Neural Networks Mediating High-Level Mentalizing in Patients With Right Cerebral Hemispheric Gliomas

右侧大脑半球胶质瘤患者高级心理化能力的神经网络

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Abstract

Mentalizing is the ability to understand others' mental state through external cues. It consists of two networks, namely low-level and high-level metalizing. Although it is an essential function in our daily social life, surgical resection of right cerebral hemisphere disturbs mentalizing processing with high possibility. In the past, little was known about the white matter related to high-level mentalizing, and the conservation of high-level mentalizing during surgery has not been a focus of attention. Therefore, the main purpose of this study was to examine the neural networks underlying high-level mentalizing and then, secondarily, investigate the usefulness of awake surgery in preserving the mentalizing network. A total of 20 patients with glioma localized in the right hemisphere who underwent awake surgery participated in this study. All patients were assigned to two groups: with or without intraoperative assessment of high-level mentalizing. Their high-level mentalizing abilities were assessed before surgery and 1 week and 3 months after surgery. At 3 months after surgery, only patients who received the intraoperative high-level mentalizing test showed the same score as normal healthy volunteers. The tract-based lesion symptom analysis was performed to confirm the severity of damage of associated fibers and high-level mentalizing accuracy. This analysis revealed the superior longitudinal fascicles (SLF) III and fronto-striatal tract (FST) to be associated with high-level mentalizing processing. Moreover, the voxel-based lesion symptom analysis demonstrated that resection of orbito-frontal cortex (OFC) causes persistent mentalizing dysfunction. Our study indicates that damage of the OFC and structural connectivity of the SLF and FST causes the disorder of mentalizing after surgery, and assessing high-level mentalizing during surgery may be useful to preserve these pathways.

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