A review of FDG-PET biomarker analysis methods in Parkinson's disease

Abstract

Despite well-established clinical diagnostic criteria for Parkinson's disease (PD), challenges remain in early-stage diagnosis, differentiating atypical parkinsonism disorders, and understanding of underlying disease mechanisms. Neuroimaging, particularly positron emission tomography (PET) with [18F]fluorodeoxyglucose (FDG), plays a crucial role by offering in vivo assessments of brain activity through glucose metabolism, a marker of neuronal function. FDG-PET has demonstrated consistent metabolic abnormalities in PD, even in early or ambiguous stages, supporting its potential as both a diagnostic and mechanistic tool. Recent advancements in analytical methods, including regional univariate, pattern-based multivariate, network connectivity, and machine learning approaches, have allowed for more detailed exploration of PD-related brain dysfunction across multiple spatial, temporal, and organizational scales. This review provides an overview of the various FDG-PET analysis methods, discussing their principles, strengths, and limitations, with an emphasis on their utility for applications in PD, including both diagnostic applications and insights into underlying neural mechanisms.