A structural enriched functional network: An application to predict brain cognitive performance.
A structural enriched functional network: An application to predict brain cognitive performance.
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DOI：
10.1016/j.media.2021.102026
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发表时间：
2021-07
影响因子：
10.9
通讯作者：
Shen L
中科院分区：
文献类型：
--
作者：
Kim M;Bao J;Liu K;Park BY;Park H;Baik JY;Shen L
脑网络的结构-功能耦合已成为现代神经科学的重要研究课题。结构网络可以为功能网络提供骨干。功能网络与结构信息的整合可以帮助我们更好地理解大脑中的功能通信。提出了一种利用弥散磁共振成像中的结构网络来精确估计脑功能网络的方法。首先，我们采用了一个单纯形回归模型与图约束弹性网络构建的功能网络丰富的结构网络。然后，我们比较了这种方法与几个国家的最先进的竞争功能的网络模型构建的网络特性。此外，我们评估了结构丰富的功能网络模型是否提高了预测认知行为结果的性能。这些实验在人类连接组项目数据库的218名参与者身上进行。结果表明，我们的网络模型提高了网络的一致性和预测性能相比，几个国家的最先进的竞争功能的网络模型。
The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models.
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