静息态脑电信号动态功能连接分析

静息态脑功能连接分析是近年来脑研究的一个热点问题, 对于某些脑疾病的诊断及成因理解具有重要意义. 已有的脑功能连接研究基本上都假设功能连接网络在一段时间内是稳定不变的, 但越来越多的证据表明它应该是随时间动态变化的. 对25名被试睁眼和闭眼状态的64电极脑电生理信号, 采用独立成分分析、滑动时间窗、低分辨率脑电断层溯源、图论等方法和技术进行动态功能连接分析, 展现了睁眼和闭眼两种基线状态下视觉网络、默认网络等功能连接网络随时间的动态变化, 并对动态连接矩阵进行主成分分析得到了在整个时间段内具有代表意义的功能连接模式. 该结论支持和补充了传统稳态脑功能连接的研究, 也将为相关实验设计以及脑电信号临床研究提供基线选择依据.     

基于持久同调的复杂脑网络动态演化分析

有研究表明阿尔茨海默病（Alzheimer's disease,AD）的认知状态与动态功能连接时间特性的改变有关,持久同调指标分析方法可为AD动态脑网络的研究提供更深的见解,但是目前研究主要集中在空间演化方面,尚未有针对时变方面的脑网络演化研究．本文基于静息态功能磁共振成像（resting state-functional magnetic resonanceimaging,rs-fMRI）,对AD患者和正常被试（normal controls,NC）的静态脑网络和基于滑动窗口构建的动态脑网络进行功能连接性分析．对基于持久同调和基于图论的分析结果进行了比较,并采用k均值聚类进行了时间属性的分析．结果表明相对图论指标,持久同调的指标在AD患者和NC被试间具有更显著的差异性;而且相对于静态脑网络,基于持久同调的动态脑网络演化分析可为脑功能网络标志物的检测提供新思路．     

人脑默认模式网络的动力学行为

大脑具有自适应、自组织、多稳态等重要特征,是典型的复杂系统.人脑在静息态下的关键功能子网络--默认模式网络(DMN)的激活处于多状态间持续跳转的非平衡过程,揭示该过程背后的动力学机制具有重要的科学意义和临床应用前景.本文基于功能磁共振获得的血氧水平依赖(BOLD)信号,建立了DMN吸引子跳转非平衡过程的能量图景、吸引子非联通图、跳转关系网络等;以高级视觉皮层和听觉等皮层活动为例,通过对应激活DMN状态空间的分布,以及XGBoost、深度神经网络等算法验证了DMN状态变化与外部脑区状态的密切依赖关系;通过偏相关、收敛交叉映射等方法分析了DMN内各个脑区之间的相互作用.本文结果有助于理解静息态下大脑内在非平衡过程的动力学机制,以及从动力学的角度探索具有临床意义的脑功能障碍生物标志物.     

融合t-分布随机邻域嵌入与自动谱聚类的脑功能精细分区方法

本文针对目前脑功能分区不够准确的问题，基于静息态功能磁共振数据，提出了一种融合t-分布随机邻域嵌入（t-SNE）与自动谱聚类（ASC）的人脑功能精细分区的算法.首先，基于静息态功能磁共振图像，对需功能划分的脑区与全脑的时间序列作相关分析，得到需划分脑区的功能连接模式；然后，利用t-SNE算法提取高维功能连接模式特征；最后，通过基于本征间隙的ASC算法自动确定聚类数目，并对降维后的脑区特征分类，得到精细划分的脑亚区.模拟种子区域上的实验结果表明，相较谱聚类算法，以及结合主成分分析的谱聚类算法，本文方法对脑功能体素划分更优.进一步将本方法应用到真实人脑的功能分区中，成功地将海马旁回分为左右半球各3个亚区.本研究表明使用t-SNE与ASC融合的算法可提高脑功能分区准确性，是脑功能精细分区、进而构建脑功能图谱的一种有效方法.     

网络游戏障碍人群大脑功能网络rich club结构的改变

网络游戏障碍（internet gaming disorder,IGD）极大的影响了青少年的学习与生活．IGD于2013年被纳入《精神障碍诊断与统计手册》第5版（DSM-5）,但其神经机制还不清楚．本研究通过静息态功能磁共振成像研究30例IGD被试以及年龄、性别与之匹配的30例健康对照,分析他们大脑功能网络的rich club结构的差异．结果表明,IGD组和对照组均存在rich club结构,rich club脑区主要包含默认模式、执行控制、突显、感觉运动、听觉与视觉网络中的脑区;同时IGD组的rich club连接增加;此外,IGD组的右侧眶部额下回的度显著高于健康对照组.这些发现表明了IGD人群大脑功能网络的rich club结构发生了改变．     

光激活磁共振脑功能成像的研究现状及前景

磁共振脑功能成像经过20年的发展已经成为大脑认知神经科学研究中最主要的技术.光激活磁共振脑功能成像是将它与新发展起来的光遗传学相结合的新兴技术,具有高时空选择性地分析特定神经回路或特定类型的神经元在整个功能网络中的作用的巨大潜力,为理解人类脑功能和揭示神经精神疾病机理可提供新的技术手段.     

