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脑磁图（MEG）简介及其临床应用

A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications.

作者信息

Fred Alfred Lenin, Kumar Subbiahpillai Neelakantapillai, Kumar Haridhas Ajay, Ghosh Sayantan, Purushothaman Bhuvana Harishita, Sim Wei Khang Jeremy, Vimalan Vijayaragavan, Givo Fredin Arun Sedly, Jousmäki Veikko, Padmanabhan Parasuraman, Gulyás Balázs

机构信息

Department of CSE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India.

Department of EEE, Amal Jyothi College of Engineering, Kanjirappally 686518, Kerala, India.

出版信息

Brain Sci. 2022 Jun 15;12(6):788. doi: 10.3390/brainsci12060788.

DOI:10.3390/brainsci12060788
PMID:35741673
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9221302/
Abstract

Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG = 2.2 db, SNREEG < 1 db) and spatial resolution (SRMEG = 2−3 mm, SREEG = 7−10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.

摘要

脑磁图（MEG）在脑部疾病的诊断中起着关键作用。在本综述中，我们研究了MEG在分析脑部疾病方面的潜在应用。MEG的信噪比（SNRMEG = 2.2分贝，SNREEG < 1分贝）和空间分辨率（SRMEG = 2 - 3毫米，SREEG = 7 - 10毫米）高于脑电图（EEG），因此MEG有可能促进对皮质活动的精确监测。我们发现，直接的电生理MEG信号反映了神经系统疾病的生理状态，在疾病诊断中起着至关重要的作用。利用静息状态和特定任务期间采集的MEG数据进行的单通道连通性分析以及脑网络分析，已被用于癫痫、阿尔茨海默病、帕金森症、自闭症和精神分裂症等神经系统疾病的诊断。还讨论了MEG的工作流程及其在疾病诊断和治疗规划中的潜在应用。我们预测，计算机辅助算法在未来神经系统疾病的诊断和预测中将发挥重要作用。这篇叙述性综述的结果将有助于研究人员在诊断中利用MEG。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/0161499caae5/brainsci-12-00788-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/ff1910d7ec57/brainsci-12-00788-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/37144245098a/brainsci-12-00788-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/f029c4a9a9e6/brainsci-12-00788-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/5b395afce861/brainsci-12-00788-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/d1a4f5e84dfc/brainsci-12-00788-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/b62a4b61e83f/brainsci-12-00788-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/8089fe80033e/brainsci-12-00788-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/b56371a5c4db/brainsci-12-00788-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/0161499caae5/brainsci-12-00788-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/ff1910d7ec57/brainsci-12-00788-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/37144245098a/brainsci-12-00788-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/f029c4a9a9e6/brainsci-12-00788-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/5b395afce861/brainsci-12-00788-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/d1a4f5e84dfc/brainsci-12-00788-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/b62a4b61e83f/brainsci-12-00788-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/8089fe80033e/brainsci-12-00788-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/b56371a5c4db/brainsci-12-00788-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22d0/9221302/0161499caae5/brainsci-12-00788-g009.jpg

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