This paper introduces a novel approach for modelling time-varying connectivity in neuroimaging data, focusing on the slow fluctuations in synaptic efficacy that mediate neuronal dynamics. Building on the framework of Dynamic Causal Modelling (DCM), we propose a method that incorporates temporal basis functions into neural models, allowing for the explicit representation of slow parameter changes. This approach balances expressivity and computational efficiency by modelling these fluctuations as a Gaussian process, offering a middle ground between existing methods that either strongly constrain or excessively relax parameter fluctuations. We validate the ensuing model through simulations and real data from an auditory roving oddball paradigm, demonstrating its potential to explain key aspects of brain dynamics. This work aims to equip researchers with a robust tool for investigating time-varying connectivity, particularly in the context of synaptic modulation and its role in both healthy and pathological brain function.

翻译：本文提出了一种新颖的方法，用于建模神经影像数据中的时变连接性，重点关注介导神经元动态的突触效能慢波动。基于动态因果建模（DCM）框架，我们提出了一种将时间基函数纳入神经模型的方法，从而能够显式表示参数的慢变化。该方法通过将此类波动建模为高斯过程，在表达能力和计算效率之间取得了平衡，为现有方法（要么对参数波动施加过强约束，要么过度放松约束）提供了一种折中方案。我们通过模拟实验和来自听觉游走奇异范式的真实数据验证了所提出的模型，证明了其在解释大脑动态关键方面的潜力。本工作旨在为研究者提供一个强大的工具，用于探究时变连接性，特别是在突触调制及其在健康与病理大脑功能中作用的背景下。