Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease

Publication date: Available online 26 January 2020Source: NeuroImage: ClinicalAuthor(s): Joseph Giorgio, Susan Landau, William Jagust, Peter Tino, Zoe Kourtzi, for the Alzheimer's Disease Neuroimaging InitiativeAbstractAlzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation– using partial least squares regression– and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual c...

Source: NeuroImage: Clinical - January 28, 2020 Category: Radiology Source Type: research