PLATA aims to develop an algorithm to identify vocal biomarkers of Alzheimer's dementia.
Using data collected as part of routine care, speech patterns will be compared to known biomarkers of Alzheimer's disease, such as amyloid 1-42 and p-Tau in CSF (cerebrospinal fluid).
If biomarkers of speech can be identified in Alzheimer's disease, it is possible that patients and research participants will no longer need to undergo need to undergo the intensive and invasive baseline biomarker methods currently used, such as lumbar punctures and PET scans.
Age ≥ 50 years
Diagnosis relevant biomarker and neuropsychological data already available
Cognitively healthy to very mild dementia (CDR score max. 0.5)
Sufficient knowledge of the study language to understand study information, non opposition form,and questionnaires
Hearing problems
Patient protected by law, under guardianship or curator ship, or not able to participate in a clinical study according to the article L.1121-16 of the French Public Health Code
Participants will be recruited through the Memory Clinic at the Geriatric Hospital in Nice (Centre de Mémoire de Ressources et de Recherche, CHU de Nice, Institut Claude Pompidou 10 rue Molière, 06 100 NICE) with a minor or major neurocognitive disorder whose etiology is either: * Probable Alzheimer's disease (positive AD biomarkers) * Mood disorder * Not related to a mood disorder and with negative AD biomarkers
Cohort
Prospective
Series of cognitive tasks during a semi-automated call
Other interventionTasks: * Verbal learning recall (immediate) or Story Recall task (immediate) * Narrative Storytelling /free speech * Verbal fluency task * Verbal learning recall (delayed) or Story Recall task (delayed)
Build and validate speech-based machine learning models for relevant Phenotype detection through access to phenotyped patients from reference memory center.
Speech biomarker algorithm(s)
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