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

Research output: Contribution to journalArticle

Authors

  • Alzheimer’s Disease Neuroimaging Initiative
  • Joseph Giorgio
  • Susan Landau
  • William Jagust
  • Peter Tino
  • Zoe Kourtzi

Colleges, School and Institutes

External organisations

  • University of Cambridge
  • University of California, Berkeley

Abstract

Alzheimer’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 cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data
(memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same classification task on biological data (mean cortical β-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). However, training the model with biological (r=-0.68) rather than cognitive data (r=-0.4) shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline). Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.

Details

Original languageEnglish
Article number102199
Pages (from-to)1-14
Number of pages14
JournalNeuroImage: Clinical
Volume26
Early online date26 Jan 2020
Publication statusE-pub ahead of print - 26 Jan 2020

Keywords

  • machine learning, mild cognitive impairment, Alzheimer’s disease brain imaging, cognition