Mapping sensorimotor cortex with slow cortical potential resting-state networks while awake and under anesthesia.
Mapping sensorimotor cortex with slow cortical potential resting-state networks while awake and under anesthesia.
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DOI：
10.1227/neu.0b013e318258e5d1
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发表时间：
2012-08
期刊：
影响因子：
4.8
通讯作者：
Leuthardt EC
中科院分区：
文献类型：
--
作者：
Breshears JD;Gaona CM;Roland JL;Sharma M;Bundy DT;Shimony JS;Rashid S;Eisenman LN;Hogan RE;Snyder AZ;Leuthardt EC
对静息状态皮层网络的新见解对于理解大脑组织的基本结构非常重要。这些网络，最初是通过功能性MRI识别的，也可以在局部场电位的次低节奏的相关地形中看到。由于这些网络的基本性质及其与任务相关激活的独立性，我们假设除了它们的神经科学相关性外，这些慢皮层电位（SCP）网络也可能在临床脑制图中发挥重要作用。我们假设这些网络在识别清醒和麻醉患者的雄辩皮层（如感觉运动皮层）方面是有用的。本研究包括8名接受手术治疗的顽固性癫痫患者。在清醒和异丙酚麻醉下，从皮质表面记录scp。为了测试脑映射的实用性，使用数据驱动（种子无关）和解剖驱动（基于种子）的方法确定了慢皮层电位网络。以皮层电刺激作为比较的金标准，计算这些网络识别感觉运动皮层的敏感性和特异性。在麻醉和清醒状态下，用数据驱动方法识别的神经网络对感觉运动皮层的敏感性分别为90%和93%，特异性分别为58%和55%。在麻醉和清醒状态下通过系统种子选择确定的神经网络的敏感性分别为78%和83%，特异性分别为67%和60%。静息状态网络可能有助于调整刺激映射，并可以提供一种方法来识别麻醉下患者的雄辩区域。
The emerging insight into resting-state cortical networks has been important in understanding the fundamental architecture of brain organization. These networks, which were originally identified with functional MRI, are also seen in the correlation topography of the infraslow rhythms of local field potentials. Because of the fundamental nature of these networks and their independence from task-related activations, we posit that in addition to their neuroscientific relevance, these slow cortical potential (SCP) networks could also play an important role in clinical brain mapping. We hypothesized that these networks would be useful in identifying eloquent cortex, such as sensorimotor cortex, in patients both awake and under anesthesia. This study included eight subjects undergoing surgical treatment for intractable epilepsy. SCPs were recorded from the cortical surface while awake and under propofol anesthesia. To test brain-mapping utility, slow cortical potential networks were identified using data-driven (seed-independent) and anatomy-driven (seed-based) approaches. Using electrocortical stimulation as the gold standard for comparison, the sensitivity and specificity of these networks for identifying sensorimotor cortex was calculated. Networks identified with a data-driven approach in patients under anesthesia and awake were 90% and 93% sensitive, and 58% and 55% specific for sensorimotor cortex, respectively. Networks identified with systematic seed selection in patients under anesthesia and awake were 78% and 83% sensitive, and 67% and 60% specific, respectively. Resting-state networks may be useful for tailoring stimulation mapping and could provide a means of identifying eloquent regions in patients while under anesthesia.