脑连接模式交互分析的内在几何可视化

G. Conte, Allen Q. Ye, K. Almryde, O. Ajilore, A. Leow, A. Forbes
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引用次数: 5

摘要

了解大脑区域如何相互联系是神经影像学领域的一个重要课题。非侵入性技术的进步使得能够比以往更快地收集更大、更详细的图像。这些数据有助于创建通常被称为连接组的东西,即神经连接的综合地图。连接组数据的可用性允许提出更有趣的问题,并进行更复杂的分析。在本文中,我们提出了一种新颖的基于web的3D可视化分析工具,允许用户交互式地探索连接体的内在几何形状。也就是说,通过多维尺度(MDS)、Isomap或t分布随机邻居嵌入(t-SNE)技术等降维步骤进行转换的大脑数据。我们通过一系列现实世界的案例研究来评估我们的工具,证明它在帮助领域专家进行一系列神经成像方面的有效性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intrinsic Geometry Visualization for the Interactive Analysis of Brain Connectivity Patterns
Understanding how brain regions are interconnected is an important topic within the domain of neuroimaging. Advances in non-invasive technologies enable larger and more detailed images to be collected more quickly than ever before. These data contribute to create what is usually referred to as a connectome, that is, a comprehensive map of neural connections. The availability of connectome data allows for more interesting questions to be asked and more complex analyses to be conducted. In this paper we present a novel web-based 3D visual analytics tool that allows user to interactively explore the intrinsic geometry of the connectome. That is, brain data that has been transformed through a dimensionality reduction step, such as multidimensional scaling (MDS), Isomap, or t-distributed stochastic neighbor embedding (t-SNE) techniques. We evaluate our tool through a series of real-world case studies, demonstrating its effectiveness in aiding domain experts for a range of neuroimaging
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