Naming-related spectral responses predict neuropsychological outcome after epilepsy surgery

Abstract

This prospective study determined the use 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 recording. The Clinical Evaluation of Language Fundamentals evaluated the baseline and postoperative language function. During extra-operative intracranial EEG 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 at 70–110 Hz. We surgically removed the presumed epileptogenic zone based on the intracranial EEG and MRI abnormalities while maximally preserving the eloquent areas defined by electrical stimulation mapping. The multivariate regression model incorporating auditory naming-related high gamma augmentation predicted the postoperative changes in Core Language Score with r2 of 0.37 and in Expressive Language Index with r2 of 0.32. Independently of the effects of epilepsy and neuroimaging profiles, higher high gamma augmentation at the resected language-dominant hemispheric area predicted a more severe postoperative decline in Core Language Score and Expressive Language Index. Conversely, the model incorporating picture naming-related high gamma augmentation predicted the change in Receptive Language Index with an r2 of 0.50. Higher high gamma augmentation independently predicted a more severe postoperative decline in Receptive Language Index. Ancillary regression analysis indicated that naming-related low gamma augmentation and alpha/beta attenuation likewise independently predicted a more severe Core Language Score decline. The machine learning-based prediction model suggested that naming-related high gamma augmentation, among all spectral responses used as predictors, most strongly contributed to the improved prediction of patients showing a >5-point Core Language Score decline (reflecting the lower 25th percentile among patients). We generated the model-based atlas visualizing sites, which, if resected, would lead to such a language decline. With a 5-fold cross-validation procedure, the auditory naming-based model predicted patients who had such a postoperative language decline with an accuracy of 0.80. The model indicated that virtual resection of an electrical stimulation mapping-defined language site would have increased the relative risk of the Core Language Score decline by 5.28 (95% confidence interval: 3.47–8.02). Especially, that of an electrical stimulation mapping-defined receptive language site would have maximized it to 15.90 (95% confidence interval: 9.59–26.33). In summary, naming-related spectral responses predict neuropsychological outcomes 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 centres.

Temporally and functionally distinct large-scale brain network dynamics supporting task switching

“…This finding has the potential to drastically improve extra-operative mapping for pediatric focal epilepsy patients, since electrical stimulation mapping is not always efficacious in young children ( Haseeb et al, 2007 ) and was reported to localize language areas in less than 20% of those younger than ten years old ( Schevon et al, 2007 ). Measurement of event-related high-gamma modulations is widely practiced in tertiary epilepsy surgery centers but previously reported tasks may not have been entertaining for children ( Crone et al, 2011 ; Lachaux et al, 2012 ; Arya et al, 2018 ; Mooij et al, 2016 ; Sonoda et al, 2022 ). In our current study, patients made an average of 221.4 responses during five-minute gameplay sessions.…”

“…We measured event-related high-gamma activity, as reported in our previous studies ( Mitsuhashi et al, 2021 ; Sonoda et al, 2022 ). Using the FieldTrip toolbox ( http://www.fieldtriptoolbox.org/ ), we performed the Morlet wavelet time-frequency analysis on iEEG signals referenced to a common average ( Crone et al, 2001 ; Mitsuhashi et al, 2021 ).…”

“…To compare gameplay-related high-gamma amplitude (a measure proportional to the square root of high-gamma power) with that during the baseline period before task initiation, we sampled 2400 ms resting wakefulness epochs as many as the total number of trials played by a given patient. To minimize the potential effects of interictal spikes on gameplay-related high-gamma modulations, we excluded time-frequency bins with 30–85 Hz amplitude above an absolute z-score of 2 from further analysis ( Sonoda et al, 2022 ). Next, we computed the percent change of high-gamma (70–110 Hz) amplitude at each electrode site, compared to the non-gameplay reference mean ( Fig.…”

“…3E ). A nonparametric permutation one-sample t -test evaluated the null hypothesis that, compared to the reference period, the high gamma amplitude percent change would be zero at each 10-ms bin (1000 permutations; random sign swapping; Maris and Oostenveld, 2007 ; Cohen, 2014 ; Bassez et al, 2020 ; Sonoda et al, 2022 ). A two-sided 5% significance level was used with a false discovery rate (FDR) correction for repeated comparisons for 121 bins in a 2400 ms period ( Fig.…”

“…Notably, the iEEG’s outstanding signal fidelity ( Ball et al, 2009 ) makes trial-by-trial high-gamma analysis possible at an individual-patient level ( Vidal et al, 2012 ; Dastjerdi et al, 2013 ; Nourski et al, 2013 ; Uematsu et al, 2013 ; Coon and Schalk, 2016 ; Forseth et al, 2020 ). Considering the various iEEG frequency bands ranging from alpha through high-gamma, event-related high-gamma augmentation was reported to best predict the severity of cognitive deficits following focal brain resection ( Sonoda et al, 2022 ). Diffusion-weighted imaging (DWI) tractography can visualize the direct white matter tracts allowing neural communications between distant cortical regions concurrently involved in a cognitive process ( Sonoda et al, 2021 ).…”