Editing Magnetoencephalography

{{short description|Mapping brain activity by recording magnetic fields produced by currents in the brain}}
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{{Infobox diagnostic
|  Name        = Magnetoencephalography
|  Image       = NIMH MEG.jpg
|  Caption     = Person undergoing a MEG
|  ICD10       =
|  ICD9        =
|  MeshID      = D015225
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'''Magnetoencephalography''' ('''MEG''') is a [[functional neuroimaging]] technique for mapping brain activity by recording [[magnetic field]]s produced by [[electric current|electrical currents]] occurring naturally in the [[human brain|brain]], using very sensitive [[magnetometer]]s. Arrays of [[SQUID]]s (superconducting quantum interference devices) are currently the most common magnetometer, while the [[SERF]] (spin exchange relaxation-free) magnetometer is being investigated for future machines.<ref name="HämäläinenHari1993">{{cite journal | vauthors = Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV | title = Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain | journal = Reviews of Modern Physics | volume = 65 | issue = 2 | year = 1993 | pages = 413–497 | issn = 0034-6861 | doi = 10.1103/RevModPhys.65.413|url=https://aaltodoc.aalto.fi/bitstream/123456789/18757/1/A1_h%c3%a4m%c3%a4l%c3%a4inen_matti_1993.pdf | bibcode = 1993RvMP...65..413H }}</ref><ref name="Boto_2018">{{cite journal | vauthors = Boto E, Holmes N, Leggett J, Roberts G, Shah V, Meyer SS, Muñoz LD, Mullinger KJ, Tierney TM, Bestmann S, Barnes GR, Bowtell R, Brookes MJ | title = Moving magnetoencephalography towards real-world applications with a wearable system | language = En | journal = Nature | volume = 555 | issue = 7698 | pages = 657–661 | date = March 2018 | pmid = 29562238 | pmc = 6063354 | doi = 10.1038/nature26147 | bibcode = 2018Natur.555..657B }}</ref> Applications of MEG include basic research into perceptual and cognitive brain processes, localizing regions affected by pathology before surgical removal, determining the function of various parts of the brain, and [[neurofeedback]]. This can be applied in a clinical setting to find locations of abnormalities as well as in an experimental setting to simply measure brain activity.<ref>{{cite book | vauthors = Carlson NR | year = 2013 | title = Physiology of Behavior | url = https://archive.org/details/physiologybehavi00carl_811 | url-access = limited | location = Upper Saddle River, NJ | publisher = Pearson Education Inc. | isbn = 978-0-205-23939-9 | pages = [https://archive.org/details/physiologybehavi00carl_811/page/n172 152]–153 }}</ref>

== History ==
[[Image:MIT EarlyYEARS-261 croped.tif|thumbnail|250px|left|Dr. Cohen's shielded room at MIT, in which first MEG was measured with a SQUID]]
[[Image:MIT EarlyYears-233a.jpg|thumbnail|500px|left|First MEG measured with SQUID, in Dr. Cohen's room at MIT]]
MEG signals were first measured by University of Illinois physicist [[David Cohen (physicist)|David Cohen]] in 1968,<ref name="cohen1">{{cite journal | vauthors = Cohen D | title = Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents | journal = Science | volume = 161 | issue = 3843 | pages = 784–6 | date = August 1968 | pmid = 5663803 | doi = 10.1126/science.161.3843.784 | bibcode = 1968Sci...161..784C | s2cid = 34001253 }}</ref> 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 much better shielded room at MIT, and used one of the first SQUID detectors, just developed by [[James Edward Zimmerman|James E. Zimmerman]], a researcher at Ford Motor Company,<ref>{{cite journal | vauthors = Zimmerman JE, Theine P, Harding JT |title=Design and operation of stable rf-biased superconducting point-contact quantum devices, etc|journal=Journal of Applied Physics|year= 1970|volume=41|issue=4|pages=1572–1580|doi=10.1063/1.1659074|bibcode=1970JAP....41.1572Z }}</ref> to again measure MEG signals.<ref>{{cite journal | vauthors = Cohen D | title = Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer | journal = Science | volume = 175 | issue = 4022 | pages = 664–6 | date = February 1972 | pmid = 5009769 | doi = 10.1126/science.175.4022.664 | url = http://davidcohen.mit.edu/sites/default/files/documents/1972ScienceV175(SquidMEG).pdf | bibcode = 1972Sci...175..664C | s2cid = 29638065 }}</ref> This time the signals were almost as clear as those of [[Electroencephalography|EEG]]. This stimulated the interest of physicists who had been looking for uses of SQUIDs. Subsequent to this, 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.<ref>Yamamoto T, Williamson SJ, Kaufman L, Nicholson C, Llinas R: Magnetic localization of neuronal activity in the human brain. Proc Natl Acad Sci 85:8732-8736,1988[9]</ref> Present-day MEG arrays are set in a helmet-shaped [[vacuum flask]] 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.

