Measuring Brain Network Dynamics Using Magnetoencephalography: Methods Development and Applications in Schizophrenia

使用脑磁图测量大脑网络动态：精神分裂症的方法开发和应用

• 批准号: MR/M006301/1
• 负责人: Matthew Brookes
• 金额: $ 62.13万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FM006301%2F1
• 关键词: Measuring; Brain; Network; Dynamics; Using

The human brain can be divided into multiple regions which are responsible for different aspects of behaviour and healthy brain function relies upon efficient communication between those regions. For example a region in the left brain controls the right hand and a region in the right brain controls the left hand, these two regions must communicate when we coordinate both hands to undertake a task. In recent years, neuroscience has been revolutionised by measurement of this communication, which is termed 'connectivity'. We now know that multiple regions join together to form 'networks'. Furthermore, multiple different networks exist, some associated with basic function (e.g. movement) and others supporting high level aspects of behaviour (e.g. attention). It is clear is that connectivity is key to healthy brain function. Moreover, it is abnormal in a variety of disorders including childhood conditions (e.g. ADHD), severe mental disorders (e.g. schizophrenia) and brain degeneration (e.g. Parkinson's disease). If we are to develop successful treatments for such conditions then developing an understanding of brain networks is critical.Our principal means to examine networks is a technique called fMRI, which measures changes in blood flow. When activity in some brain region increases, an increased amount of energy is required; this necessitates an increase in blood flow. Measurement of changes in blood flow thus generates pictures of brain activity. However, the brain itself operates based on electrical currents; indeed it is these currents that allow communication between brain areas. Blood flow based measurements cannot directly measure these currents. Further, blood flow measures lack temporal precision because when a brain area becomes active, it takes around 6 seconds for the blood flow change to occur. This means that deriving a means to assess electrical activity in networks directly would represent a major advance. MEG is a brain imaging technique which can assess electrical brain activity: All electrical currents, including those in the brain, generate magnetic fields. MEG detects the magnetic fields outside the head generated by electrical current in the brain, and uses the fields to build a picture of electrical brain activity. MEG is non-invasive, and a MEG scanner forms an environment that is well tolerated by patients. Recently, we have shown that the brain networks usually examined by fMRI can also be seen in MEG. This opens up opportunities to provide a fundamentally new way to measure and understand brain networks.In this grant I aim to realise the unique potential of MEG to examine networks. I will introduce novel ways to measure the activity within brain regions. I will then use this to develop new ways to measure communication between those regions. I will test how electrical signals in the brain mediate communication within and between the networks that have previously only been seen with fMRI. By assessing electrical activity (rather than blood flow) I will be able to examine multiple different kinds of connection. Further, my methods will allow us to probe how connectivity changes in time; e.g. how might a network change when an individual undertakes a mental task? All electrical brain activity is underpinned by chemicals known as neurotransmitters. Using parallel experiments in a technique called MRS, I will test how the electrical connectivities measured in MEG are related to the amount of different kinds of neurotransmitter in the brain. Most importantly, these techniques will provide unique insights into how connectivity breaks down in diseases. Schizophrenia is a poorly understood condition with high socio-economic costs. A prevailing theory on the mechanisms of schizophrenia involves breakdown of communication between the back and the front of the brain. Using MEG and MRS to investigate this will enable new insights that will have great impact on how this highly debilitating disorder is treated.

