Building multi-site clinical research capacity in Magnetoencephalography (MEG)

建立脑磁图 (MEG) 多站点临床研究能力

• 批准号: MR/K005464/1
• 负责人: Krishna Singh
• 金额: $ 106.31万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FK005464%2F1
• 关键词: Building; multi; site; clinical; research

One of the key challenges in understanding the human brain is "bridging the gap" between the microscopic level (neurons) and the full richness of behaviour that we know humans are capable of. As well as being one of the most important and fundamental questions about ourselves, understanding brain structure and function at multiple scales is crucial for increasing our knowledge of what is going wrong in neurological and psychiatric diseases, such as Epilepsy, Schizophrenia, Depression and Alzheimer's.In terms of brain function, we know that information is represented and processed in the electrical signals generated by neurons and that information is transmitted electrically between brain areas across white matter fibre pathways. Currently, the most popular imaging technique we have studying brain function is fMRI, which cannot measure the brain's electrical activity directly, but instead measures the increase in oxygenated blood that occurs in brain regions when they are active. Rather than this indirect measure, ideally we would like to non-invasively detect the patterns of electrical activity that flow within and between brain areas as we perform various cognitive tasks. One promising technique is Magnetoencephalography (MEG), which measures the weak magnetic fields associated with neuronal electric currents. These pass transparently through the scalp/skull and can then be detected using an array of superconducting detectors. As well as being a direct window onto the brain's electrical activity, MEG can measure activity with millisecond time-resolution, allowing us to follow the rapid sweep of electrical signals across the cortex as the brain brings various networks of areas together to process information. This is something that fMRI simply cannot do. MEG also has an advantage over conventional EEG electrodes in that it is relatively easier to work out exactly where in the brain the electrical sources of activity are as it does not suffer from the smearing of information that occurs when weak electrical signals have to leak through the skull and scalp to the surface.The basic technology behind MEG has been around since the 1970s, but it is only really now that the technology has matured so that we have robust whole-head multiple-channel systems (200-300 sensors). The first such system in the UK was installed at Aston University in 2001, and over the last 10 years another seven UK sites have opened (York, UCL, Cardiff, Nottingham, Glasgow, Oxford and Cambridge). However several challenges still remain, particularly if we wish to make best use of MEG for clinical research: 2) It is a novel technique and there is a need for developing training to build UK critical research mass in this area. 2) MEG data contains a complex mix of neural signals that are difficult to interpret and many of the groups are developing novel advanced analysis tools to try and solve this problem - but there is still no standard analysis approach. 3) There are few standards for the experimental protocols that we wish to use for recording clinical MEG research data. 4) The most powerful clinical research applications involve MEG scans on large numbers of patients - this is difficult for one site to do on its own. For all of these reasons, all of the eight UK MEG research groups wish to come together to form a research partnership. The proposal consists of a mixture of academic networking activities, training programmes, joint studentships and establishment of unified approaches to 1) Performing experiments 2) Analysing MEG data 3) Storing data for future large-scale collaborative projects. As part of the partnership programme we will also collect data from a limited number of participants (80 at each site) so that we can pilot the establishment of shared databases of MEG experimental data. This is essential if we wish to perform large collaborative studies on specific clinical populations.