Recent developments attempt to increase portability of MEG scanners by using [[SERF|spin exchange relaxation-free]] (SERF) magnetometers. SERF magnetometers are relatively small, as they do not require bulky cooling systems to operate. At the same time, they feature sensitivity equivalent to that of SQUIDs. In 2012, it was demonstrated that MEG could work with a chip-scale atomic magnetometer (CSAM, type of SERF).<ref>{{cite journal | vauthors = Sander TH, Preusser J, Mhaskar R, Kitching J, Trahms L, Knappe S | title = Magnetoencephalography with a chip-scale atomic magnetometer | journal = Biomedical Optics Express | volume = 3 | issue = 5 | pages = 981–90 | date = May 2012 | pmid = 22567591 | pmc = 3342203 | doi = 10.1364/BOE.3.000981 }}</ref> More recently, in 2017, researchers built a working prototype that uses SERF magnetometers installed into portable individually 3D-printed helmets,<ref name="Boto_2018" /> which they noted in interviews could be replaced with something easier to use in future, such as a bike helmet.

== The basis of the MEG signal ==
[[Neural synchronization|Synchronized neuronal currents]] induce weak magnetic fields. The brain's magnetic field, measuring at 10 [[femto]][[tesla (unit)|tesla]] (fT) for [[cerebral cortex|cortical]] activity and 10<sup>3</sup> fT for the human [[alpha rhythm]], is considerably smaller than the ambient magnetic noise in an urban environment, which is on the order of 10<sup>8</sup> fT or 0.1 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.

[[Image:Magnetoencephalography.svg|thumbnail|250px|right|Origin of the brain's magnetic field. The electric current also produces the EEG signal.]]The MEG (and EEG) signals derive from the net effect of ionic currents flowing in the [[dendrite]]s of neurons during [[synapse|synaptic]] transmission. In accordance with [[Maxwell's equations]], any electrical current will produce a magnetic field, and it is this field that is measured. The net currents can be thought of as [[dipole|current dipoles]],<ref name="HämäläinenHari1993" /> i.e. currents with a position, orientation, and magnitude, but no spatial extent.{{dubious|reason=No such thing, currents are at least one-dimensional and run in a loop (Kirchoff's current law) or end at a charge sink (capacitor) which doesn't apply here.|date=March 2018}} According to the [[right-hand rule]], a current dipole gives rise to a magnetic field that points around the axis of its vector component.

To generate a signal that is detectable, approximately 50,000 active neurons are needed.<ref>{{cite book | vauthors = Okada Y | year =  1983 | chapter = Neurogenesis of evoked magnetic fields | chapter-url = https://books.google.com/books?id=7x3aBwAAQBAJ&pg=PA399 | veditors = Williamson SH, Romani GL, Kaufman L, Modena I | title = Biomagnetism: an Interdisciplinary Approach | location = New York | publisher = Plenum Press | pages = 399–408 | isbn = 978-1-4757-1785-3 }}</ref> Since current dipoles must have similar orientations to generate magnetic fields that reinforce each other, it is often the layer of [[pyramidal cell]]s, which are situated perpendicular to the cortical surface, that gives 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 [[sulcus (neuroanatomy)|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.

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 nerve system.

== Magnetic shielding ==
Since the magnetic signals emitted by the brain are on the order of a few femtoteslas, shielding from external magnetic signals, including the [[Earth's magnetic field]], is necessary. Appropriate [[magnetic shielding]] can be obtained by constructing rooms made of [[aluminium]] and [[mu-metal]] for reducing high-frequency and low-frequency [[signal noise|noise]], respectively.
[[Image:MSR layered door.jpg|thumbnail|250px|left|Entrance to MSR, showing the separate shielding layers]]

=== Magnetically shielded room (MSR) ===
A magnetically shielded room (MSR) model consists of three nested main layers. Each of these layers is made of a pure aluminium layer plus a high-permeability [[Ferromagnetism|ferromagnetic]] layer, similar in composition to molybdenum [[permalloy]]. The ferromagnetic layer is supplied as 1&nbsp;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 to ensure that each main layer is electrically isolated. This helps eliminate [[radio frequency]] radiation, which would degrade SQUID performance. Electrical continuity of the aluminium is also maintained by aluminium overlay strips to ensure [[alternating current|AC]] [[eddy current]] shielding, which is important at frequencies greater than 1&nbsp;Hz. The junctions of the inner layer are often electroplated with silver or gold to improve conductivity of the aluminium layers.<ref>{{cite journal | vauthors = Cohen D, Schläpfer U, Ahlfors S, Hämäläinen M, Halgren E | title = New Six-Layer Magnetically-Shielded Room for MEG | journal = Proceedings of the 13th International Conference on Biomagnetism | date = August 2002 | volume = 10 | pages = 919–921 | location = Jena, Germany | publisher = VDE Verlag | s2cid = 27016664 | url = https://pdfs.semanticscholar.org/92c5/d54611a8c35f262f82bd81b1c6223d71d29f.pdf | archive-url = https://web.archive.org/web/20200803165556/https://pdfs.semanticscholar.org/92c5/d54611a8c35f262f82bd81b1c6223d71d29f.pdf | url-status = dead | archive-date = 2020-08-03 }}</ref>

