GraphNet-based imaging biomarker model to explain levodopa-induced dyskinesia in Parkinson's disease

Abstract

© 2020 Elsevier B.V. Background and objective: Levodopa-induced dyskinesia (LID) is a disabling complication of Parkinson’s disease (PD). Imaging-based measurements, especially those related to the surface shape of the basal ganglia, have shown potential for explaining the severity of LID in PD. Here, we aimed to explore a novel application of the methodology to find biomarkers of LID severity in PD using regularization. Methods: We proposed an application of graph-constrained elastic net (GraphNet) regularization to detect surface-based shape biomarkers explaining the severity of LID and compared the approach with other conventional regularization methods. To examine the methods, we used two independent datasets, one as a training dataset to build the model, and the other dataset was used to validate the constructed model. Results: We found that the left striatum (putamen was the greatest and the caudate was second) was the most significant surface-based biomarker related to the severity of LID. Our results improved the interpretability of identified surface-based biomarkers compared to competing methods. We also found that GraphNet regularization improved prediction of the severity of LID better than the conventional reg- ularization methods. Our model performed better in terms of root-mean-squared error and correlation coefficient between predicted and actual clinical scores. Conclusion: The proposed algorithm offers an advantage of interpretable anatomical variations related to the deformation of the cortical surface. The experimental results showed that GraphNet regulariza- tion was robust to identify surface-based shape biomarkers related to both hypokinetic and hyperkinetic movement disorders.