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作为言语运动控制中功能连接性度量的高伽马波段额颞叶相干性

High-gamma band fronto-temporal coherence as a measure of functional connectivity in speech motor control.

作者信息

Kingyon J, Behroozmand R, Kelley R, Oya H, Kawasaki H, Narayanan N S, Greenlee J D W

机构信息

Department of Neurology, University of Iowa, Iowa City, IA, United States.

Department of Neurosurgery, University of Iowa, Iowa City, IA, United States; Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United States.

出版信息

Neuroscience. 2015 Oct 1;305:15-25. doi: 10.1016/j.neuroscience.2015.07.069. Epub 2015 Jul 29.

Abstract

The neural basis of human speech is unclear. Intracranial electrophysiological recordings have revealed that high-gamma band oscillations (70-150Hz) are observed in the frontal lobe during speech production and in the temporal lobe during speech perception. Here, we tested the hypothesis that the frontal and temporal brain regions had high-gamma coherence during speech. We recorded electrocorticography (ECoG) from the frontal and temporal cortices of five humans who underwent surgery for medically intractable epilepsy, and studied coherence between the frontal and temporal cortex during vocalization and playback of vocalization. We report two novel results. First, we observed high-gamma band as well as theta (4-8Hz) coherence between frontal and temporal lobes. Second, both high-gamma and theta coherence were stronger when subjects were actively vocalizing as compared to playback of the same vocalizations. These findings provide evidence that coupling between sensory-motor networks measured by high-gamma coherence plays a key role in feedback-based monitoring and control of vocal output for human vocalization.

摘要

人类言语的神经基础尚不清楚。颅内电生理记录显示,在言语产生过程中额叶会观察到高伽马波段振荡(70 - 150赫兹),而在言语感知过程中颞叶会出现这种振荡。在此,我们检验了这样一个假设:在言语过程中额叶和颞叶脑区存在高伽马相干性。我们记录了五名因药物难治性癫痫接受手术治疗的患者额叶和颞叶皮质的脑电皮层电图(ECoG),并研究了发声和发声回放过程中额叶与颞叶皮质之间的相干性。我们报告了两个新发现。第一,我们观察到额叶和颞叶之间存在高伽马波段以及theta(4 - 8赫兹)相干性。第二,与相同发声的回放相比,当受试者主动发声时,高伽马和theta相干性都更强。这些发现提供了证据,表明通过高伽马相干性测量的感觉运动网络之间的耦合在基于反馈的人类发声输出监测和控制中起关键作用。

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