运动能力不同的脑卒中患者脑静息态功能连接的特征分析

摘要 目的：用功能性近红外光谱技术(functional near-infrared spectroscopy,fNIRS)观察运动功能不同的脑卒中患者脑静息态功能连接情况,可为临床制定非侵入性脑刺激方案提供治疗靶点。 方法：应用fNIRS基于氧合血红蛋白和脱氧血红蛋白,观察46例脑卒中后偏瘫患者及21例健康对照组静息状态下脑区之间的功能连接情况。 结果：基于氧合血红蛋白情况下,严重运动障碍组同源感觉运动网络(sensorimotor network,SEN)及腹侧注意网络(ventral attention network,VAN)功能连接强度与健康对照组相比有显著降低(P<0.05);严重运动障碍组非同源脑网络(SEN-VAN,SEN-默认模式网络(default mode network,DMN),SEN-额顶网络（frontoparietal network,FPN),VAN-DMN）功能连接强度与健康对照组相比有显著降低（P<0.05)。基于脱氧血红蛋白情况下,轻度运动障碍组同源脑网络（VAN）功能连接强度与健康对照组相比有显著性降低(P<0.05),严重运动障碍组同源脑网络(SEN,VAN,DMN）功能连接强度与健康对照组相比有显著降低（P<0.05);严重运动障碍组非同源脑网络(DMN-VAN,VAN-FPN,FPN-DMN）功能连接强度与健康对照组相比有显著降低（P<0.05)。 结论：fNIRS适用于全脑静息态功能连接研究,重度功能障碍的脑卒中患者同源、非同源网络的功能连接强度显著降低。     

基于独立成分分析的精神分裂症患者动态脑功能网络分析新方法

目的 提出一种新的基于独立成分分析法进行动态脑功能网络分析的方法,并应用该方法探讨精神分裂症患者在动态全脑功能网络上的变异。方法 首先基于滑动时间窗方法计算正常被试和精神分裂症患者的动态全脑功能网络,然后使用组信息指导独立成分分析方法,提取每个被试的动态全脑功能网络的功能连接状态及相应的时间波动,比较正常被试和精神分裂症患者在功能连接状态上的差异。结果 两组的最重要功能连接状态的模式有相似性。正常被试在额叶、顶叶相关区域较精神分裂症患者具有更强的正功能连接;在小脑相关区域精神分裂症患者呈现出更多的正功能连接,而正常被试呈现出更多的负功能连接。结论 组信息指导独立成分分析方法可有效提取动态脑功能网络的功能连接状态,可揭示精神分裂症患者在动态脑功能网络的变异。     

阿尔茨海默症功能脑网络研究进展

近年来功能磁共振成像作为一种新兴的技术,被广泛应用于功能脑网络的研究中,一些功能脑网络被定义,如感觉运动网络、语言网络、默认模式网络、背侧注意网络、额顶叶控制网络、突显网络、中央执行网络等。阿尔茨海默症作为一种严重的神经退行性疾病,一直是脑科学中的研究热点,功能脑网络的研究为揭示其发病机制及早期诊断提供了可靠的依据。本文对功能脑网络用于阿尔茨海默症的研究进展进行综述。     

镜像疗法治疗脑卒中后感觉功能障碍的研究现状

镜像疗法是一种有效促进脑卒中后患者感觉运动功能恢复的治疗手段。本文对镜像疗法治疗脑卒中后感觉功能障碍的现状作一综述,旨在总结镜像疗法治疗脑卒中后感觉功能障碍的优势与不足,探讨其中可能机制,为临床的进一步应用提供参考。     

