Metabolic brain imaging in genetic Parkinson's disease: from genes to network trajectories

Genetic forms of Parkinson's disease (PD) provide a unique model to investigate how distinct molecular perturbations reshape large-scale brain networks. Although most genetic variants ultimately converge on nigrostriatal dopaminergic degeneration, accumulating evidence suggests that different mutations modulate distributed motor and cognitive circuits along genotype-specific trajectories. In this narrative review, we synthesize findings from 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) and cerebral perfusion single-photon emission computed tomography (SPECT) in monogenic and high-risk forms of PD, including LRRK2, GBA1, SNCA, PRKN, PINK1, DJ-1, RAB39B, and RAB32. Spatial covariance analyses have identified reproducible disease-related metabolic networks, notably the Parkinson's disease-related motor pattern (PDRP) and the Parkinson's disease cognitive pattern (PDCP), which can be quantified at the single-subject level and tracked longitudinally. Across genotypes, mutations appear to modulate the topology, resilience, and temporal evolution of shared network architectures rather than generating distinct metabolic signatures The strongest evidence concerns severe GBA1 variants, which are associated with early posterior cortical involvement and greater PDCP expression, whereas limited case-based data suggest more diffuse cortical involvement in selected SNCA multiplication carriers. In contrast, mitochondrial- and kinase-related mutations often show metabolic alterations largely confined to subcortical motor circuits. We propose a trajectory-based framework in which genetic background shapes network vulnerability and compensatory capacity rather than defining separate metabolic entities. In the era of gene-targeted therapies, imaging-defined network phenotypes may serve as functional biomarkers for risk stratification, longitudinal monitoring, and mechanistic therapeutic trials, bridging genotype and systems-level neurodegeneration.