Voxel-based logistic analysis of PPMI control and Parkinson's disease DaTscans

- Voxel-wise analysis of PPMI Control and Parkinson's DaTscan images gives accurate classification and identifies voxels that are informative.
- New analysis called Logistic Principal Components reveals sources of variation in control and PD images that affect classification.
- Logistic features are related to MDS-UPDRS scores.
- Logistic features interact with sex and age, but not with handedness.

A comprehensive analysis of the Parkinson's Progression Markers Initiative (PPMI) Dopamine Transporter Single Photon Emission Computed Tomography (DaTscan) images is carried out using a voxel-based logistic lasso model. The model reveals that sub-regional voxels in the caudate, the putamen, as well as in the globus pallidus are informative for classifying images into control and PD classes. Further, a new technique called logistic component analysis is developed. This technique reveals that intra-population differences in dopamine transporter concentration and imperfect normalization are significant factors influencing logistic analysis. The interactions with handedness, sex, and age are also evaluated.