Descrizione del progetto
Screening massivo in remoto di cervelli anziani
Le popolazioni che invecchiano presentano un rischio maggiore di sviluppare malattie neurodegenerative come il morbo di Alzheimer e il morbo di Parkinson. Poiché i cervelli più anziani sono altamente predisposti alle malattie neurodegenerative, è essenziale poter contare su tecniche di diagnostica efficaci. Gli approcci attuali si basano su costose scansioni cerebrali e laboriose batterie di test neuropsicologici, che richiedono urgentemente di essere aggiornate per migliorare l’efficienza del rilevamento delle malattie neurodegenerative. Il progetto MULTI-LAND, finanziato dall’UE, affronterà questo problema attraverso un quadro interdisciplinare originale e innovativo che utilizza marcatori di linguaggio naturale con il supporto dell’apprendimento automatico. Unito alla prima convalida intermetodologica (comportamentale, fMRI, EEG) e intercentrica (America Latina ed Europa) di marcatori di linguaggio naturale in pazienti con malattie neurodegenerative, il progetto contribuirà a ottimizzare lo screening massivo delle popolazioni anziane effettuato in remoto.
Obiettivo
By 2030, people aged 65 and over are expected to account for more than 25% of the European population. This fact foreshadows a dramatic growth of aging-related neurodegenerative disorders (NDs), including Alzheimer's and Parkinson's disease. Critically, this scenario will place a severe strain on healthcare systems as NDs incapacitate patients, burden their families, and entail major costs of diagnostics evaluation, including time-consuming neuropsychological batteries and expensive brain scans. Though currently irreplaceable, this approach is often unaffordable and non-viable for remote application -a key requisite during pandemic lockdowns. Thus, these procedures must be complemented with urgent innovations that boost diagnosis and symptom severity detection using low-cost tools applicable to large sections of the population remotely. A promising interdisciplinary framework rooted in natural language markers (NLMs) can offer key solutions to this crisis. This novel framework is based on linguistic features derived from patient's natural speech and analysed via machine learning algorithms. NLMs are characterized by high ecological validity, minimal stress, low costs and adaptability for remote and massive screening. Despite the increasing application of NLMs in the field of mental health, their use in NDs evaluation is still scarce. Building on a unique synergy of international expertise, we will perform the first cross-methodological (behavioural, f/MRI, EEG) and cross-centre (Latin-America and Europe) validation of NLMs in patients with NDs. Specifically, we aim to: (1) establish NLMs diagnostic sensitivity, (2) unveil potential links between NLMs and brain network disruptions, (3) estimate their robustness and generalisation power. The ultimate translational goal of this project is to identify the best-performing set of NLMs to develop a frontline mobile-phone app with clinical value, capable of capturing natural speech features for remote patient evaluation.
Campo scientifico
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
20009 San Sebastian
Spagna