A unified framework for multimodal structure-function mapping based on eigenmodes
A unified framework for multimodal structure-function mapping based on eigenmodes
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DOI：
10.1016/j.media.2020.101799
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发表时间：
2020-12-01
影响因子：
10.9
通讯作者：
Deriche, Rachid
中科院分区：
文献类型：
--
作者：
Deslauriers-Gauthier, Samuel;Zucchelli, Mauro;Deriche, Rachid
描述大脑结构和大脑功能之间的联系对于理解行为如何从底层解剖学中出现至关重要。许多研究表明，白色物质的网络结构塑造了功能连接。因此，在给定结构网络的情况下，至少可以部分地预测功能连接性。在文献中已经提出了许多结构-功能映射，包括结构和功能连接矩阵之间的几个直接映射。然而，目前的文献是支离破碎的，并没有提供一个统一的治疗目前的方法的基础上特征分解。特别是，现有的方法从来没有被相互比较，它们的关系明确地推导出大脑结构功能映射的上下文中。在这项工作中，我们提出了一个统一的计算框架，概括了最近提出的结构功能映射的基础上本征模。使用这个统一的框架，我们突出了现有模型之间的联系，并展示了如何通过我们框架的参数的具体选择来获得它们。通过将我们的框架应用于人类连接组项目的50个受试者，我们重现了6个最近发表的结果，设计了两个新的模型，并提供了所有映射之间的直接比较。最后，我们表明，玻璃天花板上的性能映射的基础上本征模似乎达到并得出结论，可能的方法来打破这一性能限制。(C)2020爱思唯尔B. V.保留所有权利。
Characterizing the connection between brain structure and brain function is essential for understanding how behaviour emerges from the underlying anatomy. A number of studies have shown that the network structure of the white matter shapes functional connectivity. Therefore, it should be possible to predict, at least partially, functional connectivity given the structural network. Many structure-function mappings have been proposed in the literature, including several direct mappings between the structural and functional connectivity matrices. However, the current literature is fragmented and does not provide a uniform treatment of current methods based on eigendecompositions. In particular, existing methods have never been compared to each other and their relationship explicitly derived in the context of brain structure-function mapping. In this work, we propose a unified computational framework that generalizes recently proposed structure-function mappings based on eigenmodes. Using this unified framework, we highlight the link between existing models and show how they can be obtained by specific choices of the parameters of our framework. By applying our framework to 50 subjects of the Human Connectome Project, we reproduce 6 recently published results, devise two new models and provide a direct comparison between all mappings. Finally, we show that a glass ceiling on the performance of mappings based on eigenmodes seems to be reached and conclude with possible approaches to break this performance limit. (C) 2020 Elsevier B.V. All rights reserved.