Songdechakraiwut Tananun, Shen Li, Chung Moo
University of Wisconsin-Madison, USA.
University of Pennsylvania, USA.
Med Image Comput Comput Assist Interv. 2021 Sep-Oct;12902:166-176. doi: 10.1007/978-3-030-87196-3_16. Epub 2021 Sep 21.
A long-standing challenge in multimodal brain network analyses is to integrate topologically different brain networks obtained from diffusion and functional MRI in a coherent statistical framework. Existing multimodal frameworks will inevitably destroy the topological difference of the networks. In this paper, we propose a novel topological learning framework that integrates networks of different topology through persistent homology. Such challenging task is made possible through the introduction of a new topological loss that bypasses intrinsic computational bottlenecks and thus enables us to perform various topological computations and optimizations with ease. We validate the topological loss in extensive statistical simulations with ground truth to assess its effectiveness of discriminating networks. Among many possible applications, we demonstrate the versatility of topological loss in the twin imaging study where we determine the extend to which brain networks are genetically heritable.
多模态脑网络分析中一个长期存在的挑战是，在一个连贯的统计框架内整合从扩散磁共振成像和功能磁共振成像中获得的拓扑结构不同的脑网络。现有的多模态框架将不可避免地破坏网络的拓扑差异。在本文中，我们提出了一种新颖的拓扑学习框架，该框架通过持久同调整合不同拓扑结构的网络。通过引入一种新的拓扑损失，绕过了内在的计算瓶颈，从而使我们能够轻松地执行各种拓扑计算和优化，这使得如此具有挑战性的任务成为可能。我们在具有真实数据的广泛统计模拟中验证了拓扑损失，以评估其区分网络的有效性。在众多可能的应用中，我们展示了拓扑损失在双胞胎成像研究中的多功能性，在该研究中我们确定了脑网络在多大程度上是可遗传的。
