论文标题
熵超连接细胞计算和分析
Entropic Hyper-Connectomes Computation and Analysis
论文作者
论文摘要
大脑功能和连通性是与许多疾病相关的医学上的一个紧迫的谜团。已经研究了具有图理论方法的图形，包括拓扑方法。从几何学和拓扑显着不同的超图模型和方法开始。我们定义了一个称为“超连接体”的超图，具有联合信息熵和总相关性。我们给出了从有限样品计算的伪代码。我们给出了这种概括的拓扑结构和几何学对随机变量的理论重要性，然后证明对预测和分类是必需的。我们通过模拟研究和计算确认。我们证明了具有有限样品的连续随机变量的近似值。我们比较了使用线性支持向量机基于fMRI数据集预测受试者中的精神分裂症的连接组与超连接组。超连接组的准确性（最高56 \％）和F1得分（最高0.52）的性能要比连接组更好。我们以95 \％拒绝零假设，p值= 0.00074。
Brain function and connectivity is a pressing mystery in medicine related to many diseases. Neural connectomes have been studied as graphs with graph theory methods including topological methods. Work has started on hypergraph models and methods where the geometry and topology is significantly different. We define a hypergraph called the hyper-connectome with joint information entropy and total correlation. We give the pseudocode for computation from finite samples. We give the theoretic importance of this generalization's topology and geometry with respect to random variables and then prove the hypergraph can be necessary for prediction and classification. We confirm with a simulation study and computation. We prove the approximation for continuous random variables with finite samples. We compare connectome versus hyper-connectome for predicting schizophrenia in subjects based on a fMRI dataset using a linear support vector machine. The hyper-connectome achieves better performance in accuracy (up to 56\%) and F1 score (up to 0.52) than the connectome. We reject null hypothesis at 95\% with p-value = 0.00074.
