Characterization of Brain Dynamics Using Beamformer Techniques: Advantages and Limitations

The objective of biomagnetic neuroimaging is to characterize the spatiotemporal distribution of electrical activity within the human brain by measuring the magnetic field outside the head. Magnetoencephalography (MEG) provides such a tool where measurements are performed non-invasively at numerous locations surrounding the head. The high sensitivity of Superconducting Quantum Interference Devices (SQUID) utilized by current MEG systems combined with advanced hardware and software noise cancellation techniques allow the detection of minute magnetic fields generated by electrical neural activity with good accuracy. The ability to directly observe the neuronal fields allows MEG to have millisecond time resolution, orders of magnitude higher than the time resolution of fMRI and PET. While the electrical signals measured by electroencephalography (EEG) are strongly influenced by inhomogeneities in the head, the magnetic fields measured by MEG are produced mainly by current flow in the relatively homogenous intracranial space, permitting more accurate spatial localization (Hamalainen et al. 1993). Although electromagnetic fields are fully described by Maxwell’s equations, the problem of obtaining the spatiotemporal distributions of the neural current generators from MEG data (referred to as the inverse problem) poses a challenge since it is inherently ill-defined (Hamalainen et al., 1993). Since the neural current generators lie within the human head, a conducting medium, it is not possible to develop a mathematical model that provides a unique solution. Instead, several methods with different assumptions have been developed, each of which has its own advantages and limitations (Pascarella et al., 2010; Sekihara and Nagarajan, 2008; Wipf and Nagarajan, 2009). Generally, inverse models in MEG can be separated into two classes: parametric models and imaging models. Parametric models are global solutions that attempt to account for the measured fields in their entirety in terms of a small number of sources. The most popular model in this class is the equivalent current dipole model (ECD), where non-linear leastsquare fits are used to estimate the parameters of the current dipoles requiring the number of sources to be known a priori, and are not easily able to reconstruct spatially distributed or extended sources. More advanced techniques have been developed in this class such as