Group information guided ICA for fMRI data analysis
Group information guided ICA for fMRI data analysis
复制标题
用于 fMRI 数据分析的群体信息引导 ICA
DOI：
10.1016/j.neuroimage.2012.11.008
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发表时间：
2013-04-01
期刊：
影响因子：
5.7
通讯作者：
Fan, Yong
中科院分区：
文献类型：
--
作者：
Du, Yuhui;Fan, Yong
组独立成分分析（ICA）已广泛应用于多被试功能磁共振成像（fMRI）数据的研究，用于计算具有被试间对应性的被试特异性独立成分。然而，在现有的组ICA方法中，源自组ICA的被试特异性独立成分（ICs）的独立性尚未得到明确优化。为了在被试层面保持ICs的独立性，同时建立ICs在被试间的对应性，我们提出了一个获取被试特异性ICs的新框架，我们将其命名为组信息引导的ICA（GIG - ICA）。在这个框架中，通过标准ICA在组层面捕获的组信息被用作指导，利用多目标优化策略计算个体被试特异性ICs。具体而言，我们提出了一个包含两个阶段的框架：首先，使用标准组ICA工具获得组独立成分（GICs），然后将GICs作为参考，在具有空间参考的新的单单元ICA（ICA - R）中使用多目标优化求解器。在模拟和真实fMRI数据上与反向重建（GICA1和GICA3）以及双重回归进行的对比实验表明，GIG - ICA除了具有更高的空间和时间准确性外，还能够获得在不同被试间具有更强独立性和更好空间对应性的被试特异性ICs。（C）2012爱思唯尔公司。保留所有权利。
Group independent component analysis (ICA) has been widely applied to studies of multi-subject fMRI data for computing subject specific independent components with correspondence across subjects. However, the independence of subject specific independent components (ICs) derived from group ICA has not been explicitly optimized in existing group ICA methods. In order to preserve independence of ICs at the subject level and simultaneously establish correspondence of ICs across subjects, we present a new framework for obtaining subject specific ICs, which we coined group-information guided ICA (GIG-ICA). In this framework, group information captured by standard ICA on the group level is exploited as guidance to compute individual subject specific ICs using a multi-objective optimization strategy. Specifically, we propose a framework with two stages: at first, group ICs (GICs) are obtained using standard group ICA tools, and then the GICs are used as references in a new one-unit ICA with spatial reference (ICA-R) using a multi-objective optimization solver. Comparison experiments with back-reconstruction (GICA1 and GICA3) and dual regression on simulated and real fMRI data have demonstrated that GIG-ICA is able to obtain subject specific ICs with stronger independence and better spatial correspondence across different subjects in addition to higher spatial and temporal accuracy. (C) 2012 Elsevier Inc. All rights reserved.