基于Granger因果分析的癫痫发作间歇期EEG在δ频段的功能连接模式

目的 应用频域Granger因果分析方法,研究颞叶癫痫发作间歇期16导脑电图(EEG)在与癫痫发作相关的δ频段的过度放电功能连接特性.方法 实验数据来自颞叶癫痫9例患者(6例左颞叶癫痫,3例右颞叶癫痫),9例正常对照受试者.记录每例颞叶癫痫受试者在发作间歇期的痫样放电、非痫样放电以及正常对照组的共3个状态的16导EEG;每个状态下各记录10个EEG数据段,每个数据段长度为20s,采样频率为200 Hz;应用带通滤波提取EEG的δ分量(1～4 Hz).应用频域Granger因果分析方法,分别计算痫样放电组、非痫样放电组和正常组10次记录的16通道EEGδ频段分量之间的频域因果度量平均值Iδ;分析以上3个组颞叶区(左颞叶癫痫:T3、T5,右颞叶癫痫:T4、T6)与额区(Fp1、Fp2、F3、F4)和顶区(C3、C4)之间EEG在δ频段的功能连接模式.结果 痫样放电组:下颞叶区(左下颞叶区T5,右下颞叶区T6)与额区、顶区之间Iδ在0.1323±0.0329～0.1670±0.0289;非痫样放电组:下颞叶区与额区、顶区之间的Iδ在0.0300±0.0130～0.0420±0.0072;正常对照组:下颞叶区与额区、顶区之间的Iδ在0.0153±0.0028～0.0193±0.0057.统计结果表明:痫样放电组下颞叶区与额区、顶区之间的Iδ值与非痫样放电组相比差异有统计学意义(P＜0.05),与正常对照组相比差异有统计学意义(P＜0.01);非痫样放电组下颞叶区与额叶、顶叶之间的Iδ值和正常对照组相比差异无统计学意义(P＞0.05).结论 颞叶癫痫发作间歇期在痫样放电状态下,EEGδ频段在下颞叶区与额区、顶区之间存在较强连接,过度放电从下颞叶区传递到额区和顶区.非痫样放电组和正常组的EEGδ频段,下颞叶区与额叶、顶叶之间连接弱,下颞叶区不是EEG信号传导的起始区.     

独立成分分析在生物医学信号处理中的应用

独立成分分析(independent component analysis，ICA)已经成功地应用到生物医学信号处理中，并被证明是一种分析生物医学信号的强有力的工具，近年来一直受到国内外学的广泛关注。本系统地介绍了独立成分分析在生物医学信号(EEG，MEG，fMRI)处理中的应用，分析了其应用方法，最后简要地探讨了独立成分分析应用到生物医学信号中的优势及存在的一些不足。     

双语脑功能机制的脑磁图研究进展

双语者经常使用两种语言已被证明对语言和认知功能有广泛的影响,但是双语使用影响大脑的机制目前尚不清楚.脑磁图通过非侵入性方式测量微弱的脑磁场信号,可更准确反映脑部神经活动,对于脑疾病早期诊断和脑科学前沿研究具有重大意义.在阐述脑磁图技术的发展进程、分析方法和软件的基础上,从脑发育过程的双语优势、双语切换的脑机制研究、双语...     

脑功能多种成像方式整合技术的研究及其进展

近年来,人们运用脑的局部血流技术对人体大脑功能的研究发展迅速,尤其是PET和f MRI具有极高的空间分辨率,但时间分辨率跟不上大脑神经生理变化的速度,与之相对应的EEG和MEG能够以毫秒级的速度跟踪神经元活动产生的电信号,但空间分辨率低,因此,为了获取大脑神经元活动的高时一空动态图像,需要对多种方法获得的信号进行整合。本文阐述了脑功能成像技术的物理和生理学基础,讨论了脑功能多种成像方式整合技术的各种方法,并对该领域的发展方向和所面临的挑战进行了简要的分析。     

