To avoid post-operative language impairments after surgery for drug-resistant epilepsy, electrical cortical stimulation mapping (ESM) is widely relied upon to localize language cortex, but ESM often elicits after- discharges (ADs), seizures, involuntary movements, and pain, which can limit mapping and impact patient safety. Furthermore, ESM is time-consuming and usually produces all-or-none results, providing limited insight into the function of stimulated sites. Finally, up to 30% of patients can have language dysfunction after resections that are guided by ESM. These limitations have motivated an alternative mapping method based on task-related power changes in high-gamma frequencies (?50-200 Hz), which are highly correlated with neuronal population firing rate changes. Although high-gamma mapping (HGM) overcomes the aforementioned limitations of ESM, its clinical validation has been limited to ESM-HGM comparisons in small case series with significant variations in technique across centers, yielding inconsistent results, with unexplained discrepancies between methods. Moreover, neither HGM nor ESM have been prospectively validated for predicting post-operative language impairments with either subdural electrocorticography (ECoG) or stereo-EEG (sEEG, increasingly a safer alternative to ECoG). The overall objective of this study is to use a small consortium of three large academic epilepsy surgery centers to demonstrate the diagnostic validity and safety of iEEG HGM for both ECoG and sEEG in a prospective series of 221 patients, using an innovative browser-based bedside HGM system to standardize methods and share data across centers. This study will test the hypothesis that HGM can accurately predict post-surgical language outcomes but requires a different framework for interpretation than that used for ESM. First, using traditional methods for interpreting HGM results, we will test the concordance between HGM and ESM, and compare their safety and feasibility. Based on previous studies, we predict that HGM will be a good, but imperfect, predictor of ESM results. However, we predict that due to ESM-related seizures and differences in mapping duration, HGM will be safer and better tolerated by patients. Second, we will test models that attempt to explain and reconcile HGM false negatives and false positives with respect to ESM, and we will test whether these models, incorporating both functional activation (HGM) and effective connectivity (measured with cortico-cortical evoked potentials, or CCEPs) can better predict ESM results. Third, we will develop and test models for prediction of post-operative language outcomes, based on the anatomical extent and volume of cortical resection with respect to HGM and ESM results, using voxel-lesion-symptom matching (VLSM). Since clinical decisions are currently based on ESM and clinicians will be blinded to HGM, we predict that the incidence of language deficits will be significantly higher when HGM+ electrodes are resected. This study will have a direct and profound impact on the clinical practice of language mapping for neurosurgical procures, while contributing valuable insights into the structure and dynamics of human language networks.
It is important to map and preserve language areas of the brain during surgery for drug-resistant seizures, to prevent language impairments. At present, clinicians rely primarily on electrical stimulation of the brain for this, but now, language mapping can be performed with passive recordings of the brain's electrical activity. This project seeks to understand the similarities and differences between these two brain mapping techniques and to validate passive electrical recordings as a safer and equally effective alternative to electrical stimulation mapping.
