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Published on: September 25, 2019
Analysis of acute stroke-like lesions in MELAS: Distribution, potential boundaries and spreading pattern
Huada Tang1, Junyu Liu1, Xiying Cai1
1Department of Neurology, Peking University First Hospital, 8 Xishiku Street, Xicheng District, Beijing, 100034, China.
Background:
Imaging during acute episodes of mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) primarily shows reversible cortical edema. However, the mechanisms underlying such cortical lesions in MELAS remain unclear. Analyzing MRI findings of MELAS could provide clues to these mechanisms.
Methods:
We reviewed the medical records and imaging data of MELAS patients treated at Peking University First Hospital from 2013 to 2024. 61 MRI scans from 55 patients were included in this study. The spatial distribution of SLLs was analyzed based on high-resolution cortical segmentation. Next, we established a non-negative independent component analysis (nnICA) model to separate independent SLL components. Then we assessed correlation of synaptic density and lesion load using Spearman's rho test. Finally, a simple diffusion model was used to fit the spreading behavior of SLLs.
Results:
The cortical probability map shows a peak involvement frequency of 0.46 observed in the left temporo-occipital lobe. Distribution of SLLs showed no bias between left and right hemisphere. Then we identified 8 independent cortical components of SLLs. The Probability Map showed a positive correlation with synaptic density map (Spearman's rho = 0.32, p < 0.001). Additionally, the Directed-Effect Coefficient exhibited a good proportional relationship with the Dilation Coefficient (y = 0.50x+0.00, r = 0.76).
Conclusion:
Analysis of structural MRI data suggests that MELAS SLLs exhibit a pattern of free spreading within regions and restricted spreading between regions on the cortex. The identified components indicate the presence of potential boundaries. We also showed that lesion spreading was often hindered in areas with sparse synaptic density.
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