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The basis of the MEG signal
Magnetic Shielding Because the magnetic signals emitted by the brain are on the order of a few femtoteslas (1 fT = T), shielding from external magnetic signals, including the Earth's magnetic field, is necessary. An appropriate magnetically shielded room can be constructed of aluminum and mu-metal for reducing high-frequency and low-frequency noise, respectively. Moreover, noise cancellation algorithms can reduce both low-frequency and high-frequency noise. Modern systems have a noise floor of around 2 to 3 fT per √Hz above 1 Hz. The Inverse Problem Main article: Inverse problem In order to determine the location of the activity within the brain, advanced signal processing techniques are used which use the magnetic fields measured outside the head to estimate the location of that activity's source. This is referred to as the inverse problem. (The forward problem is a situation where we know where the source(s) is (are) and we are estimating the field at a given distance from the source(s).) The primary technical difficulty is that the inverse problem does not have a unique solution (i.e., there are infinite possible "correct" answers), 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 localization 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. Localization 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 magnetic 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. Estimation algorithms 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 Use in the Field 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 intractable epilepsy. In research, MEG's primary use is the measurement of time courses of activity, as such time courses cannot be measured using functional magnetic resonance imaging (fMRI). MEG also accurately pinpoints sources in primary auditory, somatosensory and motor areas, whereas its use in creating functional maps of human cortex during more complex cognitive tasks is more limited; in those cases MEG should preferably be used in combination with e.g. fMRI. It has to be noted, however, that the neuronal (MEG) and hemodynamical (fMRI) data do not necessarily agree and the methods complement each other. Comparison with Other Imaging Techniques 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 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. Notes Further reading See also | |||||||||||
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