论文标题
使用流形学习的嵌入式fMRI静止状态功能连接网络的构建
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
论文作者
论文摘要
我们从基于线性和非线性流形学习算法的精神分裂症和健康对照的患者中获取了基于基准静止状态功能磁共振成像（RSFMRI）数据的基准静止状态功能磁共振成像（RSFMRI）数据的嵌入式功能连通性网络（FCN）数据，即多维量表（MDS），等值范围特征MAPP（ISOMAP）。此外，基于嵌入式FCN的关键全局图理论属性，我们使用机器学习技术比较了它们的分类潜力。我们还评估了两个指标的性能，这些指标被广泛用于fMRI的FCN，即欧几里得距离和滞后的互相关度量。我们表明，使用扩散图构建的FCN和滞后的互相关度量优于其他组合。
We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations.
