria at a ratio of 3:3:2:2. Kai-Xin-San has been widely used for the treatment of emotional disorders in China. However, no studies have identified the key proteins implicated in response to Kai-Xin-San treatment. In this study, rat models of chronic mild stress were established using different stress methods over 28 days. After 14 days of stress stimulation, rats received daily intragastric administrations of 600 mg/kg Kai-Xin-San. The sucrose preference test was used to determine depression-like behavior in rats, while isobaric tags were used for relative and absolute quantitation-based proteomics to identify altered proteins following Kai-Xin-San treatment. Kai-Xin-San treatment for 2 weeks noticeably improved depression-like behaviors in rats with chronic mild stress. We identified 33 differentially expressed proteins: 7 were upregulated and 26 were downregulated. Functional analysis showed that these differentially expressed proteins participate in synaptic plasticity, neurodevelopment, and neurogenesis. Our results indicate that Kai-Xin-San has an important role in regulating the key node proteins in the synaptic signaling network, and are helpful to better understand the mechanism of the antidepressive effects of Kai-Xin-San and to provide objective theoretical support for its clinical application. The study was approved by the Ethics Committee for Animal Research from the Chinese PLA General Hospital(approval No. X5-2016-07) on March 5, 2016.

基于Partial New Causality的因果脑网络情绪识别

为了研究情绪产生过程中脑区以及通道之间的因果作用,在部分格兰杰与新型因果关系的基础上,提出一种用于研究时间序列之间因果关系的部分新型因果关系(PNC)方法。在不同情绪下选取脑区内的8个通道,用PNC计算脑区内通道之间的因果连接关系,根据连接关系构建因果网络;对因果网络中节点的信息流向和介数属性进行分析,将PNC因果网络和Granger因果网络节点之间的因果连接视为一种特征送入SVM中训练分类。实验结果表明,基于PNC因果网络和Granger因果网络的平均识别精度分别为76.4%和68.5%,PNC可用于计算时间序列之间的因果关系。

阿尔茨海默病患者大脑形态学及结构协变网络的改变

目的探讨阿尔茨海默病(Alzheimer's disease,AD)患者大脑灰质体积、灰质皮层厚度及基于皮层厚度的结构协变网络(structural covariance network,SCN)的拓扑属性改变。材料与方法本研究共筛选了250例来自ADNI数据库的被试,包括AD组100人,健康对照(healthy controls,HCs)组150人。首先,利用基于体素的形态学分析方法(voxel-based morphometry,VBM)和基于表面的形态学分析方法(surface-based morphometry,SBM)分别计算每组被试的灰质体积和皮层厚度并比较其组间差异。其次,将有组间差异的脑区定义为感兴趣区(region of interest,ROI),提取每一个ROI的灰质体积和皮层厚度值,与认知量表进行偏相关分析。最后,构建基于皮层厚度的SCN并利用图论分析方法分析该网络的全局属性及局部属性的变化特征。结果第一,相较于HCs组,AD组的灰质体积和皮层厚度显著下降[体素和顶点水平总体误差(family-wise error,FWE)校正后P<0.001]。AD组灰质体积下降的脑区主要包括双侧海马、双侧眶额皮层、左侧岛叶、右侧枕下回、左侧楔前叶、左侧中央前回、左侧中央扣带回。AD组皮层厚度变薄的脑区主要包括双侧颞叶、双侧额叶、双侧顶叶、双侧扣带回、双侧梭状回、双侧岛回、双侧楔前叶等。第二,偏相关分析表明,AD组简易精神状态检查量表(Mini-Mental State Examination,MMSE)得分分别与右侧海马体积[rs=0.35,错误发现率(false discovery rate,FDR)校正后P<0.001]、左侧海马体积(r_(s)=0.38,FDR校正后P<0.001)、右侧梭状回皮层厚度(r_(s)=0.38,FDR校正后P<0.001)呈正相关;临床痴呆评定量表(Clinical Dementia Rating Sum of Boxes,CDR-SB)评分与左侧梭状回皮层厚度(r_(s)=-0.39,FDR校正后P<0.001)呈负相关。第三,脑网络分析表明,AD组SCN的全局效率(P<0.001)、局部效率(P=0.03)及小世界属性(P<0.001)高于HCs组,最短路径低于HCs组(P<0.001)。结论联合VBM、SBM的形态学分析及SCN的图论分析有助于全面理解AD患者脑网络的重组及其意义,进而为AD患者神经影像学改变提供新的见解和证据。

神经网络结构搜索在脑数据分析领域的研究进展

神经网络结构搜索(neural architecture search,NAS)是自动化机器学习的重要组成部分,已被广泛应用于多个领域,包括计算机视觉、语音识别等,能够针对特定数据、场景、任务寻找最优的深层神经网络结构.将NAS引入至脑数据分析领域,能够在图像分割、特征提取、辅助诊断等多个应用领域大幅度提升性能,展现低能耗自动化机器学习的优势.基于NAS进行脑数据分析是当前的研究热点之一,同时也具有一定挑战.目前,在此领域,国内外可供参考的综述性文献较少.对近年来国内外相关文献进行了细致地调研分析,从算法模型、研究任务、实验数据等不同方面对NAS在脑数据分析领域的研究现状进行了综述.同时,也对能够支撑NAS训练的脑数据集进行了系统性总结,并对NAS在脑数据分析中存在的挑战和未来的研究方向进行了分析和展望.

