RESUMEN
BACKGROUND: Cerebral blood flow (CBF) and dopamine transporter (DAT) images are clinically used for the differential diagnosis of parkinsonian disorders. OBJECTIVES: This study aimed to examine the correlation of CBF with striatal DAT in patients with Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) and evaluate the diagnostic power of DAT-correlated CBF in PD through machine learning with each imaging modality alone or in combination. METHODS: Fifty-eight patients with PD and 71 with APS (24 with multiple system atrophy, 21 with progressive supranuclear palsy, and 26 with corticobasal syndrome) underwent 123 I-IMP and 123 I-FP-CIT single-photon emission computed tomography. Multiple regression analyses for CBF and striatal DAT binding were conducted on each group. PD probability was predicted by machine learning and receiver operating characteristic curves. RESULTS: The PD group showed more affected striatal DAT binding positively correlated with the ipsilateral prefrontal perfusion and negatively with the bilateral cerebellar perfusion. In corticobasal syndrome, striatal DAT binding positively correlated with the ipsilateral prefrontal perfusion and negatively with the contralateral precentral perfusion. In Richardson's syndrome, striatal DAT binding positively correlated with perfusion in the ipsilateral precentral cortex and basal ganglia. Machine learning showed that the combination of CBF and DAT was better for delineating PD from APS (area under the curve [AUC] = 0.87) than either CBF (0.67) or DAT (0.50) alone. CONCLUSIONS: In PD and four-repeat tauopathy, prefrontal perfusion was related to ipsilateral nigrostriatal dopaminergic function. This dual-tracer frontostriatal relationship may be effectively used as a diagnostic tool for delineating PD from APS. © 2022 International Parkinson and Movement Disorder Society.