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

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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.