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.
复制标题
DOI：
10.1088/1741-2552/abf68a
复制
发表时间：
2021-04-29
影响因子：
4
通讯作者：
Ince NF
中科院分区：
文献类型：
--
作者：
Asman P;Prabhu S;Bastos D;Tummala S;Bhavsar S;McHugh TM;Ince NF
体感诱发电位（SSEP）与皮层脑电图（ECoG）记录中央沟（CS）的识别是一个广泛接受的程序，在常规术中神经生理监测。临床实践测试了短潜伏期SSEP在条形电极上的相位反转。然而，基于波形形态的评估由于手区域的局部性质而容易受到解释变化的影响，并且通常需要多个电极放置或电极重新定位。我们调查的可行性，无监督划定的CS使用的时空模式的SSEP捕获的ECoG网格。术中，使用放置在感觉运动皮层上的ECoG网格记录8例患者的SSEP。神经外科医生对电生理学不知情，使用基于脑沟解剖的神经导航识别感觉和运动脑回。我们量化的主要运动（M1）和躯体感觉（S1）皮质之间的SSEP的时间剖面的最具歧视性的时间点，使用Fisher判别标准。我们在2D热图上可视化SSEP的振幅梯度，以提供基于电生理学的CS描绘的视觉反馈。随后，我们使用整个SSEP波形进行谱聚类，而不选择任何时间点，并以无监督的方式对ECoG通道进行分组。一致的是，在所有患者中，两个不同的时间点提供了前通道和后通道之间几乎相等的区分，当我们将SSEP振幅分布视为空间2D热图时，其生动地勾勒出CS。第一个辨别时间点接近常规偏好的~20 ms峰（N20），第二个时间点略晚于显著高的~30 ms峰（P30）。尽管如此，这些时间点的位置在受试者之间存在明显差异。无监督聚类方法基于SSEP迹线的时间导数以96.3%的准确度分离前通道和后通道，而不需要受试者特定的时间点选择。相比之下，原始迹线的准确度为88.0%。我们表明，无监督聚类的SSEP跟踪评估硬膜下电极网格可以描绘CS自动与高精度，构建的热图可以定位运动皮层。我们预计，融合了机器学习的SSEP的时空模式可以作为一个有用的工具，以协助手术计划。
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. 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. 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%. 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.
登录
查看更多内容
影响因子：
9.9
作者：
Endisch, Christian;Storm, Christian;Leithner, Christoph
通讯作者：
Leithner, Christoph
影响因子：
--
作者：
CORKIN, S;MILNER, B;RASMUSSEN, T
通讯作者：
RASMUSSEN, T
影响因子：
3.6
作者：
Bonhomme, Vincent;Franssen, Collette;Hans, Pol
通讯作者：
Hans, Pol
影响因子：
4.1
作者：
GOLDRING, S;GREGORIE, EM
通讯作者：
GREGORIE, EM
影响因子：
3.4
作者：
Ko, Andrew L.;Weaver, Kurt E.;Ojemann, Jeffrey G.
通讯作者：
Ojemann, Jeffrey G.