Modeling autosomal dominant Alzheimer's disease with machine learning

Abstract

Introduction: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

Methods: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.

Results: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R² = 0.95), fluorodeoxyglucose (R² = 0.93), and atrophy (R² = 0.95) in mutation carriers compared to non-carriers.

Discussion: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

Keywords: Pittsburgh compound B (PiB); autosomal dominant Alzheimer's disease (ADAD); fluorodeoxyglucose (FDG); machine learning; magnetic resonance imaging (MRI).

Figure 1.

Results of Pittsburgh Compound-B (PiB) predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red). Correlation and RMSE of predicted versus actual values. The ANN was able to predict future PiB values with an average R² of 0.95 and RMSE of 0.2 in both MCs and NCs.

Figure 2.

Results of fluorodeoxyglucose (FDG) predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red) in select ROIs. Correlation and root mean squared error (RMSE) of predicted versus actual values. The ANN was able to predict future FDG values with an average R² of 0.93 and RMSE of 0.02 in MCs and NCs, with MCs showing trends of lower predicted FDG values than NCs.

Figure 3.

Results of brain volumetric predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red). Correlation and root mean squared error (RMSE) of predicted versus actual values. The ANN was able to predict changes in brain volumes with an average R² value of 0.95 and showed a general trend of MCs having more brain atrophy than NCs.

Figure 4.

(Top left) Simulated biomarker evolution for total mean cortical and subcortical Pittsburgh Compound-B (PiB), total mean cortical and subcortical fluorodeoxyglucose (FDG), and total gray matter volume (scaled to a common interval) derived from the artificial neural network (ANN) in mutation carriers (MC). Shaded region indicates model variability, with EAO marked by perpendicular line. (Top right) Simulated biomarker evolution for total mean cortical and subcortical PiB, total mean cortical and subcortical FDG, and total gray matter volume (scaled to a common interval) derived from the ANN in mutation non-carriers (NC). (Bottom left) Normalized biomarker rate of change for mean PiB, mean FDG, and total gray matter volume (scaled to a common interval) fit to a polynomial curve showing 95% confidence interval. (Bottom right) Mean absolute error of predicted (normalized) biomarker values given the amount of time in the future to predict, fit with a 2-degree polynomial curve projected into the future. Errors increased linearly with an increase in the amount of time in the future to predict.

Figure 5.

Strongest predictors of mutation carrier (MC) status for autosomal dominant Alzheimer’s disease (ADAD) as identified by Relief algorithms. The strongest predictors across all modalities were the precuneus, caudate, and anterior cingulate. Changes in amyloid PET (PiB, blue circle) were primarily seen within subcortical regions. Changes in metabolism (FDG, orange circle) showed more cortical involvement. Volumetric changes (Volume, green circle) showed both cortical and subcortical involvement.