帕金森病感觉运动区脑功能网络连通性分析

目的 采用独立成分分析和功能网络连通性分析方法,对比帕金森病患者与健康者大脑的感觉运动网络与其他网络间连通性差异,及感觉运动网络内各亚区间连通性的差异.材料与方法 纳入2019年1～12月于扬州大学附属医院神经内科就诊的原发性帕金森病患者30例和同期招募的健康对照者30名.采集静息态功能磁共振数据和结构像磁共振数据,使用Restplus软件包对图像进行预处理,并使用GIFT软件包将2组受试者的大脑划分为53个独立成分,归为7个脑网络,计算各网络之间的连接强度,统计分析两组间各网络成分的连通性差异.将2组受试者大脑感觉运动网络划分18个独立成分,归为6个亚区,计算各亚区间的连接强度,统计分析两组间各亚区间连通性差异.并将存在统计学差异的成分间的连接强度与帕金森病统一评分量表(UPDRS-Ⅲ)做相关性分析.结果 与健康对照组相比,帕金森病组感觉运动网络与高级视觉网络、注意网络间的连通性减少.在感觉运动网络内部,帕金森病患者的左侧中央前/后回与右侧中央前/回、中央旁小叶间的连通性均减少,但左侧中央前回与左侧中央后回的连通性增加.中央旁小叶与左侧中央前/后回的连接强度与UPDRS-Ⅲ评分存在显著负相关.结论 与健康者相比,帕金森病患者无论是感觉运动网络与其他脑功能网络间的连通性,还是感觉运动网络内的连通性都存在差异,且大部分呈连接强度减弱趋势,提示脑功能连接异常可能是导致帕金森病患者运动功能障碍如静止性震颤、肌强直、运动迟缓等症状产生的原因之一.     

脑卒中后完全性失语症患者功能连接模式的功能性近红外光谱成像研究

目的:采用功能性近红外光谱技术(functional near-infrared spectroscopy,f NIRS)观察脑卒中后完全性失语症患者功能连接模式的特征。方法:选取脑卒中后完全性失语症患者10例,脑卒中后非失语症患者10例为非失语症对照组,健康中老年人21例为健康对照组。采用f NIRS采集8min的静息态数据,并选择与语言相关的关键脑区,包括背外侧前额叶(dorsolateral prefrontal cortex,DLPFC)、Broca区、颞上回(superior temporal gyrus,STG)、颞中回(middle temporal gyrus,MTG)、Wernicke区、角回、辅助运动区(supplementary motor area,SMA)作为感兴趣区,分别与全脑做相关分析,获得各组的脑功能连接图。结果:与健康对照组比较,完全性失语症组全脑语言网络功能连通性和连接比下降,其中连接比下降有显著性意义(P0.05),同时在左侧DLPFC-右侧SMA、左侧DLPFC-右侧Wernicke、左侧MTG-右侧MTG、左侧SMA-右侧Wernicke、左侧DLPFC-左侧SMA、右侧DLPFC-左侧SMA、左侧SMA-右侧SMA间的功能连接差异有显著性意义(P0.05)。相较于非失语症组,完全性失语症组表现出左侧MTG-左侧角回间功能连接下降且差异有显著性意义(P0.05)。结论:脑卒中后完全性失语症患者全脑语言网络的功能连通性与连接比以及关键语言区之间的功能连接模式存在异常,相关脑区之间的功能连接减弱可能是引起脑卒中后语言功能减弱的原因之一。其中左侧MTG-左侧角回间的功能连接可能为关键功能连接,左侧MTG和角回有望成为针对脑卒中后完全性失语症患者的新的神经调控靶点。     

近红外光谱技术在脑卒中康复领域的研究进展

全球范围内,脑卒中是继缺血性心脏病之后的第二大致死原因,循证医学证实,脑卒中康复是降低致残率最有效的方法。对于脑卒中患者,脑功能成像技术能帮助判断病变周围脑功能是否存在以及脑功能区是否移位,显示脑内特定区域的功能变化与躯体局部感觉运动的关系,对脑卒中后康复治疗和预后判定有指导意义;还可以根据脑功能成像技术选择性地进行康复治疗。目前应用于脑卒中患者康复诊疗中的脑功能成像技术主要有功能性磁共振技术(fMRI)、脑电图技术(EEG)和正电子发射型计算机断层显像技术(PET),但这些传统的脑功能成像技术有其相应的局限性。近红外光谱(NIRS)技术作为一种新兴的无创性光学成像技术,具有便携性强、抗运动干扰、时间分辨力高、安全经济等突出的优点。本综述简要概述了NIRS应用于监测脑卒中患者大脑功能区域与肢体恢复的关系以及脑卒中的康复评估与治疗等方面的研究及其最新进展。     

