Semiblind spatial ICA of fMRI using spatial constraints.
Semiblind spatial ICA of fMRI using spatial constraints.
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DOI：
10.1002/hbm.20919
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发表时间：
2010-07
影响因子：
4.8
通讯作者：
Calhoun, Vince D.
中科院分区：
文献类型：
--
作者：
Lin, Qiu-Hua;Liu, Jingyu;Zheng, Yong-Rui;Liang, Hualou;Calhoun, Vince D.
利用先验信息的独立成分分析（伊卡），也称为半盲伊卡，在功能磁共振成像（fMRI）分析中表现出相当大的前景。到目前为止，有关fMRI的时间信息已被用于时间伊卡或空间伊卡作为额外的约束，以提高估计的任务相关的组件。考虑到空间模式的先验信息也是可用的，在带固定点学习的约束伊卡框架下，提出了一种利用空间模式先验信息的半盲空间伊卡算法.该方法首先与合成的fMRI样数据进行了测试，然后被应用到真实的fMRI数据从11个科目执行视觉任务。自动提取了三个感兴趣的组件，包括两个任务相关的组件和“默认模式”组件，并使用地图集定义的掩码作为空间约束。默认模式网络，一组区域，出现相关，特别是在没有任务或外部刺激，并越来越感兴趣的功能磁共振成像研究，被发现大大改善时，纳入空间先验信息。仿真和真实的fMRI数据的实验结果表明，与半盲伊卡算法和标准盲伊卡算法相比，该算法能够提高伊卡的性能。
Independent component analysis (ICA) utilizing prior information, also called semiblind ICA, has demonstrated considerable promise in the analysis of functional magnetic resonance imaging (fMRI). So far, temporal information about fMRI has been used in temporal ICA or spatial ICA as additional constraints to improve estimation of task-related components. Considering that prior information about spatial patterns is also available, a semiblind spatial ICA algorithm utilizing the spatial information was proposed within the framework of constrained ICA with fixed-point learning. The proposed approach was first tested with synthetic fMRI-like data, and then was applied to real fMRI data from 11 subjects performing a visuomotor task. Three components of interest including two task-related components and the “default mode” component were automatically extracted, and atlas-defined masks were used as the spatial constraints. The default mode network, a set of regions that appear correlated in particular in the absence of tasks or external stimuli and is of increasing interest in fMRI studies, was found to be greatly improved when incorporating spatial prior information. Results from simulation and real fMRI data demonstrate that the proposed algorithm can improve ICA performance compared to a different semiblind ICA algorithm and a standard blind ICA algorithm.
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DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
5.7
作者：
Genovese, CR;Lazar, NA;Nichols, T
通讯作者：
Nichols, T
DOI：
10.1073/pnas.0308627101
发表时间：
2004-03-30
影响因子：
11.1
作者：
Greicius, MD;Srivastava, G;Menon, V
通讯作者：
Menon, V
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H
影响因子：
10.6
作者：
Freire, L;Roche, A;Mangin, JF
通讯作者：
Mangin, JF