Golstrategy

• Meg And Dia Monster Dubstep
• Meg Donnelly

Audra Miracle This is the go-to for anyone struggling with exposure. Your knowledge and conceptual way of teaching gave me the “a ha” moment.I finally understand it! Chords for Meg & Dia - Monster (DotEXE Dubstep Remix). Play along with guitar, ukulele, or piano with interactive chords and diagrams. Includes transpose. Stream Meg & Dia - Monster (DotEXE Remix) by DotEXE from desktop or your mobile device.

Meg

• She's an adventurer in training with lots to learn.
• She's an adventurer in training who could use some pointers.
• An adventurer in training who really looks up to you.
• Rumour has it she is also known as Swagatha.
• It looks like this a little too much adventure for her.
• For the first four years of her life, her parents thought she was a housecat.
• An adventurer that makes you just a little bit proud.

Megatha Christie, commonly known as Meg, is an aspiring adventurer who describes herself as the player's biggest fan. Meg can be found in the player's player-owned port in the south-west corner. Once a week, resetting on Wednesday at approximately 00:00 UTC, Meg will ask the player for advice on how to deal with three situations that she is expecting on her adventures. After hearing some advice, she will teleport out to her adventures.

When Meg returns the following Wednesday, players receive a chatbox message upon logging in: 'You receive word that Meg has returned from her latest adventure.' The next time a player talks to Meg, she will tell them how well she did and give them a treasure chest, with a reward that depends on the quality of the advice she was given.

The advice that Meg is given acts as a bonus to her skills. It is always best to give the best possible advice from the choices offered; however, even she may fail to have a perfect result regardless of whether she was given the best possible advice to all three questions. Excellent answers only give big boosts to Meg's own skills - if the mission is above her league, she will still fail it, although excellent answers guarantee that the player will be rewarded with at least a medium lamp. Players do not need to meet the skill requirements for player-owned ports to speak with and advise Meg.

Exiting the conversation and offering to Help her again before completing the dialogue forces a player to begin the dialogue again from the beginning, allowing a player to choose different advice to give, but Meg will teleport away after she is answered three times.

Meg's cases

During the Mega May event in 2016, players assisted Meg a.k.a. Megatha Christie with her detective agency by investigating cases for her. One case was unlocked each day, and rewards included experience lamps, pets, and an outfit.

Kindred Spirits

During the course of her investigations into various thefts and kidnappings, Meg was herself kidnapped by a dragonkinesque creature. She was used as bait alongside Major Mary Rancour, Linza and Brother Samwell to force the World Guardian to play a series of games designed by Sliske involving the Barrows brothers. Brother Samwell was killed while the others were forced to help the World Guardian defeat the games. During Ahrim's trial, she was possibly tortured, and during Verac's trial, she may have been hurt making a key. During Guthan's trial, she badly impersonated the World Guardian, who was investigating Sliske's secret laboratory. Sliske saw through the guise and, when the World Guardian arrived, beat them and turned Linza into a wight. Meg, alongside Major Rancour and the World Guardian, fled to the surface after the Barrows brothers refused to kill them. She returned back to her post in the player-owned ports afterwards.

Player-owned port

The reward comes in the form of a treasure chest that contains two items: a stack of coins and an experience lamp giving experience in a random skill. Players will always receive at least a small experience lamp no matter how poorly Meg did. The lamps are equivalent to those awarded by Treasure Hunter.

Meg's questions

The first question will have four answers to select from, while the remaining two questions will have three. The responses available to you depend on your level in the skill being asked about, with the best responses only being available at higher levels. The possible types of answers are excellent, good, bad, neutral or terrible. For some questions there are two possible answers for each type, for a total of ten possible answers per question, and for others there is only one possible choice for each level of answer.

If any of your levels are lower than their base levels, the best possible responses might sometimes not be available. This includes Prayer and Summoning levels that have been drained from maximum.^{[source needed]}

Answers in this table are colour coded as follows:

A suggested method of navigating this table is searching for phrases in the question using the 'Find' function (CTRL+F, or ⌘+F on Mac).

