Neuromagnetic Source Estimation and Coherence Mapping of Brain Activities Neuromagnetic Source Estimation and Coherence Mapping of Brain Activities Neuromagnetic Source Estimation and Coherence Mapping of Brain Activities

Magnetoencephalography (MEG) non-invasively measures the electromagnetic signals induced by brain activities. It can provide spatiotemporal brain activation imaging with high temporal resolution to facilitate functional brain research in both clinical and basic neuroscience fields. In this thesis, we propose novel spatial filtering techniques for statistical mapping of neuronal sources as well as cortical oscillatory coupling by using the wholehead MEG recordings. The problem of estimating the activation sources in the brain from the MEG recordings is called the inverse problem. To solve this ill-posed problem, approximations such as equivalent current dipole for source modeling, assumptions such as a fixed number of dipoles during the task, and constraints such as anatomical constraint and minimum-norm constraint are required to limit the solution space. Among the various kinds of source estimation methods, beamforming technique, a kind of spatial filtering technique, has becoming more and more attractive during the past decade. By probing the source space in a voxel-by-voxel manner, a spatial filter is individually calculated for each position. This spatial filter can reconstruct the activation magnitude of the targeted source while suppressing the contribution from other sources by imposing the unit-gain constraint and by applying the minimum-variance criterion. However, the determination of dipole orientation can be problematic. There are three major kinds of methods proposed in the literature. First, the dipole orientation is aligned to be perpendicular to the cortical surface. Unfortunately, the surface reconstruction for the convoluted cortex is very difficult and the reconstruction deviation will decrease the accuracy of the orientation. Second, the dipole orientation is determined by (exhaustive) search, which is time-consuming. The third kinds of methods decompose the dipole into three orthogonal components, which may suffer the risk of miss-detection. In this work, we develop a novel spatial filtering technique, called the maximumcontrast beamformer, for statistical mapping of neuronal sources. In addition to the unitgain constraint and the minimum-variance criterion, as in the conventional beamformers, our method exploits a maximum-contrast criterion that can maximize the discrimination between the estimated neuronal activities in the active state and those in the control (or