Multimodel Diffusion MRI Signatures in Atypical Parkinsonian Disorders

Area of Science:

• Neuroimaging
• Neuroscience
• Radiology

Background:

• Differentiating atypical parkinsonian disorders (APS) from Parkinson's disease (PD) is clinically challenging due to overlapping symptoms.
• Accurate diagnosis is crucial for appropriate patient prognosis and treatment strategies.
• Microstructural alterations in white and gray matter are key pathological hallmarks.

Purpose of the Study:

• To characterize and compare microstructural changes in corticobasal syndrome (CBS), progressive supranuclear palsy-Richardson syndrome (PSP-RS), and PD using multimodel diffusion MRI (dMRI).
• To identify which dMRI models and regional metrics provide the strongest group-level separation between these conditions.
• To assess the association between dMRI metrics and clinical disease severity.

Main Methods:

• Analysis of 25 CBS, 42 PSP-RS, and 21 PD participants, compared to 35 healthy controls, using a 3-shell high angular resolution diffusion imaging (HARDI) protocol.
• 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-based analysis (FBA) using fixel density (FD).
• Quantification of group differentiation using covariate-adjusted Cohen's d effect sizes and Spearman correlations between dMRI metrics and clinical scales.

Main Results:

• Distinct microstructural signatures were observed across CBS, PSP-RS, and PD, with varying sensitivity among dMRI models.
• DTI and NODDI metrics showed strongest effects in midbrain/peduncular pathways for PSP-RS, while NODDI and FBA measures highlighted precentral/corticospinal alterations in CBS.
• NODDI metrics demonstrated the most robust associations with disease severity, outperforming DTI and FWE measures.

Conclusions:

• Multimodel dMRI analysis effectively differentiates between APS and PD by revealing distinct microstructural alterations.
• Different dMRI models (DTI, NODDI, FBA) offer complementary sensitivities to specific neurodegenerative patterns in these disorders.
• NODDI metrics show particular promise for assessing disease severity in APS and PD.