Multi-model Diffusion MRI Signatures in Atypical Parkinsonian Disorders

Area of Science:

• Neuroimaging
• Neurology
• Biomedical Engineering

Background:

• Distinguishing atypical parkinsonian disorders (APS) from Parkinson's disease (PD) is clinically challenging due to overlapping symptoms.
• Accurate differentiation is crucial for appropriate patient prognosis and treatment strategies.
• Diffusion MRI (dMRI) offers potential for characterizing white and gray matter microstructural alterations.

Purpose of the Study:

• To compare the diagnostic sensitivity of five complementary dMRI models in differentiating corticobasal syndrome (CBS), progressive supranuclear palsy-Richardson syndrome (PSP-RS), and PD.
• To identify the optimal dMRI model for characterizing neurodegeneration in APS and PD.
• To correlate dMRI metrics with clinical disease severity.

Main Methods:

• Analysis of 3-shell high angular resolution diffusion imaging (HARDI) data from 25 CBS, 42 PSP-RS, 21 PD participants, and 35 controls.
• Application of 11 metrics from five dMRI models: diffusion tensor imaging (DTI), free-water-eliminated DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue-weighted NODDI, and Fixel Density (FD) in Fixel-Based Analysis (FBA).
• Assessment of group differentiation using Cohen's d effect sizes and correlation of dMRI metrics with clinical scales.

Main Results:

• Distinct microstructural signatures were identified across CBS, PSP-RS, and PD, with varying sensitivities among dMRI models.
• DTI and NODDI metrics showed significant effects in midbrain and peduncular pathways for PSP-RS, while NODDI and FBA measures highlighted precentral and corticospinal tract alterations in CBS.
• NODDI metrics demonstrated the most robust associations with disease severity, whereas DTI and FWE metrics showed more limited regional effects.

Conclusions:

• Multi-model dMRI analysis reveals complementary yet distinct sensitivities in detecting neurodegeneration across different parkinsonian disorders.
• NODDI and Fixel-Based Analysis show promise for differentiating APS subtypes and correlating with disease progression.
• These findings underscore the value of advanced dMRI techniques for precise diagnosis and understanding of parkinsonian syndromes.