多变量自回归和多线性主成分分析结合的多通道信号特征提取研究

脑机接口(BCI)系统通过从脑信号中提取特征对其进行识别。针对自回归模型特征提取方法和传统主成分分析降维方法处理多通道信号的局限性,本文提出了多变量自回归(MVAR)模型和多线性主成分分析(MPCA)结合的多通道特征提取方法,并用于脑磁图/脑电图(MEG/EEG)信号识别。首先计算MEG/EEG信号的MVAR模型的系数矩阵,然后采用MPCA对系数矩阵进行降维,最后使用线性判别分析分类器对脑信号分类。创新在于将传统单通道特征提取方法扩展到多通道。选用BCI竞赛IV数据集3和1数据进行实验验证,两组实验结果表明MVAR和MPCA结合的特征提取方法处理多通道信号是可行的。     

基于Granger因果性的功能磁共振成像对内侧颞叶癫痫活动的研究

在内侧颞叶癫痫(mTLE)中,内侧颞叶与皮层及皮层下结构参与了癫痫活动的起源与传播.基于Granger因果性(GC)检验方法,对脑电联合功能磁共振(EEG-fMRI)数据进行分析,研究内侧颞叶在mTLE中的作用.以内侧颞叶激活区域为参考区域,计算参考区域与大脑其余每个体素点之间的Granger因果关系,并映射到全脑,形成Granger因果图(GCM).结果表明,内侧颞叶将癫痫活动传播到外侧颞叶、额叶、顶叶及丘脑等区域,同时受到脑岛、壳核以及丘脑等区域脑活动的影响,提示内侧颞叶在mTLE痫样发放的传播环路中具有关键作用.     

独立成分分析在生物医学信号处理中的应用

独立成分分析(independentcomponentanalysis熏ICA)已经成功地应用到生物医学信号处理中,并被证明是一种分析生物医学信号的强有力的工具,近年来一直受到国内外学者的广泛关注。本文系统地介绍了独立成分分析在生物医学信号(EEG,MEG,fMRI)处理中的应用,分析了其应用方法,最后简要地探讨了独立成分分析应用到生物医学信号中的优势及存在的一些不足。     

应用因果分析方法对癫痫发作间期头皮脑电信号进行致痫灶定侧

目的利用因果分析方法对癫痫发作间期的头皮脑电信号进行致痫灶定侧。方法在频域因果分析方法——自适应直接传递函数（ADTF）的基础上提出功率谱加权ADTF（psADTF）法,以给定频段内信号的功率谱对ADTF的标准化做加权,以适应不同癫痫波频域信息不同的特点。利用该方法对2组共30例患者的头皮脑电信号进行分析,含组1癫痫手术患者15例,组2门诊癫痫患者15例。其中组1患者共截取癫痫波样本数104个,组2患者共截取癫痫波样本数98个。结果组1患者通过psADTF分析对致痫灶定侧与手术侧一致的有96个,平均单个癫痫波致痫灶定侧准确率可达92％;组2患者通过psADTF分析对致痫灶定侧与专家判读结果一致的有94个,平均单个癫痫波致痫灶定侧准确率达96％。结论发作间期头皮脑电癫痫波信号的psADTF分析能够很好地辅助临床致痫灶定侧。     

功能磁共振在研究老年抑郁症脑功能及结构中的应用

目的:基于核磁共振下高分辨率结构成像及功能扫描评价老年抑郁症患者的脑功能及结构。方法:选取2015年1月至2016年7月于我院就诊的抑郁症老年患者50例为研究组,招募50例老年健康者为对照组,所有入组者均进行核磁共振下高分辨率结构像及功能扫描,采用MRIcro软件转化,应用REST、SPM 8软件进行图像结果分析。结果:抑郁组的汉密尔顿抑郁量表(HAMD)评分为(26.7±1.6)分,对照组为(4.7±1.7)分,两组比较差异具有统计学意义(t=66.636,P0.001)。抑郁组患者中左侧的梭状回、颞上回、楔前叶、顶下小叶、中央后回、中央旁小叶,以及右侧的枕中回、顶下小叶、额上回的ReHo较健康组显著减低(P0.05),抑郁组患者的左侧颞下回、额下回、楔前叶,右侧内侧扣带回、梭状回、颞下回、楔叶的GMV较健康组显著减低(P0.05)。结论:老年抑郁症患者中脑功能与结构异常的区域没有重叠,异常的脑功能与结构对于抑郁症神经病理机制的作用相互独立。     