Dialogue

Trivia

• The fourth option when speaking to her is 'Shut up Meg.', which is a reference to the reoccurring line in the American animated sitcom Family Guy. Choosing this option does not have any effect on your standing with Meg.
• One of her descriptions also references Family Guy by stating that for the first four years of her life, her parents thought that she was a house cat, similar to Peter sending a letter to Meg stating such.
• Meg's question about travelling across terrain faster could be a reference that players often request a 'riding skill' or 'mounts'. So far no mount or riding system exists in RuneScape.
• When Meg asks about the best way to steal money due to the Duke being rich, while his subjects are poor, is most likely a Robin Hood reference.
• The options for Meg traversing a waterfall from bad-good are a reference to the Waterfall Quest where you take a raft, before using a rope to descend the cliff.
• The riding crop she holds in her hand is a possible reference to Indiana Jones.
• The question about the rare lobsters in Morytania that are deep obsidian black is a reference to the 'Rare' Black lobsters scam, where players would attempt to sell burnt lobsters for lots of money. One of the answers for the question references the cutting gem scam.
• The question on the El Piskay Demon fish of Lumbridge River could be a word twist on El Fishy as pisc- is the latin root for fish, as well as Elpis, the Greek spirit of hope.
• The answer 'Dig a tunnel with a spoon' is a reference to the June 1962 Alcatraz escape, where three prisoners escaped Alcatraz by digging a hole through the cell wall with steel spoons.
• The answer stating 'Poke her with spoons. Witches hate spoons.' in the question about stealing a Morytanian witch's book of spells is a reference to the popular internet meme about female dogs liking leaves.
• The question of how to handle a rare shark that walks on land, and the answers 'They'll probably use sound to track prey' and 'Keep above ground', may be a possible reference to the 1990 film Tremors.
• Meg's name and examine text during Mega May are references to crime novelist Agatha Christie.
• During the Mega May event, she was replaced by Seasinger Jemi for her usual weekly adventurer role where she asks questions to the player, though her voiced dialogue still said Meg.
• The answer 'Run up and punch him in the face.' is likely a reference to a Japanese Anime called 'A Certain Magical Index', where the main premise of the show is the protagonist running up and punching magicians in the face to defeat them.

Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers. Arrays of SQUIDs (superconducting quantum interference devices) are currently the most common magnetometer, while the SERF (spin exchange relaxation-free) magnetometer is being investigated for future machines.^[1][2] 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.^[3]

• 3Magnetic shielding
• 4Source localization
• 5Use in the field
• 6Comparison with related techniques

History[edit]

MEG signals were first measured by University of Illinois physicist David Cohen in 1968,^[4] 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 E. Zimmerman, a researcher at Ford Motor Company,^[5] to again measure MEG signals.^[6] 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. 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. 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 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).^[7] More recently, in 2017, researchers built a working prototype that uses SERF magnetometers installed into portable individually 3D-printed helmets^[2], 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[edit]

Synchronized neuronal currents induce weak magnetic fields. The brain's magnetic field, measuring at 10 femtotesla (fT) for cortical activity and 10³ fT for the human alpha rhythm, is considerably smaller than the ambient magnetic noise in an urban environment, which is on the order of 10⁸ 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.

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 a magnetic field, and it is this field that is measured. The net currents can be thought of as current dipoles^{[citation needed]}, i.e. currents with a position, orientation, and magnitude, but no spatial extent^[dubious]. 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.^[8] 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 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 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 nerves.

Magnetic shielding[edit]

Meg And Dia Monster Dubstep

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 noise, respectively.

Meg Donnelly

Magnetically shielded room (MSR)[edit]

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 ferromagnetic layer, similar in composition to molybdenum 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 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 ACeddy 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 aluminium layers.^[9]

Active shielding system[edit]

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.^[4] Moreover, noise cancellation algorithms can reduce both low-frequency and high-frequency noise. Modern systems have a noise floor of around 2–3 fT/Hz^0.5 above 1 Hz.

Source localization[edit]

The inverse problem[edit]

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^[10] 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.^[11] 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: 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 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.^[12]

Magnetic source imaging[edit]

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 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, 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[edit]

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.^[13] Automated multiple dipole model algorithms such as multiple signal classification (MUSIC) and MSST (MultiStart Spatial and Temporal) modeling 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[edit]

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.^[14] 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 several parameters such as brain area, depth, orientation, number of sensors etc.^[15]

Independent component analysis (ICA)[edit]

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.^[16] However, ICA has poor resolution of highly correlated brain sources.

Use in the field[edit]

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 MRI (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 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.^[17] 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.^[18] MEG is also being used to correlate standard psychological responses, such as the emotional dependence of language comprehension.^[19]

Recent studies have reported successful classification of patients with multiple sclerosis, Alzheimer's disease, schizophrenia, Sjögren's syndrome, chronic alcoholism, facial pain and thalamocortical dysrhythmias. MEG can be used to distinguish these patients from healthy control subjects, suggesting a future role of MEG in diagnostics.^[20][21]

Focal epilepsy[edit]

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 tumors or intractable epilepsy. The goal of epilepsy surgery is to remove the epileptogenic tissue while sparing healthy brain areas.^[22] 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 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.^[23][24][25] 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[edit]

MEG has been used to study cognitive processes such as vision, audition, and language processing in fetuses and newborns.^[26]

Comparison with related techniques[edit]

