Statistical analysis of clinical and DaTSCAN SPECT imaging features' in Parkinson's Disease, SWEDD, and Healthy Control subjects

Abstract

Background: Single Photon Emission Computed Tomography (SPECT) scan is a convenient diagnostic technique for Parkinson's Disease (PD). Nevertheless, certain subjects who met the clinical diagnosis measurements for PD had normal SPECT images. They are referred to have Scans Without Evidence of Dopamine Deficiency (SWEDD). Indeed, SWEDD is a heterogeneous group of Healthy Control (HC) and early PD subjects. Method: In this research work, we developed a Stepwise Multiple Linear Regression (SMLR) method in the PD group. This process is an optimal regression model for identifying the key SPECT image-derived features, which influence clinical scores. We also investigate the interaction between these features in the three groups together (PD, SWEDD, and HC). Results: SMLR results showed that only the Specific Binding Ratio (SBR) of the Worst-Putamen and SBR of the Worst-Caudate are the factors that influence Unified Parkinson's Disease Rating Scale (UPDRS) and State Trait Anxiety Inventory (STAI) scores, respectively. In addition, two tests of Two-Way-ANOVA showed significant interactions between UPDRS and SBR of the Worst-Putamen, and between STAI and SBR of the Worst-Caudate of the three groups. Conclusion: From the SMLR results, we found that the increased UPDRS and STAI scores severity is highly dependent on the dopamine transporter density in the Worst-Putamen and the Worst-Caudate, respectively.