Ecker Christine, Reynaud Emanuelle, Williams Steven C, Brammer Michael J
Brain Image Analysis Unit, Department of Biostatistics and Computing, Institute of Psychiatry, London, United Kingdom.
Hum Brain Mapp. 2007 Sep;28(9):817-34. doi: 10.1002/hbm.20311.
This study aimed to demonstrate how a regional variant of principal component analysis (PCA) can be used to delineate the known functional subdivisions of the human visual system. Unlike conventional eigenimage analysis, PCA was carried out as a second-level analysis subsequent to model-based General Linear Model (GLM)-type functional activation mapping. Functional homogeneity of the functional magnetic resonance imaging (fMRI) time series within and between clusters was examined on several levels of the visual network, starting from the level of individual clusters up to the network level comprising two or more distinct visual regions. On each level, the number of significant components was identified and compared with the number of clusters in the data set. Eigenimages were used to examine the regional distribution of the extracted components. It was shown that voxels within individual clusters and voxels located in bilateral homologue visual regions can be represented by a single component, constituting the characteristic functional specialization of the cluster(s). If, however, PCA was applied to time series of voxels located in functionally distinct visual regions, more than one component was observed with each component being dominated by voxels in one of the investigated regions. The model of functional connections derived by PCA was in accordance with the well-known functional anatomy and anatomical connectivity of the visual system. PCA in combination with conventional activation mapping might therefore be used to identify the number of functionally distinct nodes in an fMRI data set in order to generate a model of functional connectivity within a neuroanatomical network.
本研究旨在证明主成分分析（PCA）的区域变体如何用于描绘人类视觉系统已知的功能细分。与传统的特征图像分析不同，PCA是在基于模型的通用线性模型（GLM）类型的功能激活映射之后进行的二级分析。从单个簇的层面到包含两个或更多不同视觉区域的网络层面，在视觉网络的多个层面上检查了簇内和簇间功能磁共振成像（fMRI）时间序列的功能同质性。在每个层面上，确定显著成分的数量并与数据集中的簇数量进行比较。特征图像用于检查提取成分的区域分布。结果表明，单个簇内的体素以及位于双侧同源视觉区域的体素可以由单个成分表示，构成该簇的特征性功能特化。然而，如果将PCA应用于位于功能不同视觉区域的体素的时间序列，则会观察到多个成分，每个成分由其中一个研究区域的体素主导。通过PCA得出的功能连接模型与视觉系统众所周知的功能解剖学和解剖学连接性一致。因此，PCA与传统激活映射相结合可用于识别fMRI数据集中功能不同节点的数量，以便生成神经解剖网络内的功能连接模型。
