Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study.

Area of Science:

• Neuroimaging
• Medical Physics
• Neurology

Background:

• Metabolic imaging, like PET, detects disease patterns but involves radiation exposure.
• Parkinson's disease-related pattern (PDRP) is a validated metabolic topography.
• Non-invasive methods are sought for disease network detection.

Purpose of the Study:

• To validate a non-invasive resting-state fMRI (rs-fMRI) derived Parkinson's disease pattern (fPDRP) across different patient populations and sites.
• To compare the reproducibility of fPDRP topography and expression between two independent cohorts.
• To assess the correlation of fPDRP expression with clinical motor disability in Parkinson's disease patients.

Main Methods:

• Utilized independent component analysis (ICA) and bootstrap resampling on rs-fMRI data.
• Validated the fPDRP_NS pattern in a new cohort from Cologne, Germany, and identified a local pattern, fPDRP_COL.
• Compared topographical similarity and expression levels between fPDRP_NS and fPDRP_COL, and correlated with clinical motor scores.

Main Results:

• fPDRP_NS and fPDRP_COL showed topographical similarity, with key contributions from the putamen, globus pallidus, pons, cerebellum, and thalamus.
• Expression levels of both fPDRP patterns were significantly correlated within each patient group.
• Abnormal elevations in fPDRP_COL core expression were observed in PD patients compared to controls, and correlations with motor disability were significant.

Conclusions:

• rs-fMRI derived fPDRP networks are reproducible across different patient populations, clinical sites, and scanning platforms.
• This non-invasive rs-fMRI approach offers a viable alternative to metabolic PET for quantitative assessment of disease networks in Parkinson's disease.
• The findings support the use of rs-fMRI for clinical disease monitoring and research.