Introduction
Although magnetic resonance imaging (MRI) is the most widely used brain imaging technology, magnetoencephalography is also a valuable and promising tool to fully discover neural activities. In this article, I would like to introduce the fundamental concepts of MEG, its capability, and implementation to neurological research.
Brief history of MEG
MEG was first developed by David Cohen, a physicist from University of Illinois. He used a copper induction coil as a detector, which was noisy and less sensitive than modern day MEG. As computing technology rapidly evolves, highly technical 300+ sensors cover the patient’s brain and give a vast amount of information. Especially, the introduction of SQUID (Superconducting Quantum Interference Device) allowed MEG to measure very weak magnetic fields of the brain.
Concepts
Magnetoencephalography, MEG, is the brain imaging technology that records the magnetic fields created by electrical neural activity. MEG focuses on the large-scale activity of the brain, and provides a great amount of spatial information. One of the main reasons researchers often prefer MEG is its sensitivity to produce data non-invasively. Moreover, MEG can be also used to track the specific time, within milli-seconds.
The detailed process of MEG
As mentioned above, MEG identifies the brain activity by recording magnetic fields. MEG mainly works in two ways. And to understand this you need to know the forward problem and the inverse problem.
Forward problem involves predicting the magnetic field, outer source, of the brain given the neural source inside the brain. Inverse problem, like the name implies, involves predicting the neural source inside the brain given the magnetic field outside the brain.Forward problems can be solved by modeling the brain, measuring the neural source, and calculating the outer source. Given the correct modeling process and measuring process, forward problems can be solved relatively easily.
On the other hand, solving inverse problems can be challenging. To solve the inverse problems, one needs to measure the magnetic field and find the correct neural source that is caused by the measured data. Considering different neural source configurations can cause the same magnetic field, researchers cannot identify the exact solution to each problem. Not to mention the complexity of the brain, regularization and advanced computation algorithms are essential in solving the inverse problems.
Inference done through the inverse problems are crucial for neuroscience research. To understand the cognitive process of the brain, researchers need to use MEG to map brain activity and this can further be used to create brain-computer interface. The use of MEG, particularly in solving the inverse problem, will be further explained in the next section.
Continued development in the technology will allow enhanced accuracy in MEG. Recently developed technology provides vector fields across the brain including a specific spatial data.
Use of MEG and its contribution to field of cognitive neuroscience and medical
The two main areas of MEG currently used are temporal dynamics and brain rhythms. One of the distinctive advantages of MEG is the capability to reconstruct the neural activity and provide superb temporal and spatial information. Thus, this allows MEG to unveil different stages of information processing. Not only does this enable the identification of activation in specific areas of the brain, but it also allows researchers to characterize the "meaning" of neural activity on a relatively large scale.
Researchers also use the technology to discover the rhythmicity of brian. Joachim Cross, who is part of the Institute for Biomagnetism and Biosignal Analysis (IBB) in University of Muenster, argues that rhythmicity of the brain is key to figuring out principles of neural dynamics. As rhythmicity of brain activity tends to be apparent with the precisely timed interactions of neurons, the MEG can be used to find those specific rhythms.
MEG is utilized in the medical field in order to map the patient’s brain before the operation or epilepsy surgery. The most important process before the epilepsy surgery is determining the exact location of seizure focus. MEG heavily contributes to finding the precise location and also helps identifying the area that can be safely resected during the operation. In most of the cases, EEG also works together to gather collective data. MEG is also showing promising potential in early diagnosis of autistic children and Alzheimer’s.
References
Cross, J. (2019, October 23). Neuron Primer. https://www.cell.com/neuron/fulltext/S0896-6273(19)30599-9?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627319305999%3Fshowall%3Dtrue
Singh, S. P. (2014). Magnetoencephalography: Basic principles. Annals of Indian Academy of Neurology, 17(5), 107. https://doi.org/10.4103/0972-2327.128676
Ray, A., & Bowyer, S. (2010). Clinical applications of magnetoencephalography in epilepsy. Annals of Indian Academy of Neurology, 13(1), 14. https://doi.org/10.4103/0972-2327.61271
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