论文标题
静止状态fMRI数据的时间动态模型：一种神经普通微分方程方法
Temporal Dynamic Model for Resting State fMRI Data: A Neural Ordinary Differential Equation approach
论文作者
论文摘要
本文的目的是为静止状态功能磁共振成像（fMRI）轨迹提供时间动力学模型，以根据给定序列预测未来的脑图像。为此，我们提出了利用表示学习和神经普通微分方程（神经ode）的模型，将fMRI图像数据压缩到潜在表示中，并学会预测差分方程后的轨迹。通过高斯混合模型分析潜在空间。学习的FMRI轨迹嵌入可用于解释轨迹的方差，并预测每个受试者的人类特征。对于整个预测的轨迹，该方法达到了平均0.5空间相关性，并提供了训练有素的ODE参数以进行进一步分析。
The objective of this paper is to provide a temporal dynamic model for resting state functional Magnetic Resonance Imaging (fMRI) trajectory to predict future brain images based on the given sequence. To this end, we came up with the model that takes advantage of representation learning and Neural Ordinary Differential Equation (Neural ODE) to compress the fMRI image data into latent representation and learn to predict the trajectory following differential equation. Latent space was analyzed by Gaussian Mixture Model. The learned fMRI trajectory embedding can be used to explain the variance of the trajectory and predict human traits for each subject. This method achieves average 0.5 spatial correlation for the whole predicted trajectory, and provide trained ODE parameter for further analysis.
