mteb/BrightProPsychologyRetrieval huggingface dataset:
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  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   implants  "Implant
\(medicine\)") are present), non-invasiveness, use of no ionizing radiation,
as opposed to PET and high temporal resolution as opposed to fMRI.
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 tumors  or
intractable epilepsy. The goal of epilepsy surgery is to remove the
epileptogenic tissue while sparing healthy brain areas.  [30]  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.  [31]  [32]  [33]  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.
DO - 10.1016/0896-6273(95)90095-0
JF - Neuron
#  Meta
353

Citations (Scopus)
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  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.
About web accessibility
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.  [16]  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.  [17]
*  How to run Half Life 2 Natively on Jetson Nano 
*  Analyzing Connectivity between MEG Channels using PLV 
*  MEG Signal Classification using PLV and Neural Networks 
*  MEG Signal Modeling using Recurrent Neural Networks 
*  Project: 80C188EB Single Board Computer
Further, my analysis here is based on visual inspection of the time series. It
would be interesting to analyze channel-to-channel connectivity of the ground
truth and predictions (using e.g.  Phase Lag Index
 ) and see if these
share any characteristics. That would given an unbiased estimate how well the
network models connectivity between channels. This might be a good topic for
my next post…
All content on this site: Copyright © 2024  Elsevier B.V.
 or its licensors and contributors. All rights are
reserved, including those for text and data mining, AI training, and similar
technologies. For all open access content, the Creative Commons licensing
terms apply
*  Link to publication in Scopus
@article{02667c75f40649808d698e485bfe1cba,
The basis of the MEG signal  

 Synchronized neuronal currents  induce weak magnetic fields. The brain's magnetic field,
measuring at 10  femto   tesla 
"Tesla \(unit\)") (fT) for  cortical  activity and 10  3  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  8
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.

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  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
 ,  [9]  i.e. currents with a position, orientation,
and magnitude, but no spatial extent.  [ _[ dubious
](/wiki/Wikipedia:Accuracy_dispute
JO - Neuron
year = "1995",
##  Cite this
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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.  [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.
publisher = "Cell Press",
Mechanism  

Shunting inhibition is theorized to be a type of  gain control
 mechanism, regulating the responses
of  neurons  .  [5]  [6]  Simple inhibition such as 
hyperpolarization  "Hyperpolarization
\(biology\)") has a subtractive effect on the  depolarization
 caused by concurrent excitation,
whereas shunting inhibition can in some cases account for a divisive effect.
[7]

Some evidence exists that shunting inhibition can have a divisive effect on
neuronal responses, at least on  subthreshold postsynaptic potentials
 .  [8]  In a 2005
article, researchers Abbott and Chance state that "Although the importance of
gain modulation and multiplicative interaction in general has been appreciated
for many years, it has proven difficult to uncover a realistic biophysical
mechanism by which it can occur. It is important to note that, despite
comments in the literature to the contrary (see above), divisive inhibition of
neuronal responses cannot arise from shunting inhibition. This has been shown
theoretically as well as experimentally – inhibition has the same subtractive
effect on firing rates whether it is of the shunting or hyperpolarizing
variety."  [7]  Thus, shunting inhibition does not provide a plausible
mechanism for  neuronal gain modulation  .  [7]
Dubious
"Talk:Magnetoencephalography") _ ]  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.  [10]  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
 "Sulcus \(neuroanatomy\)") . 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.
Since the LSTMs are popular today, I chose to use them also in my
experimentation. In case you are interested to learn more about LSTMs, take a
look on  colah’s blog post
 .
Y1 - 1995/11
|   Analyzing Connectivi…
 on  MEG Signal Classification usin…

---|---
 |   MEG Signal Modeling…
 on  Modeling MEG signals using…
*  https://doi.org/10.1016/0896-6273(95)90095-0  90095-0)
language = "English",
#   Nexton's Blog
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##  Random projects by Arto Meriläinen
*  Electronics 
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Report vulnerability
##  Network for Modeling the Data-set
RNNs were developed already in 1980s to overcome the issues in feed-forward
networks by allowing dependencies between time steps, however, they were found
to be impossible to train: The early RNN architectures suffered from an issue
of vanishing/exploding gradients. Advances in network architectures allowed to
overcome the issue and today there are few different solutions to the issue.
One of the most used solutions is called Long Short-Term Memory (LSTM) which
was introduced already in 1997. Today LSTMs are used in multiple areas
including  speech recognition  ,  machine translation
 and  time series prediction for
generating text  .
Stuart, G   & Sakmann,
B 1995, '  Amplification of EPSPs by axosomatic sodium channels in
neocortical pyramidal neurons
 ',  _Neuron_ , vol. 15, no. 5, pp.
1065-1076.  https://doi.org/10.1016/0896-6273(95)90095-0
90095-0)
Stuart G   , Sakmann B.
 Amplification of EPSPs by axosomatic sodium channels in neocortical
pyramidal neurons
 .  Neuron  . 1995
Nov;15(5):1065-1076. doi: 10.1016/0896-6273(95)90095-0
Shunting inhibition

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PY - 1995/11
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References  

