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  Magnetoencephalography (MEG) is an imaging technique used to measure the magnetic fields produced by electrical activity in the brain via extremely sensitive devices such as superconducting quantum interference devices (SQUIDs). These measurements are commonly used in both research and clinical settings. There are many uses for the MEG, including assisting surgeons in localizing a pathology, assisting researchers in determining the function of various parts of the brain, neurofeedback, and others.


History of the MEG

The MEG was first measured by David Cohen[1] in 1968, before the availability of the SQUID, using only a copper induction coil as the detector. To reduce the magnetic background noise, the measurements were made in a magnetically shielded room. However, the insensitivity of this detector resulted in poor, noisy MEG signals, which were difficult to use. Then, later at MIT, he built a better shielded room, and used one of the first SQUID detectors (just developed by Zimmerman[2]) to again measure the MEG[3]. This time the signals were almost as clear as the EEG, and stimulated the interest of physicists who had begun looking for uses of SQUIDs. Thus, the MEG began to be used, so that various types of spontaneous and evoked MEG’s began to be measured.

At first only a single SQUID detector was used, to successively measure the magnetic field at a number of points around the subject’s head. This was cumbersome, and in the 1980’s, MEG manufacurers began to increase the number of sensors in the dewar to cover a larger area of the head, using a correspondingly larger dewar. Present-day MEG dewars are helmet-shaped and contain as many as 300 sensors, covering most of the head, as shown in the first figure. In this way, MEG’s of a subject or patient can now be accumulated rapidly and efficiently.

The basis of the MEG signal

Synchronized neuronal currents induce very weak magnetic fields that can be measured on MEG. However, the magnetic field of the brain is considerably smaller at 10 fT (femtotesla) for cortical activity and 103 fT for the human alpha rhythm than the ambient magnetic noise in an urban environment, which is on the order of 108 fT. Two essential problems of biomagnetism arise: weakness of the signal and strength of the competing environmental noise. The development of extremely sensitive measurement devices, SQUIDs, facilitates analysis of the brain's magnetic field and confronts the aforementioned problems.

 The MEG (and EEG) signals derive from the net effect of ionic currents flowing in the dendrites of neurons during synaptic transmission. In accordance with Maxwell's equations, any electrical current will produce an orthogonally oriented magnetic field. It is this field which is measured with MEG. 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.

In order to generate a signal that is detectable, approximately 50,000 active neurons are needed.[4] 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. Researchers are experimenting with various signal processing methods to try to find methods that will allow deep brain (i.e., non-cortical) signal to be detected, but as of yet there is no clinically useful method available.

It is worth noting that action potentials do not usually produce an observable field, mainly because the currents associated with action potentials flow in opposite directions and the magnetic fields cancel out. However, action fields have been measured from peripheral nerves.

Magnetic shielding

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. Appropriate magnetic shielding can be obtained by constructing rooms made of aluminum and mu-metal for reducing high-frequency and low-frequency noise, respectively.


Magnetically Shielded Rooms

A Magnetically Shielded Room (MSR) model consists of three nested main layers. Each of these layers is made of a pure aluminum layer plus a high permeability ferromagnetic layer, similar in composition to Moly Permalloy. The ferromagnetic layer is supplied as 1 mm sheets, while the innermost layer is composed of four sheets in close contact, and the outer two layers are composed of three sheets each. Magnetic continuity is maintained by overlay strips. Insulating washers are used in the screw assemblies so that each main layer is electrically isolated to help eliminate radio frequencies, which degrade SQUID performance. Electrical continuity of the aluminum is also maintained by aluminum overlay strips to allow Alternating Current eddy-current shielding which is important at frequencies greater than 1 Hz. The junctions of the inner layer are often electroplated with silver or gold to improve conductivity of the aluminum layers. [5]

Active shielding system

Active systems are designed for three dimensional noise cancellation. To implement an active system, low-noise fluxgate magnetometers are mounted at the center of each surface and oriented orthogonally to it. This negatively feeds a DC amplifier through a low-pass network with a slow falloff to minimize positive feedback and oscillation. Built into the system are shaking and degaussing wires. Shaking wires increase the magnetic permeability, while the permanent degaussing wires are applied to all surfaces of the inner main layer to degauss the surfaces. [1] 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.

Source localization

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.

The source models can be either overdetermined or underdetermined. An overdetermined model may consist of a few point-like sources, whose locations are then estimated from the data. The underdetermined models may be used in cases where many different distributed areas are activated; there are several possible current distributions explaining the measurement results, but the most likely is selected. It is believed by some researchers in the field that more complex source models increase the quality of a solution. However this may decrease the robustness of the estimation and increasing the effects of forward 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,[6] 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.

