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ID-earlyMCI

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Objective home based EEG prediction of aMCI Identification of a predictive elec...

Objective home based EEG prediction of aMCI Identification of a predictive electrophysiological model of cognitive function in amnesic mild cognitive impairment. 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 precu... ver más

30/09/2022

BrainWaveBank

185K€

Presupuesto del proyecto: 185K€

Líder del proyecto

BRAINWAVEBANK LTD. 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 H2020 notifico la concesión del proyecto el día 2022-09-30

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

BrainWaveBank

184.59K€ | Lider

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Duración del proyecto: 30 meses Fecha Inicio: 2020-03-24
Fecha Fin: 2022-09-30

Líder del proyecto

BRAINWAVEBANK LTD. No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 185K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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