Principal Component Analysis of Multimodal Neuromelanin MRI and Dopamine Transporter PET Data Provides a Specific Metric for the Nigral Dopaminergic Neuronal Density.

The loss of dopaminergic (DA) neurons in the substantia nigra (SN) is a major pathophysiological feature of patients with Parkinson's disease (PD). As nigral DA neurons contain both neuromelanin (NM) and dopamine transporter (DAT), decreased intensities in both NM-sensitive MRI and DAT PET reflect decreased DA neuronal density. This study demonstrates that a more specific metric for the nigral DA neuronal density can be derived with multimodal MRI and PET. Participants were 11 clinically diagnosed PD patients and 10 age and gender matched healthy controls (HCs). Two quantities, the NM-related index (RNM) and the binding potential of the radiotracer [18F]FE-PE2I to DAT (BPND) in SN, were measured for each subject using MRI and PET, respectively. Principal component analysis (PCA) was applied to the multimodal data set to estimate principal components. One of the components, PCP, corresponds to a basis vector oriented in a direction where both BPND and RNM increase. The ability of BPND, RNM and PCP to discriminate between HC and PD groups was compared. Correlation analyses between the motor score of the unified Parkinson's disease rating scale and each metric were also performed. PCP, BPND and RNM for PD patients were significantly lower than those for HCs (F = 16.26, P<0.001; F = 6.05, P = 0.008; F = 7.31, P = 0.034, respectively). The differential diagnostic performance between the HC and PD groups as assessed by the area under the receiver-operating characteristic curve was best for PCP (0.94, 95% CI: 0.66-1.00). A significant negative correlation was found between the motor severity score and PCp (R = -0.70, P<0.001) and RNM (R = -0.52, P = 0.015), but not for BPND (R = -0.36, P = 0.110). PCA of multimodal NM-sensitive MRI and DAT PET data provides a metric for nigral DA neuronal density that will help illuminate the pathophysiology of PD in SN. Further studies are required to explore whether PCA is useful for other parkinsonian syndromes.