清醒猴功能磁共振成像技术平台

功能磁共振成像对脑与认知科学和神经医学的重大推动作用,已为二十多年来数以万计基于人类被试的实验所证实。清醒猴功能磁共振成像因兼具功能磁共振成像和清醒状态非人灵长类动物实验二者的技术特点,在脑科学及脑疾病的研究方面具有独特的优势,近年来在国际脑科学研究中受到了广泛的重视。     

脑功能激活光诱发机理小世界网络分析研究

研究光诱发和静息两种状态下的脑功能网络的信息传输枢纽、网络聚合能力和信息传输的最小路径的差异性。采用小世界网络理论对脑功能网络进行建模,通过对脑功能网络连接度、簇系数和最小路径进行分析,得出光诱发状态下的信息传输重要枢纽为岛叶、后扣带回功能区;丘脑、海马两处功能网络有较大聚合能力。光诱发过程从额上回经颞中回传输到枕中回。静息状态下的信息传输重要枢纽为楔叶、舌回;中央旁小叶、颞上回脑功能网络有较大聚合能力。静息状态下的左半区最佳信息传输路径为左额上回、左颞中回、右楔叶最后到左枕中回;右脑半区的为右额上回、右前扣带回、左枕下回最后到右枕中回。光诱发状态与静息状态的最佳传输路径有明显的区别。     

功能磁共振成像技术及其在脑科学研究中的应用

功能磁共振成像(functional magnetic resonance imaging,fMRI)是用磁共振成像的方法研究人脑和神经系统的功能,它是磁共振成像的一种应用和深入发展.磁共振成像(magnetic resonance imaging,MRI)是核磁共振成像的简称,它是基于核磁共振(nuclear magnetic resonance,NMR)这一物理现象发展起来的.1946年物理学家首先发现核磁共振现象,直到70年代初,它一直沿着高分辨核磁共振波谱学的方向发展.1972年达马迪安(R.Damadian)提出磁共振成像的设想,并指出可以用磁共振成像仪扫描人体检查疾病.1973年劳特伯(P.Lauterbur)在<自然>杂志上发表了用试管样品得到的磁共振截面像,显示了磁共振成像的可能性.从此开始了磁共振成像的发展时期.1980年在实验室中获得了足够清晰的有医学诊断意义的人的头部磁共振图像.磁共振成像仪逐渐形成产业,开始进入医院,主要用于观测人体内部解剖学结构,确定肿瘤和其他疾病的位置.1990年对动物的实验表明,有可能用磁共振成像研究大脑功能.1991年发表了第一幅有意义的人的大脑功能的图像,显示出视觉刺激在大脑的反应,开始了脑功能磁共振成像的研究.至今刚刚过了几年的时间,这一研究领域已经得到了迅速发展.     

脑功能磁共振成像在人类嗅觉研究中的应用

在人类的5种主要感觉中,嗅觉是最广泛、古老、直接和内在的感觉.这些特性使人们对人类嗅觉的研究异常艰难,以致于直到今天人们对嗅觉的功能仍不清楚,而对大脑的功能机制所知更少.与其他基于物理原理的方法一样,磁共振成像技术的广泛应用极大地推动了整个生命科学的发展.脑功能磁共振成像的优势(高分辨率、高对比度、无损性和无放射性等)为人们研究嗅觉高级中枢以及与嗅觉相关行为的脑机制等提供了强有力的技术手段.文章在简单介绍嗅觉知识的基础上,着重讨论了近十年来,脑功能磁共振成像技术在人类嗅觉研究中所取得的成果.     

Biophysical and neural basis of resting state functional connectivity: Evidence from non-human primates

Functional MRI (fMRI) has evolved from simple observations of regional changes in MRI signals caused by cortical activity induced by a task or stimulus, to task-free acquisitions of images in a resting state. Such resting state signals contain low frequency fluctuations which may be correlated between voxels, and strongly correlated regions are deemed to reflect functional connectivity within synchronized circuits. Resting state functional connectivity (rsFC) measures have been widely adopted by the neuroscience community, and are being used and interpreted as indicators of intrinsic neural circuits and their functional states in a broad range of applications, both basic and clinical. However, there has been relatively little work reported that validates whether inter-regional correlations in resting state fluctuations of fMRI (rsfMRI) signals actually measure functional connectivity between brain regions, or to establish how MRI data correlate with other metrics of functional connectivity. In this mini-review, we summarize recent studies of rsFC within mesoscopic scale cortical networks (100 μm–10 mm) within a well defined functional region of primary somatosensory cortex (S1), as well as spinal cord and brain white matter in non-human primates, in which we have measured spatial patterns of resting state correlations and validated their interpretation with electrophysiological signals and anatomic connections. Moreover, we emphasize that low frequency correlations are a general feature of neural systems, as evidenced by their presence in the spinal cord as well as white matter. These studies demonstrate the valuable role of high field MRI and invasive measurements in an animal model to inform the interpretation of human imaging studies.     

