IEEE Trans Med Imaging. 2021 Dec;40(12):3326-3336. doi: 10.1109/TMI.2021.3083984. Epub 2021 Nov 30.
Estimating effective connectivity from functional magnetic resonance imaging (fMRI) time series data has become a very hot topic in neuroinformatics and brain informatics. However, it is hard for the current methods to accurately estimate the effective connectivity due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for estimating effective connectivity based on recurrent generative adversarial networks, called EC-RGAN. The proposed framework employs the generator that consists of a set of effective connectivity generators based on recurrent neural networks to generate the fMRI time series of each brain region, and uses the discriminator to distinguish between the joint distributions of the real and generated fMRI time series. When the model is well-trained and generated fMRI data is similar to real fMRI data, EC-RGAN outputs the effective connectivity by means of the causal parameters of the effective connectivity generators. Experimental results on both simulated and real-world fMRI time series data demonstrate the efficacy of our proposed framework.
从功能磁共振成像 (fMRI) 时间序列数据估计有效连通性已成为神经信息学和脑信息学中的一个非常热门的话题。然而，由于 fMRI 数据的高噪声和小样本量，当前的方法很难准确估计有效连通性。在本文中，我们提出了一种基于递归生成对抗网络的有效连通性估计的新框架，称为 EC-RGAN。所提出的框架采用由一组基于递归神经网络的有效连通性生成器组成的生成器来生成每个脑区的 fMRI 时间序列，并使用鉴别器来区分真实和生成的 fMRI 时间序列的联合分布。当模型训练良好并且生成的 fMRI 数据与真实的 fMRI 数据相似时，EC-RGAN 通过有效连通性生成器的因果参数输出有效连通性。在模拟和真实 fMRI 时间序列数据上的实验结果表明了我们提出的框架的有效性。
