Guo Xiaoxuan, Mai Guangting, Mohammadi Yousef, Benzaquén Ester, Yukhnovich Ekaterina A, Sedley Will, Griffiths Timothy D
Auditory Cognition Lab, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom.
Psychology and Language Sciences, Faculty of Brain Sciences, University College London, WC1N 1PF, United Kingdom; NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, NG1 5DU, United Kingdom.
Neuroimage. 2025 Jul 1;314:121270. doi: 10.1016/j.neuroimage.2025.121270. Epub 2025 May 13.
Neural entrainment to certain acoustic features can predict speech-in-noise perception, but these features are difficult to separate. We measured neural responses to both natural speech-in-noise and stimuli (auditory figure-ground) that simulate speech-in-noise without any acoustic or linguistic confounds such as stress contour and semantics. The figure-ground stimulus is formed by multiple temporally coherent pure-tone components embedded in a random tone cloud. Previous work has shown that discrimination of dynamic figure-ground based on the fundamental frequency (F0) of natural speech predicts speech-in-noise recognition independent of hearing and age. In this study, we compared the brain substrate for the figure-ground analysis based on the F0 contour and a statistically similar '1/f' contour with speech-in-noise. We used the temporal response function to predict the electroencephalography responses to the frequency trajectories of the auditory targets. We demonstrate that the brain significantly tracked the pitch changes in both AFG conditions (F0 and 1/F tracking) and a sentence-in-noise condition (F0 tracking) at similar latencies, but at similar magnitudes only when tracking the F0 contour. The pitch-tracking accuracy was consistently high across the delta and theta bands for the AFG condition but not for speech. Sensor-space analysis revealed that speech-in-noise performance correlated with the positive peak amplitude of the F0 figure-ground at 100 ms. Source-space analysis revealed bilateral temporal lobe and hippocampal generators, and strong tracking in the superior parietal lobe for auditory figures and natural speech. In conclusion, our findings demonstrate that the human brain reliably tracks the F0 trajectory of both speech and a non-linguistic figure in noise, with speech tracking showing reduced accuracy in the theta band compared to figure-ground tracking. Despite the difference in prediction accuracy, we reveal striking similarities in neural entrainment patterns and source locations between the two paradigms. These results suggest that neural entrainment engages high-level cortical mechanisms independent of linguistic content. Furthermore, we show that TRF peak amplitude serves as a potential biomarker for speech-in-noise ability, highlighting possible clinical applications.
神经对某些声学特征的同步能够预测噪声中的言语感知，但这些特征难以分离。我们测量了对自然噪声中的言语以及模拟噪声中的言语（听觉图形-背景）的神经反应，后者不存在任何声学或语言方面的混淆因素，如重音轮廓和语义。图形-背景刺激由嵌入随机音调云的多个时间上连贯的纯音成分构成。先前的研究表明，基于自然言语的基频（F0）对动态图形-背景的辨别能够预测噪声中的言语识别，且不受听力和年龄影响。在本研究中，我们比较了基于F0轮廓和统计上相似的“1/f”轮廓进行图形-背景分析时的脑基质与噪声中的言语。我们使用时间响应函数来预测脑电图对听觉目标频率轨迹的反应。我们证明，大脑在相似的潜伏期内显著追踪了两种听觉图形-背景条件（F0和1/F追踪）以及噪声中句子条件（F0追踪）下的音高变化，但仅在追踪F0轮廓时幅度相似。对于听觉图形-背景条件，跨δ和θ频段的音高追踪准确率始终较高，但对于言语则不然。传感器空间分析显示，噪声中言语的表现与100毫秒时F0图形-背景的正峰值幅度相关。源空间分析揭示了双侧颞叶和海马体发生器，以及顶叶上部对听觉图形和自然言语的强烈追踪。总之，我们的研究结果表明，人类大脑能够可靠地追踪噪声中言语和非语言图形的F0轨迹，与图形-背景追踪相比，言语追踪在θ频段的准确率较低。尽管预测准确率存在差异，但我们揭示了两种范式在神经同步模式和源位置上的显著相似性。这些结果表明，神经同步涉及独立于语言内容的高级皮质机制。此外，我们表明时间响应函数峰值幅度可作为噪声中言语能力的潜在生物标志物，凸显了可能的临床应用。
