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

ABSTRACT: 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.

SUBMITTER: Asman P

PROVIDER: S-EPMC8718352 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recent studies have suggested that cortical gamma-oscillations are tightly linked with various forms of physiological activity. In the present study, the dynamic changes of intracranially recorded median-nerve somatosensory-evoked potentials (SEPs) and somatosensory-induced gamma-oscillations were animated on a three-dimensional MR image, and the temporal and spatial characteristics of these activities were analysed in 10 children being evaluated for epilepsy surgery. Visual and quantitative assessments revealed that short-latency SEPs and somatosensory-induced gamma-oscillations predominantly involved the post-central gyrus and less intensely involved the pre-central gyrus and the anterior parietal lobule. Formation of a dipole of N20 peak with opposite polarities across the central sulcus was well delineated in animation movies. High-frequency (100-250 Hz) somatosensory-induced gamma-oscillations emerged in the post-central gyrus at 13.6-17.5 ms after median-nerve stimulation, gradually slowed down in frequency around and below 100 Hz, and progressively involved the neighbouring areas. A substantial proportion of somatosensory-induced gamma-oscillations was initially phase-locked and the proportion of a non-phase-locked component gradually increased over time. The primary motor hand areas proven by cortical stimulation frequently coincided with the sites showing the largest N20 peak and the largest somatosensory-induced gamma oscillations. In vivo animation of SEPs and somatosensory-induced gamma oscillations both may be utilized to localize the primary sensory-motor hand area in pre-surgical evaluation. The dipole on SEPs is consistent with the previously accepted notion that the cortices along the central sulcus are activated. The high-frequency somatosensory-induced gamma-oscillations in the post-central gyrus may represent the initial neural processing for external somatosensory stimuli, whereas the subsequent lower-frequency oscillations might represent the reafferent cortical activity occurring in larger cortical networks.

Project description:BackgroundScience continues to search for a neuroprotective drug therapy to improve outcomes after cardiac arrest (CA). The use of glibenclamide (GBC) has shown promise in preclinical studies, but its effects on neuroprognostication tools are not well understood. We aimed to investigate the effect of GBC on somatosensory evoked potential (SSEP) waveform recovery post CA and how this relates to the early prediction of functional outcome, with close attention to arousal and somatosensory recovery, in a rodent model of CA.MethodsSixteen male Wistar rats were subjected to 8-min asphyxia CA and assigned to GBC treatment (n = 8) or control (n = 8) groups. GBC was administered as a loading dose of 10 μg/kg intraperitoneally 10 min after the return of spontaneous circulation, followed by a maintenance dosage of 1.6 μg/kg every 8 h for 24 h. SSEPs were recorded from baseline until 150 min following CA. Coma recovery, arousal, and brainstem function, measured by subsets of the neurological deficit score (NDS), were compared between both groups. SSEP N10 amplitudes were compared between the two groups at 30, 60, 90, and 120 min post CA.ResultsRats treated with GBC had higher sub-NDS scores post CA, with improved arousal and brainstem function recovery (P = 0.007). Both groups showed a gradual improvement of SSEP N10 amplitude over time, from 30 to 120 min post CA. Rats treated with GBC showed significantly better SSEP recovery at every time point (P < 0.001 for 30, 60, and 90 min; P = 0.003 for 120 min). In the GBC group, the N10 amplitude recovered to baseline by 120 min post CA. Quantified Cresyl violet staining revealed a significantly greater percentage of damage in the control group compared with the GBC treatment group (P = 0.004).ConclusionsGlibenclamide improves coma recovery, arousal, and brainstem function after CA with decreased number of ischemic neurons in a rat model. GBC improves SSEP recovery post CA, with N10 amplitude reaching the baseline value by 120 min, suggesting early electrophysiologic recovery with this treatment. This medication warrants further exploration as a potential drug therapy to improve functional outcomes in patients after CA.

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

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