Effective connectivity analysis in functional magnetic resonance imaging (fMRI) studies directional interactions among brain regions and experimental stimuli. Dynamic causal modeling (DCM) is a widely used method to estimate effective connectivity, based on a state-space representation consisting of a latent neural signal model and an observation model transforming the neural signal into the observed blood-oxygen-level-dependent (BOLD) response. A standard DCM combines ordinary differential equation (ODE) dynamics for the latent signal with a complex neural-hemodynamic system for the observation model, and typically uses variational Bayes for parameter estimation. While physically well-motivated, this approach can lead to practical challenges such as inexact solutions and underestimated uncertainty. We introduce Canonical DCM (CDCM), a Markov chain Monte Carlo (MCMC)-based method that adopts a simpler observation model and the No-U-Turn Sampler for posterior sampling. The simpler observation model admits a piecewise analytic solution to the neural ODE, increasing computational efficiency and enabling explicit derivation of sufficient conditions for parameter identifiability. The results indicate that CDCM provides reliable uncertainty quantification and consistent estimation of parameters related to experimental inputs for simulated and real data. We use publicly available data from the Wellcome Centre for Human Neuroimaging and the Human Connectome Project (HCP) to benchmark CDCM against standard DCM methods and examine replicability of estimated connectivity patterns in small- and large-scale neuroimaging settings.

翻译：功能磁共振成像中的有效连接分析研究大脑区域与实验刺激之间的方向性相互作用。动态因果建模是估计有效连接的广泛使用的方法，基于由潜在神经信号模型和将神经信号转化为观测的血氧水平依赖响应的观测模型组成的状态空间表示。标准DCM将潜在信号的常微分方程动力学与观测模型的复杂神经-血流动力学系统相结合，通常使用变分贝叶斯进行参数估计。尽管该方法在物理上具有合理性，但可能导致实际问题，如非精确解和低估的不确定性。我们提出典范DCM，一种基于马尔可夫链蒙特卡洛的方法，采用更简单的观测模型和No-U-Turn采样器进行后验采样。更简单的观测模型允许神经ODE的分段解析解，提高了计算效率，并能显式推导参数可辨识性的充分条件。结果表明，CDCM为模拟和真实数据提供了可靠的不确定性量化和与实验输入相关参数的一致估计。我们使用来自Wellcome人类神经影像中心和人类连接组计划的公开数据，将CDCM与标准DCM方法进行基准测试，并检查小规模和大规模神经影像设置中估计连接模式的可重复性。