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。     

A general probabilistic model for group independent component analysis and its estimation methods

Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a prespecified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multisubject spatiotemporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood (ML) method is used for estimating this general group ICA model. We propose two expectation-maximization (EM) algorithms to obtain the ML estimates. The first method is an exact EM algorithm, which provides an exact E-step and an explicit noniterative M-step. The second method is a variational approximation EM algorithm, which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods.     

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。     

The functional anatomy of cerebral reorganisation after focal brain injury

Stroke is a major cause of disability in all age groups. Although the value of specific rehabilitative therapies is now acknowledged, the mechanisms of impairment and recovery are not well understood. There is growing interest in the role that central nervous system reorganisation might play in the recovery process, and in particular whether this reorganisation can be manipulated to provide clinical benefits for patients. The careful use of non-invasive techniques such as functional magnetic resonance imaging and transcranial magnetic stimulation allows the study of the working human brain, and studies in humans suggest that functionally relevant adaptive changes occur in cerebral networks following stroke. An understanding of how these changes influence the recovery process will facilitate the development of novel therapeutic techniques that are based on neurobiological principles and will allow the delivery of specific therapies to appropriately targeted patients suffering from stroke.     

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。     

一种独立分量分析的迭代算法和实验结果

介绍盲信源分离中一种独立分量分析方法,基于信息论原理,给出了一个衡量输出分量统计独立的目标函数。最优化该目标函数,得出一种用于独立分量分析的迭代算法。相对于其他大多数独立分量分析方法来说,该算法的优点在于迭代过程中不需要计算信号的高阶统计量,收敛速度快。通过脑电信号和其他信号的计算机仿真和实验结果表明了算法的有效性。     

多通道脑电信号的盲分离

提出一种新的多通道脑电信号盲分离的方法,将小波变换和独立分量分析(independent component analysis,ICA)相结合,利用小波变换的滤噪作用,将混合在原始脑电的部分高频噪声滤除后,再重构原始脑电作为ICA的输入信号,有效地克服了现有ICA算法不能区分噪声的缺陷。实验结果表明,该方法对多通道脑电的盲分离是很有效的。