The spectral DCM analyses were performed using DCM 12, which was implemented in SPM 12 software (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). The five ROIs for DCM analysis were determined based on the results obtained from our MVPA. The pre-processed resting-state data were modeled by applying a General Linear Model (GLM), which included six nuisance regressors capturing head motion from each session, as well as the confound time series derived from the WM and CSF. A high-pass filter was utilized to eliminate potential ultraslow fluctuations (<0.0078 Hz) [39]. After extracting the confounds-adjusted time series values of all ROIs, we assumed a uniform model across all participants and specified a “full” connected model, where each ROI was connected to every other ROI (52 = 25 connectivity parameters, comprising five inherent self-connections) for each subject. The spectral DCM encompassed endogenous connections and was measured through matrix parameters 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
