scholarly journals Knowledge of language function and underlying neural networks gained from focal seizures and epilepsy surgery

2019 ◽  
Vol 189 ◽  
pp. 20-33 ◽  
Author(s):  
Daniel L. Drane ◽  
Nigel P. Pedersen
Keyword(s):  
Neural Networks ◽  
Epilepsy Surgery ◽  
Language Function ◽  
Focal Seizures ◽  
Knowledge Of Language

scholarly journals Intracerebral Microdialysis of Extracellular Amino Acids in the Human Epileptic Focus

1992 ◽  
Vol 12 (5) ◽  
pp. 873-876 ◽  
Author(s):  
Elisabeth Ronne-Engström ◽  
Lars Hillered ◽  
Roland Flink ◽  
BO Spännare ◽  
Urban Ungerstedt ◽  
...  
Keyword(s):  
Amino Acids ◽  
Brain Damage ◽  
Epilepsy Surgery ◽  
Seizure Activity ◽  
Intractable Epilepsy ◽  
The Other ◽  
Epileptic Focus ◽  
Intracerebral Microdialysis ◽  
Focal Seizures

Extracellular levels of aspartate (ASP), glutamate (GLU), serine (SER), asparagine (ASN), glycine (GLY), threonine (THR), arginine (ARG), alanine (ALA), taurine (TAU), tyrosine (TYR), phenylalanine (PHE), isoleucine (ILEU), and leucine (LEU) were monitored by using intracerebral microdialysis in seven patients with medically intractable epilepsy, undergoing epilepsy surgery. In association with focal seizures, dramatic increases of the extracellular ASP, GLU, GLY, and SER concentrations were observed. The other amino acids analyzed, including TAU, showed small changes. The results support the hypothesis that ASP, GLU, GLY, and possibly SER, play an important role in the mechanism of seizure activity and seizure-related brain damage in the human epileptic focus.

Partial resection of presurgical fMRI activation is associated with a postsurgical loss of language function after frontal lobe epilepsy surgery

Neurocase ◽  
2017 ◽  
Vol 23 (3-4) ◽  
pp. 239-248 ◽  
Author(s):  
Kirsten Labudda ◽  
Markus Mertens ◽  
Thilo Kalbhenn ◽  
Reinhard Schulz ◽  
Friedrich G Woermann
Keyword(s):  
Frontal Lobe ◽  
Epilepsy Surgery ◽  
Partial Resection ◽  
Frontal Lobe Epilepsy ◽  
Language Function

Neural network analysis of preoperative variables and outcome in epilepsy surgery

1999 ◽  
Vol 90 (6) ◽  
pp. 998-1004 ◽  
Author(s):  
Jeffrey E. Arle ◽  
Kenneth Perrine ◽  
Orrin Devinsky ◽  
Werner K. Doyle
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Epilepsy Surgery ◽  
Multivariate Techniques ◽  
Chi Square ◽  
Content Type ◽  
Individual Variables ◽  
Postoperative Seizure ◽  
Chi Square Analysis ◽  
Fine Print

Object. Because appropriate patient selection is essential for achieving successful outcomes after epilepsy surgery, the need for more robust methods of predicting postoperative seizure control has been created. Standard multivariate techniques have been only 75 to 80% accurate in this regard. Recent use of artificial intelligence techniques, including neural networks, for analyzing multivariate clinical data has been successful in predicting medical outcome.Methods. The authors applied neural network techniques to 80 consecutive patients undergoing epilepsy surgery in whom data on demographic, seizure, operative, and clinical variables to predict postoperative seizures were collected.Neural networks could be used to predict postoperative seizures in up to 98% of cases. Student's t-tests or chi-square analysis performed on individual variables revealed that only the preoperative medication index was significantly different (p = 0.02) between the two outcome groups. Six different combinations of input variables were used to train the networks. Neural network accuracies differed in their ability to predict seizures: using all data (96%); all data minus electroencephalography concordance and operative side (93%); all data except intra- or postoperative variables such as tissue pathological category (98%); all data excluding pathological category, intelligence quotient (IQ) data, and Wada results (84%); only demographics and tissue pathological category (65%); and only IQ data (63%).Conclusions. Analysis of the results reveals that several networks that are trained with the usual accepted variables characterizing the typical evaluation of epilepsy patients can predict postoperative seizures with greater than 95% accuracy.

scholarly journals Neural network analysis of preoperative variables and outcome in epilepsy surgery

1999 ◽  
Vol 6 (2) ◽  
pp. E6
Author(s):  
Jeffrey E. Arle ◽  
Kenneth Perrine ◽  
Orrin Devinsky ◽  
Werner K. Doyle
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Epilepsy Surgery ◽  
Multivariate Techniques ◽  
Chi Square ◽  
Individual Variables ◽  
Input Variables ◽  
Student’S T ◽  
Postoperative Seizure ◽  
Chi Square Analysis

Because appropriate patient selection is essential for achieving successful outcomes after epilepsy surgery, the need for more robust methods of predicting postoperative seizure control has been created. Standard multivariate techniques have been only 75 to 80% accurate in this regard. Recent use of artificial intelligence techniques, including neural networks, for analyzing multivariate clinical data, has been successful in predicting medical outcome. The authors applied neural network techniques to 80 consecutive patients undergoing epilepsy surgery in whom demographic, seizure, operative, and clinical variables to predict postoperative seizures data were obtained. Neural networks were able to predict postoperative seizures in up to 98% of cases. Student's t tests or chi-square analysis performed on individual variables revealed that only the preoperative medication index was significantly different (p = 0.02) between the two outcome groups. Six different combinations of input variables were used to train the networks. Neural network accuracies differed in their ability to predict seizures using all data (96%); all data minus electroencephalography concordance and operative side (93%); all data except intra- or postoperative variables such as tissue pathology (98%); all data excluding pathology, intelligence quotient (IQ) data, and Wada results (84%); only using demographics and tissue pathology (65%); and only using IQ data (63%). Analysis of the results reveals that several networks that are trained with the usual accepted variables characterizing the typical evaluation of epilepsy patients can predict postoperative seizures with greater than 95% accuracy.

scholarly journals Naming-related spectral responses predict neuropsychological outcome after epilepsy surgery

2021 ◽  
Author(s):  
Masaki Sonoda ◽  
Robert Rothermel ◽  
Alanna Carlson ◽  
Jeong-Won Jeong ◽  
Min-Hee Lee ◽  
...  
Keyword(s):  
Prediction Model ◽  
Epilepsy Surgery ◽  
Picture Naming ◽  
Receptive Language ◽  
Epileptogenic Zone ◽  
Neuropsychological Outcome ◽  
Language Function ◽  
Time Frequency ◽  
Naming Tasks ◽  
Spectral Responses

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.

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