AI Case Study

"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.

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).

The overarching goal of this work is to provide a longitudinal analysis framework for predicting symptom progression in AD that addresses the aforementioned challenges pertaining to task definition (model output) as well as ensemble feature representation (model input). The contributions of this work are two-fold. First, we present a novel data-driven approach for modeling long-term symptom trajectories derived solely from clustering of longitudinal clinical assessments. We show that the resultant trajectory classes represent relatively stable and declining trans-diagnostic subgroups of the subject population. Second, we present a novel machine-learning (ML) model called longitudinal Siamese network (LSN) for prediction of these symptom trajectories based on multimodal and longitudinal data. Specifically, we use cortical thickness as our MR measure due to its higher robustness against typical confounds, such as head size, total brain volume, etc., compared to local volumetric measures [32] and its previous use in biomarker development and clinical applications in AD [16,21,33,34]. The choice of excluding other potential biomarkers related to AD-progression, such as PET or CSF data in the analysis was based on their invasive acquisition and lack of availability in practice and in the databases leveraged in this work."