Dai Peishan, He Zhuang, Ou Yilin, Luo Jialin, Liao Shenghui, Yi Xiaoping
School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, China.
Phys Eng Sci Med. 2025 May 22. doi: 10.1007/s13246-025-01543-z.
Effective Connectivity (EC) reflects the causal influence between brain regions. Identifying Effective Connectivity Networks (ECN) in the brain can enhance our understanding of brain functions and reveal the impact of mental illnesses on these functions. However, existing EC estimation methods face challenges in extracting deep features from functional magnetic resonance imaging (fMRI) data. In this study, we propose a novel Time Series to Effective Connectivity (TS2EC) prediction model based on recurrent neural networks, which directly extracts deep features from fMRI time series without relying on a fixed model order. Specifically, we introduce a method for generating EC labels from electrocortical stimulation fMRI (es-fMRI) data, representing the first attempt to use es-fMRI for EC estimation. We evaluated TS2EC on three datasets: an es-fMRI dataset with 23 subjects (augmented to 7,082 samples), a multivariate autoregressive simulated dataset, and a Smith simulated dataset. On the es-fMRI dataset, TS2EC achieved a mean squared error of 0.0057, significantly outperforming existing methods. Experiments on the simulated datasets demonstrated that TS2EC attained superior performance in accuracy, recall, structural Hamming distance, and F1-score. Experimental results demonstrate that the EC prediction performance of TS2EC is significantly higher than current EC analysis methods. TS2EC holds promise as a novel tool for the analysis of ECN in the brain.
有效连接性（EC）反映了脑区之间的因果影响。识别大脑中的有效连接性网络（ECN）可以增强我们对脑功能的理解，并揭示精神疾病对这些功能的影响。然而，现有的EC估计方法在从功能磁共振成像（fMRI）数据中提取深度特征方面面临挑战。在本研究中，我们提出了一种基于循环神经网络的新型时间序列到有效连接性（TS2EC）预测模型，该模型直接从fMRI时间序列中提取深度特征，而无需依赖固定的模型阶数。具体而言，我们介绍了一种从皮层电刺激fMRI（es-fMRI）数据生成EC标签的方法，这是首次尝试将es-fMRI用于EC估计。我们在三个数据集上评估了TS2EC：一个包含23名受试者的es-fMRI数据集（扩充至7082个样本）、一个多元自回归模拟数据集和一个史密斯模拟数据集。在es-fMRI数据集上，TS2EC的均方误差为0.0057，显著优于现有方法。在模拟数据集上的实验表明，TS2EC在准确率、召回率、结构汉明距离和F1分数方面表现优异。实验结果表明，TS2EC的EC预测性能显著高于当前的EC分析方法。TS2EC有望成为一种用于分析大脑中ECN的新型工具。
