Computational Neuroscience of Speech and Hearing, Department of Computational Linguistics, University of Zurich, 8050, Zurich, Switzerland.
International Max Planck Research School on the Life Course (IMPRS LIFE), University of Zurich, 8050, Zurich, Switzerland.
Sci Rep. 2024 Aug 17;14(1):19105. doi: 10.1038/s41598-024-69602-1.
The multivariate temporal response function (mTRF) is an effective tool for investigating the neural encoding of acoustic and complex linguistic features in natural continuous speech. In this study, we investigated how neural representations of speech features derived from natural stimuli are related to early signs of cognitive decline in older adults, taking into account the effects of hearing. Participants without ( ) and with ( ) early signs of cognitive decline listened to an audiobook while their electroencephalography responses were recorded. Using the mTRF framework, we modeled the relationship between speech input and neural response via different acoustic, segmented and linguistic encoding models and examined the response functions in terms of encoding accuracy, signal power, peak amplitudes and latencies. Our results showed no significant effect of cognitive decline or hearing ability on the neural encoding of acoustic and linguistic speech features. However, we found a significant interaction between hearing ability and the word-level segmentation model, suggesting that hearing impairment specifically affects encoding accuracy for this model, while other features were not affected by hearing ability. These results suggest that while speech processing markers remain unaffected by cognitive decline and hearing loss per se, neural encoding of word-level segmented speech features in older adults is affected by hearing loss but not by cognitive decline. This study emphasises the effectiveness of mTRF analysis in studying the neural encoding of speech and argues for an extension of research to investigate its clinical impact on hearing loss and cognition.
多变量时间响应函数 (mTRF) 是一种研究自然连续语音中声学和复杂语言特征的神经编码的有效工具。在这项研究中，我们调查了从自然刺激中得出的语音特征的神经表示与老年人认知能力下降的早期迹象之间的关系，同时考虑了听力的影响。没有（ ）和有（ ）认知能力下降早期迹象的参与者在听有声读物时记录了他们的脑电图反应。我们使用 mTRF 框架，通过不同的声学、分段和语言编码模型来模拟语音输入和神经反应之间的关系，并根据编码准确性、信号功率、峰值幅度和潜伏期来检查响应函数。我们的结果表明，认知能力下降或听力能力对语音特征的神经编码没有显著影响。然而，我们发现听力能力和单词级分段模型之间存在显著的相互作用，这表明听力障碍特别影响了该模型的编码准确性，而其他特征不受听力能力的影响。这些结果表明，虽然言语处理标志物不受认知能力下降和听力损失本身的影响，但老年人对单词级分段言语特征的神经编码受到听力损失的影响，但不受认知能力下降的影响。这项研究强调了 mTRF 分析在研究言语的神经编码方面的有效性，并呼吁进一步研究其对听力损失和认知能力的临床影响。
