Lin Shang-Hua N, Lin Geng-Hong, Tsai Pei-Jung, Hsu Ai-Ling, Lo Men-Tzung, Yang Albert C, Lin Ching-Po, Wu Changwei W
Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.
Graduate Institute of Biomedical Engineering, National Central University, Taoyuan, Taiwan.
J Neurosci Methods. 2016 Jan 30;258:56-66. doi: 10.1016/j.jneumeth.2015.10.009. Epub 2015 Oct 30.
Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience.
To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise.
We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD.
COMPARISON WITH EXISTING METHOD(S): Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution.
We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.
功能磁共振成像（fMRI）被广泛用于研究神经学和心理学问题中的动态脑功能；然而，高噪声水平限制了其在神经科学领域进行深入和复杂研究的适用性。
为了解决灵敏度低和动态信号这两个问题，我们使用了总体经验模态分解（EEMD），这是一种用于非平稳和非线性特征的自适应数据驱动分析方法，以从原始fMRI信号中滤除与任务无关的噪声。通过模拟和具有代表性的fMRI数据，我们优化了解析参数，并识别出无意义的本征模态函数（IMF）以去除噪声。
我们揭示了EEMD在fMRI分析中的以下优势：（1）EEMD对任务参与实现了高检测性；（2）通过基于EEMD去除与任务无关的伪影，功能灵敏度显著提高。
与其他噪声去除方法（如带通滤波和独立成分分析）相比，基于EEMD的伪影去除方法表现出更好的空间特异性以及所得t分数分布的更高高斯性。
我们发现EEMD方法能有效提高诱发fMRI的功能灵敏度。同样的策略一般也适用于静息态fMRI信号。
