Abstract #4897

Time Dependent Regularization for Functional Magnetic Resonance Inverse Imaging

Aapo Nummenmaa^1,2, Matti S. Hamalainen¹, Fa-Hsuan Lin^1,3

¹MGH-MIT-HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States; ²Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, Espoo, Finland; ³Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan

We propose a novel method for time dependent regularization of functional magnetic resonance Inverse Imaging (InI). A Variational Bayesian approximation with a dynamic model for the regularization is constructed to obtain an automatic, temporally adaptive estimation algorithm. The proposed method is compared with the standard Minimum-Norm Estimate (MNE) by using simulated InI data. The dynamic dMNE shows significant improvements in determining the activation onset from the baseline period.