Aapo
Nummenmaa1,2, Matti S. Hamalainen1, Fa-Hsuan Lin1,3
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.