1Magnetoencephalography (MEG) is a medical imaging technology that provides unprecedented insight into the workings of the human brain through the measurement of electromagnetic activity.

By measuring the magnetic fields created by the electric current flowing within the neurons, MEG identifies brain activity associated with various human functions in real time, with millimeter spatial accuracy.

Above: CTF MEG Magnetoencephalography Brain Imaging System


Magnetoencephalography (MEG) is a technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using arrays of SQUIDs (superconducting quantum interference devices). Applications of MEG include localizing regions affected by pathology before surgical removal, determining the function of various parts of the brain, and neurofeedback.  This non-invasive approach can positively impact patient outcomes, providing clinicians with the invaluable information they need to evaluate neurological disorders and plan surgical treatments.  Both spontaneous brain function (for example, normal alpha waves or pathological epileptic spikes) and evoked brain activity (caused by an external stimulus such as visual, auditory or tactile input) can be measured using MEG. In both cases, electrical currents in a group of neurons within the brain create very small magnetic fields. Special detectors called superconducting quantum interference devices (SQUIDs), cooled to the temperature of liquid helium (about -273°C), can measure these tiny magnetic fields.


History of Magnetoencephalography


MEG signals were first measured by University of Illinois physicist David Cohen in 1968, before the availability of the SQUID, using a copper induction coil as the detector. To reduce the magnetic background noise, the measurements were made in a magnetically shielded room.


The coil detector was barely sensitive enough, resulting in poor, noisy MEG measurements that were difficult to use. Later, Cohen built a better shielded room at MIT, and used one of the first SQUID detectors, just developed by James E. Zimmerman, a researcher at Ford Motor Company, to again measure MEG signals. This time the signals were almost as clear as those of EEG. This stimulated the interest of physicists who had been looking for uses of SQUIDs. Subsequently, various types of spontaneous and evoked MEGs began to be measured.


At first, 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 1980s, MEG manufacturers began to arrange multiple sensors into arrays to cover a larger area of the head. Present-day MEG arrays are set in helmet-shaped dewar that typically contain 300 sensors, covering most of the head. In this way, MEGs of a subject or patient can now be accumulated rapidly and efficiently.


MEG signals

Synchronized neuronal currents induce weak magnetic fields. At 10 femtotesla (fT) for cortical activity and 103 fT for the human alpha rhythm, the brain's magnetic field is considerably smaller than the ambient magnetic noise in an urban environment, which is on the order of 108 fT or 10 µT. The essential problem of biomagnetism is thus the weakness of the signal relative to the sensitivity of the detectors, and to the competing environmental noise.  The electric current also produces the EEG signal. 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. The net currents can be thought of as electric dipoles, ie. currents with a 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.


To generate a signal that is detectable, approximately 50,000 active neurons are needed. Since current dipoles must have similar orientations to generate magnetic fields that reinforce each other, it is often the layer of pyramidal cells, which are situated perpendicular to the cortical surface, that give rise to measurable magnetic fields. Bundles of these neurons that are orientated tangentially to the scalp surface project measurable portions of their magnetic fields outside of the head, and these bundles are typically located in the sulci. Researchers are experimenting with various signal processing methods in the search for methods that detect deep brain (i.e., non-cortical) signal, but no clinically useful method is currently available. 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.


MEG and EEG are closely related, the latter detecting the electric potentials generated by neural currents instead of the corresponding magnetic fields. However, it turns out that the task of inferring the sites of brain activation is often more straightforward from MEG than from EEG. This is due to the electric and magnetic properties of the tissues in the cranium and also to the fact that MEG is selectively sensitive to currents flowing tangential to the scalp, corresponding to sulcal activations. On the other hand, the interpretation of EEG is often complicated by the simultaneous presense of both sulcal and gyral sources, the latter corresponding to radial currents.


The MEG system is operated in a shielded room that minimizes interference from external magnetic disturbances, including the Earth's magnetic field, noise generated by electrical equipment, radiofrequency signals, and low frequency magnetic fields produced by moving magnetic objects like elevators, cars, and trains.  Both MEG and EEG raw data are often presented as time-dependent signals, arranged in a topographical layout. These data may either represent averages over repeated sensory stimuli or motor responses or continuous raw data. The latter are routinely employed in the analysis of abnormal epileptic activity or in the characterization of ongoing rhythmic activity. 


Multimodal imaging

Often the non-uniqueness of the MEG (and EEG) source estimation problem can be alleviated by incorporating information from other imaging modalities as an a priori constraint. The most natural companion to MEG is EEG, which provides additional information about the radial current sources.



What are its limitations?

A major technical problem associated with MEG is that the localization of sources of electrical activity within the brain from magnetic measurement outside the head is complicated and does not have a unique solution. This is known as the ill-posed inverse problem, and is itself the subject of research. However, as indicated above, feasible solutions can be often obtained by using relatively simple models.  Due to the increased distance to sources and the almost spherical symmetry of the head it is difficult to provide reliable information about subcortical sources of brain activity.


MEG does not provide structural/anatomical information. Therefore MEG data often must be combined with MR data into a composite image of function overlaid on anatomy to produce activation maps.  Because the neuromagnetic signals are very weak compared to the magnetic fields in a normal laboratory environment, the MEG measurements have to be taken in a magnetically shielded room with two or more layers using a sensitive SQUID magnetometer.   Clinically, MEG is used to detect and localize epileptiform spiking activity in patients with epilepsy. It is also used to localize brain areas important for speech, which should be avoided by the surgeon in planning for removal of brain tumours. Researchers use MEG, often in conjunction with EEG, MRI, fMRI, and optical imaging to obtain maps of brain activity in cognitive neuroscience studies carefully designed to investigate the workings of the normal and damaged brain.




Compiled and edited by John Sandham IEng MIHEEM MIET
May 2010

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