像素标注分析

通常是目标导向型的分析方式

一种常见连接组学数据分析方式是目的导向的，研究人员基于连接组学像素化标注结果的进行形态学分析，不需要对所有数据完成标注，只需要对感兴趣的区域和内容进行重点标注即可。标注所得的体式重构数据一方面提供了前所未有的高分辨细节，另一方面还能够从多个角度展示对应的神经结构，因此使得研究人员能够真正“身临其境”地深入神经系统内部探索其结构奥秘。这种分析方式可以常用于亚细胞级的细胞器研究，例如小鼠视感受器内段线粒体的对称性分布1以及斑马鱼视觉感受器线粒体的昼夜动态变化2；也可以用于发现新的细胞间神经环路，例如灵长类视网膜用于非成像视觉的色觉神经环路3以及灵长类视网膜上用于边缘检测的短视锥神经环路4;甚至用于系统级神经科学规律的验证，例如在视网膜5、海马体6和新皮层7的数据均对突触形成和物理距离之间的相关性提出了质疑。这种分析方式对于数据量要求不高，但是对数据标注的可靠性以及研究人员的领域知识有很强的依赖，整体分析难度不高，因此相比于分析工具来说，有效地对形态结果进行可视化展示更加重要，通常数据标注软件自带的可视化工具就能满足需求，除此以外Blender8也是常用于此类研究的可视化渲染工具。

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  7. Narayanan Kasthuri, Kenneth Jeffrey Hayworth, Daniel Raimund Berger, Richard Lee Schalek, José Angel Conchello, Seymour Knowles-Barley, Dongil Lee, Amelio Vázquez-Reina, Verena Kaynig, Thouis Raymond Jones, and others. Saturated reconstruction of a volume of neocortex. Cell, 162(3):648–661, 2015. ↩

  8. Blender Online Community. Blender—a 3d modelling and rendering package. Blender Foundation, 2018. ↩