基于自回归模型和关联向量机的癫痫脑电信号自动分类

癫痫脑电信号自动分类方法的研究具有重要意义.基于自回归模型和关联向量机,实现癫痫脑电信号的自动分类.采用自回归模型,进行脑电信号特征提取;通过引入主成分分析和线性判别分析两种特征变换方法,降低特征空间维数;采用关联向量机作为分类器,提高模型稀疏性并可以得到概率式输出.在对波恩大学癫痫研究中心脑电信号的分类中,所提出的方法最高准确率可以达到99.875％;在将特征空间维数降至原始维数的1/15时,分类准确率仍可达到99.500％;采用关联向量机作为分类器,模型稀疏性大幅提高,与支持向量机相比,同等条件下关联向量数仅为支持向量数的几十分之一.所提方法可以很好地应用于癫痫脑电信号的自动分类.     

Investigation of the effective connectivity of resting state networks in Alzheimer's disease: a functional MRI study combining independent components analysis and multivariate Granger causality analysis

Recent neuroimaging studies have shown that the cognitive and memory decline in patients with Alzheimer's disease (AD) is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses in small‐world attributes. However, the causal interactions among the spatially isolated, but functionally related, resting state networks (RSNs) are still largely unexplored. In this study, we first identified eight RSNs by independent components analysis from resting state functional MRI data of 18 patients with AD and 18 age‐matched healthy subjects. We then performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that patients with AD exhibited decreased causal interactions among the RSNs in both intensity and quantity relative to normal controls. Results from mGCA indicated that the causal interactions involving the default mode network and auditory network were weaker in patients with AD, whereas stronger causal connectivity emerged in relation to the memory network and executive control network. Our findings suggest that the default mode network plays a less important role in patients with AD. Increased causal connectivity of the memory network and self‐referential network may elucidate the dysfunctional and compensatory processes in the brain networks of patients with AD. These preliminary findings may provide a new pathway towards the determination of the neurophysiological mechanisms of AD. Copyright © 2012 John Wiley & Sons, Ltd.     

基于多变量格兰杰因果关系的运动想象因效网络构建

运动想象神经活动规律的探索为脑损伤肢体瘫痪患者康复训练新方法研发等提供理论指导。基于格兰杰因果关系构建的因效网络是分析运动想象神经活动状态的重要工具,但是格兰杰因果关系只能反映两个变量之间的相互作用,而一个简单的运动想象过程也需要多个神经节点参与,针对该问题,本研究引入可反映一个集群中多个变量之间相互作用的多变量格兰杰因果分析,优化运动想象因效网络构建方法。针对4位受试者,利用多变量及传统格兰杰因果关系,分别构建同一人两种不同运动想象模式的因效网络,并提取网络特征进行运动想象模式分类。结果表明,基于多变量格兰杰因效网络进行4位受试者运动想象模式分类的正确率分别为90.4%、88.8%、91.1%、90.3%,基于格兰杰因效网络的正确率为88.5%、89.3%、90.2%、89.7%。与传统格兰杰因果关系相比,基于多变量格兰杰因果关系构建因效网络,能更准确地反映运动想象神经活动特征状态。     

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.     

Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis

Vector autoregression (VAR) and structural equation modeling (SEM) are two popular brain-network modeling tools. VAR, which is a data-driven approach, assumes that connected regions exert time-lagged influences on one another. In contrast, the hypothesis-driven SEM is used to validate an existing connectivity model where connected regions have contemporaneous interactions among them. We present the two models in detail and discuss their applicability to FMRI data, and their interpretational limits. We also propose a unified approach that models both lagged and contemporaneous effects. The unifying model, structural vector autoregression (SVAR), may improve statistical and explanatory power, and avoid some prevalent pitfalls that can occur when VAR and SEM are utilized separately.     

