NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity.
NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity.
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DOI：
10.1371/journal.pone.0153404
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Clayden JD
中科院分区：
文献类型：
--
作者：
Deligianni F;Carmichael DW;Zhang GH;Clark CA;Clayden JD
在弥散加权磁共振成像（DWI）中，信号受神经元细胞的生物物理特性及其相对位置以及细胞外组织区室的影响。通常，微观结构指标，如分数各向异性（FA）和平均扩散率（MD），是基于张量模型的，不能区分这些参数的影响。最近，神经突定向弥散和密度成像（NODDI）利用多壳采集协议来模拟扩散信号作为三个组织区室的贡献。细胞内体积分数（ICVF）和取向弥散指数（ODI）分别与神经元密度和取向弥散有直接关系。检查神经纤维中这些微观结构指标的神经生理作用的一种方法是研究它们与大脑功能的关系。在这里，我们利用基于稀疏典型相关分析（sCCA）和随机Lasso的统计框架来识别与同时使用EEG-fMRI测量的静息状态功能连接高度相关的结构连接。我们的研究结果揭示了每个微观结构指标的不同结构指纹，也反映了它们之间的相互关系。
In Diffusion Weighted MR Imaging (DWI), the signal is affected by the biophysical properties of neuronal cells and their relative placement, as well as extra-cellular tissue compartments. Typically, microstructural indices, such as fractional anisotropy (FA) and mean diffusivity (MD), are based on a tensor model that cannot disentangle the influence of these parameters. Recently, Neurite Orientation Dispersion and Density Imaging (NODDI) has exploited multi-shell acquisition protocols to model the diffusion signal as the contribution of three tissue compartments. NODDI microstructural indices, such as intra-cellular volume fraction (ICVF) and orientation dispersion index (ODI) are directly related to neuronal density and orientation dispersion, respectively. One way of examining the neurophysiological role of these microstructural indices across neuronal fibres is to look into how they relate to brain function. Here we exploit a statistical framework based on sparse Canonical Correlation Analysis (sCCA) and randomised Lasso to identify structural connections that are highly correlated with resting-state functional connectivity measured with simultaneous EEG-fMRI. Our results reveal distinct structural fingerprints for each microstructural index that also reflect their inter-relationships.