缺血性脑卒中认知功能障碍患者静息态下海马功能连接的功能性磁共振成像研究

目的采用静息态功能性磁共振成像（fMRI）观察缺血性脑卒中认知功能障碍患者海马,尤其是与默认网络系统的功能连接模式的异常情况及其可能的机制。 方法选取缺血性脑卒中后认知功能障碍患者15例为病例组,健康老年人10例为健康对照组。采用静息态fMRI,选择左、右两侧海马作为感兴趣区,分别与全脑做相关分析,获得健康对照组和病例组的脑功能连接激活图,观察其左右海马与全脑的功能连接模式。 结果与健康对照组比较,病例组与海马功能连接减弱脑区主要包括扣带回、额叶（上、中、下回）、顶下小叶、颞上回等,组间差异有统计学意义（P<0.05）;而功能连接增强的脑区主要包括小脑后叶、枕叶、颞叶内侧、楔前叶、距状沟等,组间差异有统计学意（P<0.05）。 结论脑卒中后认知功能障碍患者海马功能连接模式存在异常,相关脑区之间的功能连接减弱可能是引起脑卒中后认知功能减弱的原因之一,而功能连接增强提示脑卒中认知功能障碍患者可能同时存在相应的代偿机制。     

基于独立成分分析的脑卒中感觉运动网络功能连接异常的研究

目的:运用独立成分分析(independent component analysis,ICA)探讨脑卒中偏瘫患者感觉运动网络功能连接变化。方法:收集33例慢性期左侧皮质下脑卒中患者和34例年龄、性别相匹配的健康志愿者的静息态功能磁共振成像(resting-state functional magnetic resonance imaging,rs-fMRI)数据,采用组ICA方法提取出健康对照组与脑卒中患者组的感觉运动网络,并运用双样本t检验(P0.05,AlphaSim校正)比较其组间差异。结果:与健康对照组相比,ICA提取出的感觉运动网络(包括背侧、腹侧、左侧和右侧感觉运动网络)在患者组均显示其内功能连接明显下降,具体涉及脑区有:背侧感觉运动网络内的左侧中央前回和右侧中央后回,腹侧感觉运动网络内的左侧中央后回,左侧感觉运动网络内的左侧辅助运动皮质和右侧中央后回,右侧感觉运动网络内的左侧中央前后回。结论:脑卒中偏瘫患者涉及全身感觉运动功能连接网络损伤,ICA为更加全面了解感觉运动功能损伤机制提供了一种新的有效途径。     

失语性脑卒中后抑郁测评工具的研究进展

脑卒中后抑郁(post-stroke depression,PSD)是脑卒中后引发的一种情绪障碍,它阻碍了患者卒中后功能的恢复,增加病死率,甚至可能造成患者自残,给家庭、社会带来了沉重负担[1]。失语是大脑语言中枢受到损伤,导致后天习得的语言功能受损或丧失的一种语言障碍,是卒中的主要后遗症之一[2]。研究表明,失语患者易产生抑郁情绪,脑卒中后失语患者更易发生抑     

Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain

The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected brain, in which voxels are mostly connected with their direct neighbors forming clustered sub-networks, which are held together by a small number of highly connected hub-voxels that ensure a high level of overall connectivity.     

Changes in neuronal connectivity after stroke in rats as studied by serial manganese-enhanced MRI

Loss of function and subsequent spontaneous recovery after stroke have been associated with physiological and anatomical alterations in neuronal networks in the brain. However, the spatiotemporal pattern of such changes has been incompletely characterized. Manganese-enhanced MRI (MEMRI) provides a unique tool for in vivo investigation of neuronal connectivity. In this study, we measured manganese-induced changes in longitudinal relaxation rate, R(1), to assess the spatiotemporal pattern of manganese distribution after focal injection into the intact sensorimotor cortex in control rats (n=10), and in rats at 2 weeks after 90-min unilateral occlusion of the middle cerebral artery (n=10). MEMRI data were compared with results from conventional tract tracing with wheat-germ agglutinin horseradish peroxidase (WGA-HRP). Distinct areas of the sensorimotor pathway were clearly visualized with MEMRI. At 2 weeks after stroke, manganese-induced changes in R(1) were significantly delayed and diminished in the ipsilateral caudate putamen, thalamus and substantia nigra. Loss of connectivity between areas of the sensorimotor network was also identified from reduced WGA-HRP staining in these areas on post-mortem brain sections. This study demonstrates that MEMRI enables in vivo assessment of spatiotemporal alterations in neuronal connectivity after stroke, which may lead to improved insights in mechanisms underlying functional loss and recovery after stroke.     

Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches

The cognitive activity of the human brain benefits from the functional connectivity of multiple brain regions that form specific, functional brain networks. Recent studies have indicated that the relationship between brain regions can be investigated by examining the temporal interaction (known as functional connectivity) of spontaneous blood oxygen level-dependent (BOLD) signals derived from resting-state functional MRI. Most of these studies plausibly assumed that inter-regional interactions were temporally stationary. However, little is known about the dynamic characteristics of resting-state functional connectivity (RSFC). In this study, we thoroughly examined this question within and between multiple functional brain networks. Twenty-two healthy subjects were scanned in a resting state. Several of the RSFC networks observed, including the default-mode, motor, attention, memory, auditory, visual, language and subcortical networks, were first identified using a conventional voxel-wise correlation analysis with predefined region of interests (ROIs). Then, a variable parameter regression model combined with the Kalman filtering method was employed to detect the dynamic interactions between each ROI and all other brain voxels within each of the RSFC maps extracted above. Experimental results revealed that the functional interactions within each RSFC map showed time-varying properties, and that approximately 10-20% of the voxels within each RSFC map showed significant functional connectivity to each ROI during the scanning session. This dynamic pattern was also observed for the interactions between different functional networks. In addition, the spatial pattern of dynamic connectivity maps obtained from neighboring time points had a high similarity. Overall, this study provides insights into the dynamic properties of resting-state functional networks.     

Functional connectivity of the insula in the resting brain

The human insula is hidden in the depth of the cerebral hemisphere by the overlying frontal and temporal opercula, and consists of three cytoarchitectonically distinct regions: the anterior agranular area, posterior granular area, and the transitional dysgranular zone; each has distinct histochemical staining patterns and specific connectivity. Even though there are several studies reporting the functional connectivity of the insula with the cingulated cortex, its relationships with other brain areas remain elusive in humans. Therefore, we decided to use resting state functional connectivity to elucidate in details its connectivity, in terms of cortical and subcortical areas, and also of lateralization. We investigated correlations in BOLD fluctuations between specific regions of interest of the insula and other brain areas of right-handed healthy volunteers, on both sides of the brain. Our findings document two major complementary networks involving the ventral-anterior and dorsal-posterior insula: one network links the anterior insula to the middle and inferior temporal cortex and anterior cingulate cortex, and is primarily related to limbic regions which play a role in emotional aspects; the second links the middle-posterior insula to premotor, sensorimotor, supplementary motor and middle-posterior cingulate cortices, indicating a role for the insula in sensorimotor integration. The clear bipartition of the insula was confirmed by negative correlation analysis. Correlation maps are partially lateralized: the salience network, related to the ventral anterior insula, displays stronger connections with the anterior cingulate cortex on the right side, and with the frontal cortex on the left side; the posterior network has stronger connections with the superior temporal cortex and the occipital cortex on the right side. These results are in agreement with connectivity studies in primates, and support the use of resting state functional analysis to investigate connectivity in the living human brain.     

Acupuncture modulates resting state connectivity in default and sensorimotor brain networks