=== Active shielding system ===
Active systems are designed for three-dimensional noise cancellation. To implement an active system, low-noise fluxgate [[magnetometer]]s are mounted at the center of each surface and oriented orthogonally to it. This negatively feeds a [[direct current|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.<ref name="cohen1" /> Moreover, noise cancellation algorithms can reduce both low-frequency and high-frequency noise. Modern systems have a [[noise floor]] of around 2–3&nbsp;fT/Hz<sup>0.5</sup> above 1&nbsp;Hz.

== Source localization ==

=== The inverse problem ===
{{main|Inverse problem}}

The challenge posed by MEG is to determine the location of electric activity within the brain from the induced magnetic fields outside the head. Problems such as this, where model parameters (the location of the activity) have to be estimated from measured data (the SQUID signals) are referred to as ''inverse problems'' (in contrast to ''forward problems''<ref>{{cite thesis | url = http://lib.tkk.fi/Diss/2006/isbn9512280914/ | vauthors = Tanzer IO | year = 2006 | title = Numerical Modeling in Electro- and Magnetoencephalography | degree = Ph.D. | publisher = Helsinki University of Technology | location = Finland }}</ref> where the model parameters (e.g. source location) are known and the data (e.g. the field at a given distance) is to be estimated.) The primary difficulty is that the inverse problem does not have a unique solution (i.e., there are infinite possible "correct" answers), and the problem of defining the "best" solution is itself the subject of intensive research<!--

-->.<ref name="HaukWakemanHenson">{{cite journal | vauthors = Hauk O, Wakeman DG, Henson R | title = Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics | journal = NeuroImage | volume = 54 | issue = 3 | pages = 1966–74 | date = February 2011 | pmid = 20884360 | pmc = 3018574 | doi = 10.1016/j.neuroimage.2010.09.053 }}</ref> Possible solutions can be derived using models involving prior knowledge of brain activity.

The source models can be either over-determined or under-determined. An over-determined model may consist of a few point-like sources ("equivalent dipoles"), whose locations are then estimated from the data. Under-determined models may be used in cases where many different distributed areas are activated ("distributed source solutions"): there are infinitely many possible current distributions explaining the measurement results, but the most likely is selected. Localization algorithms make use of given source and head models to find a likely location for an underlying focal field generator.

One type of localization algorithm for overdetermined models operates by [[Expectation-maximization algorithm|expectation-maximization]]: the system is initialized with a first guess.  A loop is started, in which a forward model is used to simulate the magnetic field that would result from the current guess. The guess is adjusted to reduce the discrepancy between the simulated field and the measured field.  This process is iterated until convergence.

Another common technique is [[beamforming]], wherein a theoretical model of the magnetic field produced by a given current dipole is used as a prior, along with second-order statistics of the data in the form of a [[covariance matrix]], to calculate a linear weighting of the [[sensor array]] (the beamformer) via the [[Backus–Gilbert method|Backus-Gilbert inverse]]. This is also known as a linearly constrained minimum variance (LCMV) beamformer. When the beamformer is applied to the data, it produces an estimate of the power in a "virtual channel" at the source location.

The extent to which the constraint-free MEG inverse problem is ill-posed cannot be overemphasized. If one's goal is to estimate the current density within the human brain with say a 5mm resolution then it is well established that the vast majority of the information needed to perform a unique inversion must come not from the magnetic field measurement but rather from the constraints applied to the problem. Furthermore, even when a unique inversion is possible in the presence of such constraints said inversion can be unstable. These conclusions are easily deduced from published works.<ref>{{cite journal | vauthors = Sheltraw D, Coutsias E| journal=Journal of Applied Physics |volume=94|number=8|year=2003 | url = http://www.math.unm.edu/~vageli/papers/JApplPhys_94_5307.pdf  | doi = 10.1063/1.1611262 | title=Invertibility of current density from near-field electromagnetic data|pages=5307–5315| bibcode=2003JAP....94.5307S }}</ref>