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 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 ferromagneticimplants 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[edit]

Although EEG and MEG signals originate from the same neurophysiological processes, there are important differences.^[27] 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^[28] 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[edit]

References[edit]

1. ^Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993). 'Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain'(PDF). Reviews of Modern Physics. 65 (2): 413–497. Bibcode:1993RvMP...65..413H. doi:10.1103/RevModPhys.65.413. ISSN0034-6861.
2. ^ ^abBoto, Elena; Holmes, Niall; Leggett, James; Roberts, Gillian; Shah, Vishal; Meyer, Sofie S.; Muñoz, Leonardo Duque; Mullinger, Karen J.; Tierney, Tim M. (March 2018). 'Moving magnetoencephalography towards real-world applications with a wearable system'. Nature. 555 (7698): 657–661. Bibcode:2018Natur.555..657B. doi:10.1038/nature26147. ISSN1476-4687. PMC6063354. PMID29562238.
3. ^Carlson NR (2013). Physiology of Behavior. Upper Saddle River, NJ: Pearson Education Inc. pp. 152–153. ISBN978-0-205-23939-9.
4. ^ ^abCohen D (August 1968). 'Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents'. Science. 161 (3843): 784–6. Bibcode:1968Sci...161..784C. doi:10.1126/science.161.3843.784. PMID5663803.
5. ^Zimmerman JE, Theine P, Harding JT (1970). 'Design and operation of stable rf-biased superconducting point-contact quantum devices, etc'. Journal of Applied Physics. 41 (4): 1572–1580. doi:10.1063/1.1659074.
6. ^Cohen D (February 1972). 'Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer'(PDF). Science. 175 (4022): 664–6. Bibcode:1972Sci...175..664C. doi:10.1126/science.175.4022.664. PMID5009769.
7. ^Sander TH, Preusser J, Mhaskar R, Kitching J, Trahms L, Knappe S (May 2012). 'Magnetoencephalography with a chip-scale atomic magnetometer'. Biomedical Optics Express. 3 (5): 981–90. doi:10.1364/BOE.3.000981. PMC3342203. PMID22567591.
8. ^Okada Y (1983). 'Neurogenesis of evoked magnetic fields'. In Williamson SH, Romani GL, Kaufman L, Modena I (eds.). Biomagnetism: an Interdisciplinary Approach. New York: Plenum Press. pp. 399–408. ISBN978-1-4757-1785-3.
9. ^Cohen D, Schläpfer U, Ahlfors S, Hämäläinen M, Halgren E. 'New Six-Layer Magnetically-Shielded Room for MEG'. Charlestown, Massachusetts: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital.Missing or empty |url= (help)
10. ^Tanzer IO (2006). Numerical Modeling in Electro- and Magnetoencephalography (Ph.D. thesis). Finland: Helsinki University of Technology.
11. ^Hauk O, Wakeman DG, Henson R (February 2011). 'Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics'. NeuroImage. 54 (3): 1966–74. doi:10.1016/j.neuroimage.2010.09.053. PMC3018574. PMID20884360.
12. ^Sheltraw D, Coutsias E (2003). 'Invertibility of current density from near-field electromagnetic data'(PDF). Journal of Applied Physics. 94 (8): 5307–5315. Bibcode:2003JAP....94.5307S. doi:10.1063/1.1611262.
13. ^Huang MX, Dale AM, Song T, Halgren E, Harrington DL, Podgorny I, Canive JM, Lewis S, Lee RR (July 2006). 'Vector-based spatial-temporal minimum L1-norm solution for MEG'. NeuroImage. 31 (3): 1025–37. doi:10.1016/j.neuroimage.2006.01.029. PMID16542857.
14. ^Hämäläinen MS, Ilmoniemi RJ (January 1994). 'Interpreting magnetic fields of the brain: minimum norm estimates'. Medical & Biological Engineering & Computing. 32 (1): 35–42. doi:10.1007/BF02512476. PMID8182960.
15. ^Molins A, Stufflebeam SM, Brown EN, Hämäläinen MS (September 2008). 'Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation'. NeuroImage. 42 (3): 1069–77. doi:10.1016/j.neuroimage.2008.05.064. PMID18602485.
16. ^Jung TP, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski TJ (October 2000). 'Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects'(PDF). Clinical Neurophysiology. 111 (10): 1745–58. CiteSeerX10.1.1.164.9941. doi:10.1016/S1388-2457(00)00386-2. PMID11018488.
17. ^Cui R, Cunnington R, Beisteiner R, Deecke L (2012). 'Effects of force-load on cortical activity preceding voluntary finger movement'. Neurology, Psychiatry and Brain Research. 18 (3): 97–104. doi:10.1016/j.npbr.2012.03.001.
18. ^Hirano Y, Hirano S, Maekawa T, Obayashi C, Oribe N, Monji A, Kasai K, Kanba S, Onitsuka T (March 2010). 'Auditory gating deficit to human voices in schizophrenia: a MEG study'. Schizophrenia Research. 117 (1): 61–7. doi:10.1016/j.schres.2009.09.003. PMID19783406.
19. ^Ihara A, Wei Q, Matani A, Fujimaki N, Yagura H, Nogai T, Umehara H, Murata T (January 2012). 'Language comprehension dependent on emotional context: a magnetoencephalography study'. Neuroscience Research. 72 (1): 50–8. doi:10.1016/j.neures.2011.09.011. PMID22001763.
20. ^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 (December 2007). 'Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders'. Journal of Neural Engineering. 4 (4): 349–55. Bibcode:2007JNEng...4..349G. doi:10.1088/1741-2560/4/4/001. hdl:10161/12446. PMID18057502.
21. ^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 (February 2009). 'Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease'. Proceedings of the National Academy of Sciences of the United States of America. 106 (5): 1614–9. Bibcode:2009PNAS..106.1614M. doi:10.1073/pnas.0811699106. PMC2635782. PMID19164579.
22. ^Luders HO (1992). Epilepsy Surgery. New York Raven Press.
23. ^Sutherling WW, Crandall PH, Darcey TM, Becker DP, Levesque MF, Barth DS (November 1988). 'The magnetic and electric fields agree with intracranial localizations of somatosensory cortex'. Neurology. 38 (11): 1705–14. doi:10.1212/WNL.38.11.1705. PMID3185905.
24. ^Rowley HA, Roberts TP (November 1995). 'Functional localization by magnetoencephalography'. Neuroimaging Clinics of North America. 5 (4): 695–710. PMID8564291.
25. ^Gallen CC, Hirschkoff EC, Buchanan DS (May 1995). 'Magnetoencephalography and magnetic source imaging. Capabilities and limitations'. Neuroimaging Clinics of North America. 5 (2): 227–49. PMID7640886.
26. ^Sheridan CJ, Matuz T, Draganova R, Eswaran H, Preissl H (2010). 'Fetal Magnetoencephalography - Achievements and Challenges in the Study of Prenatal and Early Postnatal Brain Responses: A Review'. Infant and Child Development. 19 (1): 80–93. doi:10.1002/icd.657. PMC2830651. PMID20209112.
27. ^Cohen D, Cuffin BN (July 1983). 'Demonstration of useful differences between magnetoencephalogram and electroencephalogram'. Electroencephalography and Clinical Neurophysiology. 56 (1): 38–51. doi:10.1016/0013-4694(83)90005-6. PMID6190632.
28. ^Barth DS, Sutherling W, Beatty J (March 1986). 'Intracellular currents of interictal penicillin spikes: evidence from neuromagnetic mapping'. Brain Research. 368 (1): 36–48. doi:10.1016/0006-8993(86)91040-1. PMID3955364.

