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

• Published in: PLoS computational biology Vol. 14; no. 9; p. e1006376
• Main Authors: Bhagwat, Nikhil, Viviano, Joseph D, Voineskos, Aristotle N, Chakravarty, M Mallar
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.09.2018; Public Library of Science (PLoS)
• Subjects: Accuracy; Addictions; Aged; Aging; Algorithms; Alzheimer Disease - diagnosis; Alzheimer Disease - physiopathology; Alzheimer's disease; Area Under Curve; Australia; Automation; Biology and Life Sciences; Biomarkers; Biomedical engineering; Biomedical materials; Brain research; Clinical trials; Cluster analysis; Clustering; Collaboration; Computation; Computational neuroscience; Computer and Information Sciences; Datasets; Development and progression; Diagnosis; Disease Progression; Female; Follow-Up Studies; Humans; Laboratories; Learning algorithms; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Male; Mathematical models; Medical imaging; Medical research; Medical treatment; Medicine and Health Sciences; Mental health; Models, Neurological; Nerve Net; Neural networks; Neuroimaging; Neurology; Performance evaluation; Physical Sciences; Predictions; Replication; Reproducibility of Results; Research and Analysis Methods; Social Sciences; Software; Studies; Symptom Assessment; Trajectory analysis; Australia; Toronto Ontario Canada; Canada; Quebec Canada
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006376

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.