Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data
Fig 1
Analysis workflow of the longitudinal framework.
The workflow comprises two tasks, 1) trajectory modeling (TM), and 2) trajectory prediction (TP). Data from 69 ADNI-1 MCI subjects with 9 visits within 6 years are used for TM task using hierarchical clustering. 1116 ADNI subjects pooled from ADNI1, ADNIGO, and ADNI2 cohorts are used towards TP task. Data (CA: clinical attributes, CT: cortical thickness) from baseline and a follow-up timepoint is used towards trajectory prediction. The trained models from k-fold cross validation of ADNI subjects are then tested on 117 AIBL subjects as part of the replication analysis.
doi: https://doi.org/10.1371/journal.pcbi.1006376.g001