Magnetoencephalography

From Academic Kids

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 femtotesla (1 fT = <math>10^{-15}<math> 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, while signals from individual neurons are much weaker, generally 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 axons during action potentials, although net currents flowing in opposite directions down an axon from the point of action potential propogation give rise to magnetic fields that tend to cancel each other out.

These net currents can be thought of as Current Dipoles; vectors defined to have an associated position, orientation, and length in space as well as an associated current, but no width. 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 far to weak to measure, even with SQUIDs. Hence it takes the combined fields from a region of about 50,000 active neurons to 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 rather than cancel each other out, 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.

Neuromagnetic fields are subject to a great deal of flux because the brain currents that give rise to them are forever changing. However, this magnetic flux can induce current in magnetometers which, in turn, induce current in their associated SQUIDs. A SQUID may contain one or more Josephson Junctions. Altogether, the SQUID is superconducting until a certain threshold of current is induced and then the Josephson Junctions begin to resist the current flow. Since a base voltage is applied across the SQUID, watching how voltage changes as currents are induced by a fluctuating neuromagnetic field gives a way of quantising the neuromagnetic flux. Measurements thereof provide the raw data for reconstructing the neuromagnetic field at each time point of an MEG session.

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") is ill posed, and is itself the subject of intensive research. Adequate solutions can be derived using models of brain activity and the head as well as localisation algorithms. Generally, the more complex but realistic source and head models increase the quality of a solution but certain investigations may only require the use of 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 arrive at a plausible solution.

Generally, localisation algorithms start by making a first attempt to solve what the source activity in the brain was. This first solution is used as input for the Forward Problem whereby the magnetic field that would arise from it is calculated. The theoretical magnetic field is then compared against the measured magnetic field and an attempt to minimise the difference between the two is made by finding a new solution based on the first attempt. This process is repeated; an old solution is used to calculate a theoretical neuromagnetic field and a new solution is found based on the old that attempts to minimise the difference between the theoretical and measured magnetic fields. When, if possible, the new solution matches the measured magnetic field with an acceptable degree of accuracy it is taken and used.

A solution can then be combined with Magnetic Resonance Imaging (MRI) images to create Magnetic Source Images. 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. Location of the fiducial points in each data set is used them to define a common coordinate system so that superimposing the functional MEG data onto the structural MRI data is possible.

MEG has been in development since the 1970's 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 1ms); since MEG takes its measurements directly from the activity of the neurons themselves its temporal resolution is comparable with that of intracranial electrodes. Its strengths complement 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. In research MEG is used to functionally map the cortex.

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