发展性阅读障碍的脑机制——来自脑成像研究的证据

发展性阅读障碍是一种特殊的学习障碍,发展性阅读障碍的脑机制一直是研究者们关心的一个重要问题．随着脑成像技术的应用,人们在发展性阅读障碍的脑机制研究方面取得了重大进展．脑结构研究发现,发展性阅读障碍者在颞-顶叶、颞-枕叶、额下回、小脑等区域都存在一定的脑结构异常,这些脑结构异常要么表现在某个脑区的结构上,要么表现某个脑区结构的左右不对称性上．脑功能研究发现,发展性阅读障碍者出现脑结构异常的区域也大多表现出脑功能的异常．脑功能连接的研究发现,发展性阅读障碍者脑功能连接的异常不仅涉及到同侧脑区前后部分的连接,还涉及双侧脑区相应部分的连接．另外,中文发展性阅读障碍的研究发现了与拼音文字发展性阅读障碍不同的脑机制．这些研究成果为进一步揭示发展性阅读障碍的脑机制以及拓展中文发展性阅读障碍的研究提供了借鉴．     

偏头痛患者体感刺激下脑电信号的功能连接（英文）

目的 偏头痛是一种复杂的脑功能障碍性疾病,全球范围内患病率为14.4%。功能连接测量两个神经信号之间的统计学相互依赖性,不同的功能连接反映了大脑区域协同工作的不同模式。因此,研究不同脑区的功能连接对于理解偏头痛的病理生理机制具有十分重要的意义。以往基于脑电图对偏头痛患者脑功能连接的分析主要集中在视觉和疼痛刺激。本文尝试研究偏头痛患者在发作间期对体感刺激的皮质反应,以进一步了解偏头痛的神经功能障碍,为偏头痛的预防和治疗提供线索。方法 招募23例无先兆偏头痛患者,10例有先兆偏头痛患者,28名健康对照者。所有受试者均进行详细的基本资料和病史采集,完善量表评估,在正中神经体感刺激下进行脑电图记录。计算68个脑区的相干性作为功能连接,并评估功能连接与临床参数的相关性。结果 在正中神经体感刺激下,无先兆偏头痛和有先兆偏头痛患者的脑电功能连接与对照组相比存在差异,异常的脑电功能连接主要位于感觉辨别、疼痛调节、情绪认知和视觉处理等区域。无先兆偏头痛和有先兆偏头痛患者的大脑皮层对体感刺激可能具有相同的反应方式。偏头痛患者的功能连接异常与临床特征之间存在相关性,可以部分反映偏头痛的严重程度。结论 本研究...     

基于复杂网络的脑疾病功能网络研究进展

脑功能连接能够反映脑区间的相互联系状况,目前已成为脑功能研究的主要方法。复杂网络方法源于图论分析,可以对功能连接所构网络进行量化分析,提供多种量化指标。本文介绍了当前复杂网络的多种基本概念,及其在几种典型脑部疾病上的应用情况。     

基于脑结构子网络分析的颞叶肿瘤患者认知功能研究

本研究旨在结合图论讨论颞叶肿瘤对整个大脑结构网络的影响以及患者认知功能变化情况.本文基于弥散张量成像技术,构建颞叶肿瘤患者以及正常对照组脑结构网络,依据纤维束连接长度,将整个脑网络分为3个子网络,对比分析各个子网络的网络参数全局效率和局部效率,进而对颞叶肿瘤患者大脑内纤维束的再生以及消亡情况做简单分析.并对两组受试者进行认知能力评估,分析患者认知能力与节点效率之间的相关性,从而得到患者大脑认知分布情况.研究结果显示,全局效率和局部效率在全脑网络结构中均无显著差异,子网络拓扑参数显示,仅在短距离子网络中,患者组较正常组有显著升高,局部参数研究结果显示,正常组均优于患者组,且患者认知能力普遍低于正常组.脑区内部纤维束再生以及不同功能区之间纤维束连接发生变化从而使得颞叶肿瘤患者脑结构网络发生重组,且患者在注意力、记忆功能、学习能力等方面较正常人有显著降低,患者额颞-顶叶部分脑区参与认知过程,特殊脑区信息处理效率的变化可间接反映认知变化情况.     

带状疱疹后神经痛患者杏仁核的功能连接改变研究

带状疱疹后神经痛(postherpetic neuralgia,PHN)是一种常见的神经病理性疼痛,但其中枢机制尚不明了.杏仁核在疼痛反应中的作用近年来受到关注.本研究的目的在于通过功能磁共振成像,研究带状疱疹后神经痛患者杏仁核各个亚区功能连接(functional connectivity,FC)的改变,探索慢性神经病理性疼痛的中枢机制.8位带状疱疹后神经痛患者和8位健康者进行了普通核磁共振和静息态功能磁共振扫描.将杏仁核各个亚区分别进行的功能连接分析,并将功能连接和被试者的病程、视觉模拟评分(visual analog scale,VAS)进行了相关分析.与健康志愿者相比,PHN患者杏仁核的基底外侧部(laterobasal groups,LB)和皮质部(superficial groups,SF)与多个脑区的FC表现出增强,主要位于颞叶和额叶.同时SF与多个区域的FC出现减低,主要位于额叶和顶叶.颞叶和额叶部分区域与LB的FC强度、与病程长短和VAS评分表现出关联性.研究结果提示,PHN患者杏仁核功能连接的改变提示了在慢性神经病理性疼痛的产生和发展中,杏仁核以及多个涉及情绪、认知、注意的脑区发挥了重要作用.     

