Clinical, Dopaminergic, and Metabolic Correlations in Parkinson Disease

多巴胺能 帕金森病 医学 神经影像学 神经科学 多巴胺 疾病 内科学 心理学 精神科
作者
Feng‐Tao Liu,Jingjie Ge,Jianjun Wu,Ping Wu,Yilong Ma,Chuantao Zuo,Jian Wang
出处
期刊:Clinical Nuclear Medicine [Lippincott Williams & Wilkins]
卷期号:43 (8): 562-571 被引量:46
标识
DOI:10.1097/rlu.0000000000002148
摘要

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.

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