LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Neuroimage. 2011 Jun 15;56(4):2058-67. doi: 10.1016/j.neuroimage.2011.03.051. Epub 2011 Apr 2.
The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders. Rather than analyzing each network encoded by a spatial independent component separately, we propose a novel algorithm for discriminant analysis of functional brain networks jointly at an individual level. The functional brain networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based Riemannian distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional brain networks that are informative for schizophrenia diagnosis.
使用独立成分分析从 fMRI 图像中提取的功能脑网络已被证明对于区分认知功能和大脑障碍的脑状态具有信息性。我们提出了一种新的算法，用于在个体水平上联合进行功能脑网络的判别分析，而不是分别分析每个空间独立成分编码的网络。每个个体的功能脑网络用作线性子空间的基础，称为功能连接模式，这有助于全面描述 fMRI 数据。通过采用基于主角的黎曼距离，在 Grassmann 流形上分析不同个体的功能连接模式。结合支持向量机分类器，提出了一种前向成分选择技术，用于选择独立成分来构建最具判别力的功能连接模式。该判别分析方法已应用于一项基于 fMRI 的精神分裂症研究，其中包括 31 名精神分裂症患者和 31 名健康个体。实验结果表明，该方法不仅在区分精神分裂症患者和健康对照者方面取得了有前途的分类性能，而且还确定了对精神分裂症诊断具有信息性的有判别力的功能脑网络。
