Electromagnetic Mapping of Brain Activity


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 kind 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 reconstructed neuronal activities in the active state and those in the control (or resting) state. The maximum-contrast criterion helps to analytically determine the dipole orientation in a closed-form manner and the spatial filter can be obtained very efficiently for each targeted position. Once the neuronal activity waveform is reconstructed in the source space by spatially filtering the MEG recordings, F-statistic map can be calculated to reveal cortical regions with significant difference of activities between the control and active states.


Experiments with simulation, phantom, and real data are conducted to verify the correctness and to assess the capability of the proposed methods. According to the experiments with simulation and phantom data, our methods indeed can efficiently and accurately calculate the dipole orientation. Also, our methods correctly locate the sources with significant variance and significant time-frequency coherence. When applied to a finger-lifting study, F-statistic map computed from the reconstructed neuronal activities on the cortical surface clearly identify the sensorimotor area with high contrast.


Figure : F-static map of the estimated source by maximum contrast beamformer according the simulated recordings. Three significant sources are simulated in the source space.(a)(b)(c) Estimated results are viewed form sagittal, coronal and transverse view aimed at the location of the putative source1, 2, 3.(d)Viewing the whole source space slice by slice. The map is normalized and truncated the part lower than 0.27 times the maximum value in the map.


Demo Video : Cortical activities of subjects performing self-paced right index-finger lifting task.