A Novel Brain Networks Enhancement Model (BNEM) for BOLD fMRI Data Analysis With Highly Spatial Reproducibility
A Novel Brain Networks Enhancement Model (BNEM) for BOLD fMRI Data Analysis With Highly Spatial Reproducibility
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DOI：
10.1109/jbhi.2015.2439685
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发表时间：
2016-07
影响因子：
7.7
通讯作者：
Ni-zhuan Wang;Weiming Zeng;Dongtailang Chen;Jun Yin;Lei Chen
中科院分区：
文献类型：
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作者：
Ni-zhuan Wang;Weiming Zeng;Dongtailang Chen;Jun Yin;Lei Chen
独立成分分析旨在检测离散大脑皮层区域之间的功能连接，已被广泛用于探索功能磁共振成像数据。尽管独立元件（IC）具有相对高的质量，但是IC中的噪声嵌入对真实有源/无源区域推断和再现性具有很大影响，在后处理阶段，例如，统计参数图（SPM）的提取。本文提出了一种新的脑网络增强模型（BNEM），主要包括两个关键技术：1）对有意义的IC进行三维小波噪声滤波（3DWNF），有效抑制噪声，增强SPM的真实的激活推理; 2）空间再现性增强算法（SREA），提高SPM的再现性。仿真实验表明，3DWNF滤波后的信号比预滤波后的信号具有更高的相关性和更小的归一化均方误差; SREA可以进一步提高大多数后滤波信号的质量，同时保持与3DWNF的一致性。真实的数据实验还表明：1）3DWNF能够正确识别未增强SPM中高比例的误分类体素，从而更准确地保留真阳性体素：2）SREA能够进一步提高3DWNF去噪IC对应的SPM的活动/非活动体素的分类精度; 3）3DWNF和SREA都有助于提高BNEM再现SPM的再现性。因此，BNEM有望在神经科学和临床领域具有广泛的适用性。
Independent component analysis aiming at detecting the functional connectivity among discrete cortical brain regions has been extensively used to explore the functional magnetic resonance imaging data. Although the independent components (ICs) were with relatively high quality, the noise embedding in ICs has a great impact on the true active/inactive region inference and the reproducibility, in postprocessing stage, e.g., the extraction of statistical parametrical maps (SPMs). In this paper, a novel brain network enhancement model (BNEM) is proposed, which mainly consists of two key techniques: 1) 3-D wavelet noise filter (3DWNF) for the meaningful ICs, which greatly suppresses noise and enforces the real activation inference of SPMs; and 2) a spatial reproducibility enhancement algorithm (SREA), aiming to improve the reproducibility of SPMs. The simulated experiment demonstrated that the postfiltering signals by 3DWNF were with higher correlation and less normalized mean square error to the ground truths than the prefiltering ones; SREA could further enhance the quality of most postfiltering ones, preserving the consistency with 3DWNF. The real data experiments also revealed that 1) 3DWNF could lead to more accurate preservation of the true positive voxels by correctly identifying the high proportionally misclassified voxels of the nonenhanced SPMs; 2) SREA could further improve the classification accuracy of the active/inactive voxels of SPMs corresponding to the 3DWNF denoised ICs; and 3) both 3DWNF and SREA contribute to the reproducibility enhancement of the reproduced SPMs by BNEM. Thus, BNEM is expected to have wide applicability in the neuroscience and clinical domain.