Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet.

Mansu Kim, Jingxaun Bao, Kefei Liu, Bo-Yong Park, Hyunjin Park, Li Shen
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引用次数: 1

Abstract

The connectivity analysis is a powerful technique for investigating a hard-wired brain architecture as well as flexible, functional dynamics tied to human cognition. Recent multi-modal connectivity studies had the challenge of combining functional and structural connectivity information into one integrated network. In this paper, we proposed a simplex regression model with graph-constrained Elastic Net (GraphNet) to estimate functional networks enriched by structural connectivity in a biologically meaningful way with a low model complexity. Our model constructed the functional networks using sparse simplex regression framework and enriched structural connectivity information based on GraphNet constraint. We applied our model on the real neuroimaging datasets to show its ability for predicting a clinical score. Our results demonstrated that integrating multi-modal features could detect more sensitive and subtle brain biomarkers than using a single modality.

使用GraphNet的单纯形回归增强结构连接性的功能性脑网络。
连通性分析是一种强大的技术,用于研究与人类认知相关的硬连线大脑结构以及灵活的功能动力学。最近的多模态连通性研究面临着将功能和结构连通性信息结合到一个综合网络中的挑战。在本文中,我们提出了一个具有图约束弹性网的单纯形回归模型(GraphNet),以低模型复杂度,以生物学意义的方式估计富含结构连通性的函数网络。我们的模型使用稀疏单纯形回归框架构建了函数网络,并基于GraphNet约束丰富了结构连通性信息。我们将我们的模型应用于真实的神经成像数据集,以显示其预测临床评分的能力。我们的研究结果表明,与使用单一模态相比,整合多模态特征可以检测出更敏感、更微妙的大脑生物标志物。
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