Are posterior default-mode networks more robust than anterior default-mode networks? Evidence from resting-state fMRI data analysis

Intrinsic brain activity known as default-mode networks (DMNs) has been observed predominantly within the medial/superior frontal areas, anterior/posterior cingulate gyri, and precuneus using blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI). Despite anecdotal evidence of distinct spatial patterns reflecting neuropsychiatric conditions in these DMNs, rigorous analysis of the characteristic traits of DMNs has been limited in previous studies. In this letter, the reproducibility and potential variability of the anterior and posterior DMNs were evaluated based on individual-level variations in effect sizes, activated areas, and causal interactions. Our results indicated that the DMNs were indeed reproducible between sessions/subjects. Region-specific traits were also observed: the posterior DMN seemed more robust to individual-level variations than the anterior DMN. The proposed analytical methods and reported findings may be useful in the development of a wide range of applications, including those involving clinical populations, which utilize the characteristic traits of DMNs.     

Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis

Fast changes, in the range of milliseconds, in the optical properties of cerebral tissue are associated with brain activity and can be detected using noninvasive near-infrared spectroscopy (NIRS). These changes are assumed to be caused by changes in the light scattering properties of the neuronal tissue. The aim of this study was to develop highly sensitive data analysi algorithms to detect this fast signal, which is small compared with other physiological signals. A frequency-domain tissue oximeter, whose laser diodes were intensity modulated at 110 MHz, was used. The amplitude, mean intensity and phase of the modulated optical signal were measured at a sample rate of 96 Hz. The probe, consisting of four crossed source detector pairs was placed above the motor cortex, contralateral to the hand performing a tapping exercise consisting of alternating rest and tapping periods of 20 s each. An adaptive filter was used to remove the arterial pulsatility from the optical signals. Independent component analysis allowed further separation of a signal component containing the fast signal. In nine out of 14 subjects, a significant fast neuronal signal related to the finger tapping was found in the intensity signals. In the phase signals, indications of the fast signal were found in only two subjects.     

Implication of spinal protein kinase Cgamma isoform in activation of the mouse brain by intrathecal injection of the protein kinase C activator phorbol 12,13-dibutyrate using functional magnetic resonance imaging analysis

The aim of the present study was to investigate whether direct activation of protein kinase C (PKC) in the spinal cord could change brain activation using a functional magnetic resonance imaging (fMRI) analysis in mice that lack the PKCgamma gene. The activation of spinal PKC by intrathecal (i.t.) injection with phorbol 12,13-dibutyrate (PDBu), a specific PKC activator, caused a time-dependent decrease in paw-withdrawal latency to the heat thermal stimulus. In contrast, i.t. injection of PDBu failed to cause thermal hyperalgesia in mice which lacked the PKCgamma gene. We found that the i.t. injection with PDBu caused a remarkable increase in the activity of several brain regions in wild-type mice compared with vehicle injection. In the somatosensory cortex and lateral and medial thalamus, i.t. injection of PDBu produced a dramatic and time-dependent increase in signal intensity at 1-6h after i.t. PDBu injection. In contrast, i.t. injection of PDBu produced a delayed but significant increase in signal intensity at 3-6h in the cingulate cortex, at 4-6h in the nucleus accumbens and at 3-6h in the ventral tegmental area. In addition, all effects of PDBu were abolished in mice that lacked the PKCgamma gene. These results suggest that the activation of spinal PKCgamma associated with the activation of ascending pain transmission may be an important factor in chronic pain-like hyperalgesia with changes in emotionality.     

静息态脑功能网络分析的假设驱动和数据驱动方法综述

基于功能磁共振成像的研究已发现在脑区之间存在低频波动一致的脑功能网络.脑功能网络有助于认识脑功能和诊断神经精神疾病,而脑功能网络分析方法在其中具有重要地位.本文首先综述了假设驱动和数据驱动进行脑功能网络分析的两类方法,接着对常用的基于感兴趣区、基于体素、基于独立成分分析、基于主成分分析和基于聚类方法的原理、优缺点做了详细介绍,并着重阐述了最近提出的组信息指导的独立成分分析方法,及基于半监督学习技术的感兴趣区选择方法,最后对改进方向进行了展望.