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NIMBLE

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Cerrado

Neuromagnetic Imaging Multiobject Bayesian Localization and Estimation

Functional neuro-imaging is a lively field in contemporary science, with a large number of open research problems and many clinical applications including the non-invasive diagnosis and monitoring of brain conditions such as epile... ver más

02/09/2012

UNIVERSITY OF WARW...

173K€

Presupuesto del proyecto: 173K€

Líder del proyecto

UNIVERSITY OF WARWICK No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2012-09-02 No tenemos la información de la convocatoria

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1 Participantes

UNIVERSITY OF WARWICK

0.00€ | Lider

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Líder del proyecto

UNIVERSITY OF WARWICK No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 173K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Functional neuro-imaging is a lively field in contemporary science, with a large number of open research problems and many clinical applications including the non-invasive diagnosis and monitoring of brain conditions such as epilepsy, Alzheimer, Parkinson’s, and brain cancer. In this context, Magnetoencephalography is characterized by an outstanding temporal resolution, and is currently the best available technique for investigating the activity of different brain regions on a millisecond time scale. However, a key open problem for MEG data analysis is the lack of a robust framework for automatic source detection and parameter estimation using MEG data; these are in fact routinely analyzed using labour-intensive manual methods heavily relying on the expertise of the user. The NIMBLE project (Neuromagnetic Imaging: Multiobject Bayesian Localisation and Estimation) aims at developing novel Bayesian methodology for automatic source estimation from MEG data, based on stochastic geometry and point processes. The project is consistent with the research profile of the candidate, who has developed a significant experience with MEG and has obtained preliminary results on estimating MEG sources with Bayesian filtering. The Host Institution was selected by the Fellow in light of its rich research environment in neuroscience including, among others, a world-renowned expert in stochastic geometry and several young researchers with expertise in statistical modeling for neuroimaging, Bayesian inference and sequential Monte Carlo methods for Bayesian filtering. The NIMBLE project is highly multidisciplinary, involving the statistics of point processes, the modeling of neural sources and the efficient implementation of high-dimensional sequential Monte Carlo filters. Should the NIMBLE project be funded, it would contribute to strengthen the EU position in the strategic field of neuroimaging, where competition is currently strong with the United States and Japan.

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