论文标题
监督人口驱动的大脑网络估算的多人多人网络交叉扩散
Supervised Multi-topology Network Cross-diffusion for Population-driven Brain Network Atlas Estimation
论文作者
论文摘要
估计代表性和歧视性脑网络地图集（BNA）是映射健康和疾病中脑网络人群的新生研究领域。尽管有限，但现有的BNA估计方法有几个局限性。首先，它们主要依赖于相似性网络扩散和融合技术，该技术仅将节点度视为跨网络扩散过程中的拓扑度量，从而俯瞰了大脑网络的丰富拓扑度量（例如中心性）。其次，扩散和融合技术均以完全无监督的方式实施，这可能会降低估计的BNA的判别能力。为了填补这些空白，我们提出了一个有监督的多人网络交叉扩散（SM-NETFUSION）框架，用于估算BNA满足的bna：（i）富有代表性（捕获跨主题的共享特征），（ii）中心性良好（在所有受试者中取得了良好的依赖），并且（III）较高的歧视可以识别出较大的区分（均可识别有足够的依从性（有效）。对于特定类别，鉴于训练数据的群集标签，我们学习了以监督方式衍生出的拓扑扩散内核的加权组合。具体而言，我们通过使用学习的扩散核来归一化训练大脑网络来学习交叉扩散过程。与其变体和最新方法相比，我们的SM-NetFusion产生了最中心和代表性的模板，并进一步将自闭症受试者的分类提高了5-15％。 SM-NetFusion介绍了基于图形拓扑度量的监督网络交叉扩散的第一项工作，可以进一步利用该方法来设计一种有效的图形特征选择方法，用于培训网络神经科学中的预测性学习者。
Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field in mapping a population of brain networks in health and disease. Although limited, existing BNA estimation methods have several limitations. First, they primarily rely on a similarity network diffusion and fusion technique, which only considers node degree as a topological measure in the cross-network diffusion process, thereby overlooking rich topological measures of the brain network (e.g., centrality). Second, both diffusion and fusion techniques are implemented in fully unsupervised manner, which might decrease the discriminative power of the estimated BNAs. To fill these gaps, we propose a supervised multi-topology network cross-diffusion (SM-netFusion) framework for estimating a BNA satisfying : (i) well-representativeness (captures shared traits across subjects), (ii) well-centeredness (optimally close to all subjects), and (iii) high discriminativeness (can easily and efficiently identify discriminative brain connections that distinguish between two populations). For a specific class, given the cluster labels of the training data, we learn a weighted combination of the topological diffusion kernels derived from degree, closeness and eigenvector centrality measures in a supervised manner. Specifically, we learn the cross-diffusion process by normalizing the training brain networks using the learned diffusion kernels. Our SM-netFusion produces the most centered and representative template in comparison with its variants and state-of-the-art methods and further boosted the classification of autistic subjects by 5-15%. SM-netFusion presents the first work for supervised network cross-diffusion based on graph topological measures, which can be further leveraged to design an efficient graph feature selection method for training predictive learners in network neuroscience.
