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Multi-model Diffusion MRI Signatures in Atypical Parkinsonian Disorders
Yuqi Tian1, Farwa Ali2, Mary M Machulda3
1Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Abstract:
Distinguishing atypical parkinsonian disorders (APS) from Parkinson's disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multi-model diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy-Richardson syndrome (PSP-RS) and PD, with the aim of identifying which dMRI model provides optimum differentiation. We analyzed 25 CBS, 42 PSP-RS, and 21 PD participants compared to 35 age and sex-matched controls. Using a clinically feasible 3-shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models-diffusion tensor imaging (DTI), free-water-eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue-weighted NODDI, and Fixel Density (FD) in fixel-based analysis (FBA) -to comprehensively assess regional white and gray matter integrity. Group differentiation was assessed using Cohen's d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI-derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP-RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free-water-corrected metrics showed attenuated group differences. Across clinical-diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free-water, multi-compartment, and fixel-based models to APS-related neurodegeneration.
Insights
Multi-model diffusion MRI (dMRI) effectively differentiates atypical parkinsonian disorders (APS) like corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP) from Parkinson's disease (PD). Different dMRI models reveal distinct microstructural changes, with NODDI showing strong clinical associations.
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.
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