Predicting postoperative language outcome using presurgical fMRI, MEG, TMS, and high gamma ECoG

Abstract

Objective: To predict the postoperative language outcome using the support vector regression (SVR) and results of multimodal presurgical language mapping.

Methods: Eleven patients with epilepsy received presurgical language mapping using functional MRI (fMRI), magnetoencephalography (MEG), transcranial magnetic stimulation (TMS), and high-gamma electrocorticography (hgECoG), as well as pre- and postoperative neuropsychological evaluation of language. We constructed 15 (2⁴-1) SVR models by considering the extent of resected language areas identified by all subsets of four modalities as input feature vector and the postoperative language outcome as output. We trained and cross-validated SVR models, and compared the cross-validation (CV) errors of all models for prediction of language outcome.

Results: Seven patients had some level of postoperative language decline and two of them had significant postoperative decline in naming. Some parts of language areas identified by four modalities were resected in these patients. We found that an SVR model consisting of fMRI, MEG, and hgECoG provided minimum CV error, although an SVR model consisting of fMRI and MEG was the optimal model that facilitated the best trade-off between model complexity and prediction accuracy.

Conclusions: A multimodal SVR can be used to predict the language outcome.

Significance: The developed multimodal SVR models in this study can be utilized to calculate the language outcomes of different resection plans prior to surgery and select the optimal surgical plan.

Keywords: High gamma electrocorticography (hgECoG); MEG; Postoperative language outcome; TMS; fMRI.