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

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.

The last decade has seen a shift in focus in neuroimaging, from the study of discrete functional regions to the identification of large scale networks. Such networks are of fundamental importance to neuroscience: network connectivity is key to healthy brain function and altered in many neuro-pathologies. Most network studies employ fMRI, however being based on haemodynamics, fMRI cannot elucidate the electrophysiological mechanisms underlying connectivity. However, recent developments allow independent measurement of established networks using MEG. These monitor electrical brain activity directly via assessment of the magnetic fields induced by neural currents. The purpose of this grant is to realise the unique potential of MEG as a means to examine networks, and to gain a fundamentally new understanding of the nature of network connectivity in health and disease.

I will develop novel ways to measure, and understand the relationship between, the wide range electrophysiological effects observable from a single brain region. In addition I will develop entropic transforms of electrical signals as a new means to characterise local brain activity. Building upon this, I will develop new ways to measure intrinsic modes of functional coupling between regions including: i) methods to characterise linear and non-Iinear coupling and ii) measurement of dynamic changes in functional connectivity. Electrophysiological activity and connectivity is mediated by neurochemistry, and using parallel ultra-high field magnetic resonance spectroscopy, I will relate the concentration of neurotransmitters (glutamate/GABA) to electrophysiological metrics in MEG. Finally these metrics will be applied in a human model of schizophrenia, (schizotypy) in order to gain new insight into how impaired network activity and connectivity underlies the core symptoms associated with schizophrenia.

WHO WILL BENEFIT FROM THIS RESEARCH
1. Academic groups with an interesting in understanding brain network dynamics
2. Patients with schizophrenia
3. Mental health professionals involved in the care of patients with schizophrenia
4. Patients with other neurological disorders in which network dysfunction is implied
5. Potentially, pharmaceutical companies with interest in developing drugs for mental disorders

HOW WILL THEY BENEFIT

Acedemics:
The primary beneficiaries from this work will be academics who will employ the developed tools directly for their own research purposes. The proposed methods will interrogate network dynamics, in particular how neural oscillations across a wide spectral scale integrate brain activity within local and long range networks. Such neural dynamics are increasingly studied by laboratories worldwide, and are known to be perturbed across a wide range of disorders. The methods proposed will have significant impact upon research in this area. (Please also see 'Academic Beneficiaries'.)

Schizophrenia patients and their carers:
All volunteers scanned as part of this study will essentially be healthy. However, in addition to the imaging metrics, all volunteers will undergo a schizotypy measurement. Schizotypy is a personality trait marker; it is assessed by questionnaire, and probes the extent to which healthy individuals exhibit schizophrenia-like (schizotypal) characteristics. Evidence suggests that schizotypy is a good human model of the ongoing symptoms of schizophrenia such as impoverishment of mental activity, disorganisation of thought, and impaired working memory. These ongoing symptoms are poorly treated using current medication (which largely targets the dopaminergic system). More importantly, it is these symptoms (particularly impoverishment of mental activity) which reduces the capacity of patients to take part in the life of society (for example by gainful employment). The neuropathological basis of these ongoing symptoms is likely to involve both dysconnectivity and abnormalities in the glutamate and GABA systems; these are precisely the systems that this programme sets out to measure. Studying the spectrum of neural dynamics in schizotypy and schizophrenia, via investigation of brain network dynamics and their underlying neurochemistry, has the potential to generate fundamentally new techniques for diagnosis, phenotypying and tracking treatment efficacy in schizophrenia. Hence this project has significant potential to impact on patients, carers and medical professionals working in this area.

Beyond schizophrenia:
Although the present application focuses on schizophrenia, abnormalities of brain networks have been found across many pathologies including (but not limited to) Alzheimer's disease, Parkinson's disease, multiple sclerosis, ADHD and epilepsy. By delivering techniques to investigate network function and dysfunction, the present programme has the potential to have significant impact across all of these conditions. (Please see also Pathways to Impact).

Finally, the techniques to be developed may have impact in the field of neuropharmacology. The effect of therapeutic drugs on network dynamics is currently poorly understood. Studies in this area using fMRI are potentially confounded if the drug affects neurovascular coupling mechanisms as well as the neural network under study. MEG and MRS bypasses such confounds by measuring directly electrical dynamics and neurochemistry within brain networks. The methods to be developed may be used to study potential new treatment mechanisms, both in schizophrenia and other neuro-pathologies. Such study would allow pharmaceutical companies to investigate the potential mechanism of action of a novel molecule within a time scale of less than one year after Phase 1 trials have its established safety. This may reduce the time it takes to identify a strong candidate for treatment.