ArticleHuman brain mapping2022
Preoperative function-specific connectome analysis predicts surgery-related aphasia after glioma resection.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Multiscale network perspectives on glioma: from tumour biology to symptoms, survival and treatment.Nature reviews. Neurology · 2026Review
- Eloquent glioma resection assisted by brain connectomics: A new tool for awake neurosurgery.Surgical neurology international · 2026Article
- The Role of Deep Cerebral Tracts in Predicting Postoperative Aphasia: An nTMS-Based Investigation of the Corticothalamic Fibers.Human brain mapping · 2025Article
- Article
- Facilitating complete resection of intrinsic motor cortex glioma with titration of high-frequency cortico-subcortical mapping train count informed by navigated transcranial magnetic stimulation: illustrative case.Journal of neurosurgery. Case lessons · 2024Article
- Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients.European radiology · 2024Article
- Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice.Frontiers in neurology · 2024Article
- Preoperative function-specific connectome analysis predicts surgery-related aphasia after glioma resection.Human brain mapping · 2022Article
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Authors and funding
8 authors.
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Abstract
Glioma resection within language-eloquent regions poses a high risk of surgery-related aphasia (SRA). Preoperative functional mapping by navigated transcranial magnetic stimulation (nTMS) combined with diffusion tensor imaging (DTI) is increasingly used to localize cortical and subcortical language-eloquent areas. This study enrolled 60 nonaphasic patients with left hemispheric perisylvian gliomas to investigate the prediction of SRA based on function-specific connectome network properties under different fractional anisotropy (FA) thresholds. Moreover, we applied a machine learning model for training and cross-validation to predict SRA based on preoperative connectome parameters. Preoperative connectome analysis helps predict SRA development with an accuracy of 73.3% and sensitivity of 78.3%. The current study provides a new perspective of combining nTMS and function-specific connectome analysis applied in a machine learning model to investigate language in neurooncological patients and promises to advance our understanding of the intricate networks.
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