In this paper, we propose a method to reconstruct current distributions in the human brain from neuromagnetic measurements. The proposed method is based on the weighted lead-field synthetic (WLFS) filtering technique with the weighting factors calculated from the results of previous source space scanning. In this method, in addition to the depth normalization technique, weighting factors of the WLFS are determined by the cost values previously calculated based on the multiple signal classification (MUSIC) scan. We performed computer simulations of this method under noisy measurement conditions and compared the results to those obtained with the conventional WLFS method. The results of the simulations indicate that the proposed method is effective for the reconstruction of the current distributions in the human brain using magnetoencephalographic (MEG) measurements, even if the signal-to-noise ratio of the measured data is relatively low. We applied the proposed method to the magnetoencephalographic data obtained during a mental image processing task that included object recognition and mental rotation operations. The results suggest that the proposed method can extract the neural activity in the extrastriate visual region and the parietal region. These results are in agreement with the results of previous positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies.

The purpose of our study is to estimate the imaging of ischemic myocardial muscles in rats. The magnetocardiograms (MCG) of rats were measured by a 12-channel high resolution gradiometer, which consisted of 5 mm diameter pick-up coils with a 7.5 mm distance between each coil. MCGs of seven male rats were measured in a magnetically shielded room pre and post coronary artery occlusion. The source imaging was estimated by minimum norm estimation (MNE). Changes of the current source imaging pre- and post coronary artery occlusion were clarified. As a result, in the ST segment, the current distribution significantly increased at the ischemic area. In the T wave, the direction of the current distribution clearly shifted to the left thorax. We proved that the increased area of the current distribution in the ST segment was related to the ischemic area of the ventricular muscles.

In recent years, several inverse solutions of magnetoencephalography (MEG) have been proposed. Among them, the multiple signal classification (MUSIC) method utilizes spatio-temporal information obtained from magnetic fields. The conventional MUSIC method is, however, sensitive to Gaussian noise and a sufficiently large signal-to-noise ratio (SNR) is required to estimate the number of sources and to specify the precise locations of electrical neural activities. In this paper, a new algorithm for solving the inverse problem using the fourth order MUSIC (FO-MUSIC) method is proposed. We apply it to the MEG source estimation problem. Numerical simulations demonstrate that the proposed FO-MUSIC algorithm is more robust against Gaussian noise than the conventional MUSIC algorithm.