Table 1.
Summary statistics of the participants at baseline.
Table 2.
The number of available subjects in each diagnostic group for annual follow-up visits.
Table 3.
The degree of missingness (%) in different baseline data modalities for two patient groups.
Fig 1.
Feed-forward, fully-connected neural network architecture.
Nonlinear models have rectified linear units (ReLU) between layers. Final layer implements a soft-max. Ll: Number of neurons in layer l. Input features include following. Demog.: Deomographics. Clinical Assess.: Clinical Assessments. Cog. Assess.: Cognitive Assessments. Baseline DX: Baseline Diagnosis. prob.: probability.
Fig 2.
ROC curves of NMM for CN-to-MCI and MCI-to-AD conversion in five-year time horizon.
Displayed are averages of 200 train-test splits.
Fig 3.
Predictive performance of different models for different follow-up years.
ROC AUC values are averaged across 200 80–20 data splits. Error bars indicate the standard error across these splits. LSM, Linear Single-year Model; NSM, Nonlinear Single-year Model; NMM, Nonlinear Multi-year Model.
Fig 4.
Δ ROC AUC values obtained with NMM by the addition of various biomarker combinations to the clinical data (participant demographics, clinical assessments, and cognitive assessments).
ROC AUC values are averaged across 200 80–20 data splits. Error bars indicate the standard error across these splits. +: Used together. CD, Clinical data; AV45, Florbetapir PET; CSF, Cerebrospinal Fluid; FDG, Fluorine-18-Fluorodeoxyglucose PET; MRI, Magnetic Resonance Imaging; ICV, Intracranial Volume.
Fig 5.
Conversion risk predictions of NMM for CN baseline participants with different ground truth disease trajectories.
Blue line is the average MCI conversion risk with 68% confidence. Red dots represent the observed diagnosis time (x-coordinate) and the observed diagnosis (y-coordinate) of the participants with the corresponding trajectory. Grey dots are for reference.
Fig 6.
Conversion risk predictions of NMM for MCI baseline participants with different ground truth disease trajectories.
Blue line is the average AD conversion risk with 68% confidence. Red dots represent the observed diagnosis time (x-coordinate) and the observed diagnosis (y-coordinate) of the participants with the corresponding trajectory. Grey dots are for reference.