Lee Jae-Min, Hu Jing, Gao Jianbo, Crosson Bruce, Peck Kyung K, Wierenga Christina E, McGregor Keith, Zhao Qun, White Keith D
Brain Rehabilitation Research Center, Malcom Randall VAMC, Gainesville, FL 32608, USA.
Neuroimage. 2008 Mar 1;40(1):197-212. doi: 10.1016/j.neuroimage.2007.11.016. Epub 2007 Nov 22.
Functional magnetic resonance imaging (fMRI) signal changes can be separated from background noise by various processing algorithms, including the well-known deconvolution method. However, discriminating signal changes due to task-related brain activities from those due to task-related head motion or other artifacts correlated in time to the task has been little addressed. We examine whether three exploratory fractal scaling analyses correctly classify these possibilities by capturing temporal self-similarity; namely, fluctuation analysis, wavelet multi-resolution analysis, and detrended fluctuation analysis (DFA). We specifically evaluate whether these fractal analytic methods can be effective and reliable in discriminating activations from artifacts. DFA is indeed robust for such classification. Brain activation maps derived by DFA are similar, but not identical, to maps derived by deconvolution. Deconvolution explicitly utilizes task timing to extract the signals whereas DFA does not, so these methods reveal somewhat different information from the data. DFA is better than deconvolution for distinguishing fMRI activations from task-related artifacts, although a combination of these approaches is superior to either one taken alone. We also present a method for estimating noise levels in fMRI data, validated with numerical simulations suggesting that Birn's model is effective for simulating fMRI signals. Simulations further corroborate that DFA is excellent at discriminating signal changes due to task-related brain activities from those due to task-related artifacts, under a range of conditions.
功能磁共振成像（fMRI）信号变化可以通过各种处理算法与背景噪声分离，包括著名的反卷积方法。然而，区分与任务相关的大脑活动引起的信号变化与由与任务相关的头部运动或其他与任务在时间上相关的伪影引起的信号变化，这一点几乎没有得到探讨。我们研究了三种探索性分形标度分析是否通过捕捉时间自相似性来正确分类这些可能性；即波动分析、小波多分辨率分析和去趋势波动分析（DFA）。我们具体评估这些分形分析方法在区分激活与伪影方面是否有效和可靠。DFA在这种分类中确实很稳健。由DFA得出的脑激活图与由反卷积得出的图相似，但不完全相同。反卷积明确利用任务时间来提取信号，而DFA则不然，因此这些方法从数据中揭示的信息略有不同。在区分fMRI激活与任务相关伪影方面，DFA比反卷积更好，尽管这些方法的组合比单独使用任何一种方法都更优越。我们还提出了一种估计fMRI数据中噪声水平的方法，并通过数值模拟进行了验证，结果表明Birn模型在模拟fMRI信号方面是有效的。模拟进一步证实，在一系列条件下，DFA在区分与任务相关的大脑活动引起的信号变化与由任务相关伪影引起的信号变化方面表现出色。