=== Magnetic source imaging ===
The 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 [[Fiduciary marker|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 the functional MEG data onto the structural MRI data ("[[coregistration]]") 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, not because of the relatively low spatial resolution of MEG, but rather some inherent uncertainty in the 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 to be focal. This dipole fitting procedure is non-linear and over-determined, since the number of unknown dipole parameters is smaller than the number of MEG measurements.<ref>{{cite journal | vauthors = Huang MX, Dale AM, Song T, Halgren E, Harrington DL, Podgorny I, Canive JM, Lewis S, Lee RR | title = Vector-based spatial-temporal minimum L1-norm solution for MEG | journal = NeuroImage | volume = 31 | issue = 3 | pages = 1025–37 | date = July 2006 | pmid = 16542857 | doi = 10.1016/j.neuroimage.2006.01.029 | s2cid = 9607000 | url = https://escholarship.org/uc/item/4xr5z4qd }}</ref> Automated multiple dipole model algorithms such as [[multiple signal classification]] (MUSIC) and multi-start spatial and temporal modeling (MSST) are applied to the analysis of MEG responses. The limitations of dipole models for characterizing neuronal responses are (1) difficulties in localizing extended sources with ECDs, (2) problems with accurately estimating the total number of dipoles in advance, and (3) dependency on dipole location, especially depth in the brain.

=== Distributed source models ===
Unlike multiple-dipole modeling, distributed source models divide 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.<ref>{{cite journal | vauthors = Hämäläinen MS, Ilmoniemi RJ | title = Interpreting magnetic fields of the brain: minimum norm estimates | journal = Medical & Biological Engineering & Computing | volume = 32 | issue = 1 | pages = 35–42 | date = January 1994 | pmid = 8182960 | doi = 10.1007/BF02512476 | s2cid = 6796187 }}</ref> As the number of unknown dipole moments is much greater than the number of MEG sensors, the inverse solution is highly underdetermined, so additional constraints are needed to reduce ambiguity of the solution. The primary advantage of this approach is that no prior specification of the source model is necessary. However, the resulting distributions may be difficult to interpret, because they only reflect a "blurred" (or even distorted) image of the true neuronal source distribution. The matter is complicated by the fact that spatial resolution depends strongly on various parameters such as brain area, depth, orientation, number of sensors etc.<ref>{{cite journal | vauthors = Molins A, Stufflebeam SM, Brown EN, Hämäläinen MS | title = Quantification of the benefit from integrating MEG and EEG data in minimum ℓ<sub>2</sub>-norm estimation | journal = NeuroImage | volume = 42 | issue = 3 | pages = 1069–77 | date = September 2008 | pmid = 18602485 | doi = 10.1016/j.neuroimage.2008.05.064 | s2cid = 6462818 }}</ref>

=== Independent component analysis (ICA) ===
[[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.<ref>{{cite journal | vauthors = Jung TP, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski TJ | title = Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects | journal = Clinical Neurophysiology | volume = 111 | issue = 10 | pages = 1745–58 | date = October 2000 | pmid = 11018488 | doi = 10.1016/S1388-2457(00)00386-2 | citeseerx = 10.1.1.164.9941 | s2cid = 11044416 }}</ref> However, ICA has poor resolution of highly correlated brain sources.

== Use in the field ==
[[File:MEG map.png|thumb|371x371px|Over 100 MEG systems are known to operate worldwide, with Japan possessing the greatest number of MEG systems per capita and the United States possessing the greatest overall number of MEG systems. A very small number of systems worldwide are designed for infant and/or fetal recordings.]]
In research, MEG's primary use is the measurement of time courses of activity. MEG can resolve events with a precision of 10 milliseconds or faster, while [[functional magnetic resonance imaging]] (fMRI), which depends on changes in blood flow, can at best resolve events with a precision of several hundred milliseconds.  MEG also accurately pinpoints sources in primary auditory, somatosensory, and motor areas. For creating functional maps of human cortex during more complex cognitive tasks, MEG is most often combined with fMRI, as the methods complement each other. Neuronal (MEG) and [[hemodynamics|hemodynamic]] fMRI data do not necessarily agree, in spite of the tight relationship between local field potentials (LFP) and blood oxygenation level-dependent (BOLD) signals. MEG and BOLD signals may originate from the same source (though the BOLD signals are filtered through the hemodynamic response).

