Project description
Imaging technology to improve diagnostic of Parkinsonism
Parkinsonism is the second most prevalent neurodegenerative disorder after Parkinson’s disease (PD), and 20 % of patients are diagnosed with atypical parkinsonisms (AP). AP tends to be therapy-resistant and presents a faster degeneration rate than PD. The EU-funded SYNPARK project is an interdisciplinary action that will investigate the value of the different imaging markers' modalities in various parkinsonism forms. Improving diagnostic accuracy is important now, when disease-modifying therapies are becoming available for PD. The research involves a multidisciplinary approach of the in vivo synaptic molecular brain assessment using positron emission tomography (PET), the whole-brain connectomics organisation using magnetic resonance imaging and the clinical validation of a new generation PET tracer in AP/PD.
Objective
Neurodegenerative diseases affect more than 6 million people in Europe, and its prevalence is growing as population ages, hence it is a timeliness health challenge we are facing as a society. Parkinsonism is the 2nd most prevalent neurodegenerative form, being Parkinson’s disease (PD) the most frequent, whereas 20% of the patients are diagnosed with atypical parkinsonisms (AP). Despite presenting some clinical overlap, AP tends to be more therapy-resistant and have faster degeneration rates than PD. SYNPARK is an interdisciplinary project that will investigate the discriminative power of different imaging markers’ modalities in parkinsonisms. Improving diagnostic accuracy is crucial as disease-modifying therapies are becoming available for PD. For this challenge, I propose a multidisciplinary approach: from the in-vivo synaptic molecular brain assessment (using positron emission tomography, PET), the whole-brain connectomics organisation (using magnetic resonance imaging, MRI) to the clinics. I will conduct the outgoing phase in one of the world’s PET leading centres in Toronto to test the clinical validity of a new generation PET tracer in AP/PD. My host return institution (Barcelona) has pioneered the research on machine learning (ML) techniques that are revolutionising the medical sciences field to improve parkinsonisms’ differential diagnosis at the single-patient level by means of whole-brain MRI connectomics information. My current expertise and the proposed ambitious training objectives will position me at the forefront of this exciting new avenue in medical sciences and will enhance my professional independence for research leadership. Overall, the synergies established between these two leading centres are expected to have a tremendous impact on the understanding of AP/PD brain pathophysiology and its diagnostic accuracy, and ultimately enhancing a revolution in personalised medicine, a futuristic therapy that is increasingly becoming a reality.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences basic medicine physiology pathophysiology
- medical and health sciences health sciences personalized medicine
- engineering and technology medical engineering diagnostic imaging magnetic resonance imaging
- medical and health sciences basic medicine neurology parkinson
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2019
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
08007 BARCELONA
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.