Impact of tractogram filtering and graph creation for structural connectomics in subjects with Parkinson's disease.

INTRODUCTION

Structural connectomics derives subject-specific brain connectivity from diffusion-weighted MRI and has potential as a biomarker for clinical Parkinson's disease (PD) detection.

METHOD: In this study, we applied probabilistic tractography (iFOD2) to derive different types of connectomes and analyzed group discriminability between PD patients and healthy controls from the Parkinson's Progression Markers Initiative (PPMI) dataset (n = 233). Particular emphasis was placed on the streamline filtering stage with SIFT2 and the comparison of different connectivity metrics, including streamline count, fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD). We performed a three-level analysis comprising (1) connection-level statistical analysis, (2) graph theory measures at the node and whole-brain levels, and (3) classification using support vector machines (SVM) and graph neural networks.

RESULTS

We did not find any statistical difference at any level after correction for multiple comparisons. Also, the classifiers performed poorly with AUC values close to chance levels. However, we found differences between filtered and unfiltered tractograms at the node level.

DISCUSSION: Our findings suggest that structural connectivity analyses for PD are highly sensitive to specific pipeline configurations and fine-tuning.