Accurately modeling effective connectivity (EC) is critical for understanding how the brain processes and integrates sensory information. Yet, it remains a formidable challenge due to complex neural dynamics and noisy measurements such as those obtained from the electroencephalogram (EEG). Model-driven EC infers local (within a brain region) and global (between brain regions) EC parameters by fitting a generative model of neural activity onto experimental data. This approach offers a promising route for various applications, including investigating neurodevelopmental disorders. However, current approaches fail to scale to whole-brain analyses and are highly noise-sensitive. In this work, we employ three deep-learning architectures--a transformer, a long short-term memory (LSTM) network, and a convolutional neural network and bidirectional LSTM (CNN-BiLSTM) network--for inverse modeling and compare their performance with simulation-based inference in estimating the Jansen-Rit neural mass model (JR-NMM) parameters from simulated EEG data under various noise conditions. We demonstrate a reliable estimation of key local parameters, such as synaptic gains and time constants. However, other parameters like local JR-NMM connectivity cannot be evaluated reliably from evoked-related potentials (ERP). We also conduct a sensitivity analysis to characterize the influence of JR-NMM parameters on ERP and evaluate their learnability. Our results show the feasibility of deep-learning approaches to estimate the subset of learnable JR-NMM parameters.

翻译：准确建模有效连接对于理解大脑如何处理与整合感觉信息至关重要。然而，由于复杂的神经动力学以及从脑电图等测量中获取的噪声干扰，这仍然是一项艰巨的挑战。模型驱动的有效连接通过将神经活动的生成模型拟合到实验数据上，来推断局部（大脑区域内）和全局（大脑区域间）的有效连接参数。这一方法为包括研究神经发育障碍在内的多种应用提供了有前景的途径。然而，现有方法难以扩展到全脑分析，并且对噪声高度敏感。在本工作中，我们采用了三种深度学习架构——Transformer、长短期记忆网络以及卷积神经网络与双向长短期记忆网络的组合——进行逆向建模，并在不同噪声条件下，基于模拟脑电图数据估计Jansen-Rit神经质量模型参数时，将它们的性能与基于模拟的推断方法进行了比较。我们证明了对关键局部参数（如突触增益和时间常数）的可靠估计。然而，其他参数（如局部Jansen-Rit神经质量模型连接性）无法从事件相关电位中可靠评估。我们还进行了敏感性分析，以表征Jansen-Rit神经质量模型参数对事件相关电位的影响并评估其可学习性。我们的结果表明，深度学习方法是估计可学习的Jansen-Rit神经质量模型参数子集的一种可行途径。