人类大脑可以分为多个区域，负责行为的不同方面，健康的大脑功能依赖于这些区域之间的有效沟通。例如，左脑的一个区域控制右手，右脑的一个区域控制左手，当我们协调双手执行任务时，这两个区域必须进行通信。近年来，通过测量这种被称为“连通性”的通信，神经科学发生了革命性的变化。我们现在知道，多个区域连接在一起形成“网络”。此外，存在多个不同的网络，一些与基本功能（例如运动）相关联，而另一些支持行为的高级方面（例如注意力）。很明显，连通性是健康大脑功能的关键。此外，它在多种疾病中是异常的，包括儿童病症（例如ADHD）、严重精神障碍（例如精神分裂症）和脑变性（例如帕金森病）。如果我们想成功地治疗这些疾病，那么对大脑网络的理解就至关重要。我们检查网络的主要手段是一种叫做功能性磁共振成像的技术，它测量血液流动的变化。当大脑某些区域的活动增加时，需要增加的能量;这就需要增加血流量。因此，测量血流的变化就可以得到大脑活动的图像。然而，大脑本身是基于电流运行的;事实上，正是这些电流使大脑区域之间能够进行通信。基于血流的测量不能直接测量这些电流。此外，血流测量缺乏时间精度，因为当大脑区域变得活跃时，血流变化发生大约需要6秒。这意味着，获得一种直接评估网络中电活动的方法将是一个重大进步。MEG是一种大脑成像技术，可以评估大脑电活动：所有电流，包括大脑中的电流，都会产生磁场。脑磁图检测大脑中电流产生的头部外部磁场，并使用这些磁场来构建大脑电活动的图像。MEG是非侵入性的，并且MEG扫描仪形成患者耐受良好的环境。最近，我们已经表明，通常由功能磁共振成像检查的大脑网络也可以在脑磁图中看到。这为测量和理解大脑网络提供了一种全新的方法。在这项资助中，我的目标是实现MEG在检查网络方面的独特潜力。我将介绍新的方法来测量大脑区域内的活动。然后，我将利用这一点来开发新的方法来衡量这些地区之间的沟通。我将测试大脑中的电信号是如何调节网络内部和网络之间的通信的，这些通信以前只能用功能性磁共振成像来观察。通过评估电活动（而不是血流），我将能够检查多种不同类型的连接。此外，我的方法将使我们能够探索连接性如何随时间变化;例如，当一个人承担一项心理任务时，网络可能会发生什么变化？所有的脑电活动都是由一种叫做神经递质的化学物质支撑的。使用一种称为MRS的技术中的平行实验，我将测试MEG中测量的电连通性如何与大脑中不同种类的神经递质的数量相关。最重要的是，这些技术将为疾病中的连接性如何破坏提供独特的见解。精神分裂症是一种人们知之甚少的疾病，其社会经济成本很高。关于精神分裂症的机制，一个流行的理论涉及到大脑后部和前部之间的交流中断。使用MEG和MRS来调查这一点将使新的见解，这将对如何治疗这种高度衰弱的疾病产生重大影响。

项目成果

A mean field model for movement induced changes in the beta rhythm.

• DOI: 10.1007/s10827-017-0655-7
• 发表时间: 2017-10
• 期刊: Journal of computational neuroscience
• 影响因子: 1.2
• 作者: Byrne Á;Brookes MJ;Coombes S
• 通讯作者: Coombes S

A multi-layer network approach to MEG connectivity analysis.

• DOI: 10.1016/j.neuroimage.2016.02.045
• 发表时间: 2016-05-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Brookes MJ;Tewarie PK;Hunt BAE;Robson SE;Gascoyne LE;Liddle EB;Liddle PF;Morris PG
• 通讯作者: Morris PG

On the Potential of a New Generation of Magnetometers for MEG: A Beamformer Simulation Study.

• DOI: 10.1371/journal.pone.0157655
• 发表时间: 2016
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Boto E;Bowtell R;Krüger P;Fromhold TM;Morris PG;Meyer SS;Barnes GR;Brookes MJ
• 通讯作者: Brookes MJ

Abnormal task driven neural oscillations in multiple sclerosis: A visuomotor MEG study.

• DOI: 10.1002/hbm.23531
• 发表时间: 2017-05
• 期刊: Human brain mapping
• 影响因子: 4.8
• 作者: Barratt EL;Tewarie PK;Clarke MA;Hall EL;Gowland PA;Morris PG;Francis ST;Evangelou N;Brookes MJ
• 通讯作者: Brookes MJ

Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement.

• DOI: 10.3389/fphys.2015.00183
• 发表时间: 2015
• 期刊: Frontiers in physiology
• 影响因子: 4
• 作者: Botcharova M;Berthouze L;Brookes MJ;Barnes GR;Farmer SF
• 通讯作者: Farmer SF