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音型知觉学习改变了 Heschl 回和颞上回之间的有效连接。

Perceptual learning of tone patterns changes the effective connectivity between Heschl's gyrus and planum temporale.

机构信息

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark.

Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.

出版信息

Hum Brain Mapp. 2021 Mar;42(4):941-952. doi: 10.1002/hbm.25269. Epub 2020 Nov 4.

Abstract

Learning of complex auditory sequences such as music can be thought of as optimizing an internal model of regularities through unpredicted events (or "prediction errors"). We used dynamic causal modeling (DCM) and parametric empirical Bayes on functional magnetic resonance imaging (fMRI) data to identify modulation of effective brain connectivity that takes place during perceptual learning of complex tone patterns. Our approach differs from previous studies in two aspects. First, we used a complex oddball paradigm based on tone patterns as opposed to simple deviant tones. Second, the use of fMRI allowed us to identify cortical regions with high spatial accuracy. These regions served as empirical regions-of-interest for the analysis of effective connectivity. Deviant patterns induced an increased blood oxygenation level-dependent response, compared to standards, in early auditory (Heschl's gyrus [HG]) and association auditory areas (planum temporale [PT]) bilaterally. Within this network, we found a left-lateralized increase in feedforward connectivity from HG to PT during deviant responses and an increase in excitation within left HG. In contrast to previous findings, we did not find frontal activity, nor did we find modulations of backward connections in response to oddball sounds. Our results suggest that complex auditory prediction errors are encoded by changes in feedforward and intrinsic connections, confined to superior temporal gyrus.

摘要

学习复杂的听觉序列,如音乐,可以被视为通过不可预测的事件(或“预测误差”)优化内部规律模型。我们使用动态因果建模(DCM)和功能磁共振成像(fMRI)数据的参数经验贝叶斯方法,来识别在复杂音调模式的感知学习过程中发生的有效大脑连接的调制。我们的方法与之前的研究在两个方面有所不同。首先,我们使用了基于音调模式的复杂的Oddball 范式,而不是简单的偏差音调。其次,fMRI 的使用使我们能够以高空间精度识别皮质区域。这些区域作为有效连接分析的经验性感兴趣区域。与标准相比,偏差模式在双侧早期听觉(Heschl gyrus [HG])和联合听觉区域(temporal gyrus [PT])中引起了血氧水平依赖反应的增加。在这个网络中,我们发现左侧 HG 到 PT 的前馈连接在偏差反应时增加,而左侧 HG 内的兴奋增加。与之前的发现不同,我们没有发现额叶活动,也没有发现对Oddball 声音的后向连接的调制。我们的结果表明,复杂的听觉预测误差是通过前馈和内在连接的变化来编码的,这些变化局限于颞上回。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f378/7856650/7c6e71463431/HBM-42-941-g001.jpg

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