Clinical, Dopaminergic, and Metabolic Correlations in Parkinson Disease: A Dual-Tracer PET Study. (August 2018)

• Record Type: Journal Article
• Authors: Liu, Feng-Tao; Ge, Jing-Jie; Wu, Jian-Jun; Wu, Ping; Ma, Yilong; Zuo, Chuan-Tao; Wang, Jian
• Is Part Of: Clinical nuclear medicine. Volume 43:Number 8(2018)
• Publication Date: 2018-08
• Subjects: clinical rating -- dopamine imaging -- glucose metabolism -- Parkinson disease -- PET; Nuclear medicine -- Periodicals; Radioisotope scanning -- Periodicals; Nuclear Medicine -- Periodicals; 616.07575
• DOI: 10.1097/RLU.0000000000002148
• Languages: English
• ISSNs: 0363-9762

Abstract

Abstract : Purpose: Neuroimaging indicators of Parkinson disease have been developed and applied in clinical practices. Dopaminergic imaging reflects nigrostriatal dopaminergic dysfunction, and metabolic network imaging offers disease-related metabolic changes at a system level. We aimed to elucidate the association between Parkinsonian symptoms and neuroimaging, and interactions between different imaging techniques. Methods: We conducted a dual-tracer PET study for the combined assessments of dopaminergic binding ( 11 C-CFT) and glucose metabolism ( 18 F-FDG) in 103 participants with Parkinson disease (65 male and 38 female subjects). The detailed clinical rating scores were systematically collected in all members. The interactions among dopaminergic bindings, metabolic changes, and clinical manifestations were evaluated at voxel, regional, and network levels. Results: Striatal DAT binding correlated with akinesia-rigidity ( P < 0.001) but not with tremor; the metabolic PET imaging, nonspecific to the dopaminergic dysfunction, disclosed a set of brain regions correlating with the cardinal symptoms, including tremor. In addition, the unilateral symptom correlated with the contralateral nigrostriatal dopamine loss, but with bilateral metabolic changes, suggesting their differences in the application of disease-related mechanistic studies. Further imaging-imaging correlation study revealed that dopaminergic dysfunction correlated with widely distributed metabolic changes in Parkinson disease, and the modest correlations supported the findings on the clinical-imaging correlation. Conclusions: In this dual-tracer PET study, we demonstrated the robust interactions among dopaminergic dysfunction, metabolic brain changes and clinical manifestations at voxel, regional, and network levels. Our findings might promote the understanding in the proper application of dopaminergic and metabolic PET imaging in Parkinson disease and offer more evidence in support of Parkinsonian pathophysiological mechanisms.