Wang Nizhuan, Zeng Weiming, Shi Yingchao, Ren Tianlong, Jing Yanshan, Yin Jun, Yang Jiajun
Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China.
Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China.
J Neurosci Methods. 2015 May 15;246:75-96. doi: 10.1016/j.jneumeth.2015.03.011. Epub 2015 Mar 16.
Researches declared that the super-Gaussian property contributed to the success of some spatial independent component analysis (ICA) algorithms in brain fMRI source separation (e.g., Infomax and FastICA), which implied that sparse approximation transforming the sources (super-Gaussian or Gaussian-like) with stronger super-Gaussian feature would possibly improve the separation performance of these algorithms.
This paper presented a novel wavelet-shrinkage based ICA (WASICA) model, an extension of our previous SACICA, for single-subject analysis. In contrast, two main aspects had been effectively enhanced: (1) sparse approximation coefficients set formation, made up of two sub-procedures: the wavelet-shrinkage of wavelet packet (WP) tree nodes, and the automatic nodes selection and integration based on the relative WP energy; (2) ICA-based decomposition and reconstruction, composed of temporal dynamics extraction using ICA, WP reconstruction based on the sparse approximation coefficients set and least-square-based functional networks reconstruction.
The wavelet-shrinkage and the automatic nodes selection and integration simultaneously transformed both the mixtures and underlying sources into effective sparse approximation coefficients sets, exhibiting stronger super-Gaussian distribution; WP projected-back approximation with nuisance-exclusion contributed to networks reconstruction.
Simulation 1 revealed WASICA successfully recovered super-Gaussian and some Gaussian-like sources. Simulation 2 and hybrid data experiments showed that WASICA with good temporal performance had stronger source recovery ability and signal detection sensitivity spatially than FastICA, Infomax and SACICA did; the higher intra-consistency in task-related experiments denoted WASICA occupied stronger spatial robustness against smooth kernels.
WASICA was a promising brain signal separation model with charming spatial-temporal performance.
研究表明，超高斯特性有助于一些空间独立成分分析（ICA）算法在脑功能磁共振成像（fMRI）源分离中取得成功（例如，Infomax和FastICA），这意味着用具有更强超高斯特征的稀疏近似变换源（超高斯或类高斯）可能会提高这些算法的分离性能。
本文提出了一种基于小波收缩的新型ICA（WASICA）模型，它是我们之前的SACICA的扩展，用于单受试者分析。相比之下，有两个主要方面得到了有效增强：（1）稀疏近似系数集的形成，由两个子过程组成：小波包（WP）树节点的小波收缩，以及基于相对WP能量的自动节点选择和整合；（2）基于ICA的分解和重建，包括使用ICA提取时间动态、基于稀疏近似系数集的WP重建以及基于最小二乘法的功能网络重建。
小波收缩以及自动节点选择和整合同时将混合信号和潜在源都变换为有效的稀疏近似系数集，呈现出更强的超高斯分布；具有干扰排除的WP投影回近似有助于网络重建。
模拟1表明WASICA成功恢复了超高斯和一些类高斯源。模拟2和混合数据实验表明，具有良好时间性能的WASICA在空间上比FastICA、Infomax和SACICA具有更强的源恢复能力和信号检测灵敏度；任务相关实验中更高的内部一致性表明WASICA在针对平滑核时具有更强的空间鲁棒性。
WASICA是一个具有迷人时空性能的有前途的脑信号分离模型。
