Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data

Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer’s disease (AD). Individual prognosis is complicated by many factors including the definition of the prediction objective itself. In this work, we present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories using multimodal and longitudinal data. We perform primary analyses on three cohorts from Alzheimer’s Disease Neuroimaging Initiative (ADNI), and a replication analysis using subjects from Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). We model the prototypical symptom trajectory classes using clinical assessment scores from mini-mental state exam (MMSE) and Alzheimer’s Disease Assessment Scale (ADAS-13) at nine timepoints spanned over six years based on a hierarchical clustering approach. Subsequently we predict these trajectory classes for a given subject using magnetic resonance (MR) imaging, genetic, and clinical variables from two timepoints (baseline + follow-up). For prediction, we present a longitudinal Siamese neural-network (LSN) with novel architectural modules for combining multimodal data from two timepoints. The trajectory modeling yields two (stable and decline) and three (stable, slow-decline, fast-decline) trajectory classes for MMSE and ADAS-13 assessments, respectively. For the predictive tasks, LSN offers highly accurate performance with 0.900 accuracy and 0.968 AUC for binary MMSE task and 0.760 accuracy for 3-way ADAS-13 task on ADNI datasets, as well as, 0.724 accuracy and 0.883 AUC for binary MMSE task on replication AIBL dataset.Author summary: With an aging global population, the prevalence of Alzheimer’s disease (AD) is rapidly increasing, creating a heavy burden on public healthcare systems. It is, therefore, critical to identify those most likely to decline towards AD in an effort to implement preventative treatments and interventions. However, predictions are complicated by the substantial heterogeneity present in the clinical presentation in the prodromal stages of AD. Longitudinal data comprising cognitive assessments, magnetic resonance images, along with genetic and demographic information can help model and predict the symptom progression patterns at the single subject level. Additionally, recent advances in machine-learning techniques provide the computational framework for extracting combinatorial longitudinal and multimodal feature sets. To this end, we have used multiple AD datasets consisting of 1000 subjects with longitudinal visits spanned up to six years for 1) modeling stable versus declining clinical symptom trajectories and 2) predicting these trajectories using data from both baseline and a follow-up visits within one year. From a computational standpoint, we validated that a machine-learning model is capable of combining longitudinal, multimodal data towards accurate predictions. Our validations demonstrate that the presented model can be used for early detection of individuals at risk for clinical decline, and therefore holds crucial clinical utility for AD, as well as, other neurodegenerative disease interventions.