Magnetoencephalography

Magnetoencephalography (MEG) is the measurement of the magnetic fields produced by electrical activity in the brain, usually conducted externally, using extremely sensitive devices such as SQUIDs. Because the magnetic signals emitted by the brain are on the order of a few femtoteslas (1 fT = $$10^{-15}$$ T), shielding from external magnetic signals, including the Earth's magnetic field, is necessary. An appropriate magnetically shielded room can be constructed from mu-metal, which is effective at reducing high-frequency noise, while noise cancellation algorithms reduce low-frequency common mode signals.

Modern systems have roughly 300 channels situated around the head, and have a noise floor of around 5 to 7 fT above 1 Hz. The overall magnetic field of the brain is typically around 100 to 1000 fT, although signals from individual neurons are much weaker, well below the noise floor.

The signals themselves derive from the net effect of ionic currents flowing in the dendrites of neurons during synaptic transmission and in the extra-cellular medium as return currents. Action potentials do not produce an observable field because the currents associated with action potentials flow in opposite directions and the magnetic fields cancel out.

The net currents can be thought of as current dipoles which are currents defined to have an associated position, orientation, and magnitude, but no spatial extent. According to the right-hand rule, a current dipole gives rise to a magnetic field that flows around the axis of its vector component.

The magnetic field arising from the net current dipole of a single neuron is too weak to be directly detected. However the combined fields from a region of about 50,000 active neurons can give rise to a net magnetic field that is measurable. Since current dipoles must have similar orientations to generate magnetic fields that reinforce each other, it is often the layer of pyramidal cells in the cortex, which are generally perpendicular to its surface, that give rise to measurable magnetic fields. Furthermore, it is often bundles of these neurons located in the sulci of the cortex with orientations parallel to the surface of the head that project measurable portions of their magnetic fields outside of the head.

The primary technical difficulty with MEG is that the problem of inferring charge motions in the brain from magnetic measurements outside the head (the "inverse problem") does not in general have a unique solution, and the problem of finding the best solution is itself the subject of intensive research. Adequate solutions can be derived using models involving prior knowledge of brain activity and the characteristics of the head, as well as localisation algorithms. It is believed by some researchers in the field that more complex but realistic source and head models increase the quality of a solution. However this also increases the opportunity for local minima and potentially makes the numeric conditioning of the system worse, thus increasing the effects of model errors. Many experiments use simple models, reducing possible sources of error and decreasing the computation time to find a solution. Localisation algorithms make use of the given source and head models to find a likely location for an underlying focal field generator. An alternative methodology involves performing Independent Component Analysis first in order to segregate sources without using a forward model, and then localizing the separated sources individually. This method has been shown to improve the signal-to-noise ratio of the data by correctly separating non-neuronal noise sources from neuronal sources, and has shown promise in segregating focal neuronal sources.

Generally, localisation algorithms operate by successive refinement. The system is initialized with a first guess. Then a loop is entered, in which a forward model is used to generate the magnitic field the would result from the current guess, and the guess then adjusted to reduce the difference between this estimated field and the measured field. This process it iterated until convergence.

Another approach is to ignore the ill-posed inverse problem, and use an estimation algorithm to localize sources. One such approach is the second-order technique known as Synthetic Aperture Magnetometry (SAM), which uses a linear weighting of the sensor channels to focus the array on a given target location. This approach, also known as beamforming, has an advantage over more traditional source localization techniques because most sources in the brain are distributed and cannot be well described with a point source such as a current dipole.

A solution can then be combined with Magnetic Resonance Imaging (MRI) images to create Magnetic Source Images (MSI). The two sets of data are combined by measuring the location of a common set of fiducial points marked during MRI with lipid markers and marked during MEG with electrified coils of wire that give off magnetic fields. The locations of the fiducial points in each data set are then used to define a common coordinate system so that superimposing ("coregistering") the functional MEG data onto the structural MRI data is possible.

A criticism of the use of this technique in clinical practice is that it produces colored areas with definite boundaries superimposed upon an MRI scan: the untrained viewer may not realize that the colors do not represent a physiological certainty, because of the relatively low spatial resolution of MEG, but rather a probability cloud derived from statistical processes. However, when the magnetic source image corroborates other data, it can be of clinical utility.

MEG has been in development since the 1970s but has been greatly aided by recent advances in computing algorithms and hardware, and promises good spatial resolution and extremely high temporal resolution (better than 1 ms); since MEG takes its measurements directly from the activity of the neurons themselves its temporal resolution is comparable with that of intracranial electrodes. MEG's strengths complement those of other brain activity measurement techniques such as electroencephalography (EEG), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI) whose strengths, in turn, complement MEG. Other important strengths to note about MEG are that the biosignals it measures are not distorted by the body as in EEG (unless ferromagnetic implants are present) and that it is completely non-invasive, as opposed to PET and possibly MRI/fMRI.

The clinical uses of MEG are in detecting and localizing epileptiform spiking activity in patients with epilepsy, and in localizing eloquent cortex for surgical planning in patients with brain tumors or intractible epilepsy.

In research, MEG's primary use is the measurement of time courses of activity, as such time courses cannot be measured using FMRI. Due to various technical and methodological difficulties in localization of sources using MEG, its use in creating functional maps of human cortex plays a secondary role, as verification of any proposed maps would require verification using other techniques before they would be widely accepted in the brain mapping community.