Objective
Noninvasive neuroimaging techniques have shown much promise over the last decade in the investigation, diagnosis and in determining treatment of neurological disorders. However, to improve their predictive power and better understand underlying causative disease factors it will be essential to extend our investigations to empiric studies of large populations and to develop appropriate statistical models and define statistical procedure for use in such observational studies. It is the main objective of this project therefore, to develop and extend the methods of Causal Inference for use on large unstructured neuroimaging datasets. Specifically, this proposal seeks to 1) Develop and apply existing techniques from matched sampling to observational studies of imaging for Causal Inference. 2) Investigate the benefits of the same matched sampling procedures in support of classification models. 3) Industrial considerations: This is an enterprise panel proposal and will aim to integrate the developed technology into the Clinical Imaging Big-Data program at Siemens HealthCare (SHC), Erlangen. These objectives will be achieved by implementing and testing different Matched Sampling procedures using large observational neuroimaging datasets and assessing their performance through reduction in the specific forms of bias known to be present in observational data. These methods will be extended for use in classification models and the effects of matching on common prediction methods will be examined. The project is highly relevant for the work program as it will provide an opportunity to enhance training through Siemens and has the potential to facilitate a career move from academia to the non academic sector.
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 neurology dementia alzheimer
- medical and health sciences clinical medicine rheumatology
- medical and health sciences clinical medicine endocrinology diabetes
- medical and health sciences health sciences nutrition
- medical and health sciences basic medicine neurology stroke
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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-EF-SE - Society and Enterprise panel
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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-2016
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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.
91052 Erlangen
Germany
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.