The integration of large-scale neural network modeling and functional brain imaging in speech motor control.
The integration of large-scale neural network modeling and functional brain imaging in speech motor control.
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DOI：
10.1016/j.neuroimage.2009.10.023
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发表时间：
2010-09
期刊：
影响因子：
5.7
通讯作者：
Guenther, F. H.
中科院分区：
文献类型：
--
作者：
Golfinopoulos, E.;Tourville, J. A.;Guenther, F. H.
关键词：
语音的产生需要若干综合处理阶段。该系统必须编码的语音运动程序，命令运动轨迹的发音和监测瞬态时空变化的听觉和体感反馈。该系统的早期模型提出独立的神经区域执行专门的语音处理。随着技术的进步，神经成像数据显示，语音的动态感觉运动过程需要一组分布式的相互作用的神经区域。DIVA（关节运动速度方向）神经计算模型阐述了早期的理论，整合了现有的数据和当代的意识形态，提供了一个关于语音获取和产生的声学，运动学和功能性磁共振成像（fMRI）数据的机械解释。这个大规模的神经网络模型由几个相互连接的组件组成，其细胞活动和突触权重强度由微分方程控制。模型中的细胞与神经解剖基质相关，并已映射到蒙特利尔神经研究所立体定向空间的位置，提供了一种比较模拟和经验fMRI数据的方法。DIVA模型还提供了一个计算和神经生理学框架，在此框架内解释和组织流利和不流利儿童和成人扬声器的语音获取和生产的研究。这篇综述文章的目的是展示DIVA模型是如何用于激励和指导功能成像研究的。我们描述了如何使用基于体素的，基于感兴趣区域的参数分析和区域间的有效连接建模的功能磁共振成像数据模型预测进行评估。
Speech production demands a number of integrated processing stages. The system must encode the speech motor programs that command movement trajectories of the articulators and monitor transient spatiotemporal variations in auditory and somatosensory feedback. Early models of this system proposed that independent neural regions perform specialized speech processes. As technology advanced, neuroimaging data revealed that the dynamic sensorimotor processes of speech require a distributed set of interacting neural regions. The DIVA (Directions into Velocities of Articulators) neurocomputational model elaborates on early theories, integrating existing data and contemporary ideologies, to provide a mechanistic account of acoustic, kinematic, and functional magnetic resonance imaging (fMRI) data on speech acquisition and production. This large-scale neural network model is composed of several interconnected components whose cell activities and synaptic weight strengths are governed by differential equations. Cells in the model are associated with neuroanatomical substrates and have been mapped to locations in Montreal Neurological Institute stereotactic space, providing a means to compare simulated and empirical fMRI data. The DIVA model also provides a computational and neurophysiological framework within which to interpret and organize research on speech acquisition and production in fluent and dysfluent child and adult speakers. The purpose of this review article is to demonstrate how the DIVA model is used to motivate and guide functional imaging studies. We describe how model predictions are evaluated using voxel-based, region-of-interest-based parametric analyses and inter-regional effective connectivity modeling of fMRI data.
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