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

人脑可以分为多个区域，这些区域负责不同方面的行为，而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，当我们协调双手承担一项任务时，这两个区域必须进行沟通。近年来，神经科学通过测量这种交流方式而发生了革命性的变化，这种交流方式被称为“连通性”。我们现在知道，多个地区联合在一起形成了“网络”。此外，存在多个不同的网络，其中一些与基本功能(例如运动)相关联，而另一些则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，它在各种疾病中都是异常的，包括儿童疾病(例如ADHD)、严重的精神障碍(例如精神分裂症)和大脑退化(例如帕金森氏症)。如果我们要为这种疾病开发成功的治疗方法，那么发展对大脑网络的理解是至关重要的。我们检查网络的主要手段是一种名为fMRI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，就需要更多的能量；这就需要增加血流量。因此，对血流变化的测量会产生大脑活动的图片。然而，大脑本身是基于电流运行的；事实上，正是这些电流允许大脑区域之间的交流。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确度，因为当大脑区域变得活跃时，大约需要6秒才能发生血流量变化。这意味着，获得一种直接评估网络中电子活动的方法将是一项重大进步。脑磁图是一种大脑成像技术，可以评估大脑的电活动：所有电流，包括大脑中的电流，都会产生磁场。脑磁图探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活动的图像。脑磁图是非侵入性的，脑磁图扫描仪形成了一个患者可以很好耐受的环境。最近，我们发现在脑磁图中也可以看到通常由fMRI检查的脑网络。这为测量和理解大脑网络提供了一种全新的方法。在这笔拨款中，我的目标是实现MEG在研究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交流。我将测试大脑中的电信号是如何在网络内部和网络之间进行沟通的，而这些网络以前只有功能磁共振成像才能看到。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，我的方法将允许我们探索连接是如何随着时间的推移而变化的；例如，当一个人承担一项脑力任务时，网络可能会如何变化？所有的大脑电活动都是由一种被称为神经递质的化学物质支撑的。使用一种名为MRS的技术进行平行实验，我将测试以脑磁图为单位测量的电连接性与大脑中不同类型神经递质的数量之间的关系。最重要的是，这些技术将为疾病中的连通性如何中断提供独特的见解。精神分裂症是一种知之甚少的疾病，具有很高的社会经济成本。一种流行的关于精神分裂症发病机制的理论涉及大脑后部和前部之间的交流中断。使用MEG和MRS来研究这一点将使人们能够获得新的见解，这些见解将对如何治疗这种高度虚弱的疾病产生巨大影响。

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.