Assessing functional connectivity in the human brain by fMRI

Functional magnetic resonance imaging (fMRI) is widely used to detect and delineate regions of the brain that change their level of activation in response to specific stimuli and tasks. Simple activation maps depict only the average level of engagement of different regions within distributed systems. FMRI potentially can reveal additional information about the degree to which components of large-scale neural systems are functionally coupled together to achieve specific tasks. In order to better understand how brain regions contribute to functionally connected circuits, it is necessary to record activation maps either as a function of different conditions, at different times or in different subjects. Data obtained under different conditions may then be analyzed by a variety of techniques to infer correlations and couplings between nodes in networks. Several multivariate statistical methods have been adapted and applied to analyze variations within such data. An approach of particular interest that is suited to studies of connectivity within single subjects makes use of acquisitions of runs of MRI images obtained while the brain is in a so-called steady state, either at rest (i.e., without any specific stimulus or task) or in a condition of continuous activation. Interregional correlations between fluctuations of MRI signal potentially reveal functional connectivity. Recent studies have established that interregional correlations between different components of circuits in each of the visual, language, motor and working memory systems can be detected in the resting state. Correlations at baseline are changed during the performance of a continuous task. In this review, various methods available for assessing connectivity are described and evaluated.     

A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed to identify effective connectivity in the human brain with functional magnetic resonance imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pair-wise GCM has commonly been applied based on single-voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of fMRI data with GCM. To compare the effectiveness of our approach with traditional pair-wise GCM models, we applied a well-established conditional GCM to preselected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis of an fMRI data set in the temporal domain. Data sets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM-detected brain activation regions in the emotion-related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state data set, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network that can be characterized as both afferent and efferent influences on the medial prefrontal cortex and posterior cingulate cortex. These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive model can achieve greater accuracy in detecting network connectivity than the widely used pair-wise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI.     

Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets

A new approach in studying interregional functional connectivity using functional magnetic resonance imaging (fMRI) is presented. Functional connectivity may be detected by means of cross correlating time course data from functionally related brain regions. These data exhibit high temporal coherence of low frequency fluctuations due to synchronized blood flow changes. In the past, this fMRI technique for studying functional connectivity has been applied to subjects that performed no prescribed task ("resting" state). This paper presents the results of applying the same method to task-related activation datasets. Functional connectivity analysis is first performed in areas not involved with the task. Then a method is devised to remove the effects of activation from the data using independent component analysis (ICA) and functional connectivity analysis is repeated. Functional connectivity, which is demonstrated in the "resting brain," is not affected by tasks which activate unrelated brain regions. In addition, ICA effectively removes activation from the data and may allow us to study functional connectivity even in the activated regions.     

Use of random matrix theory in the discovery of resting state brain networks

Connectomics identifies brain networks in vivo in resting state functional MRI. However, the presence of noise produces spurious identification of brain networks, which have low test-retest reliability. A Network Based Statistics approach to network identification has been previously proposed that affords much better statistical power relative to Bonferroni method but nevertheless provides a sufficiently conservative, family-wise control for false positives. We propose the use of Random Matrix Theory (RMT) to discover brain networks and to associate those networks with demographic and clinical variables. We parcellated the brain into cortical and subcortical regions using either an anatomical or a functional brain atlas. We applied RMT to study functional connectivity across brain regions by first computing the correlation matrix for time courses in those brain regions and then identifying eigenvalues that deviate from the theoretical random distribution that RMT predicts, on the assumption that real brain networks would produce eigenvalues that differ significantly from the random distribution. We assessed the specificity and test-retest reliability of identified networks through application of this RMT-based approach to (1) synthetic data generated under the null-hypothesis, (2) resting state functional MRI data from 4 real-world cohorts of patients and healthy controls, and (3) synthetic data generated by the addition of increasing amounts of noise to real-world datasets. Our findings showed that RMT method was robust to the atlas used for parcellating the brain and did not discover a brain network in synthetic data when in fact a network was not present (i.e., specificity was high); RMT-identified networks in the real-world dataset had high test-retest reliability; and RMT-based method consistently discovered the same network in the presence of increasing noise in the real-world dataset.     

