Parkinson's Disease Structural Connectomes from PPMI with Tractogram Filtering Analysis

Fabian Leander Sinzinger's research dataset contains structural brain connectomes derived from diffusion-weighted MRI for 233 subjects from the Parkinson's Progression Markers Initiative. The data was used to analyze group discriminability between Parkinson's disease patients and healthy controls, with a focus on tractogram filtering and connectivity metrics. The dataset was last updated on April 23, 2026.

Use Cases

• Comparing the impact of SIFT2 streamline filtering on graph measures based on the described analysis.
• Training classifiers like support vector machines for patient group discrimination based on the derived connectomes.
• Analyzing node-level differences in brain networks using graph theory measures mentioned in the description.
• Evaluating different connectivity metrics, such as fractional anisotropy or mean diffusivity, for their biomarker potential.

Strengths

• Data is sourced from the well-known Parkinson's Progression Markers Initiative (PPMI) cohort.
• Analysis includes a substantial sample size of 233 subjects.
• Dataset is licensed under CC-BY-4.0, permitting open sharing and adaptation.

Limitations

• The primary data file is a 1.4 MB PDF, suggesting the underlying connectome data may not be directly accessible or is summarized.
• Row count and column-level documentation are unknown, limiting suitability assessment for direct computational use.
• The study results indicated no statistical differences after correction and poor classifier performance, which may limit the data's utility for certain predictive tasks.

Provenance

Source

Parkinson's Progression Markers Initiative (PPMI)

Collection Method

Probabilistic tractography (iFOD2) applied to diffusion-weighted MRI, with subsequent filtering and graph creation.

Freshness

Last updated 2026-04-23 05:35:09