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在受到听觉反馈干扰的情况下发声时的有效连通性的调制。

Modulation of effective connectivity during vocalization with perturbed auditory feedback.

机构信息

Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX 78229, USA.

出版信息

Neuropsychologia. 2013 Jul;51(8):1471-80. doi: 10.1016/j.neuropsychologia.2013.05.002. Epub 2013 May 9.

Abstract

The integration of auditory feedback with vocal motor output is important for the control of voice fundamental frequency (F0). We used a pitch-shift paradigm where subjects respond to an alteration, or shift, of voice pitch auditory feedback with a reflexive change in F0. We presented varying magnitudes of pitch shifted auditory feedback to subjects during vocalization and passive listening and measured event related potentials (ERPs) to the feedback shifts. Shifts were delivered at +100 and +400 cents (200 ms duration). The ERP data were modeled with dynamic causal modeling (DCM) techniques where the effective connectivity between the superior temporal gyrus (STG), inferior frontal gyrus and premotor areas were tested. We compared three main factors: the effect of intrinsic STG connectivity, STG modulation across hemispheres and the specific effect of hemisphere. A Bayesian model selection procedure was used to make inference about model families. Results suggest that both intrinsic STG and left to right STG connections are important in the identification of self-voice error and sensory motor integration. We identified differences in left-to-right STG connections between 100 cent and 400 cent shift conditions suggesting that self- and non-self-voice error are processed differently in the left and right hemisphere. These results also highlight the potential of DCM modeling of ERP responses to characterize specific network properties of forward models of voice control.

摘要

听觉反馈与发声运动输出的整合对于控制声音基频(F0)非常重要。我们使用了一种音高移动范式,其中受试者通过 F0 的反射性变化来响应声音音高听觉反馈的改变。我们在发声和被动聆听期间向受试者呈现不同幅度的音高移动听觉反馈,并测量对反馈移动的事件相关电位(ERP)。音高移动持续时间为 200 毫秒,移动幅度为+100 和+400 分(cent)。使用动态因果建模(DCM)技术对 ERP 数据进行建模,测试了颞上回(STG)、下额叶和运动前区之间的有效连接。我们比较了三个主要因素:内在 STG 连接的影响、半球间的 STG 调制以及特定的半球效应。使用贝叶斯模型选择程序来对模型族进行推断。结果表明,内在 STG 和左右半球间的 STG 连接对于自我声音错误和感觉运动整合的识别都很重要。我们在 100 分和 400 分音高移动条件之间识别出了左到右 STG 连接的差异,表明自我和非自我声音错误在左右半球的处理方式不同。这些结果还突出了 DCM 对 ERP 反应建模的潜力,以表征声音控制前模型的特定网络特性。

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