人类连接体数据的模式可视化。

Yishi Guo, Yang Wang, Shiaofen Fang, Hongyang Chao, Andrew J Saykin, Li Shen
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引用次数: 2

摘要

人类的大脑是一个由无数相连的神经元组成的复杂网络,可以被描述为一个“连接组”。现有的分析人类连接组数据的研究主要集中在用少量易于计算的测量来表征大脑网络,这些测量可能不足以揭示大脑功能与其结构基质之间的复杂关系。为了便于大规模的连接组分析,本文提出了一种强大而灵活的体绘制方案,以有效地可视化和交互式地探索大脑解剖学背景下的数千个网络测量,并帮助发现模式。我们通过将其应用于真实的连接体数据集来证明所提出方案的有效性。
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Pattern Visualization of Human Connectome Data.

The human brain is a complex network with countless connected neurons, and can be described as a "connectome". Existing studies on analyzing human connectome data are primarily focused on characterizing the brain networks with a small number of easily computable measures that may be inadequate for revealing complex relationship between brain function and its structural substrate. To facilitate large-scale connectomic analysis, in this paper, we propose a powerful and flexible volume rendering scheme to effectively visualize and interactively explore thousands of network measures in the context of brain anatomy, and to aid pattern discovery. We demonstrate the effectiveness of the proposed scheme by applying it to a real connectome data set.

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