| EPSRC Reference: |
EP/P033199/1 |
| Title: |
A Computational Prototype for Electroencephalographic Brain Connectomics |
| Principal Investigator: |
Chennu, Dr S |
| Other Investigators: |
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| Project Partners: |
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| Department: |
Sch of Computing |
| Organisation: |
University of Kent |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
01 September 2017 |
Ends: |
31 August 2019 |
Value (£): |
101,059
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| EPSRC Research Topic Classifications: |
| Med.Instrument.Device& Equip. |
Medical Imaging |
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Summary on Grant Application Form |
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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.kent.ac.uk |