MEG is also being used to better localize responses in the brain. The openness of the MEG setup allows external auditory and visual stimuli to be easily introduced. Some movement by the subject is also possible as long as it does not jar the subject's head. The responses in the brain before, during, and after the introduction of such stimuli/movement can then be mapped with greater spatial resolution than was previously possible with EEG.<ref>{{cite journal | vauthors = Cui R, Cunnington R, Beisteiner R, Deecke L | title = Effects of force-load on cortical activity preceding voluntary finger movement|journal=Neurology, Psychiatry and Brain Research|volume=18|issue=3|year=2012|pages=97–104|doi=10.1016/j.npbr.2012.03.001}}</ref> Psychologists are also taking advantage of MEG neuroimaging to better understand relationships between brain function and behavior. For example, a number of studies have been done comparing the MEG responses of patients with psychological troubles to control patients. There has been great success isolating unique responses in patients with schizophrenia, such as auditory gating deficits to human voices.<ref>{{cite journal | vauthors = Hirano Y, Hirano S, Maekawa T, Obayashi C, Oribe N, Monji A, Kasai K, Kanba S, Onitsuka T | title = Auditory gating deficit to human voices in schizophrenia: a MEG study | journal = Schizophrenia Research | volume = 117 | issue = 1 | pages = 61–7 | date = March 2010 | pmid = 19783406 | doi = 10.1016/j.schres.2009.09.003 | s2cid = 7845180 }}</ref> MEG is also being used to correlate standard psychological responses, such as the emotional dependence of language comprehension.<ref>{{cite journal | vauthors = Ihara A, Wei Q, Matani A, Fujimaki N, Yagura H, Nogai T, Umehara H, Murata T | title = Language comprehension dependent on emotional context: a magnetoencephalography study | journal = Neuroscience Research | volume = 72 | issue = 1 | pages = 50–8 | date = January 2012 | pmid = 22001763 | doi = 10.1016/j.neures.2011.09.011 | s2cid = 836242 }}</ref>

Recent studies have reported successful classification of patients with [[multiple sclerosis]], [[Alzheimer's disease]], [[schizophrenia]], [[Sjögren's syndrome]], [[chronic alcoholism]], [[facial pain]] and [[thalamocortical dysrhythmia]]s. MEG can be used to distinguish these patients from healthy control subjects, suggesting a future role of MEG in diagnostics.<!----><ref name="pmid18057502">{{cite journal | vauthors = Georgopoulos AP, Karageorgiou E, Leuthold AC, Lewis SM, Lynch JK, Alonso AA, Aslam Z, Carpenter AF, Georgopoulos A, Hemmy LS, Koutlas IG, Langheim FJ, McCarten JR, McPherson SE, Pardo JV, Pardo PJ, Parry GJ, Rottunda SJ, Segal BM, Sponheim SR, Stanwyck JJ, Stephane M, Westermeyer JJ | title = Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders | journal = Journal of Neural Engineering | volume = 4 | issue = 4 | pages = 349–55 | date = December 2007 | pmid = 18057502 | doi = 10.1088/1741-2560/4/4/001 | url = http://stacks.iop.org/1741-2560/4/349 | bibcode = 2007JNEng...4..349G | hdl = 10161/12446 | s2cid = 2836220 | hdl-access = free }}</ref><ref name="Montez2009">{{cite journal | vauthors = Montez T, Poil SS, Jones BF, Manshanden I, Verbunt JP, van Dijk BW, Brussaard AB, van Ooyen A, Stam CJ, Scheltens P, Linkenkaer-Hansen K | title = Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 5 | pages = 1614–9 | date = February 2009 | pmid = 19164579 | pmc = 2635782 | doi = 10.1073/pnas.0811699106 | bibcode = 2009PNAS..106.1614M | doi-access = free }}</ref>

A large part of the difficulty and cost of using MEG is the need for manual analysis of the data. Progress has been made in analysis by computer, comparing a patient's scans with those drawn from a large database of normal scans, with the potential to reduce cost greatly.<ref name=rose/>

=== Brain connectivity and neural oscillations ===
Based on its perfect temporal resolution, magnetoencephalography (MEG) is now heavily used to study oscillatory activity in the brain, both in terms of local neural synchrony and cross-area synchronisation. As an example for local neural synchrony, MEG has been used to investigate alpha rhythms in various targeted brain regions, such as in visual<ref>{{cite journal | vauthors = Bagherzadeh Y, Baldauf D, Pantazis D, Desimone R | title = Alpha Synchrony and the Neurofeedback Control of Spatial Attention | journal = Neuron | volume = 105 | issue = 3 | pages = 577–587.e5 | date = February 2020 | pmid = 31812515 | doi = 10.1016/j.neuron.2019.11.001 | hdl-access = free | s2cid = 208614924 | doi-access = free | hdl = 11572/252726 }}</ref><ref>{{cite journal | vauthors = de Vries E, Baldauf D | title = Attentional Weighting in the Face Processing Network: A Magnetic Response Image-guided Magnetoencephalography Study Using Multiple Cyclic Entrainments | journal = Journal of Cognitive Neuroscience | volume = 31 | issue = 10 | pages = 1573–1588 | date = October 2019 | pmid = 31112470 | doi = 10.1162/jocn_a_01428 | hdl-access = free | s2cid = 160012572 | hdl = 11572/252722 }}</ref> or auditory cortex.<ref>{{cite journal | vauthors = de Vries IE, Marinato G, Baldauf D | title = Decoding Object-Based Auditory Attention from Source-Reconstructed MEG Alpha Oscillations | journal = The Journal of Neuroscience | volume = 41 | issue = 41 | pages = 8603–8617 | date = October 2021 | pmid = 34429378 | pmc = 8513695 | doi = 10.1523/JNEUROSCI.0583-21.2021 }}</ref> Other studies have used MEG to study the neural interactions between different brain regions (e.g., between frontal cortex and visual cortex).<ref>{{cite journal | vauthors = Baldauf D, Desimone R | title = Neural mechanisms of object-based attention | journal = Science | volume = 344 | issue = 6182 | pages = 424–427 | date = April 2014 | pmid = 24763592 | doi = 10.1126/science.1247003 | s2cid = 34728448 | doi-access = free | bibcode = 2014Sci...344..424B }}</ref> Magnetoencephalography can also be used to study changes in neural oscillations across different stages of consciousness, such as in sleep.<ref>{{cite journal | vauthors = Brancaccio A, Tabarelli D, Bigica M, Baldauf D | title = Cortical source localization of sleep-stage specific oscillatory activity | journal = Scientific Reports | volume = 10 | issue = 1 | pages = 6976 | date = April 2020 | pmid = 32332806 | pmc = 7181624 | doi = 10.1038/s41598-020-63933-5 | bibcode = 2020NatSR..10.6976B }}</ref>

