Multivariate Variational Mode Decomposition Improves Dynamic Causal Modeling For fMRI Data

Dynamic Causal Modeling (DCM) is a Bayesian framework to investigate effective connectivity between brain regions using neuroimaging data. High noise levels can significantly affect the efficiency and reliability of DCM. Here, we propose a new multivariate method, called Multivariate Variational Mode Decomposition (MVMD) for enhanced DCM (MVMD-DCM). This method works on the extracted time-series of the regions of interest by reducing the contribution of noise, which ultimately ensures that only relevant task-related information in the time-series are fed to DCM. We demonstrate the effectiveness of MVMD-DCM in mitigating the impact of noise using simulated task-based fMRI data. Simulated data was generated at two Signal-to-Noise Ratio (SNR) levels of 0.5 (-3dB) and 1 (0dB). A comparative analysis with respect to model evidence, true model selection frequency and model parameters estimation demonstrated the superiority of MVMD-DCM over the default DCM for both SNR levels. Our study paves the way for the development of robust methods for inferring effective connectivity with DCM from noisy fMRI data.