解码灰质：大规模脑细胞形态测量分析为扩散磁共振信号的微观结构建模提供信息
Decoding Gray Matter: large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals
AI总结：
该研究针对灰质细胞形态学缺乏系统性关键特征报告的问题，通过分析跨3物种9细胞类型的11500多个三维细胞重建，建立形态学参考值并明确其与dMRI的相关性，为灰质研究提供基准并助力dMRI数据解释与脑组织模型完善。
摘要：
灰质结构长期以来一直是神经科学的关键研究焦点，因为细胞形态因类型而异，并可能受到神经系统疾病的影响。了解这些差异对于研究脑功能和疾病至关重要。扩散加权磁共振成像（dMRI）是一种强大的非侵入性工具，用于在体检查细胞微观结构。然而，为了让dMRI准确反映细胞特征，确定形态学的哪些方面影响其测量结果至关重要。dMRI数据的正确解释取决于了解其对不同细胞特征的敏感性。尽管对细胞形态学的兴趣日益浓厚，但尚未有关于定义不同神经细胞类型的关键特征的系统性报告。为解决这一问题，我们分析了跨3个物种、9种细胞类型的11500多个三维细胞重建，建立了关键形态学特征的参考值。这些特征分为三类：定义细胞骨架框架的结构特征、描述空间组织的形状特征，以及分解细胞结构以区分细胞类型的拓扑特征。除报告这些参考值外，我们还研究了它们与dMRI的相关性，确定了dMRI可检测的神经特征以及可能可区分的细胞类型。为补充统计分析，我们还提供了代表每种细胞类型和物种的高分辨率三维表面网格。这项工作为灰质研究提供了基本基准，为将神经影像测量与神经生物学联系起来提供了新指南。这些参考值将成为神经科学家和神经影像研究人员的宝贵资源，有助于影像数据的解释和脑组织模型的完善。
英文摘要：
The structure of grey matter has long been a key focus in neuroscience, as cell morphology varies by type and can be affected by neurological conditions. Understanding these variations is essential for studying brain function and disease. Diffusion-weighted MRI (dMRI) is a powerful non-invasive tool for examining cellular microstructure in vivo. However, for dMRI to accurately reflect cellular features, it is crucial to determine which aspects of morphology influence its measurements. Proper interpretation of dMRI data depends on understanding its sensitivity to different cellular characteristics. Despite growing interest in cellular morphology, there has been no systematic report on the key features defining different neural cell types. To address this, we analyzed over 11,500 three-dimensional cellular reconstructions across three species and nine cell types, establishing reference values for critical morphological traits. These traits fall into three categories: structural features that define the cell's skeletal framework, shape features that describe spatial organization, and topological features that break down cellular structure to distinguish cell types. Beyond reporting these reference values, we examine their relevance for dMRI, identifying which neural features dMRI can detect and which cell types may be distinguishable. To complement the statistical analysis, we also provide high resolution 3D surface meshes representative of each cell type and species. This work provides essential benchmarks for grey matter research, offering new guidelines on linking neuroimaging measurements to neurobiology. These reference values will be a valuable resource for neuroscientists and neuroimaging researchers, aiding in the interpretation of imaging data and the refinement of brain tissue models.
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