论文标题
嵌入功能性人脑网络在球体上
Embedding of Functional Human Brain Networks on a Sphere
论文作者
论文摘要
通常使用通过功能性磁共振成像（fMRI）获得的血氧级依赖（BOLD）信号来测量人脑活动。然后，将大脑区域之间的连通性强度测量为Pearson相关矩阵。随着大脑区域数量的增加，基质的维度增加。甚至可以看到和量化此类加权完整网络，这变得非常麻烦。为了解决这个问题，我们建议将脑网络嵌入到一个球体上，这是一个具有恒定正曲率的Riemannian歧管。球形嵌入的MATLAB代码在https://github.com/laplcebeltrami/sphericalmds中给出。
Human brain activity is often measured using the blood-oxygen-level dependent (BOLD) signals obtained through functional magnetic resonance imaging (fMRI). The strength of connectivity between brain regions is then measured as a Pearson correlation matrix. As the number of brain regions increases, the dimension of matrix increases. It becomes extremely cumbersome to even visualize and quantify such weighted complete networks. To remedy the problem, we propose to embed brain networks onto a sphere, which is a Riemannian manifold with constant positive curvature. The Matlab code for the spherical embedding is given in https://github.com/laplcebeltrami/sphericalMDS.
