Project description
A prediction model of dementia
Dementia refers to an age-related group of progressive brain disorders such as Alzheimer's disease (AD) that affect millions of adults in Europe. AD is usually preceded by amnestic mild cognitive impairment (aMCI), which is characterised by reduced memory skills. Being able to diagnose and characterise aMCI promptly would facilitate the delivery of early interventions, thereby delaying AD onset. Scientists of the EU-funded ID-earlyMCI project propose to use electroencephalography (EEG) alongside behavioural practices to measure brain activity in patients and develop a predictive model of cognitive function. Advanced machine learning methods will be used on a large EEG database, contributing to improved healthcare services for patients with aMCI.
Objective
Dementia is an umbrella term for age-related brain disease, of which Alzheimer’s Disease (AD) is the most common. Around 5-7% of adults over 60 years suffer dementia worldwide, with approx. 8.7 m people in the EU. A frequent precursor of AD is amnestic mild cognitive impairment (aMCI), a clinical condition characterised by declines in memory skills. By predicting
aMCI progression, health-care services will have new opportunities to deliver early interventions that could delay AD onset. This will ultimately promote functional independence in vulnerable adults and meet the societal challenge Health, Demographic Change and Wellbeing of Horizon 2020.
Clinical outcomes of aMCI patients are influenced by the severity of cognitive (dys-) function. However, these deficits may occur at an advanced stage of neurodegeneration. This fellowship aims to identify a predictive model of cognitive function based on brain activity measured with electro-encephalography (EEG). Previous studies suggest that the capacity to learn a new task (practice effects) can help classify a person into healthy, aMCI or AD. Also, cross-sectional studies using EEG have found differences between normal controls, aMCI and AD patients during rest and cognitive tasks. The behavioural and EEG evidence combined shows the potential of using behavioural practice and EEG measures to predict cognitive function.
This potential will be investigated and exploited in this fellowship via advanced machine learning methods on a large EEG
data sample. This fellowship will take place in BrainWaveBank (BWB), an innovative company developing the largest database of EEG data in older adults along with cutting-edge analytics. This fellowship will allow the researcher to apply her experience on neural engineering and expand her knowledge and expertise to machine learning and clinical neuroscience in BWB. This will build the researcher’s independence and build prospects for a career in the medical technology 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.
- natural sciences biological sciences neurobiology cognitive neuroscience
- medical and health sciences basic medicine neurology dementia alzheimer
- social sciences sociology demography
- medical and health sciences health sciences health care services
- 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.
BT39DT BELFAST
United Kingdom
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
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.