Institute of Neuroscience and Psychology, University of Glasgow, 58 Hillhead Street, Glasgow, G12 8QB, Scotland, United Kingdom.
Institute of Biomagnetism and Biosignalanalysis, University of Muenster, Malmedyweg 15, 48149, Muenster, Germany.
Sci Rep. 2018 Sep 18;8(1):14007. doi: 10.1038/s41598-018-32385-3.
Emerging evidence supports the role of neural oscillations as a mechanism for predictive information processing across large-scale networks. However, the oscillatory signatures underlying auditory mismatch detection and information flow between brain regions remain unclear. To address this issue, we examined the contribution of oscillatory activity at theta/alpha-bands (4-8/8-13 Hz) and assessed directed connectivity in magnetoencephalographic data while 17 human participants were presented with sound sequences containing predictable repetitions and order manipulations that elicited prediction-error responses. We characterized the spectro-temporal properties of neural generators using a minimum-norm approach and assessed directed connectivity using Granger Causality analysis. Mismatching sequences elicited increased theta power and phase-locking in auditory, hippocampal and prefrontal cortices, suggesting that theta-band oscillations underlie prediction-error generation in cortical-subcortical networks. Furthermore, enhanced feedforward theta/alpha-band connectivity was observed in auditory-prefrontal networks during mismatching sequences, while increased feedback connectivity in the alpha-band was observed between hippocampus and auditory regions during predictable sounds. Our findings highlight the involvement of hippocampal theta/alpha-band oscillations towards auditory prediction-error generation and suggest a spectral dissociation between inter-areal feedforward vs. feedback signalling, thus providing novel insights into the oscillatory mechanisms underlying auditory predictive processing.
新出现的证据支持神经振荡作为跨大规模网络进行预测信息处理的机制。然而,听觉不匹配检测和脑区之间信息流的振荡特征尚不清楚。为了解决这个问题,我们研究了在θ/α频段(4-8/8-13 Hz)的振荡活动的贡献,并评估了磁源性脑电图数据中的定向连通性,同时 17 名人类参与者被呈现包含可预测重复和顺序操作的声音序列,这些序列会引起预测误差反应。我们使用最小范数方法描述了神经发生器的谱时特性,并使用格兰杰因果分析评估了定向连通性。不匹配序列引起听觉、海马体和前额叶皮质中θ功率和相位锁定增加,表明θ频段振荡是皮质下网络中预测误差产生的基础。此外,在不匹配序列期间观察到听觉-前额叶网络中增强的前馈θ/α频带连通性,而在可预测声音期间观察到海马体和听觉区域之间的α频带中增强的反馈连通性。我们的发现强调了海马体θ/α频段振荡对听觉预测误差产生的参与,并表明在区域间前馈与反馈信号之间存在频谱分离,从而为听觉预测处理的振荡机制提供了新的见解。