Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA.
School of Arts and Sciences, University of Pennsylvania, PA, USA.
Med Image Anal. 2021 Jul;71:102026. doi: 10.1016/j.media.2021.102026. Epub 2021 Mar 4.
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.
脑网络的结构-功能耦合已成为现代神经科学的一个重要研究课题。结构网络可以为功能网络提供骨干。将功能网络与结构信息集成可以帮助我们更好地理解大脑中的功能通讯。本文提出了一种从弥散磁共振成像中准确估计结构网络丰富的脑功能网络的方法。首先，我们采用了具有图约束弹性网络的单形回归模型来构建结构网络丰富的功能网络。然后，我们将该方法构建的网络特征与几种最先进的竞争功能网络模型进行了比较。此外，我们还评估了结构丰富的功能网络模型是否可以提高预测认知行为结果的性能。实验是在来自人类连接组计划数据库的 218 名参与者上进行的。结果表明，与几种最先进的竞争功能网络模型相比，我们的网络模型提高了网络的一致性及其预测性能。
