Purpose: Spatial covariance analysis has been used with FDG PET to identify a specific metabolic network associated with Parkinson's disease (PD). In the current study, we utilized a new, fully automated voxel-based method to quantify network expression in ECD SPECT images from patients with classical PD, patients with multiple system atrophy (MSA), and healthy control subjects.
Methods: We applied a previously validated voxel-based PD-related covariance pattern (PDRP) to quantify network expression in the ECD SPECT scans of 35 PD patients, 15 age- and disease severity-matched MSA patients, and 35 age-matched healthy control subjects. PDRP scores were compared across groups using analysis of variance. The sensitivity and specificity of the prospectively computed PDRP scores in the differential diagnosis of individual subjects were assessed by receiver operating characteristic (ROC) analysis.
Results: PDRP scores were significantly increased (p < 0.001) in the PD group relative to the MSA and control groups. ROC analysis indicated that the overall diagnostic accuracy of the PDRP measures was 0.91 (AUC). The optimal cutoff value was consistent with a sensitivity of 0.97 and a specificity of 0.80 and 0.71 for discriminating PD patients from MSA and normal controls, respectively.
Conclusion: Our findings suggest that fully automated voxel-based network assessment techniques can be used to quantify network expression in the ECD SPECT scans of parkinsonian patients.