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Objective home-based EEG prediction of aMCI: Identification of a predictive electrophysiological model of cognitive function in amnesic mild cognitive impairment.

Descrizione del progetto

Un modello predittivo della demenza

La demenza si riferisce a un gruppo di disturbi cerebrali progressivi legati all’età, come il morbo di Alzheimer, che in Europa colpisce milioni di adulti. Il morbo di Alzheimer è solitamente preceduto da un deterioramento cognitivo lieve (MCI, Mild Cognitive Impairment) amnesico, caratterizzato da una ridotta capacità di memoria. Riuscire a diagnosticare e caratterizzare tempestivamente l’MCI amnesico faciliterebbe l’erogazione di interventi precoci, ritardando così l’insorgenza del morbo. Gli scienziati del progetto ID-earlyMCI, finanziato dall’UE, propongono di utilizzare l’elettroencefalografia (EEG) accanto alle pratiche comportamentali per misurare l’attività cerebrale nei pazienti e sviluppare un modello predittivo delle funzioni cognitive. Metodi avanzati di apprendimento automatico saranno utilizzati su un vasto database EEG, contribuendo a migliorare i servizi sanitari per i pazienti con MCI amnesico.

Obiettivo

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.

Campo scientifico (EuroSciVoc)

Parole chiave

Programma(i)

Argomento(i)

Meccanismo di finanziamento

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Invito a presentare proposte

(si apre in una nuova finestra) H2020-MSCA-IF-2019

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Coordinatore

BRAINWAVEBANK LTD.
Contributo netto dell'UE
€ 184 590,72
Indirizzo
THE INNOVATION CENTRE, UNIT 4, QUEE
BT39DT BELFAST
Regno Unito
PMI
Sì
Regione
Northern Ireland Northern Ireland Belfast
Tipo di attività
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Collegamenti
Costo totale
€ 184 590,72