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Published on: June 26, 2013
Complemental Value of Microstructural and Macrostructural MRI in the Discrimination of Neurodegenerative Parkinson
Nils Schröter1, Philipp G Arnold2,3, Jonas A Hosp1
1Department of Neurology and Clinical Neuroscience, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Purpose:
Various MRI-based techniques were tested for the differentiation of neurodegenerative Parkinson syndromes (NPS); the value of these techniques in direct comparison and combination is uncertain. We thus compared the diagnostic performance of macrostructural, single compartmental, and multicompartmental MRI in the differentiation of NPS.
Methods:
We retrospectively included patients with NPS, including 136 Parkinson's disease (PD), 41 multiple system atrophy (MSA) and 32 progressive supranuclear palsy (PSP) and 27 healthy controls (HC). Macrostructural tissue probability values (TPV) were obtained by CAT12. The microstructure was assessed using a mesoscopic approach by diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI). After an atlas-based read-out, a linear support vector machine (SVM) was trained on a training set (n = 196) and validated in an independent test cohort (n = 40). The diagnostic performance of the SVM was compared for different inputs individually and in combination.
Results:
Regarding the inputs separately, we observed the best diagnostic performance for DMI. Overall, the combination of DMI and TPV performed best and correctly classified 88% of the patients. The corresponding area under the receiver operating characteristic curve was 0.87 for HC, 0.97 for PD, 1.0 for MSA, and 0.99 for PSP.
Conclusion:
We were able to demonstrate that (1) MRI parameters that approximate the microstructure provided substantial added value over conventional macrostructural imaging, (2) multicompartmental biophysically motivated models performed better than the single compartmental DTI and (3) combining macrostructural and microstructural information classified NPS and HC with satisfactory performance, thus suggesting a complementary value of both approaches.
Insights
Advanced MRI techniques, particularly diffusion microstructure imaging (DMI) combined with macrostructural measures, significantly improved the differentiation of neurodegenerative Parkinson syndromes (NPS) compared to conventional methods.
Area of Science:
- Neuroimaging
- Radiology
- Neurology
Background:
- Neurodegenerative Parkinson syndromes (NPS) present diagnostic challenges.
- Various MRI techniques exist for NPS differentiation, but their comparative value is unclear.
Purpose of the Study:
- To compare the diagnostic performance of macrostructural, single-compartmental, and multicompartmental MRI for differentiating NPS.
- To evaluate the combined diagnostic utility of these MRI techniques.
Main Methods:
- Retrospective analysis of MRI data from patients with Parkinson's disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and healthy controls (HC).
- Assessment of macrostructural tissue probability values (TPV) and microstructural parameters using diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI).
- Machine learning (support vector machine) model trained and validated for classification performance.
Main Results:
- Diffusion microstructure imaging (DMI) showed the highest diagnostic performance individually.
- The combination of DMI and TPV achieved the best classification rate (88%) for NPS and HC.
- High area under the curve values were observed for differentiating HC, PD, MSA, and PSP.
Conclusions:
- Microstructural MRI parameters offer significant advantages over macrostructural imaging for NPS differentiation.
- Multicompartmental models outperform single-compartmental DTI.
- Combining macrostructural and microstructural MRI data provides satisfactory classification performance, highlighting their complementary value.
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