Previous studies have defined low-frequency, spatially consistent networks in resting fMRI data which may reflect functional connectivity. We sought to explore how a complex somatosensory stimulation, acupuncture, influences intrinsic connectivity in two of these networks: the default mode network (DMN) and sensorimotor network (SMN). We analyzed resting fMRI data taken before and after verum and sham acupuncture. Electrocardiography data were used to infer autonomic modulation through measures of heart rate variability (HRV). Probabilistic independent component analysis was used to separate resting fMRI data into DMN and SMN components. Following verum, but not sham, acupuncture there was increased DMN connectivity with pain (anterior cingulate cortex (ACC), periaqueductal gray), affective (amygdala, ACC), and memory (hippocampal formation, middle temporal gyrus) related brain regions. Furthermore, increased DMN connectivity with the hippocampal formation, a region known to support memory and interconnected with autonomic brain regions, was negatively correlated with acupuncture-induced increase in a sympathetic related HRV metric (LFu), and positively correlated with a parasympathetic related metric (HFu). Following verum, but not sham, acupuncture there was also increased SMN connectivity with pain-related brain regions (ACC, cerebellum). We attribute differences between verum and sham acupuncture to more varied and stronger sensations evoked by verum acupuncture. Our results demonstrate for the first time that acupuncture can enhance the post-stimulation spatial extent of resting brain networks to include anti-nociceptive, memory, and affective brain regions. This modulation and sympathovagal response may relate to acupuncture analgesia and other potential therapeutic effects.     

Weight-conserving characterization of complex functional brain networks

Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.     

Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy

The brain is a complex network with time-varying functional connectivity (FC) and network organization. However, it remains largely unknown whether resting-state fNIRS measurements can be used to characterize dynamic characteristics of intrinsic brain organization. In this study, for the first time, we used the whole-cortical fNIRS time series and a sliding-window correlation approach to demonstrate that fNIRS measurement can be ultimately used to quantify the dynamic characteristics of resting-state brain connectivity. Our results reveal that the fNIRS-derived FC is time-varying, and the variability strength (Q) is correlated negatively with the time-averaged, static FC. Furthermore, the Q values also show significant differences in connectivity between different spatial locations (e.g., intrahemispheric and homotopic connections). The findings are reproducible across both sliding-window lengths and different brain scanning sessions, suggesting that the dynamic characteristics in fNIRS-derived cerebral functional correlation results from true cerebral fluctuation.OCIS codes: (170.2655) Functional monitoring and imaging, (170.5380) Physiology, (170.3880) Medical and biological imaging     

Age-related changes in modular organization of human brain functional networks

Graph theory allows us to quantify any complex system, e.g., in social sciences, biology or technology, that can be abstractly described as a set of nodes and links. Here we derived human brain functional networks from fMRI measurements of endogenous, low frequency, correlated oscillations in 90 cortical and subcortical regions for two groups of healthy (young and older) participants. We investigated the modular structure of these networks and tested the hypothesis that normal brain aging might be associated with changes in modularity of sparse networks. Newman's modularity metric was maximised and topological roles were assigned to brain regions depending on their specific contributions to intra- and inter-modular connectivity. Both young and older brain networks demonstrated significantly non-random modularity. The young brain network was decomposed into 3 major modules: central and posterior modules, which comprised mainly nodes with few inter-modular connections, and a dorsal fronto-cingulo-parietal module, which comprised mainly nodes with extensive inter-modular connections. The mean network in the older group also included posterior, superior central and dorsal fronto-striato-thalamic modules but the number of intermodular connections to frontal modular regions was significantly reduced, whereas the number of connector nodes in posterior and central modules was increased.     

Motor learning-induced changes in functional brain connectivity as revealed by means of graph-theoretical network analysis

Complex bimanual motor learning causes specific changes in activation across brain regions. However, there is little information on how motor learning changes the functional connectivity between these regions, and whether this is influenced by different sensory feedback modalities. We applied graph-theoretical network analysis (GTNA) to examine functional networks based on motor-task-related fMRI activations. Two groups learned a complex 90° out-of-phase bimanual coordination pattern, receiving either visual or auditory feedback. 3T fMRI scanning occurred before (day 0) and after (day 5) training. In both groups, improved motor performance coincided with increased functional network connectivity (increased clustering coefficients, higher number of network connections and increased connection strength, and shorter communication distances). Day×feedback interactions were absent but, when examining network metrics across all examined brain regions, the visual group had a marginally better connectivity, higher connection strength, and more direct communication pathways. Removal of feedback had no acute effect on the functional connectivity of the trained networks. Hub analyses showed an importance of specific brain regions not apparent in the standard fMRI analyses. These findings indicate that GTNA can make unique contributions to the examination of functional brain connectivity in motor learning.