Université de Montréal
Session: reproducible neuroscience + open science
Dealing with clinical heterogeneity in the discovery of new biomarkers of Alzheimer’s disease
Magnetic resonance imaging is routinely used as a biomarker for the prognosis of Alzheimer’s dementia. Prognosis based on machine learning models however has limited positive predictive value, likely due to the heterogeneity of clinical populations.
I will present data-driven algorithms aimed at identifying homogeneous subgroups of individuals at very high risk of progression to dementia, using a combination of multimodal imaging and behavioural data.
