Preoperative function‐specific connectome analysis predicts surgery‐related aphasia after glioma resection
Sebastian Ille, Haosu Zhang, Lisa Sogerer, Maximilian Schwendner, Axel Schöder, Bernhard Meyer, Benedikt Wiestler, Sandro M. Krieg
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.