基于典型相关分析的脑网络研究方法综述

脑网络分析在研究大脑的认知活动、探究大脑的信息处理模式和辅助精神类疾病的诊断等方面都起着重要作用。近年来,基于多变量数据集的脑网络研究方法得到了普遍关注。典型相关分析(CCA)作为一种基于数据驱动的多元统计方法,能够有效捕捉多变量数据间的隐含关系,被广泛地应用于脑网络研究。综述CCA在脑网络研究中的作用、具体应用模式、存在的优势和局限性。首先,对传统的CCA其及常见变体的算法原理进行归纳总结;然后,阐述基于CCA分析方法在脑网络构建、脑网络分析、脑网络标记物识别方面的研究现状;最后,对基于CCA的脑网络研究方法进行总结并探讨未来研究的方向。

基于独立成分分析的终末期肾病患者静态及动态功能网络连接研究

目的:探索终末期肾病(ESRD)患者功能网络连接(FNC)的静态及动态变化特点。方法:收集33例ESRD患者及34例健康对照组作为研究对象。首先基于独立成分分析识别到6个静息态功能网络,即听觉网络、凸显网络、视觉网络、感觉运动网络(SMN)、执行控制网络(ECN)及默认网络。然后比较两组静态功能网络连接(sFNC)和动态功能网络连接(dFNC)相关参数的差异,并与神经心理测试进行相关分析。结果:sFNC分析显示ESRD组ECN与SMN的sFNC强度显著高于健康对照组(P<0.05,FDR校正),而且与执行功能评分[连线追踪测试A(TMT-A)]呈显著正相关(r=0.429,P=0.018)。dFNC分析显示ESRD组状态3的时间分数和平均驻留时间显著低于健康对照组(P<0.05);状态2的时间分数(r=0.503,P=0.005)和平均驻留时间(r=0.412,P=0.024)与TMT-A评分呈显著正相关;状态4的时间分数与焦虑评分呈显著负相关(r=-0.372,P=0.043)。结论:本研究采用独立成分分析的方法揭示了ESRD患者静态及动态功能网络连接的特点,为深入理解ESRD患者神经病理损害机制提供了新视角。

经颅直流电刺激不同靶点治疗帕金森病效果的网状Meta分析

目的:系统评价经颅直流电刺激对帕金森患者运动功能的康复疗效,并比较经颅直流电刺激作用于不同靶点对帕金森患者运动功能的疗效差异,为临床中经颅直流电刺激的靶点选择提供理论依据。方法:计算机检索Cochrane Library、PubMed、Web of Science、中国知网、维普和万方数据库,以“帕金森、经颅直流电刺激”为中文检索词,以“Parkinson,transcranial direct current stimulation”为英文检索词,收集从各数据库建库至2023年1月发表的关于经颅直流电刺激改善帕金森患者运动功能的随机对照试验。使用Cochrane 5.1.0偏倚风险评估工具和PEDro量表对纳入研究进行质量评价。采用RevMan 5.4和Stata 17.0软件对结局指标进行Meta分析。结果:①最终纳入15项随机对照试验,PEDro量表评估显示均为高质量或极高质量研究。②Meta分析显示,与对照组相比经颅直流电刺激可显著提高UPDRS-Ⅲ评分(MD=-2.49,95%CI:-4.42至-0.55,P<0.05)、步频评分(MD=0.07,95%CI:0.03-0.11,P<0.05)和步速评分(MD=0.02,95%CI:0.00-0.05,P<0.05),但对BBS评分(MD=2.57,95%CI:-0.74-5.87,P>0.05)的提高不明显。③网状Meta分析概率排序结果显示,在UPDRS-Ⅲ评分方面,刺激靶点疗效的概率排序结果为背外侧前额叶皮质(52.4%)>初级皮质运动区(45.8%)>大脑中央点(1.8%)>常规康复治疗(0%);在步频评分方面,刺激靶点疗效的概率排序结果为小脑(50.1%)>大脑中央点(45.8%)>背外侧前额叶皮质(3.9%)>初级皮质运动区(0.2%)>常规康复治疗(0%);在步速评分方面,刺激靶点疗效的概率排序结果为小脑(64.8%)>背外侧前额叶皮质(23.8%)>大脑中央点(9.4%)>初级皮质运动区(1.7%)>常规康复治疗(0.4%);在BBS评分方面,刺激靶点疗效的概率排序结果为:小脑(77.4%)>背外侧前额叶皮质(20.7%)>大脑中央点(0.7%)>常规康复治疗(0.2%)。结论:经颅直流电刺激可显著改善帕金森患者运动功能,其中刺激背外侧前额叶皮质区域对改善帕金森患者运动协调方面疗效更佳,而刺激小脑区域对改善帕金森患者步行和平衡方面疗效更佳。

