INTRODUCTION: Parkinson's disease ( METHODS: Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson's Progression Markers Initiative ( RESULTS: Using data from 2396 subjects, we show that CONCLUSION: Our findings suggest that, while PD is generally associated with a larger DAT deficit in specific brain structures of the neostriatum, it exhibits intrinsic heterogeneity across individuals, which may stem from genetic factors. Such heterogeneity can be characterized by ML models and optimally mapped into network states, providing new insights to consider when developing personalized drugs.