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White matter abnormalities and multivariate pattern analysis in anti-NMDA receptor encephalitis.
Shengyu Yang1, Ying Wu1, Lanfeng Sun1
1Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Frontiers in Psychiatry
|October 10, 2022
Summary
White matter damage is common in anti-NMDA receptor encephalitis and correlates with disease severity. Diffusion tensor imaging and machine learning can identify these alterations and aid diagnosis.
Area of Science:
- Neuroimaging
- Neurology
- Medical Science
Background:
- Anti-NMDA receptor (NMDAR) encephalitis is a severe autoimmune neurological disorder.
- Understanding white matter (WM) microstructural changes is crucial for diagnosing and managing NMDAR encephalitis.
Purpose of the Study:
- To investigate white matter (WM) microstructural alterations in anti-NMDAR receptor encephalitis.
- To explore the correlation between WM changes and disease severity.
- To evaluate the potential of imaging biomarkers for discriminating patients from healthy controls.
Main Methods:
- Diffusion tensor imaging (DTI) was performed on 32 patients with anti-NMDAR encephalitis and 26 healthy controls.
- Tract-based spatial statistics (TBSS) and atlas-based analysis were used to assess WM microstructural integrity.
- Multivariate pattern analysis (MVPA), a machine learning algorithm, was employed for classification.
Main Results:
- Patients showed reduced fractional anisotropy (FA) in multiple WM tracts, including the corpus callosum and cingulum.
- Increased mean diffusivity (MD) and radial diffusivity (RD) were observed in various WM regions.
- WM abnormalities in the cingulum, fornix, and internal capsule correlated significantly with disease severity.
Conclusions:
- Widespread white matter lesions are characteristic of anti-NMDAR encephalitis.
- WM alterations play a significant role in the pathophysiology of the disease.
- Combining TBSS and MVPA offers valuable insights for both group and individual patient assessment.
