Using diffusion anisotropy to characterize neuronal morphology in gray matter: the orientation distribution of axons and dendrites in the NeuroMorpho.org database.
Using diffusion anisotropy to characterize neuronal morphology in gray matter: the orientation distribution of axons and dendrites in the NeuroMorpho.org database.
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DOI：
10.3389/fnint.2013.00031
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发表时间：
2013
影响因子：
3.5
通讯作者：
Kroenke CD
中科院分区：
文献类型：
--
作者：
Hansen MB;Jespersen SN;Leigland LA;Kroenke CD
准确的数学建模对于解释脑灰质（GM）细胞结构方面的扩散磁共振（MR）成像数据的能力是不可或缺的。在之前的工作中，我们推导了表达式，以便从扩散MR数据中确定轴突和树突过程的方向分布。在这里，我们利用NeuroMorpho数据库（www.neuromorpho.org）中提供的神经元重建来评估我们提出的模型的有效性，通过将神经元的形态特性与基于重建神经元模型的扩散MR模拟的预测进行比较。最初，直接确定神经突取向分布的方法被证明不依赖于用于量化圆柱元素的线长。神经元形态的进一步变异与神经元类型、物种和起源实验室有关。随后，基于人类新皮层神经元重建模拟扩散MR信号。这揭示了扩散MR数据预测神经元取向分布具有人为低各向异性的偏见。这种偏倚是由于扩散的高斯近似的缺点（已经处于相对较低的扩散权重）引起的，在限制性障碍的存在下，涉及较高累积膨胀矩的数据分析方法被证明能够降低观察到的偏倚的大小。
Accurate mathematical modeling is integral to the ability to interpret diffusion magnetic resonance (MR) imaging data in terms of cellular structure in brain gray matter (GM). In previous work, we derived expressions to facilitate the determination of the orientation distribution of axonal and dendritic processes from diffusion MR data. Here we utilize neuron reconstructions available in the NeuroMorpho database (www.neuromorpho.org) to assess the validity of the model we proposed by comparing morphological properties of the neurons to predictions based on diffusion MR simulations using the reconstructed neuron models. Initially, the method for directly determining neurite orientation distributions is shown to not depend on the line length used to quantify cylindrical elements. Further variability in neuron morphology is characterized relative to neuron type, species, and laboratory of origin. Subsequently, diffusion MR signals are simulated based on human neocortical neuron reconstructions. This reveals a bias in which diffusion MR data predict neuron orientation distributions to have artificially low anisotropy. This bias is shown to arise from shortcomings (already at relatively low diffusion weighting) in the Gaussian approximation of diffusion, in the presence of restrictive barriers, and data analysis methods involving higher moments of the cumulant expansion are shown to be capable of reducing the magnitude of the observed bias.