空间独立成分分析实现fMRI信号的盲分离

独立成分分析（ICA）在功能核磁共振成像（fMRI)技术中的应用是近年来人们关注的一个热点。简要介绍了空间独立成分分析（SICA）的模型和方法,将fMRI信号分析看作是一种盲源分离问题,用快速算法实现fMRI信号的盲源分离。对fMRI信号的研究大多是在假定已知事件相关时间过程曲线的情况下,利用相关性分析得到脑的激活区域。在不清楚有哪几种因素对fMRI信号有贡献、也不清楚其时间过程曲线的情况下,用SICA可以对fMRI信号进行盲源分离,提取不同独立成分得到任务相关成分、头动成分、瞬时任务相关成分、噪声干扰、以及其它产生fMRI信号的多种源信号。     

新的独立成分分析算法实现功能磁共振成像信号的盲分离

采用独立成分分析(independent component analysis,ICA)的一种新的牛顿型算法来提取功能磁共振成像(functional magnetic rasonance imaging,fMRI)信号中的各种独立成分(包括与实验设计相关的成分以及各种噪声)。与fastICA相比,该算法减少了运算量,提高了运算速度,而且能够很好地分离出各个独立成分。结果表明该算法是一种有效的fMRI信号分析手段。     

基于ICA的颞叶癫痫缺省模式网络的研究

很多fMRI研究表明部分癫痫患者缺省模式网络存在中断现象,但均采用广义线性模型的假设驱动方法。作者尝试运用独立成分分析（independent component analysis, ICA）分离出l5例单侧颞叶癫痫（temporal lobe epilepsy,TLE）患者和17例正常对照的缺省模式网络,并采用拟合度值（goodness-of-fit scores）分析对感兴趣成分进行挑选,将其结果进行组内分析和组间分析。结果表明颞叶癫痫患者的缺省模式网络犬部分区域功能连接度下降,以前额叶和同侧颞上回为著,这可能是由于颞叶癫痫患者的大脑功能内源性组织发生破坏所致。拟合度值下降表明缺省模式网络激活区域为单侧TLE患者提供了一个灵敏的生物信号特征。     

fMRI颞叶癫痫默认模式网络的研究

许多功能磁共振研究已经发现人脑的一些皮层区域在静息状态下出现共激活,这些区域形成连通的功能网络,称为"默认模式网络"。本文研究颞叶癫痫患者的默认模式网络,运用独立成分分析(Independent component analysis)分离出12例颞叶癫痫患者和12例正常对照的默认模式网络,进行组内分析得到两组被试的统计图,进行组间分析比较两组被试的默认模式网络的差异。结果表明默认模式网络均存在于颞叶癫痫患者和正常对照中,然而,在默认模式包含的网络中,颞叶癫痫患者前扣带回腹侧(ventral anterior cingulated cortex,vACC)、前额中分(medial prefrontal cortex,MPFC)、楔前叶(precuneus)、以及海马旁回区域比正常对照表现出代谢增强。这一结果有助于从脑功能的角度了解癫痫患者某些临床症状的发病机理,为今后癫痫诊治的发展提供一定的帮助。     

用独立分量分析和小波变换提取听觉诱发电位晚成分的强度变化

诱发电位的提取通常依靠相干平均方法,需要进行多次的重复刺激,实验时间较长.随着实验时间的增加,受试者生理因素及环境因素的变化,会影响诱发电位的正常形态(波形、强度和相位).利用独立分量分析和小波变换方法,通过时域信息和空域信息的综合应用,可成功提取到听觉诱发电位晚成分的强度在实验过程中的变化,对由于实验时间增加对晚成分的影响做出定量评价.结果表明,在10 min左右的实验过程中,听觉诱发电位晚成分的幅度会下降约40%.     

