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Multivariate machine learning-based language mapping in glioma patients based on lesion topography
Nan Zhang1,2, Binke Yuan3,4,5, Jing Yan6
1Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, Hefei, China.
Brain Imaging and Behavior
|February 23, 2021
Summary
Tumor location in the brain
Area of Science:
- Neuroscience
- Neurology
- Oncology
Background:
- Brain tumors infiltrating language areas cause functional reorganization.
- The macrostructural basis of resulting language deficits remains unclear.
Purpose of the Study:
- To investigate the macrostructural basis of language deficits in patients with gliomas.
- To correlate glioma grade with brain plasticity and language network reorganization.
Main Methods:
- Multivariate machine learning-based lesion-language mapping analysis.
- Utilized preoperative lesion topography data from 137 patients with left cerebral language-network gliomas (81 low-grade, 56 high-grade).
Main Results:
- Tumor location in the left posterior middle temporal gyrus predicted speech and naming deficits in high-grade gliomas.
- No significant lesion-language mapping results were found in low-grade gliomas, indicating substantial functional reorganization.
Conclusions:
- Glioma grade influences macrostructural plasticity and brain-behavior relationships in language networks.
- Tumor location within critical language pathways (posterior middle temporal gyrus) is key for high-grade glioma-related deficits.
