Implications of cortical balanced excitation and inhibition, functional heterogeneity, and sparseness of neuronal activity in fMRI.
Implications of cortical balanced excitation and inhibition, functional heterogeneity, and sparseness of neuronal activity in fMRI.
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DOI：
10.1016/j.neubiorev.2015.08.018
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发表时间：
2015-10
影响因子：
8.2
通讯作者：
Xu J
中科院分区：
文献类型：
--
作者：
Xu J
血液氧合水平依赖(BOLD)功能磁共振成像(FMRI)研究经常报告不一致的结果，可能是由于大脑的特性，如平衡的兴奋和抑制以及功能异质性。这些特性表明，同一体素中的不同神经元可能表现出不同的活动，包括同时激活和去激活，BOLD信号和神经活动(即神经血管耦合)之间的关系是复杂的，并且BOLD信号的增加可能反映了去激活的减少或激活的增加，或两者兼而有之。传统的基于一般线性模型的分析(GLM-BA)是一种单变量方法，不能从相同的体素中分离出BOLD信号混合的不同分量，并且可能导致fMRI结果不一致。空间独立分量分析(SICA)是一种多变量分析方法，可以将来自每个体素的粗信号混合分离成不同的源信号，并分别测量每个源信号，从而可以协调GLM-BA先前产生的相互矛盾的结果。我们建议经常使用能够分离混合信号的方法，如SICA，以便更准确、更完整地提取嵌入在fMRI数据集中的信息。
Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies often report inconsistent findings, probably due to brain properties such as balanced excitation and inhibition and functional heterogeneity. These properties indicate that different neurons in the same voxels may show variable activities including concurrent activation and deactivation, that the relationships between BOLD signal and neural activity (i.e., neurovascular coupling) are complex, and that increased BOLD signal may reflect reduced deactivation, increased activation, or both. The traditional general-linear-model-based-analysis (GLM-BA) is a univariate approach, cannot separate different components of BOLD signal mixtures from the same voxels, and may contribute to inconsistent findings of fMRI. Spatial independent component analysis (sICA) is a multivariate approach, can separate the BOLD signal mixture from each voxel into different source signals and measure each separately, and thus may reconcile previous conflicting findings generated by GLM-BA. We propose that methods capable of separating mixed signals such as sICA should be regularly used for more accurately and completely extracting information embedded in fMRI datasets.