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.
Objective:
This study aimed to investigate white matter (WM) microstructural alterations and their relationship correlation with disease severity in anti-NMDA receptor (NMDAR) encephalitis. Multivariate pattern analysis (MVPA) was applied to discriminate between patients and healthy controls and explore potential imaging biomarkers.
Methods:
Thirty-two patients with anti-NMDAR encephalitis and 26 matched healthy controls underwent diffusion tensor imaging. Tract-based spatial statistics and atlas-based analysis were used to determine WM microstructural alterations between the two groups. MVPA, based on a machine-learning algorithm, was applied to classify patients and healthy controls.
Results:
Patients exhibited significantly reduced fractional anisotropy in the corpus callosum, fornix, cingulum, anterior limb of the internal capsule, and corona radiata. Moreover, mean diffusivity was increased in the anterior corona radiata and body of the corpus callosum. On the other hand, radial diffusivity was increased in the anterior limb of the internal capsule, cingulum, corpus callosum, corona radiata, and fornix. WM changes in the cingulum, fornix, and retrolenticular part of the internal capsule were correlated with disease severity. The accuracy, sensitivity, and specificity of fractional anisotropy-based classification were each 78.33%, while they were 67.71, 65.83, and 70% for radial diffusivity.
Conclusion:
Widespread WM lesions were detected in anti-NMDAR encephalitis. The correlation between WM abnormalities and disease severity suggests that these alterations may serve a key role in the pathophysiological mechanisms of anti-NMDAR encephalitis. The combination of tract-based spatial statistics and MVPA may provide more specific and complementary information at the group and individual levels.
Insights
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.
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