=== Focal epilepsy ===
The clinical uses of MEG are in detecting and localizing pathological activity in patients with [[epilepsy]], and in localizing [[eloquent cortex]] for surgical planning in patients with [[brain tumor]]s or intractable epilepsy. The goal of epilepsy surgery is to remove the epileptogenic tissue while sparing healthy brain areas.<ref>{{cite book | vauthors = Luders HO| title = Epilepsy Surgery|publisher=New York Raven Press|year=1992}}</ref> Knowing the exact position of essential brain regions (such as the [[primary motor cortex]] and [[primary sensory cortex]], [[visual cortex]], and areas involved in speech production and comprehension) helps to avoid surgically induced neurological deficits. Direct cortical stimulation and somatosensory evoked potentials recorded on [[electrocorticography]] (ECoG) are considered the gold standard for localizing essential brain regions. These procedures can be performed either intraoperatively or from chronically indwelling subdural grid electrodes. Both are invasive.

Noninvasive MEG localizations of the central sulcus obtained from somatosensory evoked magnetic fields show strong agreement with these invasive recordings.<ref>{{cite journal | vauthors = Sutherling WW, Crandall PH, Darcey TM, Becker DP, Levesque MF, Barth DS | title = The magnetic and electric fields agree with intracranial localizations of somatosensory cortex | journal = Neurology | volume = 38 | issue = 11 | pages = 1705–14 | date = November 1988 | pmid = 3185905 | doi = 10.1212/WNL.38.11.1705 | s2cid = 8828767 }}</ref><ref>{{cite journal | vauthors = Rowley HA, Roberts TP | title = Functional localization by magnetoencephalography | journal = Neuroimaging Clinics of North America | volume = 5 | issue = 4 | pages = 695–710 | date = November 1995 | pmid = 8564291 }}</ref><ref>{{cite journal | vauthors = Gallen CC, Hirschkoff EC, Buchanan DS | title = Magnetoencephalography and magnetic source imaging. Capabilities and limitations | journal = Neuroimaging Clinics of North America | volume = 5 | issue = 2 | pages = 227–49 | date = May 1995 | pmid = 7640886 }}</ref> 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 localization of cortical tissue and MEG recordings shows the effectiveness of MEG analysis and indicates that MEG may substitute invasive procedures in the future.

