@article {4448, title = {Passive functional mapping of receptive language cortex during general anesthesia using electrocorticography.}, journal = {Clin Neurophysiol}, volume = {147}, year = {2023}, month = {03/2023}, pages = {31-44}, abstract = {

OBJECTIVE: To investigate the feasibility of passive functional mapping in the receptive language cortex during general anesthesia using electrocorticographic (ECoG) signals.

METHODS: We used subdurally placed ECoG grids to record cortical responses to speech stimuli during awake and anesthesia conditions. We identified the cortical areas with significant responses to the stimuli using the spectro-temporal consistency of the brain signal in the broadband gamma (BBG) frequency band (70-170~Hz).

RESULTS: We found that ECoG BBG responses during general anesthesia effectively identify cortical regions associated with receptive language function. Our analyses demonstrated that the ability to identify receptive language cortex varies across different states and depths of anesthesia. We confirmed these results by comparing them to receptive language areas identified during the awake condition. Quantification of these results demonstrated an average sensitivity and specificity of passive language mapping during general anesthesia to be 49{\textpm}7.7\% and 100\%, respectively.

CONCLUSION: Our results demonstrate that mapping receptive language cortex in patients during general anesthesia is feasible.

SIGNIFICANCE: Our proposed protocol could greatly expand the population of patients that can benefit from passive language mapping techniques, and could eliminate the risks associated with electrocortical stimulation during an awake craniotomy.

}, keywords = {Anesthesia, General, Brain, Brain Mapping, Cerebral Cortex, Electrocorticography, Humans, Language}, issn = {1872-8952}, doi = {10.1016/j.clinph.2022.11.021}, author = {Nourmohammadi, Amin and Swift, James R and de Pesters, Adriana and Guay, Christian S and Adamo, Matthew A and Dalfino, John C and Ritaccio, Anthony L and Schalk, Gerwin and Brunner, Peter} } @article {4343, title = {Potential differences between monolingual and bilingual patients in approach and outcome after awake brain surgery.}, journal = {J Neurooncol}, volume = {148}, year = {2020}, month = {07/2020}, pages = {587-598}, abstract = {

INTRODUCTION: 20.8\% of the United States population and 67\% of the European population speak two or more languages. Intraoperative different languages, mapping, and localization are crucial. This investigation aims to address three questions between BL and ML patients: (1) Are there differences in complications (i.e. seizures) and DECS techniques during intra-operative brain mapping? (2) Is EOR different? and (3) Are there differences in the recovery pattern post-surgery?

METHODS: Data from 56 patients that underwent left-sided awake craniotomy for tumors infiltrating possible dominant hemisphere language areas from September 2016 to June 2019 were identified and analyzed in this study; 14 BL and 42 ML control patients. Patient demographics, education level, and the age of language acquisition were documented and evaluated. fMRI was performed on all participants.

RESULTS: 0 (0\%) BL and 3 (7\%) ML experienced intraoperative seizures (P = 0.73). BL patients received a higher direct DECS current in comparison to the ML patients (average = 4.7, 3.8, respectively, P = 0.03). The extent of resection was higher in ML patients in comparison to the BL patients (80.9 vs. 64.8, respectively, P = 0.04). The post-operative KPS scores were higher in BL patients in comparison to ML patients (84.3, 77.4, respectively, P = 0.03). BL showed lower drop in post-operative KPS in comparison to ML patients (- 4.3, - 8.7, respectively, P = 0.03).

CONCLUSION: We show that BL patients have a lower incidence of intra-operative seizures, lower EOR, higher post-operative KPS and tolerate higher DECS current, in comparison to ML patients.

}, keywords = {Brain Mapping, Brain Neoplasms, Craniotomy, Female, Follow-Up Studies, Glioma, Humans, Incidence, Language, Male, Middle Aged, Monitoring, Intraoperative, Prognosis, Retrospective Studies, Seizures, United States, Wakefulness}, issn = {1573-7373}, doi = {10.1007/s11060-020-03554-0}, author = {ReFaey, Karim and Tripathi, Shashwat and Bhargav, Adip G and Grewal, Sanjeet S and Middlebrooks, Erik H and Sabsevitz, David S and Jentoft, Mark and Peter Brunner and Wu, Adela and Tatum, William O and Ritaccio, Anthony and Chaichana, Kaisorn L and Quinones-Hinojosa, Alfredo} } @article {3407, title = {Real-time functional mapping: potential tool for improving language outcome in pediatric epilepsy surgery.}, journal = {J Neurosurg Pediatr}, volume = {14}, year = {2014}, month = {09/2014}, pages = {287-95}, abstract = {

Accurate language localization expands surgical treatment options for epilepsy patients and reduces the risk of postsurgery language deficits. Electrical cortical stimulation mapping (ESM) is considered to be the clinical gold standard for language localization. While ESM affords clinically valuable results, it can be poorly tolerated by children, requires active participation and compliance, carries a risk of inducing seizures, is highly time consuming, and is labor intensive. Given these limitations, alternative and/or complementary functional localization methods such as analysis of electrocorticographic (ECoG) activity in high gamma frequency band in real time are needed to precisely identify eloquent cortex in children. In this case report, the authors examined 1) the use of real-time functional mapping (RTFM) for language localization in a high gamma frequency band derived from ECoG to guide surgery in an epileptic pediatric patient and 2) the relationship of RTFM mapping results to postsurgical language outcomes. The authors found that RTFM demonstrated relatively high sensitivity (75\%) and high specificity (90\%) when compared with ESM in a "next-neighbor" analysis. While overlapping with ESM in the superior temporal region, RTFM showed a few other areas of activation related to expressive language function, areas that were eventually resected during the surgery. The authors speculate that this resection may be associated with observed postsurgical expressive language deficits. With additional validation in more subjects, this finding would suggest that surgical planning and associated assessment of the risk/benefit ratio would benefit from information provided by RTFM mapping.

}, keywords = {Adolescent, Anticonvulsants, Brain Mapping, Cerebral Cortex, Electric Stimulation, Electroencephalography, Epilepsies, Partial, Female, Humans, Language, Neuropsychological Tests, Sensitivity and Specificity, Speech}, issn = {1933-0715}, doi = {10.3171/2014.6.PEDS13477}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24995815}, author = {Korostenskaja, Milena and Chen, Po-Ching and Salinas, Christine M and Westerveld, Michael and Peter Brunner and Gerwin Schalk and Cook, Jane C and Baumgartner, James and Lee, Ki H} } @article {2133, title = {A graphical model framework for decoding in the visual ERP-based BCI speller.}, journal = {Neural Comput}, volume = {23}, year = {2011}, month = {01/2011}, pages = {160-82}, abstract = {

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating\ brain\ signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between\ brain\ signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the\ brain\ signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

}, keywords = {Artificial Intelligence, Computer User Training, Discrimination Learning, Electroencephalography, Evoked Potentials, Evoked Potentials, Visual, Humans, Language, Models, Neurological, Models, Theoretical, Reading, Signal Processing, Computer-Assisted, User-Computer Interface, Visual Cortex, Visual Perception}, issn = {1530-888X}, doi = {10.1162/NECO_a_00066}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20964540}, author = {Martens, S M M and Mooij, J M and Jeremy Jeremy Hill and Farquhar, Jason and Sch{\"o}lkopf, B} }