AI-Driven Language Biomarkers for Early Detection and Progression of Cognitive Decline
Description: Language is a cognitive function often impacted in early cognitive decline, potentially signalling early-stage dementia. Yet, despite extensive research, subtle linguistic markers in at-risk individuals remain poorly understood, highlighting the need for new investigative approaches. We propose integrating speech and language analysis with neuropsychological tests and biomarkers, using large-scale, clinically validated datasets for robust, scalable analysis.We aim to answer:Which speech, language, cognitive, and behavioral markers best distinguish early-stage cognitive impairment, and how do they correlate with neuropsychological test scores in a large-scale population study?Does combining linguistic markers with behavioral, cognitive, and traditional biomarkers improve diagnostic accuracy for early cognitive decline compared to single-modality measures?What measurable linguistic changes over time can speech and language analysis detect, and how do they relate to cognitive decline, disease progression, and prognosis?The questions are addressed through state-of-the-art AI technologies: natural language processing, machine & deep learning, including algorithms for data preprocessing, feature selection, classification, and clustering. The project spans four years: one year for finalizing follow-up data collection—initiated in 2025, to be completed in mid-2026—and infrastructural development (cf. 5.2); followed by three years of model development, validation, and analysis.
Funder: Vetenskapsrådet
Amount: SEK 6,620,000
Funding type: research
Funder scheme: Project grant
Start date: 2026-01-01
End date: 2029-12-31
Investigator: Dimitrios Kokkinakis
Institution: University of Gothenburg
Funded outputs count: 0
Funder award ID: 2025-00765
OpenAlex ID: G2950172912