Localization algorithms using overdetermined models 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 that 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 is iterated until convergence.

Another approach is to ignore the ill-posed inverse problem and estimate the current at a fixed location. 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.

Magnetic source imaging

The estimated source locations can 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.

Dipole model source localization

A widely accepted source-modeling technique for MEG involves calculating a set of Equivalent Current Dipoles (ECDs), which assumes the underlying neuronal sources are focal. This dipole fitting procedure is non-linear at over-determined as the number of unknown dipole parameters is less than the number of MEG measurements [7]. Automated multiple dipole model algorithms such as Multiple Signal Classification (Multiple Signal Classification ) and MSST (Multistart spatial and temporal) modeling are applied to analysis of MEG responses. The limitations of dipole models to characterize neuronal responses has three main drawbacks: (1) significant difficulties in localizing extended sources with ECDs, (2) problems with accurately estimating the total number of dipoles in advance, and (3) the sensitivity of dipole location, especially with respect to depth in the brain.

Lead-field-based imaging approach

Unlike multiple-dipole modeling, lead-field-based modeling divides the source space into a grid containing a large number of dipoles. The inverse problem is to obtain the dipole moments for the grid nodes [8]. As the number of unknown dipole moments is much greater than the number of MEG sensors, the inverse solution is highly underdetermined. To compensate for this, additional constraints are needed to reduce non-uniqueness of the solution. The primary advantage of this system is that no prior specification for source model must be made. Other strengths include relatively low computation load and smooth source time-courses, both of which lead to simple statistical comparison. A weakness is that the spatial resolution is quite poor, and tends to provide distributed statistical reconstruction models, despite having focal generators.

Independent Component Analysis

Independent Component Analysis (ICA), is another signal processing solution that separates different signals that are statistically independent in time. It is primarily used to remove artifacts such as blinking, eye muscle movement, facial muscle artifacts, cardiac artifacts, etc. from MEG and EEG signals that may be contaminated with outside noise [9]. However, ICA has poor resolution of highly correlated brain sources due to its fundamental statistical independence.

MEG Use in the Field

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 fMRI. It should be noted, however, that neuronal (MEG) and hemodynamic (fMRI) data do not necessarily agree and the methods complement each other. However, the two signals may have a common source: it is known that there is a tight relationship between LFP (local field potentials) and BOLD (blood oxgenation level dependent) signals. Since the LFP is the source signal of MEG/EEG, MEG and BOLD signals may derive from the same source (though the BOLD signals are filtered through the hemodynamic response).

Focal Epilepsy

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. The goal of epilepsy surgery is to remove the epileptogenic tissue while sparing essential brain areas to avoid neurologic deficits [10]. Knowing the exact position of essential brain regions (such as the primary motor cortex and primary sensory cortex, visual cortex, and speech cortex) is of utmost importance. Direct cortical stimulation and somatosensory evoked potentials recorded on ECoG are considered the gold standard for localization of essential brain regions. These procedures can be performed either intraoperatively or from chronically indwelling subdural grid electrodes; however, they are both invasive to the patient. MEG localizations of the central sulcus obtained from somatosensory evoked magnetic fields show strong agreement with these invasive recordings [11][12][13]. MEG studies assist in clarification of the functional organization of primary somatosensory cortex and to delineate the spatial extent of hand somatosensory cortex by stimulation of the individual digits. This agreement between invasive measures of localization of cortical tissue and MEG recordings implies the effectiveness of MEG analysis.

Controversy of MEG Studies

MEG has also recently been used somewhat more controversially to study more sophisticated cognitive processes such as audition and language processing.

Comparison with Other Imaging Techniques

MEG has been in development since the 1960s but has been greatly aided by recent advances in computing algorithms and hardware, and promises improved spatial resolution coupled with 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 do not depend on head geometry as much as EEG does (unless ferromagnetic implants are present) and that it is completely non-invasive, as opposed to PET and possibly MRI/fMRI.


Although EEG and MEG are generated by the same neurophysiologic processes, there are important differences concerning the neurogenesis of MEG and EEG [14]. In contrast to electric fields, magnetic fields are less distorted by the resistive properties of the skull and scalp, which result in a better spatial resolution of the MEG. As Electric and magnetic fields are oriented perpendicular to each other, the directions of highest sensitivity, usually the direction between the field maxima, are orthogonal to each other. Whereas scalp EEG is sensitive to both tangential and radial components of a current source in a spherical volume conductor, MEG detects only its tangential components. This shows MEG selectively measures the activity in the sulci, whereas scalp EEG measures activity both in the sulci and at the top of the cortical gyri but appears to be dominated by radial sources.