Single-subject manual independent component analysis and resting state fMRI connectivity outcomes in patients with juvenile absence epilepsy

The quality of fMRI data impacts functional connectivity measures and consequently, the decisions that clinicians and researchers make regarding functional connectivity interpretation. The present study used resting state fMRI to investigate resting state network connectivity in a sample of patients with Juvenile Absence Epilepsy. Single-subject manual independent component analysis was used in two levels, whereby all noise components were removed, and cerebrospinal fluid pulsation components only were isolated and removed. Improved temporal signal to noise ratios and functional connectivity metrics were observed in each of the cleaning levels for both epilepsy and control cohorts. Results showed full, single-subject manual independent component analysis reduced the number of functional connectivity correlations and increased the strength of these correlations. Similar effects were also observed for the cerebrospinal fluid pulsation only cleaned data relative to the uncleaned, and fully cleaned data. Single-subject manual independent component analysis coupled with short TR multiband acquisition can significantly improve the validity of findings derived from fMRI data sets.     

Fine-scale functional connectivity in somatosensory cortex revealed by high-resolution fMRI

High-resolution functional magnetic resonance imaging (fMRI) at high field (9.4 T) has been used to measure functional connectivity between subregions within the primary somatosensory (SI) cortex of the squirrel monkey brain. The hand-face region within the SI cortex of the squirrel monkey has been previously well mapped with functional imaging and electrophysiological and anatomical methods, and the orderly topographic map of the hand region is characterized by a lateral to medial representation of individual digits in four subregions of areas 3a, 3b, 1 and 2. With submillimeter resolution, we are able to detect not only the separate islands of activation corresponding to vibrotactile stimulations of single digits but also, in subsequent acquisitions, the degree of correlation between voxels within the SI cortex in the resting state. The results suggest that connectivity patterns are very similar to stimulus-driven distributions of activity and that connectivity varies on the scale of millimeters within the same primary region. Connectivity strength is not a reflection of global larger-scale changes in blood flow and is not directly dependent on distance between regions. Preliminary electrophysiological recordings agree well with the fMRI data. In human studies at 7 T, high-resolution fMRI may also be used to identify the same subregions and assess responses to sensory as well as painful stimuli, and to measure connectivity dynamically before and after such stimulations.     

Analysis of functional networks involved in motor execution and motor imagery using combined hierarchical clustering analysis and independent component analysis

Cognitive experiments involving motor execution (ME) and motor imagery (MI) have been intensively studied using functional magnetic resonance imaging (fMRI). However, the functional networks of a multitask paradigm which include ME and MI were not widely explored. In this article, we aimed to investigate the functional networks involved in MI and ME using a method combining the hierarchical clustering analysis (HCA) and the independent component analysis (ICA). Ten right-handed subjects were recruited to participate a multitask experiment with conditions such as visual cue, MI, ME and rest. The results showed that four activation clusters were found including parts of the visual network, ME network, the MI network and parts of the resting state network. Furthermore, the integration among these functional networks was also revealed. The findings further demonstrated that the combined HCA with ICA approach was an effective method to analyze the fMRI data of multitasks.     

Brain modifications after acute alcohol consumption analyzed by resting state fMRI

Resting-state functional magnetic resonance imaging (fMRI) is a recent breakthrough in neuroimaging research able to describe “in vivo” the spontaneous baseline neuronal activity characterized by blood oxygen level dependent (BOLD) signal fluctuations at slow frequency (0.01–0.1 Hz) that, in the absence of any task, forms spatially distributed functional connectivity networks, called resting state networks (RSNs). The aim of this study was to investigate, in the young and healthy population, the changing of the RSNs after acute ingestion of an alcohol dose able to determine a blood concentration (0.5 g/L) that barely exceeds the legal limits for driving in the majority of European Countries. Fifteen healthy volunteers underwent two fMRI sessions using a 1.5 T MR scanner before and after alcohol oral consumption. The main sequence acquired was EPI 2D BOLD, one per each session. To prevent the excessive alcohol consumption the subjects underwent the estimation of blood rate by breath test and after the stabilization of blood alcohol level (BAL) at 0.5 g/L the subjects underwent the second fMRI session. Functional data elaboration was carried out using the probabilistic independent component analysis (PICA). Spatial maps so obtained were further organized, with MELODIC multisession temporal concatenation FSL option, in a cluster representing the group of pre-alcohol sessions and the group of post-alcohol sessions, followed by the dual regression approach in order to evaluate the increase or decrease in terms of connectivity in the RSNs between the two sessions at group level.The results we obtained reveal that acute consumption of alcohol reduces in a significant way the BOLD signal fluctuations in the resting brain selectively in the sub-callosal cortex (SCC), in left temporal fusiform cortex (TFC) and left inferior temporal gyrus (ITG), which are cognitive regions known to be part of the reward brain network and the ventral visual system.     

A SVM-based quantitative fMRI method for resting-state functional network detection

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.