TV‐L1 Ordinal Logistic Regression Reveals New Morphometric Patterns Related to Parkinsonian Symptom Severity: An ENIGMA‐PD study. (20th December 2022)

• Record Type: Journal Article
• Authors: Zhao, Yuji; van Heese, Eva; Laansma, Max A.; Al‐Bachari, Sarah; Anderson, Tim; Assogna, Francesca; Berendse, Henk W.; Bright, Joanna; Cendes, Fernando; Dalrymple‐Alford, John; Debove, Ines; Dirkx, Michiel; Druzgal, T. Jason; Emsley, Hedley; Fouche, JP; Garraux, Gaëtan; Guimarães, Rachel; Helmich, Rick; Jahanshad, Neda; Kim, Ho Bin; Klein, Johannes C; Lochner, Christine; Mackay, Clare; McMillan, Corey T; Melzer, Tracy R; Newman, Benjamin T; Owens‐Walton, Conor; Parkes, Laura; Piras, Fabrizio; Pitcher, Toni; Poston, Kathleen L; Rango, Mario; Ribeiro, Leticia Franchescheti; Rocha, Cristiane; Roos, Annerine; Rummel, Christian; Santos, Lucas; Schmidt, Reinhold; Spalletta, Gianfranco; Squarcina, Letizia; Schwingenschuh, Petra; Vecchio, Daniela; Vriend, Chris; Wang, Jiun‐Jie; Weintraub, Daniel; Wiest, Roland; Yasuda, Clarissa; Thompson, Paul M; van der Werf, Ysbrand D.; Gutman, Boris A;
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 6
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.067037
• Languages: English
• ISSNs: 1552-5260

Abstract

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.