Scalp EEG is sensitive to extracellular volume currents produced by postsynaptic potentials, MEG primarily detects intracellular currents associated with these synaptic potentials because the field components generated by volume currents tend to cancel out in a spherical volume conductor [15] The decay of magnetic fields as a function of distance is more pronounced than for electric fields. MEG is therefore more sensitive to superficial cortical activity, which should be useful for the study of neocortical epilepsy. Finally, MEG is reference-free which is in contrast to scalp EEG, where an active reference can lead to serious difficulties in the interpretation of the data.


Primary Sources

  1. ^ Cohen D. Magnetoencephalography: evidence of magnetic fields produced by alpha rhythm currents. Science 1968;161:784-6.
  2. ^ Zimmerman, J.E., Theine, P., and Harding, J.T. Design and operation of stable rf-biased superconducting point-contact quantum devices, etc. Journal of Applied Physics 1970; 41:1572-1580.
  3. ^ Cohen D. Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer. Science 1972;175:664-66
  4. ^ Okada Y (1983): Neurogenesis of evoked magnetic fields. In: Williamson SH, Romani GL, Kaufman L, Modena I, editors. Biomagnetism: an Interdisciplinary Approach. New York: Plenum Press, pp 399-408
  5. ^ D. Cohen, U. Schläpfer. Ahlfors, M. Hämäläinen, and E. Halgren. New Six-Layer Magnetically-Shielded Room for MEG. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA; Mass.Inst.of Tech.; Imedco AG, Hägendorf, Switzerland; Low Temp. Lab., Helsinki Univ. of Technology.
  6. ^ The Forward model - the imaging process by Björn Gustavsson (2000)
  7. ^ Huang, M; Dale, A M; Song, T; Halgren, E; Harrington, D L; Podgorny, I; Canive, J M; Lewis, S; Lee, R R. Vector-based spatial-temporal minimum L1-norm solution for MEG. NeuroImage, 2005; 31:1025-1037.
  8. ^ Hamalainen, M S; Ilmoniemi, R J. Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 1994; 32: 35-42.
  9. ^ Jung, T P; Makeig, S; Westerfield, M; Townsend, J; Courchesne, E; Sejnowski, TJ. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin. Neuro-physiol, 1997; 111: 1745-1758.
  10. ^ Luders HO. Epilepsy surgery. New York Raven Press, 1992.
  11. ^ Sutherling WW, Crandall PH, Darcey TM, Becker DP, Levesque MF, Barth DS. The magnetic and electric fields agree with intracranial localizations of somatosensory cortex. Neurology 198838:1705-14.
  12. ^ Rowley HA, Roberts TP. Functional localization by rnagnetoencephalography.Neuroimaging Clin North Am, 1995;5:695-710
  13. ^ Gallen CC, Hirschkoff EC, Buchanan DS. Magnetoencephalograpby and magnetic source imaging. Capabilities and limitations. Neuroimaging Clin North Am, 1995;5:22749
  14. ^ Cohen D, Cuffin BN. Demonstration of useful differences between the magnetoencephalogram and electroencephalogram. Electroencephalogr Clin Neurophysiol, 1983;56:38-51.
  15. ^ Barth DS, Sutherling WW, Beatty J. Intracellular currents of interictal penicillin spikes: evidence from neuromagnetic mapping. Brain Res, 1986;368:36-48.

Further reading

  • Suk, J., Ribary, U., Cappell,J. Yamamoto, T. and Llinas, R. Anatomical localization revealed by MEG recordings of the human somatosensory system. EEG J 78:185-196, 1991.
  • Hämäläinen, M., Hari, R., Ilmoniemi, R., Knuutila, J. and Lounasmaa, O. V. (1993) "Magnetoencephalography – theory, instrumentation, and applications to noninvasive studies of signal processing in the human brain" in Reviews of Modern Physics 1993, 65: pp. 413–497
  • Tanzer I.O., (2006) Numerical Modeling in Electro- and Magnetoencephalography, Ph.D. Thesis, Helsinki University of Technology,

(Available online:

  • Baillet S., Mosher J. C., Leahy R. M.(2001) "Electromagnetic Brain Mapping" in IEEE Signal Processing Magazine, November 2001, 14-30.
  • Cohen, D. "Boston and the history of biomagnetism". Neurology and Clinical Neurophysiology 2004; 30: 1.
  • Cohen, D., Halgren, E. (2004). "Magnetoencephalography". In: Encyclopedia of Neuroscience, Adelman G., Smith B., editors Elsevier, 1st, 2nd and 3rd (2004) editions.
  • Murakami S, Okada Y. Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals. J Physiol. 2006 Sep 15;575(Pt 3):925-36.
  • for very basic though clear information about MEG

See also

This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Magnetoencephalography". A list of authors is available in Wikipedia.
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