综述:阿尔茨海默病的神经影像学研究进展

阿尔茨海默病(Alzheimer's disease,AD)是以记忆和其他高级认知功能下降为特征的神经退行性疾病．早期的神经影像学研究通常是探索AD患者局部脑区的结构和功能变化．随着多模态神经影像技术和人脑连接组学研究方法的发展,研究者已经能够考察AD患者脑结构和功能连接通路．采用这些方法,最近的研究已经发现,AD患者脑网络的连接强度、网络效率、模块化组织和核心脑区连接的下降,并发现这些变化与患者的记忆评分等密切相关．这些新方法和新技术的出现不仅提供了新颖的观点来解释AD病的脑区失连接病理生理机制,而且发现的AD异常脑连接模式可能作为敏感特征应用于AD早期辅助诊断的影像标记物研究．特别重要的是,研究表明,在AD患者脑神经网络出现的异常连接模式,在AD前期即轻度认知障碍期患者中也已出现,表明了将AD影像学研究的重点前移到AD前期这一可治疗阶段的重要性和迫切性．     

人脑功能连通性研究进展

对人脑结构和功能的深入研究,已经要求脑成像技术不能仅仅局限于研究简单的脑功能定位问题,即寻找和定位与特定认知任务相关的某一块或者一组大脑皮层功能区,而必须研究分析各功能区间的动态功能连通和整合问题,即描述特定脑功能区域间的交互作用以及这些交互作用如何受认知任务的影响.已有几种非常规的脑成像技术和数据分析方法,包括时间相关性分析、心理生理交互作用(PPI)、结构方程模型(SEM)、动态因果模型(DCM)、弥散张量成像(DTI)等等,被成功用于人脑功能连通性和有效连通性的研究.脑功能连通性研究的发展,有利于深入理解人脑在系统水平上的动态运作方式,是今后认知神经科学发展的一个重要方向.     

老化和精神疾病引起的皮层抑制功能变化

脑神经网络信息加工的实现方式主要依赖于兴奋性和抑制性突触连接.脑内抑制性神经元数量较少,但在信息加工和神经可塑性等方面作用极其重要,而且抑制系统失常与多种脑功能障碍有关联.脑内抑制性神经环路可粗略分为皮层内和皮层间(包括前馈和反馈)两种,分别介导同一脑区内和不同脑区间的抑制作用.本文先围绕中心-外周抑制和运动方向互斥介绍了皮层间、皮层内抑制的行为表现和作用机制,然后以老化和精神疾病为例综述了脑功能障碍与视觉系统皮层抑制功能变化间的联系,希望能对相关研究工作有所助益.     

运动想象的脑机制及其在运动功能康复中应用的研究进展

运动想象对大脑相关功能有明显的改善作用,目前正在大量应用于运动训练和康复治疗领域.近年来随着诸如功能磁共振等成像工具的出现,神经成像的复杂程度得到了不断提高,从而推动了人们对运动想象的脑机制尤其是涉及多脑区之间的协同作用机制的认识逐步深入.针对运动想象的多脑区之间相互协作机制及运动想象在运动功能康复中的应用作了详细介绍.     

脑科学和脑功能MR成像

目的：在对大脑认知功能进行脑功能成像研究之中,随着磁共振成像技术的发展,人们现在可以对脑的认知功能,如视觉、运动、语言和记忆等功能中枢进行成像。本文首先介绍了脑科学的发展历程,并从脑功能MR成像的方法出发,分析了其成像机理,探讨了用脑功能MR成像为手段对脑科学—认知科学进行的方法研究,最后对脑功能MR成像应用于脑科学的研究作了展望。     

基于空间ICA和时间相关方法的人脑视觉皮层V5区的功能连通性研究

利用功能磁共振成像技术,将空间ICA和时间相关方法相结合来研究不同活动状态下人脑视觉皮层V5区的功能连通性。首先利用空间ICA处理组块视觉运动刺激的数据,定位V5区;然后分别计算静息和连续视觉运动刺激两种稳态下V5区与其它脑区低频振荡的时间相关,检测出该区的功能连通网络。实验结果表明,静息时V5区的功能连通网络更广泛,且与已知的解剖连通一致;当被试接受连续视觉运动刺激时,与V5区连通的脑区网络局限在视觉皮层,此时的网络特定于处理视觉运动这一任务。     

