Abstract
Background
Parkinson’s disease (PD) is heterogeneous, both phenotypically and in terms of temporal progression. The Hoehn and Yahr (HY) scale is a well-established PD staging approach, and identifies 5 stages of the disease. Morphometric effects in deep gray matter regions of the brain associated with HY stages are complex; a recent large-scale ENIGMA-PD study showed higher local subcortical volumes in early HY stages relative to controls, followed by a precipitous decrease after stage 2 [1]. This finding motivates a closer look at fine-level morphometry beyond gross volume measures. Here, we developed and applied a novel machine learning algorithm to reveal the subcortical shape signatures of HY staging.
Method
We computed shape features in 7 bilateral subcortical regions [2] based on T1-weighted MRI data from 2,322 PD subjects and 1,207 controls from 20 ENIGMA-PD cohorts (HY stages in Table 1). We developed a sparse, spatially coherent (total variation/TV-L1) ordinal linear logistic classifier [3] to predict HY stages with a single linear model. We applied the model to vertex-wise medial thickness features. We optimized regularization parameters for balanced recall (sensitivity) and precision using a 4-fold cross-validation grid search. Very low numbers of HY4 and HY5 samples necessitated merging stages 3-5 into one category. For comparison, we also trained 4 binary TV-L1 logit models on the same features [4], discriminating (1) PD-Control; (2) HY1-HY2; (3) HY1-HY345; (4) HY2-HY345, using ROC area-under-the-curve (AUC) evaluation.
Result
Across-stage mean out-of-sample precision and recall were 0.43, and 0.393, respectively (chance=0.33). Table 2 shows the confusion matrix and precision/recall for each HY stage. All models’ linear coefficient maps are displayed in Figures 1,2. Binary classification ROC-AUC was 0.66 for PD-Control, and ranged from 0.62 to 0.73 for HY prediction (Figure 2).
Conclusion
We developed an ordit machine learning model for morphometric shape-based ordinal classification of disease stages, training it for Parkinson’s Disease Hoehn and Yahr stage prediction on a large MRI collection. Performance was substantially above chance. Model weight maps indicate early increased thalamic thickness, followed by a complex thinning pattern associated with later HY stages.
Parkinson’s disease (PD) is heterogeneous, both phenotypically and in terms of temporal progression. The Hoehn and Yahr (HY) scale is a well-established PD staging approach, and identifies 5 stages of the disease. Morphometric effects in deep gray matter regions of the brain associated with HY stages are complex; a recent large-scale ENIGMA-PD study showed higher local subcortical volumes in early HY stages relative to controls, followed by a precipitous decrease after stage 2 [1]. This finding motivates a closer look at fine-level morphometry beyond gross volume measures. Here, we developed and applied a novel machine learning algorithm to reveal the subcortical shape signatures of HY staging.
Method
We computed shape features in 7 bilateral subcortical regions [2] based on T1-weighted MRI data from 2,322 PD subjects and 1,207 controls from 20 ENIGMA-PD cohorts (HY stages in Table 1). We developed a sparse, spatially coherent (total variation/TV-L1) ordinal linear logistic classifier [3] to predict HY stages with a single linear model. We applied the model to vertex-wise medial thickness features. We optimized regularization parameters for balanced recall (sensitivity) and precision using a 4-fold cross-validation grid search. Very low numbers of HY4 and HY5 samples necessitated merging stages 3-5 into one category. For comparison, we also trained 4 binary TV-L1 logit models on the same features [4], discriminating (1) PD-Control; (2) HY1-HY2; (3) HY1-HY345; (4) HY2-HY345, using ROC area-under-the-curve (AUC) evaluation.
Result
Across-stage mean out-of-sample precision and recall were 0.43, and 0.393, respectively (chance=0.33). Table 2 shows the confusion matrix and precision/recall for each HY stage. All models’ linear coefficient maps are displayed in Figures 1,2. Binary classification ROC-AUC was 0.66 for PD-Control, and ranged from 0.62 to 0.73 for HY prediction (Figure 2).
Conclusion
We developed an ordit machine learning model for morphometric shape-based ordinal classification of disease stages, training it for Parkinson’s Disease Hoehn and Yahr stage prediction on a large MRI collection. Performance was substantially above chance. Model weight maps indicate early increased thalamic thickness, followed by a complex thinning pattern associated with later HY stages.
| Original language | English |
|---|---|
| Journal | Alzheimer's & Dementia |
| Volume | 18 |
| Issue number | S6 |
| DOIs | |
| Publication status | Published - 1 Dec 2022 |