Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University, Espoo, Finland.
PLoS Biol. 2024 Mar 11;22(3):e3002534. doi: 10.1371/journal.pbio.3002534. eCollection 2024 Mar.
Selective attention-related top-down modulation plays a significant role in separating relevant speech from irrelevant background speech when vocal attributes separating concurrent speakers are small and continuously evolving. Electrophysiological studies have shown that such top-down modulation enhances neural tracking of attended speech. Yet, the specific cortical regions involved remain unclear due to the limited spatial resolution of most electrophysiological techniques. To overcome such limitations, we collected both electroencephalography (EEG) (high temporal resolution) and functional magnetic resonance imaging (fMRI) (high spatial resolution), while human participants selectively attended to speakers in audiovisual scenes containing overlapping cocktail party speech. To utilise the advantages of the respective techniques, we analysed neural tracking of speech using the EEG data and performed representational dissimilarity-based EEG-fMRI fusion. We observed that attention enhanced neural tracking and modulated EEG correlates throughout the latencies studied. Further, attention-related enhancement of neural tracking fluctuated in predictable temporal profiles. We discuss how such temporal dynamics could arise from a combination of interactions between attention and prediction as well as plastic properties of the auditory cortex. EEG-fMRI fusion revealed attention-related iterative feedforward-feedback loops between hierarchically organised nodes of the ventral auditory object related processing stream. Our findings support models where attention facilitates dynamic neural changes in the auditory cortex, ultimately aiding discrimination of relevant sounds from irrelevant ones while conserving neural resources.
选择性注意相关的自上而下的调制在语音属性分离同时说话者较小且不断变化时，对于从无关的背景语音中分离相关语音起着重要作用。电生理研究表明，这种自上而下的调制增强了对注意语音的神经跟踪。然而，由于大多数电生理技术的空间分辨率有限，涉及的特定皮质区域仍不清楚。为了克服这些限制，我们同时收集了脑电图 (EEG)（高时间分辨率）和功能磁共振成像 (fMRI)（高空间分辨率），同时人类参与者选择性地关注视听场景中重叠的鸡尾酒会语音中的说话者。为了利用各自技术的优势，我们使用 EEG 数据分析了语音的神经跟踪，并进行了基于表示相似性的 EEG-fMRI 融合。我们观察到，注意增强了神经跟踪，并在研究的潜伏期内调节了 EEG 相关物。此外，神经跟踪的注意相关增强呈可预测的时间模式波动。我们讨论了这种时间动态如何源自注意力与预测之间的相互作用以及听觉皮层的可塑性的组合。EEG-fMRI 融合揭示了在听觉对象相关处理流的层次化节点之间存在注意相关的迭代前馈-反馈循环。我们的发现支持了注意力促进听觉皮层中动态神经变化的模型，最终有助于区分相关声音和无关声音，同时节省神经资源。