Effective Connectivity Extracted from Resting-State fMRI Images Using Transfer Entropy

Background and objectiveBased on magnetic resonance imaging (MRI), macroscopic structural and functional connectivity of human brain has been widely explored in the last decade. However, little work has been done on effective connectivity between individual brain parcels. In this preliminary study, we aim to investigate whole-brain effective connectivity networks from resting-state functional MRI (rs-fMRI) images.Material and methodsAfter the functional connectivity networks of 26 healthy subjects (aged from 25 to 35 years old) from Human Connectome Project database were derived from rs-fMRI images with dynamic time warping, proportional thresholding (PT) was performed on the functional connectivity matrices by retaining the PT% strongest functional connections. PT% ranges from 40% to 10% in steps of 5%. Then, effective connections corresponding to the PT% strongest functional connections, both bi-directional and unidirectional, were estimated with Renyi's 2-order transfer entropy (TE) method. Topological metrics of the built functional and effective connectivity networks were further characterized, including clustering coefficient, transitivity, and modularity.ResultsIt is found that the effective connectivity networks exhibit small world attributes, and that the networks contain a subset of highly interactive regions, including right frontal pole (in-degree 6), left middle frontal gyrus (in-degree 8, out-degree 1), right precentral gyrus (out-degree 9), left precentral gyrus (out-degree 7), right posterior division of supramarginal gyrus (in-degree 2, out-degree 3), left angular gyrus (out-degree 6), left inferior division of lateral occipital cortex (out-degree 6), right occipital pole (in-degree 5), right cerebellum 7b parcel (in-degree 15), and right cerebellum 8 parcel (in-degree 7, out-degree 1).ConclusionsThe observations in this study provide information about the casual interactions among brain parcels in resting state, helping reveal how different subregions of large-scale distributed neural networks are coupled together in performing cognitive functions.     

WLPVG approach to the analysis of EEG-based functional brain network under manual acupuncture

Functional brain network, one of the main methods for brain functional studies, can provide the connectivity information among brain regions. In this research, EEG-based functional brain network is built and analyzed through a new wavelet limited penetrable visibility graph (WLPVG) approach. This approach first decompose EEG into δ, θ, α, β sub-bands, then extracting nonlinear features from single channel signal, in addition forming a functional brain network for each sub-band. Manual acupuncture (MA) as a stimulation to the human nerve system, may evoke varied modulating effects in brain activities. To investigating whether and how this happens, WLPVG approach is used to analyze the EEGs of 15 healthy subjects with MA at acupoint ST36 on the right leg. It is found that MA can influence the complexity of EEG sub-bands in different ways and lead the functional brain networks to obtain higher efficiency and stronger small-world property compared with pre-acupuncture control state.     

Imaging the default mode network in aging and dementia

Although in the last decade brain activation in healthy aging and dementia was mainly studied using task-activation fMRI, there is increasing interest in task-induced decreases in brain activity, termed deactivations. These deactivations occur in the so-called default mode network (DMN). In parallel a growing number of studies focused on spontaneous, ongoing ‘baseline’ activity in the DMN. These resting state fMRI studies explored the functional connectivity in the DMN. Here we review whether normal aging and dementia affect task-induced deactivation and functional connectivity in the DMN. The majority of studies show a decreased DMN functional connectivity and task-induced DMN deactivations along a continuum from normal aging to mild cognitive impairment and to Alzheimer's disease (AD). Even subjects at risk for developing AD, either in terms of having amyloid plaques or carrying the APOE4 allele, showed disruptions in the DMN. While fMRI is a useful tool for detecting changes in DMN functional connectivity and deactivation, more work needs to be conducted to conclude whether these measures will become useful as a clinical diagnostic tool in AD. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.     

Dynamic Changes in Brain Activations and Functional Connectivity during Affectively Different Tactile Stimuli

In the present study, we compared brain activations produced by pleasant, neutral and unpleasant touch, to the anterior lateral surface of lower leg of human subjects. It was found that several brain regions, including the contralateral primary somatosensory area (SI), bilateral secondary somatosensory area (SII), as well as contralateral middle and posterior insula cortex were commonly activated under the three touch conditions. In addition, pleasant and unpleasant touch conditions shared a few brain regions including the contralateral posterior parietal cortex (PPC) and bilateral premotor cortex (PMC). Unpleasant touch specifically activated a set of pain-related brain regions such as contralateral supplementary motor area (SMA) and dorsal parts of bilateral anterior cingulated cortex, etc. Brain regions specifically activated by pleasant touch comprised bilateral lateral orbitofrontal cortex (OFC), posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), intraparietal cortex and left dorsal lateral prefrontal cortex (DLPFC). Using a novel functional connectivity model based on graph theory, we showed that a series of brain regions related to affectively different touch had significant functional connectivity during the resting state. Furthermore, it was found that such a network can be modulated between affectively different touch conditions.     

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.     

Cerebral cartography and connectomics

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.     

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.