1. ** ^  ** Koch C.  "Coding & Vision Lesson 4: Cell Types"  . Allen Institute.
2. ^  _**a** _ _**b** _ Javier Alvarez-Leefmans F, Delpire E (2010-01-01). "Chapter 5 - Thermodynamics and Kinetics of Chloride Transport in Neurons: An Outline". In Javier Alvarez-Leefmans F, Delpire E (eds.). _Physiology and Pathology of Chloride Transporters and Channels in the Nervous System_ . San Diego: Academic Press. pp. 81–108.  doi  "Doi \(identifier\)") :  10.1016/b978-0-12-374373-2.00005-4  .
3. ** ^  ** Isaacson JS, Scanziani M (October 2011).  "How inhibition shapes cortical activity"  . _Neuron_ . **72** (2): 231–243.  doi  "Doi \(identifier\)") :  10.1016/j.neuron.2011.09.027  .  PMC  "PMC \(identifier\)")  3236361  .  PMID  "PMID \(identifier\)")  22017986  .
4. ** ^  ** Fatt P, Katz B (August 1953).  "The effect of inhibitory nerve impulses on a crustacean muscle fibre"  . _The Journal of Physiology_ . **121** (2): 374–389.  doi  "Doi \(identifier\)") :  10.1113/jphysiol.1953.sp004952  .  PMC  "PMC \(identifier\)")  1366081  .  PMID  "PMID \(identifier\)")  13085341  .
5. ** ^  **  Eccles JC  (1964). _The Physiology of Synapses_ . Berlin: Springer-Verlag.
6. ** ^  ** Blomfield S (March 1974). "Arithmetical operations performed by nerve cells". _Brain Research_ . **69** (1): 115–124.  doi  "Doi \(identifier\)") :  10.1016/0006-8993(74)90375-8  .  PMID  "PMID \(identifier\)")  4817903  .
7. ^  _**a** _ _**b** _ _**c** _ Abbott LF, Chance FS (2005). "Drivers and modulators from push-pull and balanced synaptic input".  _Drivers and modulators from push-pull balanced synaptic input_  . Progress in Brain Research. Vol. 149. pp. 147–155.  doi  "Doi \(identifier\)") :  10.1016/S0079-6123(05)49011-1  .  ISBN  "ISBN \(identifier\)")  9780444516794   .  PMID  "PMID \(identifier\)")  16226582  . Archived from  the original  on 2013-02-02.
8. ** ^  ** Holt GR, Koch C (July 1997). "Shunting inhibition does not have a divisive effect on firing rates". _Neural Computation_ . **9** (5): 1001–1013.  CiteSeerX  "CiteSeerX \(identifier\)")  10.1.1.27.8715  .  doi  "Doi \(identifier\)") :  10.1162/neco.1997.9.5.1001  .  PMID  "PMID \(identifier\)")  9188191  .  S2CID  "S2CID \(identifier\)")  7566057  .

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See also  

*  Auditory evoked field 
*  Direct brain interfaces 
*  Electrophysiology 
*  Evoked field 
*  FieldTrip 
*  Magnetocardiography 
*  Magnetogastrography 
*  Magnetometer 
*  Magnetomyography 
*  SQUID 
*  Whole brain emulation
History  

 Dr. Cohen's shielded room at MIT,
in which first MEG was measured with a SQUID   First MEG
measured with SQUID, in Dr. Cohen's room at MIT

MEG signals were first measured by University of Illinois physicist  David
Cohen  "David Cohen \(physicist\)") 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.  [7]  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).  [8]  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.
##  Code
EP - 1076
This is a short update to my earlier post from 2015 on  modeling MEG signals
using CRBMs  . CRBMs were outdated already at the time I wrote the
post and one of the development areas was to try out recurrent neural networks
(RNNs). I’m describing here the results of my weekend project on applying RNNs
to time series prediction of MEG data.
volume = "15",
Search by expertise, name or affiliation
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.  [18]  However, ICA has
poor resolution of highly correlated brain sources.
Discovery  

Shunting inhibition was discovered by Fatt and Katz in 1953.  [2]  [4]
##  Other files and links
*  __ Overview
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.  [15]  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.
See also  

 Synaptic depression
Create a free website or blog at WordPress.com.
Stuart, G.   , &
Sakmann, B. (1995).  Amplification of EPSPs by axosomatic sodium channels in
neocortical pyramidal neurons
 .  _Neuron_ , _15_ (5), 1065-1076. 
https://doi.org/10.1016/0896-6273(95)90095-0
90095-0)
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, and are often misdiagnosed as  post-traumatic
stress disorder  (PTSD).  [24]
VL - 15
MEG Signal Classification using PLV and Neural Networks  →
Monash University Home
pages = "1065--1076",
#  Categories
Greg Stuart   , Bert
Sakmann
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  [25]  [26]  or auditory cortex.
[27]  Other studies have used MEG to study the neural interactions between
different brain regions (e.g., between frontal cortex and visual cortex).
[28]  Magnetoencephalography can also be used to study changes in neural
oscillations across different stages of consciousness, such as in sleep.  [29]
###  Jaa:
MEG in comparison to EEG  

Although EEG and MEG signals originate from the same neurophysiological
processes, there are important differences.  [37]  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.  [38]  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.
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From Wikipedia, the free encyclopedia

Mapping brain activity by recording magnetic fields produced by currents in
the brain

Magnetoencephalography
---

Person undergoing a MEG

 MeSH  |  
D015225 

**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]
Stuart, Greg   ;
Sakmann, Bert. /  **Amplification of EPSPs by axosomatic sodium channels in
neocortical pyramidal neurons**
 . In:  Neuron  . 1995 ; Vol. 15, No.
5. pp. 1065-1076.
issn = "0896-6273",