MULTI-LAND · Multicentric Language Markers of NeuroDegeneration
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2021-09-01 → 2024-08-31
- Финансиране от ЕС
- 224 497 €
- Участници
- 2
- Схема
- MSCA-IF
Линиите свързват координатора с партньорите.
Накратко на български
Езиковите маркери в речта, като промени в речника или граматиката, се анализират чрез машинно обучение за разпознаване на болести като Алцхаймер и Паркинсон. Това помага за по-бърза и достъпна диагностика, която да замени скъпите скенерни изследвания и стресиращите тестове.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Multicentric Language Markers of NeuroDegeneration
In the next decade, Europe will experience a significant demographic shift, with over 35% of its population surpassing the age of 65. This foreshadows a dramatic growth of aging-related neurodegenerative diseases (NDs). Among the most prevalent NDs are Alzheimer's disease (AD) and Parkinson's disease (PD). While these conditions share some underlying neuropathological features, they exhibit distinct cognitive and brain connectivity profiles. AD primarily affects temporo-parietal networks, leading to memory impairments, whereas PD disrupts fronto-striatal networks, impacting motor skills and executive functions. Moreover, each disease involves distinct language difficulties, with AD affecting lexico-semantic skills and PD impacting semantic control, action meaning processing and morpho-syntactic skills. Currently, diagnosing and monitoring NDs rely on lengthy, stressful cognitive tests and costly brain scans, which can be particularly challenging in regions with economic disparities. To overcome these limitations, MULTI-LAND aims to implement a ground-breaking approach focused on natural language markers (NLMs)—linguistic features extracted from patients’ speech that are analysed using machine learning in an automated fashion to identify ND conditions. While NLMs have shown promise in detecting mental health conditions, their application to NDs is limited. Additionally, no study has yet (i) examined the link between NLMs and disease-specific brain network disruptions, (ii) evaluated their reliability compared to cognitive tests, or (iii) tested their diagnostic potential across multiple research centres and countries. The primary research objective of MULTI-LAND is to conduct a comprehensive, cross-methodological (behavioural, MRI/fMRI, EEG) and multi-centric (i.e., the BCBL in Europe and the CNC-UdeSA in Argentina) validation of NLMs. The overarching hypothesis is that NLMs will robustly discriminate patients from healthy controls, while unveiling disease-specific neurocognitive patterns, offering a cost-effective, scalable, and remotely applicable approach for ND detection and monitoring.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
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.
Оригинален текст от CORDIS (на английски).
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Данни: CORDIS, © Европейски съюз