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Multivariate Variational Mode Decomposition Improves Dynamic Causal Modeling For fMRI Data
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  • Charalampos Lamprou,
  • Aamna Alshehhi,
  • Leontios J Hadjileontiadis,
  • Mohamed L Seghier
Charalampos Lamprou
Department of Biomedical Eng, Khalifa University

Corresponding Author:[email protected]

Author Profile
Aamna Alshehhi
Department of Biomedical Eng, Khalifa University
Leontios J Hadjileontiadis
Department of Electrical & Computer Eng, Aristotle University of Thessaloniki, Department of Biomedical Eng, Khalifa University
Mohamed L Seghier
Department of Biomedical Eng, Khalifa University

Abstract

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.
05 Jun 2024Submitted to TechRxiv
07 Jun 2024Published in TechRxiv