A Computational Prototype for Electroencephalographic Brain Connectomics

What are the neural signatures of consciousness? This elusive yet fascinating challenge for neuroscience takes on an immediate clinical and societal significance in patients diagnosed to be in vegetative and minimally conscious states, collectively termed disorders of consciousness (DoC). In recent years, rapid advancements in the study of human brain connectomics have generated valuable insights into how networks of interactions between specific brain regions support consciousness, and underlie neurological and psychiatric disorders. This research has the potential to improve diagnosis and prognosis for DoC patients, with significant ethical and healthcare implications. To realise this potential, this project will develop and validate robust brain connectomics software for assessing consciousness in patients at the bedside.

Despite significant advances in the neuroscience of consciousness, there are no brain-based tests of conscious state available clinically for diagnostic and prognostic applications in DoC. Current guidance in clinical practice relies primarily on behavioural assessment to characterise the level of consciousness, which has been shown to incur a risk of misdiagnosis as high as 40%. The use of brain-based measures has yet to find implementation and acceptance in clinical practice, and in an individual patient's rehabilitation journey in particular.

This proposal aims to directly contribute to addressing this serious limitation, by creating a software prototype that bring together state of the art computational analysis of brain activity data. This data has already been acquired right at the patient's bedside using electroencephalography (EEG), a non-invasive, portable brain mapping technology. This prototype will incorporate a computational data analytics pipeline designed to enable detailed visualisation and quantification of brain connectivity networks in DoC patients. These networks will be estimated by applying sophisticated signal processing methods to data collected from patients by clinical partners. We will develop and validate metrics derived from graph theory to characterise the topological and topographical properties of brain networks measured with these data. Further, the pipeline will apply machine learning to automatically classify the state of consciousness in individual patients based on the signatures in their brain networks. Most importantly, the software prototype will be designed in such a way that it can be easily deployed at the bedside in clinical rehabilitation centres where DoC patients are resident.

If realised, this project will be one of the first to directly contribute to the scientific advancement and validation of healthcare technology with real-world impact in the assessment of consciousness in the clinic. It will be of particular interest to patients' families and carers, in addition to medical professionals involved in their care, treatment and management. It has the potential to enable them to have access to objective, discriminative, and cost-effective information about brain activity related to consciousness, available right at the bedside. This will ultimately facilitate informed decision-making on behalf of patients, and the employment of more effective therapeutic interventions. This advance will eventually also have broader implications for the ethical and legal debate in the public surrounding the assessment of consciousness in the clinic.

Ultimately, the primary beneficiaries of this research will be patients diagnosed with disorders of consciousness (including the vegetative and minimally conscious states). According to recent estimates, 4000-16000 patients in the UK have a diagnosis of vegetative state, with three times as many in minimally conscious states. This poses a serious clinical challenge, as the level of consciousness in as many as 40% of patients could be misdiagnosed. The central aim of this proposal is the development of a novel prototype for characterising brain networks in patients. Once the proposed software prototype has been developed, tested and validated, we aim to pilot its deployment it at the patient's bedside at both centres. Eventually, through the development of a turnkey product or service developed for clinical end users, the results of such assessments will contribute to more accurate diagnosis of consciousness, and better prognostication of recovery. We will enable this by working with clinicians to define best practice for incorporating brain-based assessments into clinical decision making in individual cases.

Clinical professionals, including neurorehabilitation experts and allied health professionals working with DoC patients will hence benefit from access to the outcomes of this research. These outcomes will not only complement and augment existing clinical tools, they will enable clinicians to more accurately track the variable response to the specific pharmacological and therapeutic interventions they trial with individual patients. Ultimately, the availability of EEG-based brain imaging assessments at the bedside will enable clinicians to adopt a precision medicine approach toward more accurate patient stratification. This will ultimately enable them to provide patients with improved access to tailored care and therapy that best meets the needs of each individual.

The PI has contributed to increased interaction between science and policy making, by providing input to a recent Parliament Office of Science and Technology document on prolonged DoC. He also contributed to a special event on diagnostic and prognostic challenges, hosted at the Houses of Parliament, bringing together academics, clinicians, members of the judiciary, policy makers, patients' families and carers. As the proposed brain network analysis software reaches maturity, the PI will inform and disseminate knowledge about it at similar public forums in the future. He will also make contact, interact with and disseminate knowledge within the clinical bodies involved in policy making and guideline development in this context.

This proposal will also have indirect impact for patients in a related and equally complex clinical context: monitoring consciousness during general anaesthesia. The risks of intra-operative awareness and consequent post-operative delirium, which are particularly challenging in some patient groups, could be minimised with advances in brain monitoring with EEG. The work here on developing robust computational tools to track consciousness at the bedside will also influence future academic and clinical research and development into viable solutions for monitoring consciousness in the operating theatre.

More broadly, this research will also have impact amongst the general public, and students in particular. Acute and chronic disorders of consciousness have attracted considerable public interest, due to the complex ethical, legal and moral questions it raises for a caring society. Over the course of the proposed research, the PI will engage with the ongoing public debate in this context. This will include at least two public talks each year during the lifetime of this grant, at science events and festivals. At these forums, our research outcomes will demonstrate current advances in translational and personalised medicine, and the showcase the potential for novel healthcare technologies to inform the debate.