=== Fetal ===
MEG has been used to study cognitive processes such as [[visual perception|vision]], [[Hearing (sense)|audition]], and [[language processing]] in fetuses and newborns.<ref>{{cite journal | vauthors = Sheridan CJ, Matuz T, Draganova R, Eswaran H, Preissl H | title = Fetal Magnetoencephalography - Achievements and Challenges in the Study of Prenatal and Early Postnatal Brain Responses: A Review | journal = Infant and Child Development | volume = 19 | issue = 1 | pages = 80–93 | year = 2010 | pmid = 20209112 | pmc = 2830651 | doi = 10.1002/icd.657 }}</ref> Only two bespoke MEG systems, designed specifically for fetal recordings, operate worldwide.<ref name="Frohlich_2023">{{cite journal | vauthors = Frohlich J, Bayne T, Crone JS, DallaVecchia A, Kirkeby-Hinrup A, Mediano PA, Moser J, Talar K, Gharabaghi A, Preissl H | title = Not with a "zap" but with a "beep": Measuring the origins of perinatal experience | journal = NeuroImage | volume = 273 | pages = 120057 | date = June 2023 | pmid = 37001834 | doi = 10.1016/j.neuroimage.2023.120057 | s2cid = 257807321 | doi-access = free | url = https://psyarxiv.com/65zsc/download }}</ref> The first was installed at the [[University of Arkansas at Little Rock|University of Arkansas]] in 2000, and the second was installed at the [[University of Tübingen]] in 2008. Both devices are referred to as [[Superconducting QUantum Interference Device|SQUID]] arrays for reproductive assessment (SARA) and utilize a concave sensor array whose shape compliments the abdomen of a pregnant woman. Fetal recordings of cortical activity are feasible with a SARA device from a gestational age of approximately 25 weeks onward until birth. Although built for fetal recordings, SARA systems can also record from infants placed in a cradle head-first toward the sensory array.<ref name="Frohlich_2023" /> A third high density custom-made unit with similar whole abdomen coverage has been installed in 2002 at the University of Kansas Medical Center to assess fetal electrophysiology.<ref>{{Cite web |date=2002-12-10 |title=CTF installs MEG system at KUMC |url=https://www.auntminnie.com/industry-news/article/15563780/ctf-installs-meg-system-at-kumc |access-date=2024-10-22 |website=AuntMinnie |language=en-us}}</ref><ref>{{cite journal | vauthors = Minai U, Gustafson K, Fiorentino R, Jongman A, Sereno J | title = Fetal rhythm-based language discrimination: a biomagnetometry study | journal = NeuroReport | volume = 28 | issue = 10 | pages = 561–564 | date = July 2017 | pmid = 28538518 | pmc = 5611858 | doi = 10.1097/WNR.0000000000000794 }}</ref> While only a small number of devices worldwide are capable of fetal MEG recordings as of 2023, the proliferation of [[Optically pumped atomic magnetometer|optically pumped magnetometers]] for MEG in neuroscience research<ref>{{cite journal | vauthors = Brookes MJ, Leggett J, Rea M, Hill RM, Holmes N, Boto E, Bowtell R | title = Magnetoencephalography with optically pumped magnetometers (OPM-MEG): the next generation of functional neuroimaging | journal = Trends in Neurosciences | volume = 45 | issue = 8 | pages = 621–634 | date = August 2022 | pmid = 35779970 | pmc = 10465236 | doi = 10.1016/j.tins.2022.05.008 | s2cid = 250122240 | doi-access = free }}</ref> will likely result in a greater number of research centers capable of recording and publishing fetal MEG data in the near future.<ref name="Frohlich_2023" />

===Traumatic brain injury===
MEG can be used to identify traumatic brain injury, which is particularly common among soldiers exposed to explosions. Such injuries are not easily diagnosed by other methods, as the symptoms (e.g. sleep disturbances, memory problems) overlap with those from frequent co-comorbidities such as [[post-traumatic stress disorder]] (PTSD).<ref name=rose>{{cite news |title=British army veterans denied treatment for brain injuries | vauthors = Rose D |newspaper=The Observer |date=20 February 2022 |url= https://www.theguardian.com/society/2022/feb/20/british-army-veterans-denied-treatment-for-brain-injuries}}</ref>

==Comparison with related 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 [[millisecond|ms]]). Since the MEG signal is a direct measure of neuronal activity, its temporal resolution is comparable with that of intracranial electrodes.

MEG complements other brain activity measurement techniques such as [[electroencephalography]] (EEG), [[positron emission tomography]] (PET), and [[fMRI]].  Its strengths consist in independence of head geometry compared to EEG (unless [[ferromagnetic]] [[implant (medicine)|implants]] are present), non-invasiveness, use of no ionizing radiation, as opposed to PET and high temporal resolution as opposed to fMRI.

=== MEG in comparison to EEG ===
Although EEG and MEG signals originate from the same neurophysiological processes, there are important differences.<ref>{{cite journal | vauthors = Cohen D, Cuffin BN | title = Demonstration of useful differences between magnetoencephalogram and electroencephalogram | journal = Electroencephalography and Clinical Neurophysiology | volume = 56 | issue = 1 | pages = 38–51 | date = July 1983 | pmid = 6190632 | doi = 10.1016/0013-4694(83)90005-6 }}</ref> Magnetic fields are less distorted than electric fields by the skull and scalp, which results in a better spatial resolution of the MEG. 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. Scalp EEG can, therefore, detect activity both in the sulci and at the top of the cortical gyri, whereas MEG is most sensitive to activity originating in sulci. EEG is, therefore, sensitive to activity in more brain areas, but activity that is visible in MEG can also be localized with more accuracy.

