Assessing a signal model and identifying brain activity from fMRI data by a detrending-based fractal analysis
Assessing a signal model and identifying brain activity from fMRI data by a detrending-based fractal analysis
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DOI：
10.1007/s00429-007-0166-9
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发表时间：
2008-02-01
影响因子：
3.1
通讯作者：
Crosson, Bruce
中科院分区：
文献类型：
--
作者：
Hu, Jing;Lee, Jae-Min;Crosson, Bruce
功能性磁共振成像（fMRI）数据分析的主要挑战之一是开发简单可靠的方法将大脑区域与功能相关联。在本文中，我们采用了一种基于去趋势的分形方法，称为去趋势波动分析（DFA），从功能磁共振成像数据识别大脑活动。我们完成了三个任务：（a）从实验fMRI数据中估计噪声水平：（B）评估Birn等人最近提出的信号模型;以及（c）评估DFA用于区分脑激活与伪影的有效性。通过计算受试者工作特征（ROC）曲线，我们发现实验数据的ROC曲线与具有相似信噪比（SNR）的模拟数据的ROC曲线相似。这表明，所提出的算法估计噪声水平是非常有效的，Birn的模型符合我们的实验数据非常好。由DFA导出的实验数据的脑激活图类似于使用广泛使用的软件AFNI通过去卷积导出的图。考虑到去卷积明确使用有关实验范式的信息来提取激活模式，而DFA不使用，是否可以有效地整合这两种方法以提高检测与功能活动相关的大脑区域的准确性还有待观察。
One of the major challenges of functional magnetic resonance imaging (fMRI) data analysis is to develop simple and reliable methods to correlate brain regions with functionality. In this paper, we employ a detrending-based fractal method, called detrended fluctuation analysis (DFA), to identify brain activity from fMRI data. We perform three tasks: (a) Estimating noise level from experimental fMRI data; (b) Assessing a signal model recently introduced by Birn et al.; and (c) Evaluating the effectiveness of DFA for discriminating brain activations from artifacts. By computing the receiver operating characteristic (ROC) curves, we find that the ROC curve for experimental data is similar to the curve for simulated data with similar signal-to-noise ratio (SNR). This suggests that the proposed algorithm for estimating noise level is very effective and that Birn's model fits our experimental data very well. The brain activation maps for experimental data derived by DFA are similar to maps derived by deconvolution using a widely used software, AFNI. Considering that deconvolution explicitly uses the information about the experimental paradigm to extract the activation patterns whereas DFA does not, it remains to be seen whether one can effectively integrate the two methods to improve accuracy for detecting brain areas related to functional activity.