Naming-related spectral responses predict neuropsychological outcome after epilepsy surgery
Authors: Sonoda Masaki , Rothermel Robert , Carlson Alanna , Jeong Jeong-Won , Lee Min-Hee , Hayashi Takahiro , Luat Aimee F. , Sood Sandeep , Asano Eishi 
Published: April 2021

SUMMARYThis prospective study determined the utility of intracranially-recorded spectral responses during naming tasks in predicting neuropsychological performance following epilepsy surgery. We recruited 65 patients with drug-resistant focal epilepsy who underwent preoperative neuropsychological assessment and intracranial EEG (iEEG) recording. The Clinical Evaluation of Language Fundamentals (CELF) evaluated the baseline and postoperative language function. During extraoperative iEEG recording, we assigned patients to undergo auditory and picture naming tasks. Time-frequency analysis determined the spatiotemporal characteristics of naming-related amplitude modulations, including high gamma augmentation (HGA) at 70-110 Hz. We surgically removed the presumed epileptogenic zone based on the extent of iEEG and MRI abnormalities while maximally preserving the eloquent areas defined by electrical stimulation mapping (ESM). The multivariate regression model incorporating auditory naming-related HGA predicted the postoperative changes in Core Language Score (CLS) on CELF with r2 of 0.37 (p = 0.015) and in Expressive Language Index (ELI) with r2 of 0.32 (p = 0.047). Independently of the effects of epilepsy and neuroimaging profiles, higher HGA at the resected language-dominant hemispheric area predicted a more severe postoperative decline in CLS (p = 0.004) and ELI (p = 0.012). Conversely, the model incorporating picture naming-related HGA predicted the change in Receptive Language Index (RLI) with r2 of 0.50 (p < 0.001). Higher HGA independently predicted a more severe postoperative decline in RLI (p = 0.03). Ancillary regression analysis indicated that naming-related low gamma augmentation as well as alpha/beta attenuation likewise independently predicted a more severe CLS decline. The machine learning-based prediction model, referred to as the boosted tree ensemble model, suggested that naming-related HGA, among all spectral responses utilized as predictors, most strongly contributed to the improved prediction of patients showing a >5-point CLS decline (reflecting the lower 25 percentile among patients). We generated the model-based atlas visualizing sites, which, if resected, would lead to such a CLS decline. The auditory naming-based model predicted patients who developed the CLS decline with an accuracy of 0.80. The model indicated that virtual resection of an ESM-defined language site would have increased the relative risk of the CLS decline by 5.28 (95%CI: 3.47 to 8.02). Especially, that of an ESM-defined receptive language site would have maximized it to 15.90 (95%CI: 9.59-26.33). In summary, naming-related spectral responses predict objectively-measured neuropsychological outcome after epilepsy surgery. We have provided our prediction model as an open-source material, which will indicate the postoperative language function of future patients and facilitate external validation at tertiary epilepsy centers.