Further reading[edit]

• Baillet S, Mosher JC, Leahy RM (November 2001). 'Electromagnetic Brain Mapping'. IEEE Signal Processing Magazine. 18 (6): 14–30. Bibcode:2001ISPM...18...14B. doi:10.1109/79.962275.
• Cohen D (2004). 'Boston and the history of biomagnetism'. Neurology and Clinical Neurophysiology. 30 (1).
• Cohen D, Halgren E (2004). 'Magnetoencephalography'. In Adelman G, Smith B (eds.). Encyclopedia of Neuroscience. Elsevier.
• Hämäläinen M, Hari R, Ilmoniemi R, Knuutila J, Lounasmaa OV (1993). 'Magnetoencephalography – theory, instrumentation, and applications to noninvasive studies of signal processing in the human brain'(PDF). Reviews of Modern Physics. 65 (2): 413–497. doi:10.1103/revmodphys.65.413.
• Hansen PC, Kringelbach ML, Salmelin R (2010). MEG: An Introduction to Methods. New York: Oxford University Press Inc.
• Murakami S, Okada Y (September 2006). 'Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals'. The Journal of Physiology. 575 (Pt 3): 925–36. doi:10.1113/jphysiol.2006.105379. PMC1995687. PMID16613883.
• Suk J, Ribary U, Cappell J, Yamamoto T, Llinás R (March 1991). 'Anatomical localization revealed by MEG recordings of the human somatosensory system'. Electroencephalography and Clinical Neurophysiology. 78 (3): 185–96. doi:10.1016/0013-4694(91)90032-y. PMID1707790.
• Tanzer OI (2006). Numerical Modeling in Electro- and Magnetoencephalography (Ph.D. thesis). Finland: Helsinki University of Technology.