Mapping Cortical Function with Event-Related Electrocorticography

Towle, Vernon L.; Dai, Zhongtian; Zheng, Weili; Issa, Naoum P.

doi:10.1007/978-3-319-23383-3_6

Cited by 4 publications

(3 citation statements)

References 66 publications

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“…It may also be due to the placement of strip electrode away from the localized nature of the hand area [ 24 , 33 ]. Several investigators have described how several recordings with the use of a single strip in different locations might be needed to find the appropriate position to target the somatosensory cortex [ 13 , 25 , 29 , 31 , 33 , 53 ], whose success needs to be further corroborated by DCS [ 4 , 24 , 35 ] when there is persistent doubt. The multiple electrode relocation and placements require significant time and the additional risk of hemorrhage and trauma, while DCS requires a rigorous anesthetic regimen [ 39 ] and may induce seizures that could jeopardize the surgical procedure or complicate the motor monitorization.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised machine learning can delineate central sulcus by using the spatiotemporal characteristic of somatosensory evoked potentials

et al. 2021

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Objective. Somatosensory evoked potentials (SSEPs) recorded with electrocorticography (ECoG) for central sulcus (CS) identification is a widely accepted procedure in routine intraoperative neurophysiological monitoring. Clinical practices test the short-latency SSEPs for the phase reversal over strip electrodes. However, assessments based on waveform morphology are susceptible to variations in interpretations due to the hand area’s localized nature and usually require multiple electrode placements or electrode relocation. We investigated the feasibility of unsupervised delineation of the CS by using the spatiotemporal patterns of the SSEP captured with the ECoG grid. Approach. Intraoperatively, SSEPs were recorded from eight patients using ECoG grids placed over the sensorimotor cortex. Neurosurgeons blinded to the electrophysiology identified the sensory and motor gyri using neuronavigation based on sulcal anatomy. We quantified the most discriminatory time points in SSEPs temporal profile between the primary motor (M1) and somatosensory (S1) cortex using the Fisher discrimination criterion. We visualized the amplitude gradient of the SSEP over a 2D heat map to provide visual feedback for the delineation of the CS based on electrophysiology. Subsequently, we employed spectral clustering using the entire the SSEP waveform without selecting any time points and grouped ECoG channels in an unsupervised fashion. Main results. Consistently in all patients, two different time points provided almost equal discrimination between anterior and posterior channels, which vividly outlined the CS when we viewed the SSEP amplitude distribution as a spatial 2D heat map. The first discriminative time point was in proximity to the conventionally favored ∼20 ms peak (N20), and the second time point was slightly later than the markedly high ∼30 ms peak (P30). Still, the location of these time points varied noticeably across subjects. Unsupervised clustering approach separated the anterior and posterior channels with an accuracy of 96.3% based on the time derivative of the SSEP trace without the need for a subject-specific time point selection. In contrast, the raw trace resulted in an accuracy of 88.0%. Significance. We show that the unsupervised clustering of the SSEP trace assessed with subdural electrode grids can delineate the CS automatically with high precision, and the constructed heat maps can localize the motor cortex. We anticipate that the spatiotemporal patterns of SSEP fused with machine learning can serve as a useful tool to assist in surgical planning.

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Section: Discussionmentioning

confidence: 99%

Unsupervised machine learning can delineate central sulcus by using the spatiotemporal characteristic of somatosensory evoked potentials

et al. 2021

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“…ECoG is more and more frequently being used in neurolinguistic experiments [26,27], and it has proven well suited to the study of the neuronal signatures of expressive language. Previous ECoG research has shown that spatially and temporally specific neuronal effects can be obtained in the fronto-temporo-parietal cortex during overt speech production [28].…”

Section: Developments In Ecog Research On Speech Production Using Incmentioning

confidence: 99%

Neurolinguistic and machine-learning perspectives on direct speech BCIs for restoration of naturalistic communication

Iljina

Derix

Schirrmeister

et al. 2017

Brain-Computer Interfaces

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a GrK 1624 'frequency effects in language' , university of freiburg, ABSTRACT The ultimate goal of brain-computer-interface (BCI) research on speech restoration is to develop devices which will be able to reconstruct spontaneous, naturally spoken language from the underlying neuronal signals. From this it follows that thorough understanding of brain activity and its functional dynamics during real-world speech will be required. Here, we review current developments in intracranial neurolinguistic and BCI research on speech production under increasingly naturalistic conditions. With an example of neurolinguistic data from our ongoing research, we illustrate the plausibility of neurolinguistic investigations in non-experimental, out-of-the-lab conditions of speech production. We argue that interdisciplinary endeavors at the interface of neuroscience and linguistics can provide valuable insight into the functional significance of speech-related neuronal data. Finally, we anticipate that work with neurolinguistic corpora composed of real-world language samples and simultaneous neuronal recordings, together with machine-learning methodology accounting for the specifics of the neurolinguistic material, will improve the functionality of speech BCIs.

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“…The neurosurgeons need to protect the functional-convinced sites while be careful about the functional-high-risk sites, and additional DECS procedure might be performed on these functional-high-risk sites for function confirmation. As mentioned by Towle et al that a formidable challenge will be to discriminate between necessary and ancillary areas of cortical function, which are probably not discrete entities, but rather points along a continuum (Towle et al, 2016 ). When combining the results of ETAM and EFAM together, the sensitivity and specificity for identifying DECS-positive sites were 90.90 and 84.53%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Electrocorticographic Temporal Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex with Movement-Related Cortical Potentials

Wu¹,

Xie²,

Yao³

et al. 2017

Front. Neurosci.

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We propose electrocorticographic temporal alteration mapping (ETAM) for motor cortex mapping by utilizing movement-related cortical potentials (MRCPs) within the low-frequency band [0.05-3] Hz. This MRCP waveform-based temporal domain approach was compared with the state-of-the-art electrocorticographic frequency alteration mapping (EFAM), which is based on frequency spectrum dynamics. Five patients (two epilepsy cases and three tumor cases) were enrolled in the study. Each patient underwent intraoperative direct electrocortical stimulation (DECS) procedure for motor cortex localization. Moreover, the patients were required to perform simple brisk wrist extension task during awake craniotomy surgery. Cross-validation results showed that the proposed ETAM method had high sensitivity (81.8%) and specificity (94.3%) in identifying sites which exhibited positive DECS motor responses. Moreover, although the sensitivity of the ETAM and EFAM approaches was not significantly different, ETAM had greater specificity compared with EFAM (94.3 vs. 86.1%). These results indicate that for the intraoperative functional brain mapping, ETAM is a promising novel approach for motor cortex localization with the potential to reduce the need for cortical electrical stimulation.

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Mapping Cortical Function with Event-Related Electrocorticography

Cited by 4 publications

References 66 publications

Unsupervised machine learning can delineate central sulcus by using the spatiotemporal characteristic of somatosensory evoked potentials

Unsupervised machine learning can delineate central sulcus by using the spatiotemporal characteristic of somatosensory evoked potentials

Neurolinguistic and machine-learning perspectives on direct speech BCIs for restoration of naturalistic communication

Electrocorticographic Temporal Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex with Movement-Related Cortical Potentials

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