due to the absence of external inputs in the model [22, 26]. The Laplace method with variational Bayes in the frequency domain was used for Model Estimation. The model’s convolution kernel was transformed into a spectrum and expressed in terms of frequency [22]. After estimating all possible full models, we utilized a DCM network discovery (DND) routine based on Bayesian model selection to perform group-level DCM structure inference. The routine employed a greedy search algorithm to explore all potential connectivity parameters of the model (28 = 256 reduced model space), and the optimal model was selected as the one with the highest posterior probability [25, 40]. We employed the Bayesian parameter averaging (BPA) approach to estimate model parameters for each group separately [24]. To correct for multiple comparisons across the 25 connectivity parameters (5 × 5), we applied FDR P < 0.05 to identify group differences in connectivity.
