Evidence for Cerebellar Contributions to Adaptive Plasticity in Speech Perception
Evidence for Cerebellar Contributions to Adaptive Plasticity in Speech Perception
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DOI：
10.1093/cercor/bht428
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发表时间：
2015-07-01
期刊：
影响因子：
3.7
通讯作者：
Fiez, Julie A.
中科院分区：
文献类型：
--
作者：
Guediche, Sara;Holt, Lori L.;Fiez, Julie A.
在不利的听力条件下，人类的言语感知会迅速适应以保持理解。例如，通过接触，听众可以适应非母语人士发出的带有浓重口音的讲话。在言语感知领域之外，感觉和运动处理的适应性变化被归因于小脑功能。本功能磁共振成像研究调查是否适应言语知觉也涉及小脑。声刺激失真使用声码加频谱偏移操纵，并在一个单词识别任务。小脑中的区域，适应前后的差异进行了鉴定，并在适应过程中的活动和随后的行为改善之间的关系进行了检查。这些分析暗示了小脑的右I脚区域在言语感知的适应性变化中的作用。一个功能相关性分析与右小腿我作为种子区域探测大脑皮层区域在适应期的共变血流动力学反应。研究结果为小脑与大脑皮层颞叶和顶叶的语言相关区域之间的功能网络提供了证据。与已知的小脑对感觉运动适应的贡献一致，小脑-小脑的相互作用可能支持依赖于言语感知中的感觉预测错误信号的监督学习机制。
Human speech perception rapidly adapts to maintain comprehension under adverse listening conditions. For example, with exposure listeners can adapt to heavily accented speech produced by a non-native speaker. Outside the domain of speech perception, adaptive changes in sensory and motor processing have been attributed to cerebellar functions. The present functional magnetic resonance imaging study investigates whether adaptation in speech perception also involves the cerebellum. Acoustic stimuli were distorted using a vocoding plus spectral-shift manipulation and presented in a word recognition task. Regions in the cerebellum that showed differences before versus after adaptation were identified, and the relationship between activity during adaptation and subsequent behavioral improvements was examined. These analyses implicated the right Crus I region of the cerebellum in adaptive changes in speech perception. A functional correlation analysis with the right Crus I as a seed region probed for cerebral cortical regions with covarying hemodynamic responses during the adaptation period. The results provided evidence of a functional network between the cerebellum and language-related regions in the temporal and parietal lobes of the cerebral cortex. Consistent with known cerebellar contributions to sensorimotor adaptation, cerebro-cerebellar interactions may support supervised learning mechanisms that rely on sensory prediction error signals in speech perception.