Automatic response assessment in regions of language cortex in epilepsy patients using ECoG-based functional mapping and machine learning

RaviPrakash, Harish; Korostenskaja, Milena; Lee, Ki; Baumgartner, James E.; Castillo, Eduardo M.; Bağcı, Ulaş

doi:10.1109/smc.2017.8122658

Cited by 4 publications

(4 citation statements)

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“…In general, with AT-AR 3 -LF 2 is our best performing model with values >80% for all performance metrics including accuracy, sensitivity and specificity. In a feasibility study toward using machine learning for ECoG-FM, a random forest classifier was used in detecting positive and negative response channels with an accuracy of 78% (RaviPrakash et al, 2017). Traditionally, the accuracy of ECoG-FM is high for mapping sensory and motor function, but relatively low for language modality.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…In our previous works (Korostenskaja et al, 2017;RaviPrakash et al, 2017), we showed the feasibility of utilizing conventional machine learning methods for channel response classification by using the whole signal spectrum (not limited to α, β & high γ) and without using the baseline recording. This was one of the first machine learning based approaches in this field with strong results and demonstrated the potential of ECoG-FM signal to be analyzed more accurately compared to conventional signal processing based descriptive methods.…”

Section: Related Work and Existing Critical Challengesmentioning

confidence: 99%

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Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery

et al. 2020

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The success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECoG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm obtained an accuracy of 83.05% in identifying language regions, an exceptional 23% improvement with respect to the conventional ECoG-FM analysis (∼60%). Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Section: Related Work and Existing Critical Challengesmentioning

confidence: 99%

Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery

et al. 2020

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“…In our previous work 23,24 , we showed the feasibility of machine learning methods to perform classification of channel response by using the whole signal spectrum (not limited to α, β & high γ) and without using the baseline recording. This was the first machine learning based approach in this field and demonstrated the potential of ECoG-FM signal to be analyzed more accurately compared to the conventional methods.…”

Section: B)mentioning

confidence: 99%

Deep Learning provides exceptional accuracy to ECoG-based Functional Language Mapping for epilepsy surgery

RaviPrakash

Korostenskaja

Castillo

et al. 2018

Preprint

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AbstractThe success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECOG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm made an exceptional 23% improvement with respect to the conventional ECoG-FM analysis (∼60%). We obtained the state-of-the-art accuracy of 83.05% in identifying language regions, which has never been achieved before. Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.

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“…These studies showed that accepting electrodes with a high response in either the gamma band, the beta band, or in both, as potentially eloquent yielded better results than using either band alone, especially in the language cortex. Likewise, machine- and deep- learning approaches ( Prakash et al, 2017 ; RaviPrakash et al, 2020 ) have shown that using the entire frequency spectrum, rather than only the gamma band, yields better mapping results, although it was not made clear which part of the spectrum offered the most additional information. In addition, while there is considerable heterogeneity in the task employed between studies, few studies have directly compared different tasks in the same group of patients.…”

Section: Introductionmentioning

confidence: 99%

Combining Gamma With Alpha and Beta Power Modulation for Enhanced Cortical Mapping in Patients With Focal Epilepsy

Archila-Meléndez

Valente

Gommer

et al. 2020

Front. Hum. Neurosci.

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About one third of patients with epilepsy have seizures refractory to the medical treatment. Electrical stimulation mapping (ESM) is the gold standard for the identification of “eloquent” areas prior to resection of epileptogenic tissue. However, it is time-consuming and may cause undesired side effects. Broadband gamma activity (55–200 Hz) recorded with extraoperative electrocorticography (ECoG) during cognitive tasks may be an alternative to ESM but until now has not proven of definitive clinical value. Considering their role in cognition, the alpha (8–12 Hz) and beta (15–25 Hz) bands could further improve the identification of eloquent cortex. We compared gamma, alpha and beta activity, and their combinations for the identification of eloquent cortical areas defined by ESM. Ten patients with intractable focal epilepsy (age: 35.9 ± 9.1 years, range: 22–48, 8 females, 9 right handed) participated in a delayed-match-to-sample task, where syllable sounds were compared to visually presented letters. We used a generalized linear model (GLM) approach to find the optimal weighting of each band for predicting ESM-defined categories and estimated the diagnostic ability by calculating the area under the receiver operating characteristic (ROC) curve. Gamma activity increased more in eloquent than in non-eloquent areas, whereas alpha and beta power decreased more in eloquent areas. Diagnostic ability of each band was close to 0.7 for all bands but depended on multiple factors including the time period of the cognitive task, the location of the electrodes and the patient’s degree of attention to the stimulus. We show that diagnostic ability can be increased by 3–5% by combining gamma and alpha and by 7.5–11% when gamma and beta were combined. We then show how ECoG power modulation from cognitive testing can be used to map the probability of eloquence in individual patients and how this probability map can be used in clinical settings to optimize ESM planning. We conclude that the combination of gamma and beta power modulation during cognitive testing can contribute to the identification of eloquent areas prior to ESM in patients with refractory focal epilepsy.

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Automatic response assessment in regions of language cortex in epilepsy patients using ECoG-based functional mapping and machine learning

Cited by 4 publications

References 15 publications

Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery

Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery

Deep Learning provides exceptional accuracy to ECoG-based Functional Language Mapping for epilepsy surgery

Combining Gamma With Alpha and Beta Power Modulation for Enhanced Cortical Mapping in Patients With Focal Epilepsy

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