Aims
To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA‐P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction.
Methods
77 IPD and 75 MSA‐P patients underwent 3.0 T multimodal MRI comprising susceptibility‐weighted imaging, resting‐state functional magnetic resonance imaging, T1‐weighted imaging, and diffusion tensor imaging. Iron‐radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification
Results
A combination of iron‐radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA‐P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen.
Conclusion
The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello‐striatal connections and facilitated accurate classification between IPD and MSA‐P. The dorsolateral putamen was the most valuable neuromarker for the classification.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.