基于多类特征的Android应用恶意行为检测系统

目前针对未知的Android恶意应用可以采用数据挖掘算法进行检测,但使用单一数据挖掘算法无法充分发挥Android应用的多类行为特征在恶意代码检测上所起的不同作用.文中首次提出了一种综合考虑Android多类行为特征的三层混合系综算法THEA(Triple Hybrid Ensemble Algorithm)用于检测Android未知恶意应用.首先,采用动静态结合的方法提取可以反映Android应用恶意行为的组件、函数调用以及系统调用类特征;然后,针对上述3类特征设计了三层混合系综算法THEA,该算法通过构建适合3类特征的最优分类器来综合评判Android应用的恶意行为;最后,基于THEA实现了Android应用恶意行为检测工具Androdect,并对现实中的1126个恶意应用和2000个非恶意应用进行检测.实验结果表明,Androdect能够利用Android应用的多类行为特征有效检测Android未知恶意应用.并且与其它相关工作对比,Androdect在检测准确率和执行效率上表现更优.

急性轻度创伤性脑损伤患者的个体形态学脑网络研究

目的探究急性轻度创伤性脑损伤(mild traumatic brain injury,mTBI)患者个体形态学脑网络的拓扑属性改变。材料与方法共纳入43例mTBI患者和37例健康对照(health control,HC),采集所有受试者高分辨率T1WI的图像,使用Freesurfer软件对数据进行预处理得到5个形态学指标(皮层厚度、灰质体积、脑表面积、脑沟深度、平均曲率)脑图并构建个体形态学脑网络,使用图论分析的方法计算网络拓扑属性,通过双样本t检验比较组间差异,并采用错误发生率(false discovery rate,FDR)对结果进行多重比较校正。结果与HC组相比,mTBI组左侧额极横回(t=-2.186,P=0.032)、中央沟(t=-2.617,P=0.011)、外侧沟水平支(t=-2.456,P=0.016)和右侧枕极(t=-2.013,P=0.048)的节点度中心性(degree centrality,DC)增高,左侧额极横回(t=-2.182,P=0.032)、中央沟(t=-2.226,P=0.029)、外侧沟水平支(t=-2.440,P=0.017)和右侧楔前叶(t=-2.207,P=0.030)的节点效率(nodal efficiency,N_(e))增高,与认知和执行功能相关;左侧楔叶(t=2.173,P=0.033)、角回(t=2.498,P=0.015)、海马旁回(t=4.009,P<0.001)的节点DC下降,左侧颞上回极平面(t=2.394,P=0.019)、角回(t=2.668,P=0.009)、海马旁回(t=4.671,P<0.001)、胼胝体沟(t=2.189,P=0.032)的N_(e)下降,与记忆和情绪调节相关。对于全局拓扑属性,二者差异无统计学意义(P>0.05)。结论急性期mTBI的个体形态学脑网络仍保持了小世界属性。mTBI异常增高的节点DC和N_(e),主要集中在与认知和执行功能相关的脑区,反映了大脑对认知功能的应激性代偿;异常减低的节点DC和N_(e),主要集中在与记忆、情绪调节相关的脑区,揭示了急性期的认知和情绪变化。这为急性期mTBI的研究提供了新的角度,对其大脑网络改变机制的探索提供了进一步线索。

非侵入式脑刺激对帕金森病患者执行功能的影响:网状meta分析

目的探索非侵入式脑刺激措施改善帕金森病患者执行功能的有效性。方法检索Web of Science、PubMed、EMBASE、中国知网、万方数据5个数据库中关于经颅磁刺激、经颅交流电刺激、经颅直流电刺激3种非侵入式脑刺激干预措施治疗帕金森病且结局指标包括执行功能的随机对照试验,根据预先确定的标准筛选文献并提取数据。采用网状meta分析方法比较3种非侵入式脑刺激干预措施对帕金森病患者执行功能障碍的疗效,使用标准化均数差(SMD)及95%贝叶斯可信区间(CrI)汇总结果,通过累积排序曲线下面积(SUCRA)对各干预措施的疗效进行排序。结果共纳入20项随机对照试验,包括809例帕金森病患者。与对照组相比,经颅磁刺激对帕金森病患者的执行功能有显著改善效果(SMD=0.16,95%CrI 0.01~0.32)。各干预措施疗效的概率排序结果显示,对帕金森病患者执行功能障碍疗效最佳的干预措施排序为经颅磁刺激>经颅交流电刺激>经颅直流电刺激>对照(SUCRA分别为0.72、0.61、0.41、0.25),经颅磁刺激最有可能是疗效最佳的干预措施。结论目前的有限证据显示,经颅磁刺激对帕金森病患者的执行功能有直接的改善效果。受纳入研究的数量及质量影响,上述结论需进行更进一步的高质量研究验证。