| EPSRC Reference: |
EP/N006771/1 |
| Title: |
Imaging dynamical brain networks using hybrid dynamical models |
| Principal Investigator: |
Trujillo-Barreto, Dr NJ |
| Other Investigators: |
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| Department: |
School of Biological Sciences |
| Organisation: |
University of Manchester, The |
| Scheme: |
EPSRC Fellowship |
| Starts: |
01 January 2016 |
Ends: |
31 December 2018 |
Value (£): |
321,227
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| EPSRC Research Topic Classifications: |
| Med.Instrument.Device& Equip. |
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Summary on Grant Application Form |
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Dynamic reorganisation of a functional brain network may be related to shifts in brain state, such as those associated with adaptation and learning. From a clinical perspective, changes in the relative contribution of brain areas (plasticity) or their interactions (effective connectivity) underpin the early stages of a number of developmental (e.g Dyslexia) and psychiatric (e.g. depression, psychosis), but most prominently neurodegenerative (e.g. Alzheimer's or Parkinson's) conditions. Due to plasticity, changes in effective connectivity may precede any changes in brain structure or in observed behaviour. Early detection and monitoring of such changes by means of non-invasive imaging techniques like EEG (or MEG) is vital if early intervention and early rehabilitation strategies were to be successful . Most current analyses of effective connectivity (EC) assume that the architecture and connection strengths of the functional network are static in time, or differ between task conditions at known time points. Thus, current approaches cannot be used to detect dynamic changes in network organisation at arbitrary time points or in the absence of a cognitive task, such as during on-going EEG monitoring and diagnosis of the above neurological conditions. I propose to develop a novel model and a toolbox for estimating the time-dependent effective connectivity of the dynamical brain network underlying the on-going EEG, and to test its accuracy and limits in detecting changes in effective brain connectivity induced by Transcranial Magnetic Stimulation (TMS). Prior anatomical connectivity information and probabilistic atlases, derived from Diffusion Weighted Magnetic Resonance Imaging (DWMRI), will be used to inform the model about the likely architecture of the functional network and about the likely anatomical location of sources, respectively. The goal is to provide a low-cost and fast application to detect, track and predict early changes in brain causal networks and their dynamics from the on-going EEG. Given the current emphasis on reducing the social and economic impact of neurodegeneration as highlighted by the Prime Minister's dementia challenge, the specific focus here is on using TMS to induce small changes in two exemplars of distributed networks that simulate semantic and motor neurodegeneration as a demonstration of what this method can detect. The proposed model has the potential to change strategies for screening and early diagnosis of neurodegenerative conditions. It opens the possibility for developing new clinical applications that impact on the study of the aging brain and mental health, as well as the analysis of a wide variety of brain activity including normal during decision-making, resting-state and social behaviour.
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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.man.ac.uk |