理解人类大脑的关键挑战之一是“弥合微观水平（神经元）和我们知道人类能够进行的丰富行为之间的差距”。了解大脑的多个尺度的结构和功能不仅是关于我们自己的最重要和基本的问题之一，对于增加我们对癫痫、精神分裂症、抑郁症和阿尔茨海默氏症等神经和精神疾病的认识至关重要。就大脑功能而言，我们知道信息是在神经元产生的电信号中表示和处理的，并且信息在大脑各区域之间以电的方式传输。 白质纤维通路。目前，我们研究大脑功能最流行的成像技术是功能磁共振成像，它不能直接测量大脑的电活动，而是测量大脑区域活跃时发生的含氧血液的增加。理想情况下，我们希望非侵入性地检测当我们执行各种认知任务时大脑区域内和大脑区域之间流动的电活动模式，而不是这种间接测量。一项有前途的技术是脑磁描记术（MEG），它可以测量与神经元电流相关的弱磁场。它们透明地穿过头皮/头骨，然后可以使用一系列超导探测器进行检测。 MEG不仅是大脑电活动的直接窗口，还可以以毫秒时间分辨率测量活动，使我们能够在大脑将各个区域网络聚集在一起处理信息时跟踪整个皮层的电信号的快速扫描。这是功能磁共振成像根本无法做到的。与传统 EEG 电极相比，MEG 还具有一个优势，即相对更容易确定大脑中活动电源的准确位置，因为它不会受到微弱电信号必须通过头骨和头皮泄漏到表面时出现的信息模糊的影响。MEG 背后的基本技术自 20 世纪 70 年代以来就已存在，但直到现在该技术才真正成熟，以便我们拥有强大的全头多通道系统 （200-300 个传感器）。英国第一个此类系统于 2001 年在阿斯顿大学安装，在过去 10 年里，英国又开设了 7 个站点（约克、伦敦大学学院、卡迪夫、诺丁汉、格拉斯哥、牛津和剑桥）。然而，仍然存在一些挑战，特别是如果我们希望充分利用 MEG 进行临床研究：2）这是一项新技术，需要开展培训以建立英国在该领域的关键研究质量。 2) MEG 数据包含难以解释的复杂神经信号组合，许多小组正在开发新颖的高级分析工具来尝试解决这个问题 - 但仍然没有标准的分析方法。 3) 我们希望用于记录临床 MEG 研究数据的实验方案标准很少。 4) 最强大的临床研究应用涉及对大量患者进行脑磁图扫描——这对于一个站点来说很难单独完成。出于所有这些原因，所有八个英国 MEG 研究小组都希望联合起来形成研究伙伴关系。该提案包括学术网络活动、培训计划、联合学生资助和建立统一方法，以实现 1) 进行实验 2) 分析 MEG 数据 3) 为未来大型合作项目存储数据。作为合作伙伴计划的一部分，我们还将收集有限数量的参与者（每个地点 80 名）的数据，以便我们可以试点建立 MEG 实验数据的共享数据库。如果我们希望对特定的临床人群进行大型合作研究，这一点至关重要。

项目成果

A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks.

• DOI: 10.1371/journal.pcbi.1006007
• 发表时间: 2018-03
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Abeysuriya RG;Hadida J;Sotiropoulos SN;Jbabdi S;Becker R;Hunt BAE;Brookes MJ;Woolrich MW
• 通讯作者: Woolrich MW

Whole-Brain Neural Dynamics of Probabilistic Reward Prediction.

• DOI: 10.1523/jneurosci.2943-16.2017
• 发表时间: 2017-04-05
• 期刊: The Journal of neuroscience : the official journal of the Society for Neuroscience
• 作者: Bach DR;Symmonds M;Barnes G;Dolan RJ
• 通讯作者: Dolan RJ

The Neural Dynamics of Fronto-Parietal Networks in Childhood Revealed using Magnetoencephalography.

• DOI: 10.1093/cercor/bhu271
• 发表时间: 2015-10
• 期刊: Cerebral cortex (New York, N.Y. : 1991)
• 作者: Astle DE;Luckhoo H;Woolrich M;Kuo BC;Nobre AC;Scerif G
• 通讯作者: Scerif G

Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia.

• DOI: 10.1016/j.nicl.2020.102485
• 发表时间: 2020
• 期刊: NeuroImage. Clinical
• 作者: Alamian G;Pascarella A;Lajnef T;Knight L;Walters J;Singh KD;Jerbi K
• 通讯作者: Jerbi K

Electrophysiological measures of resting state functional connectivity and their relationship with working memory capacity in childhood.

• DOI: 10.1111/desc.12297
• 发表时间: 2016-01
• 期刊: Developmental science
• 影响因子: 3.7
• 作者: Barnes JJ;Woolrich MW;Baker K;Colclough GL;Astle DE
• 通讯作者: Astle DE