SACICA: A sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis
SACICA: A sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis
复制标题
SACICA：基于稀疏近似系数的 ICA 模型，用于功能磁共振成像数据分析
DOI：
10.1016/j.jneumeth.2013.03.014
复制
发表时间：
2013-05
影响因子：
3
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Wang NZ;Zeng WM;Chen L
独立成分分析（伊卡）假设功能网络的来源在统计学上是独立的，已被广泛应用于功能磁共振成像（fMRI）数据的功能连接性评价。近年来，许多研究者证明稀疏性是功能磁共振信号分离的有效假设。在这项研究中，我们提出了一个稀疏近似系数为基础的伊卡（SACICA）模型来分析功能磁共振成像数据，这是一个很有前途的稀疏特征和伊卡技术的组合模型。SACICA方法包括三个步骤。小波包分解过程，它分解的fMRI数据到小波树节点具有不同程度的稀疏性，是第一。然后，稀疏逼近系数集的形成过程中，提出了一个有效的Lp范数来衡量不同的小波树节点的稀疏程度，是第二。最后利用fMRI数据的稀疏近似系数集进行伊卡分解和重构。混合数据实验结果表明，SACICA方法对非平滑fMRI数据具有更强的空间源重构能力，对平滑fMRI数据具有更好的功能信号检测灵敏度。此外，任务相关实验还表明，SAICA不仅能有效地发现功能网络，而且对视觉相关功能信号的检测灵敏度更高。此外，SACICA结合Wang等人（2012）提出的Fast-FENICA被证明可以有效地对静息状态数据集进行分组分析。
Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data to evaluate the functional connectivity, which assumes that the sources of functional networks are statistically independent. Recently, many researchers have demonstrated that sparsity is an effective assumption for fMRI signal separation. In this research, we present a sparse approximation coefficient-based ICA (SACICA) model to analyse fMRI data, which is a promising combination model of sparse features and an ICA technique. The SACICA method consists of three procedures. The wavelet packet decomposition procedure, which decomposes the fMRI data into wavelet tree nodes with different degrees of sparsity, is first. Then, the sparse approximation coefficients set formation procedure, in which an effective Lpnorm is proposed to measure the sparse degree of the distinct wavelet tree nodes, is second. The ICA decomposition and reconstruction procedure, which utilises the sparse approximation coefficients set of the fMRI data, is last. The hybrid data experimental results demonstrated that the SACICA method exhibited the stronger spatial source reconstruction ability with respect to the unsmoothed fMRI data and better detection sensitivity of the functional signal on the smoothed fMRI data than the FastICA method. Furthermore, task-related experiments also revealed that SACICA was not only effective in discovering the functional networks but also exhibited a better detection sensitivity of the visual-related functional signal. In addition, the SACICA combined with Fast-FENICA proposed by Wang et al. (2012) was demonstrated to conduct the group analysis effectively on the resting-state data set.
登录
查看更多内容
DOI：
--
发表时间：
2010-03
期刊：
--
影响因子：
--
作者：
P. Comon;C. Jutten
通讯作者：
P. Comon;C. Jutten
影响因子：
1.9
作者：
J. Sato;André Fujita;E. A. Júnior;J. Miranda;P. Morettin;M. Brammer
通讯作者：
J. Sato;André Fujita;E. A. Júnior;J. Miranda;P. Morettin;M. Brammer
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
Tao Blockindeng
通讯作者：
Tao Blockindeng
影响因子：
2.9
作者：
M. Zibulevsky;Barak A. Pearlmutter
通讯作者：
M. Zibulevsky;Barak A. Pearlmutter
影响因子：
2.5
作者：
G. Valente;Federico De Martino;Giuseppe Filosa;M. Balsi;E. Formisano
通讯作者：
G. Valente;Federico De Martino;Giuseppe Filosa;M. Balsi;E. Formisano