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Homepage > AJR OnTrend > Quantifying the Severity of Parkinson Disease

Quantifying the Severity of Parkinson Disease

Pathologically, Parkinson disease is characterized by a loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) area of the brain, resulting in presynaptic nigrostriatal dopamine dysfunction. Whereas prior research into dopaminergic neuroimaging has illustrated the independent utility of neuromelanin MRI and dopamine transporter SPECT for evaluating the severity of Parkinson disease, Hiroto Takahashi and colleagues from Japan’s Osaka University Graduate School of Medicine aimed to explore the use of these two imaging biomarkers to quantify severity during the progression of Parkinson disease. Twenty men and 20 women (mean age 68.35 years) who underwent neuromelanin MRI and dopamine transporter SPECT were included in the study. Parkinson disease severity was assessed with the Hoehn and Yahr (HY) scale (HY stage 1, 4 patients; stage 2, 18 patients; stage 3, 8 patients; stage 4, 6 patients; stage 5, 4 patients). The signal-to-noise ratio (SNR) in the SNpc on neuromelanin MR images and the striatal specific binding ratio (SBR) on dopamine transporter SPECT images were calculated based on the value of each background region. The Mann-Whitney U test was used to assess the significance of differences between the early-stage group (HY 1 and 2) and the advanced-stage group (HY 3–5) for each SNR and SBR. Overall, both SNR and SBR measured much greater in early-stage patients compared to the advanced stage group (p < 0.05). Additionally, the AUC for differentiating early and advanced stage groups was 0.73 for SNR and 0.89 for SBR. The coefficient of correlation was −0.47 for SNR versus HY stage and −0.67 for SBR versus HY stage. As Takahashi acknowledged, “The current study shows that it is possible to quantify the degeneration of dopaminergic nigrostriatal transporters in Parkinson disease using striatal SBR derived from dopamine transporter SPECT with good correlation with the HY stage.”

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