Scalp EEG is sensitive to extracellular volume currents produced by postsynaptic potentials. MEG detects intracellular currents associated primarily with these synaptic potentials because the field components generated by volume currents tend to cancel out in a spherical volume conductor.<ref>{{cite journal | vauthors = Barth DS, Sutherling W, Beatty J | title = Intracellular currents of interictal penicillin spikes: evidence from neuromagnetic mapping | journal = Brain Research | volume = 368 | issue = 1 | pages = 36–48 | date = March 1986 | pmid = 3955364 | doi = 10.1016/0006-8993(86)91040-1 | s2cid = 3078690 }}</ref> The decay of magnetic fields as a function of distance is more pronounced than for electric fields. Therefore, MEG is more sensitive to superficial cortical activity, which makes it useful for the study of neocortical epilepsy. Finally, MEG is reference-free, while scalp EEG relies on a reference that, when active, makes interpretation of the data difficult.

== See also ==
{{columns-list|colwidth=30em|
* [[Auditory evoked field]]
* [[Direct brain interfaces]]
* [[Electrophysiology]]
* [[Evoked field]]
* [[FieldTrip]]
* [[Magnetocardiography]]
* [[Magnetogastrography]]
* [[Magnetometer]]
* [[Magnetomyography]]
* [[SQUID]]
* [[Whole brain emulation]]
}}

== References ==
{{reflist|32em}}

== Further reading ==
{{refbegin|32em}}
* {{cite journal | vauthors = Baillet S, Mosher JC, Leahy RM | title = Electromagnetic Brain Mapping | journal = IEEE Signal Processing Magazine | volume = 18 | issue = 6 | date = November 2001 | pages = 14–30 | bibcode = 2001ISPM...18...14B | doi = 10.1109/79.962275 }}
* {{cite journal | vauthors = Cohen D | year = 2004 | title = Boston and the history of biomagnetism | journal = Neurology and Clinical Neurophysiology | volume = 30 | issue = 1 | page = 114 | pmid = 16012683 }}
* {{cite book | vauthors = Cohen D, Halgren E | year = 2004 | chapter = Magnetoencephalography | title = Encyclopedia of Neuroscience | veditors = Adelman G, Smith B | publisher = Elsevier }}
* {{cite journal | vauthors = Hämäläinen M, Hari R, Ilmoniemi R, Knuutila J, Lounasmaa OV | year = 1993 | title = Magnetoencephalography – theory, instrumentation, and applications to noninvasive studies of signal processing in the human brain | url = https://aaltodoc.aalto.fi:443/bitstream/123456789/18757/1/A1_h%c3%a4m%c3%a4l%c3%a4inen_matti_1993.pdf| journal = Reviews of Modern Physics | volume = 65 | issue = 2| pages = 413–497 | doi=10.1103/revmodphys.65.413 | bibcode = 1993RvMP...65..413H}}
* {{cite book | vauthors = Hansen PC, Kringelbach ML, Salmelin R | year = 2010 | title = MEG: An Introduction to Methods | location = New York | publisher = Oxford University Press Inc. }}
* {{cite journal | vauthors = Murakami S, Okada Y | title = Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals | journal = The Journal of Physiology | volume = 575 | issue = Pt 3 | pages = 925–36 | date = September 2006 | pmid = 16613883 | pmc = 1995687 | doi = 10.1113/jphysiol.2006.105379 }}
* {{cite journal | vauthors = Suk J, Ribary U, Cappell J, Yamamoto T, Llinás R | title = Anatomical localization revealed by MEG recordings of the human somatosensory system | journal = Electroencephalography and Clinical Neurophysiology | volume = 78 | issue = 3 | pages = 185–96 | date = March 1991 | pmid = 1707790 | doi = 10.1016/0013-4694(91)90032-y }}
* {{cite thesis | url = http://lib.tkk.fi/Diss/2006/isbn9512280914/ | vauthors = Tanzer OI | year = 2006 | title = Numerical Modeling in Electro- and Magnetoencephalography | degree = Ph.D. | publisher = Helsinki University of Technology | location = Finland }}
* {{cite journal | vauthors = Wolters CH, Anwander A, Tricoche X, Weinstein D, Koch MA, MacLeod RS | title = Influence of Tissue Conductivity Anisotropy on EEG/MEG Field and Return Current Computation in a realistic Head Model: A Simulation and Visualization Study using High-Resolution Finite Element Modeling. | journal = NeuroImage | volume = 30 | issue = 3 | date = 2006 | pages = 813–826 | doi = 10.1016/j.neuroimage.2005.10.014 | pmid = 16364662 | s2cid = 5578998 | url = http://edoc.mpg.de/285678 | hdl = 11858/00-001M-0000-0010-BD20-9 | hdl-access = free }}
{{refend}}

{{Electrodiagnosis}}
{{Central nervous system tests and procedures}}
{{EEG}}

[[Category:Magnetoencephalography| ]]
[[Category:Diagnostic neurology]]
[[Category:Electrodiagnosis]]
[[Category:Medical tests]]
[[Category:Neurotechnology]]
[[Category:Neuroimaging]]