Background: When using integrative functional magnetic resonance imaging–
magnetoencephalography (fMRI–MEG) methods, we have to construct a
model based on prior information from an fMRI study. However, this predetermined
model is unfortunately often insufficient because of a mismatch between the
fMRI and MEG source-activated areas. Moreover, estimated signals from MEG data
can be contaminated by sources that cannot be observed by fMRI (fMRI invisible
Methods: To address this problem, we propose a method to improve the normalized
integrative fMRI–MEG method that enables the updating of the predetermined
model using an iterative procedure, which is based on both model selection and an
estimate of the fMRI invisible sources by beamforming. This updated model reduces the effect of
fMRI invisible sources. Using the proposed method, we obtained an accurate model that accounts for
the fMRI invisible sources, resulting in more accurate estimated source activities. To validate this proposed
method, we performed simulations and assessed the accuracy of our estimates. Furthermore, we
estimated the number and locations of the fMRI invisible sources. The simulation was based on an
apparent motion perception experiment. White Gaussian noise and real MEG noise under open-eye
conditions were used in the simulation. The accuracy of the estimated time course was assessed using
the residual sum of squares.
Results: The proposed method successfully estimated the fMRI invisible sources and provided a better
estimation of the time course than that of predetermined model.
Conclusion: These results demonstrate the feasibility of the proposed method.