A new approach to neuroimaging with magnetoencephalography

Abstract

We discuss the application of beamforming techniques to the field of magnetoencephalography (MEG). We argue that beamformers have given us an insight into the dynamics of oscillatory changes across the cortex not explored previously with traditional analysis techniques that rely on averaged evoked responses. We review several experiments that have used beamformers, with special emphasis on those in which the results have been compared to those observed in functional magnetic resonance imaging (fMRI) and on those studying induced phenomena. We suggest that the success of the beamformer technique, despite the assumption that there are no linear interactions between the mesoscopic local field potentials across distinct cortical areas, may tell us something of the balance between functional integration and segregation in the human brain. What is more, MEG beamformer analysis facilitates the study of these complex interactions within cortical networks that are involved in both sensory-motor and cognitive processes.

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces

“…To move beyond the limitations of sensor‐space spatial inference in our MVPA analysis (including concerns of signal leakage, head motion and inter‐individual variability; Zhang et al, 2016), the data were projected into source space using the linearly constrained minimum variance (LCMV) beamformer (Hillebrand et al, 2005; Van Veen, van Drongelen, Yuchtman, & Suzuki, 1997). This approach combines the forward model and the data covariance matrix to construct an adaptive spatial filter.…”

Abnormal MEG Oscillatory Activity during Visual Processing in the Prefrontal Cortices and Frontal Eye-Fields of the Aging HIV Brain

“…In principle, the beamformer operator generates a spatial filter for each grid point, which passes signals without attenuation from the given neural region while minimizing interference from activity in all other brain areas. The properties of these filters are determined from the MEG covariance matrix and the forward solution for each grid point in the image space, which are used to allocate sensitivity weights to each sensor in the array for each voxel in the brain (for a review, see [27]). …”

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

“…Then, the MEG forward model was computed for two orthogonal tangential current dipoles placed on a homogeneous 2-mm grid source space covering the whole brain (MNE suite; Gramfort et al, 2014). Coherence maps were produced within the computed source space at delta (0.5 Hz; frequency corresponding to sentence level prosody) and theta (4-8 Hz; frequency corresponding to syllable production rate) using a LCMVB (Hillebrand et al, 2005;Van Veen et al, 1997). The theta coherence was obtained by simply averaging the coherence values across all frequency bins falling into that band.…”

“…Nowadays, source estimation is often performed with imaging methods such as linearly constrained minimum variance beamformer (LCMVB; Hillebrand et al, 2005;Van Veen et al, 1997) or minimum norm estimation (MNE; Dale and Sereno, 1993;H€ am€ al€ ainen and Ilmoniemi, 1994). These inversion schemes make it possible to reconstruct source time-series, based on which one can build parametric maps of, e.g., task-induced power changes or coherence with an external reference signal.…”