A novel computerized algorithm to detect microstructural brainstem pathology in Parkinson's disease using standard 3...

Area of Science:

• Neuroimaging
• Neuropathology
• Neurodegenerative Diseases

Background:

• Alpha-synuclein deposition in the brainstem is characteristic of Parkinson's disease (PD).
• The clinical relevance of brainstem pathology in PD for motor and non-motor symptoms is recognized.
• The ability of structural magnetic resonance imaging (MRI) to detect brainstem microstructural changes due to alpha-synuclein in PD is not well understood.

Purpose of the Study:

• To investigate if structural MRI can detect microstructural tissue degradation in the brainstem of PD patients caused by alpha-synuclein deposition.
• To test the hypothesis that microstructural degradation leads to reduced T1 contrast, causing misclassification of white matter as grey matter by segmentation algorithms.

Main Methods:

• High-resolution T1-weighted 3D MPRAGE MRI at 3 Tesla was used for 52 PD patients and 40 healthy controls.
• A computerized algorithm combining tissue segmentation and statistical testing was developed to assess grey matter composition at the voxel level.
• Analysis focused on the brainstem, a predilection site for alpha-synuclein deposition.

Main Results:

• A significant cluster of voxels with enhanced grey matter was identified in the pontomedullary junction of PD patients compared to controls (1,368 mm³, p < 0.05 FDR corrected).
• The PD group showed significantly higher absolute grey matter volume in the brainstem overall.
• These findings suggest MRI can detect alterations in brainstem tissue microstructure in PD.

Conclusions:

• The detected grey matter increase in the brainstem of PD patients may indicate alpha-synuclein-induced microstructural pathology.
• Structural MRI, particularly with advanced algorithms, shows potential for identifying early neuropathological changes in the brainstem in Parkinson's disease.
• This could lead to improved diagnostic or prognostic markers for PD.