用独立分量分析和小波变换方法研究实验过程中脑诱发电位信号强度的变化

诱发电位的提取通常依靠相干平均方法,需要进行多次的重复刺激,实验时间较长。随着实验时间的增加,受试者生理因素及环境因素的变化,会影响诱发电位的正常形态(波形、强度和相位)。利用独立分量分析和小波变换方法,通过时域信息和空域信息的综合应用,可成功提取到听觉诱发电位晚成分的强度在实验过程中的变化,对由于实验时间增加对晚成分的影响做出定量评价。结果表明,在10min左右的实验过程中,听觉诱发电位晚成分的幅度会下降约40％。     

香溪河水质空间分布特性研究

运用聚类分析和主成分分析对香溪河19个样点水质的理化特性进行研究,聚类分析表明,根据各采样点之间水质组分的相似性可将香溪河大致分为3个河段,分别属于不同的亚流域,各亚流域问的特征差异显著,对各河段水质的主成分分析表明,上述3河段的主要水质信息差异很大,第1河段(在河流上游)水质的信息主要体现为总碱度和硬度,第2河段(河流中游)主要体现为可溶性磷酸盐、总磷和氯离子,第3河段(河流下游)则为pH、亚硝酸盐氮、总氮和COD,文中结合香溪河流域地理环境背景,探讨了香溪河水质空间分布格局的成因,为分析流域水质状况及成因提供了一条简单有效的途径。     

MATLAB 7.X生物信息工具箱的应用———基因表达图谱分析（4）*

基因表达图谱原则上可了解整体细胞基因表达的信息,是基因组功能分析的重要研究手段。MATLAB 7.X生物信息工具箱为基因表达谱数据的分析和处理提供了一个综合环境,通过众多统计函数和绘图函数的结合使用,过滤不合格的基因数据和噪声数据,从而对基因表达数据进行聚类分析和主成分分析,绘制相关的基因表达图谱,完成基因芯片数据表达图谱的分析,分析结果可视化程度高,图表清晰、直观。本文主要以酿酒酵母Saccharomyces cerevisiae为例,详细描述了利用MATLAB 7.X生物信息工具箱对其基因表达图谱进行分析的过程。     

基于ITS序列的栓菌属部分种的分子分类初步研究

栓菌属 Trametes 的一些近缘种宏观和微观形态学非常相近,传统分类学方法难于对其进行准确分类定位。测定了 34 个分类单元的 ITS(包括 5.8SrDNA)序列,并对得到的 43 个分类单元的 ITS 序列进行系统发生分析,构建了聚类分析树状图。该树状图显示,栓菌属类群与其他属类群明显分开,Trametes versicolor 聚类到一个高支持率的独立分支。形态学上定名为 T. hirsuta 和 T. pubescens 物种聚类到同一高支持率的独立分支,试验分析表明这两个种应视为同一物种。     

荷花品种的数量分类研究

本文运用电子计算机进行了荷花品种的数量分类研究。结果表明: 1.Q型聚类分析有助于揭示品种间的亲缘关系。2.R型聚类分析说明了性状间的生物学距离。3.主成分分析初步反映了各性状所占的信息比例。     

Independent component analysis of functional MRI: what is signal and what is noise?

Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation. Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find common features within the data. This exploratory approach can be revealing when the brain activation is difficult to predict beforehand, such as with complex stimuli and internal shifts of activation that are not time-locked to an easily specified sensory or motor event. These methods can be further improved by incorporating prior knowledge regarding the temporal and spatial extent of brain activation.     

Model-free functional MRI analysis using topographic independent component analysis

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.     

Temporally and Spatially Constrained ICA of fMRI Data Analysis

Constrained independent component analysis (CICA) is capable of eliminating the order ambiguity that is found in the standard ICA and extracting the desired independent components by incorporating prior information into the ICA contrast function. However, the current CICA method produces constraints that are based on only one type of prior information (temporal/spatial), which may increase the dependency of CICA on the accuracy of the prior information. To improve the robustness of CICA and to reduce the impact of the accuracy of prior information on CICA, we proposed a temporally and spatially constrained ICA (TSCICA) method that incorporated two types of prior information, both temporal and spatial, as constraints in the ICA. The proposed approach was tested using simulated fMRI data and was applied to a real fMRI experiment using 13 subjects who performed a movement task. Additionally, the performance of TSCICA was compared with the ICA method, the temporally CICA (TCICA) method and the spatially CICA (SCICA) method. The results from the simulation and from the real fMRI data demonstrated that TSCICA outperformed TCICA, SCICA and ICA in terms of robustness to noise. Moreover, the TSCICA method displayed better robustness to prior temporal/spatial information than the TCICA/SCICA method.     

Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA

Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.     

Analysis of activity in fMRI data using affinity propagation clustering

Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) data. The huge computation load, however, makes it difficult for the practical use. We use affinity propagation clustering (APC), a new clustering algorithm especially for large data sets to detect brain functional activation from fMRI. It considers all data points as possible exemplars through the minimisation of an energy function and message-passing architecture, and obtains the optimal set of exemplars and their corresponding clusters. Four simulation studies and three in vivo fMRI studies reveal that brain functional activation can be effectively detected and that different response patterns can be distinguished using this method. Our results demonstrate that APC is superior to the k-centres clustering, as revealed by their performance measures in the weighted Jaccard coefficient and average squared error. These results suggest that the proposed APC will be useful in detecting brain functional activation from fMRI data.     

Comparison of methods for clustering independent EEG components in healthy subjects and patients with postconcussion syndrome after traumatic brain injury

The efficiencies of three clustering methods for independent components of 19-channel baseline EEG in location of pathological cerebral activity sources were compared. The samples comprised 518 healthy subjects and 87 patients with postconcussion syndrome after traumatic brain injury (TBI). Clustering of independent component topographies, the spatial coordinates of equivalent dipole sources corresponding to independent component topographies, and locations of the maximums of the equivalent source current density calculated by standardized low resolution electromagnetic tomography (sLORETA) were compared. A comparison of the power spectra of independent components showed a significant increase in the EEG power in the Δ, θ, and α bands for sources located in the frontal and temporal lobes of TBI patients compared to healthy subjects. The method of clustering of independent component topographies proved to be the most sensitive of the methods compared.     

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Functional magnetic resonance imaging (fMRI) is currently the standard method of evaluating brain function in the field of Cognitive Neuroscience, in part because fMRI data acquisition and analysis techniques are readily available. Because fMRI has excellent spatial resolution but poor temporal resolution, this method can only be used to identify the spatial location of brain activity associated with a given cognitive process (and reveals virtually nothing about the time course of brain activity). By contrast, event-related potential (ERP) recording, a method that is used much less frequently than fMRI, has excellent temporal resolution and thus can track rapid temporal modulations in neural activity. Unfortunately, ERPs are under utilized in Cognitive Neuroscience because data acquisition techniques are not readily available and low density ERP recording has poor spatial resolution. In an effort to foster the increased use of ERPs in Cognitive Neuroscience, the present article details key techniques involved in high density ERP data acquisition. Critically, high density ERPs offer the promise of excellent temporal resolution and good spatial resolution (or excellent spatial resolution if coupled with fMRI), which is necessary to capture the spatial-temporal dynamics of human brain function.Download video file.^{(101M, mp4)}     

Resting‐state networks of the neonate brain identified using independent component analysis

Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been successfully used to probe the intrinsic functional organization of the brain and to study brain development. Here, we implemented a combination of individual and group independent component analysis (ICA) of FSL on a 6‐min resting‐state data set acquired from 21 naturally sleeping term‐born (age 26 ± 6.7 d), healthy neonates to investigate the emerging functional resting‐state networks (RSNs). In line with the previous literature, we found evidence of sensorimotor, auditory/language, visual, cerebellar, thalmic, parietal, prefrontal, anterior cingulate as well as dorsal and ventral aspects of the default‐mode‐network. Additionally, we identified RSNs in frontal, parietal, and temporal regions that have not been previously described in this age group and correspond to the canonical RSNs established in adults. Importantly, we found that careful ICA‐based denoising of fMRI data increased the number of networks identified with group‐ICA, whereas the degree of spatial smoothing did not change the number of identified networks. Our results show that the infant brain has an established set of RSNs soon after birth.