Broca’s area intraoperative mapping with cortico-cortical evoked potentials

Bykanov, Andrey; Pitskhelauri, D I; Titov, Oleg; Lin, Ming‐Chin; Gulaev, E.V.; Ogurtsova, A A; Maryashev, S. A.; Zhukov, V Yu; Буклина, С Б; Lubnin, А. Yu.; Beshplav, Sh T; Конакова, Т.А.; Pronin, Igor

doi:10.17116/neiro20208406149

Cited by 3 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers rely on such parameters as the amplitude of value fluctuation and the latency to the signal peak in the analysis of CCEP data (7)(8)(9)(10)(11). We utilized the average for all signal values, the latency to the signals' peak states (local extremums), and their values in μV in addition to common parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Intraoperative registration of CCEPs [7] was performed using a 32-channel intraoperative monitoring system "Neuro-IOM" (Neurosoft LLC, Russia) and a pair of subdural electrode strips. One electrode was placed in the frontal speech region (Broca's area); the second electrode was located on the surface of the upper temporal gyrus in its posterior parts and the supramarginal gyrus.…”

Section: Methodsmentioning

confidence: 99%

“…The basic set of signal features included signal amplitude, wave type, latency up to a peak (positive or negative) value [7]. Neurophysiologists typically used them to describe the CCEPs records.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Postoperative Speech Dysfunction Based on Cortico-Cortical Evoked Potentials and Machine Learning

Ishankulov

Danilov

Pitskhelauri

et al. 2022

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

The possibility of postoperative speech dysfunction prediction in neurosurgery based on intraoperative cortico-cortical evoked potentials (CCEP) might provide a new basis to refine the criteria for the extent of intracerebral tumor resection and preserve patients’ quality of life. In this study, we aimed to test the quality of predicting postoperative speech dysfunction with machine learning based on the initial intraoperative CCEP before tumor removal. CCEP data were reported for 26 patients. We used several machine learning models to predict speech deterioration following neurosurgery: a random forest of decision trees, logistic regression, support vector machine with different types of the kernel (linear, radial, and polynomial). The best result with F1-score = 0.638 was obtained by a support vector machine with a polynomial kernel. Most models showed low specificity and high sensitivity (reached 0.993 for the best model). Our pilot study demonstrated the insufficient quality of speech dysfunction prediction by solely intraoperative CCEP recorded before glial tumor resection, grounding our further research of CCEP postresectional dynamics.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Postoperative Speech Dysfunction Based on Cortico-Cortical Evoked Potentials and Machine Learning

Ishankulov

Danilov

Pitskhelauri

et al. 2022

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

show abstract

“…CCEP recording. Intraoperative recording of CCEP [5][6][7] was carried out using a 32-channel Neuro-IOM intraoperative monitoring system (Neurosoft LLC, Russia) and a pair of subdural electrode strips. The recorded CCEP data were processed by the original software developed by Neurosoft LLC as well; that guaranteed full compatibility between the recording systems and the data formats.…”

Section: Methodsmentioning

confidence: 99%

“…The initial descriptive features of the signals were determined after consultation with an expert physician. These features (commonly used by neurophysiologists to characterize CCEP records) included signal amplitude, wave type, and latency to the peak (positive or negative) value [5,8,9]. For each patient, the expert doctor provided a description of speech dysfunctions before and after surgery.…”

Section: Generation Of Descriptive Features Of the Signalsmentioning

confidence: 99%

Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology

Ishankulov¹,

Szostek²,

Pitskhelauri³

et al. 2022

Sovrem Tehnol Med

Self Cite

View full text Add to dashboard Cite

Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients' quality of life.The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection.Materials and Methods. CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models: a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study.Results. The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of "successful" and "unsuccessful" data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370.Conclusion. This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit.

show abstract

Neuromonitoring of the language pathways using cortico-cortical evoked potentials: a systematic review and meta-analysis

et al. 2022

View full text Add to dashboard Cite

Broca’s area intraoperative mapping with cortico-cortical evoked potentials

Cited by 3 publications

References 38 publications

Prediction of Postoperative Speech Dysfunction Based on Cortico-Cortical Evoked Potentials and Machine Learning

Prediction of Postoperative Speech Dysfunction Based on Cortico-Cortical Evoked Potentials and Machine Learning

Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology

Neuromonitoring of the language pathways using cortico-cortical evoked potentials: a systematic review and meta-analysis

Contact Info

Product

Resources

About