Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA.
Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA.
Hear Res. 2023 Jun;433:108767. doi: 10.1016/j.heares.2023.108767. Epub 2023 Apr 10.
The goal of describing how the human brain responds to complex acoustic stimuli has driven auditory neuroscience research for decades. Often, a systems-based approach has been taken, in which neurophysiological responses are modeled based on features of the presented stimulus. This includes a wealth of work modeling electroencephalogram (EEG) responses to complex acoustic stimuli such as speech. Examples of the acoustic features used in such modeling include the amplitude envelope and spectrogram of speech. These models implicitly assume a direct mapping from stimulus representation to cortical activity. However, in reality, the representation of sound is transformed as it passes through early stages of the auditory pathway, such that inputs to the cortex are fundamentally different from the raw audio signal that was presented. Thus, it could be valuable to account for the transformations taking place in lower-order auditory areas, such as the auditory nerve, cochlear nucleus, and inferior colliculus (IC) when predicting cortical responses to complex sounds. Specifically, because IC responses are more similar to cortical inputs than acoustic features derived directly from the audio signal, we hypothesized that linear mappings (temporal response functions; TRFs) fit to the outputs of an IC model would better predict EEG responses to speech stimuli. To this end, we modeled responses to the acoustic stimuli as they passed through the auditory nerve, cochlear nucleus, and inferior colliculus before fitting a TRF to the output of the modeled IC responses. Results showed that using model-IC responses in traditional systems analyzes resulted in better predictions of EEG activity than using the envelope or spectrogram of a speech stimulus. Further, it was revealed that model-IC derived TRFs predict different aspects of the EEG than acoustic-feature TRFs, and combining both types of TRF models provides a more accurate prediction of the EEG response.
描述人类大脑如何对复杂声音刺激做出反应的目标推动了听觉神经科学研究数十年。通常采用基于系统的方法，根据呈现刺激的特征来模拟神经生理反应。这包括大量对复杂声音（如语音）的脑电图（EEG）反应进行建模的工作。在这种建模中使用的声学特征的示例包括语音的幅度包络和频谱图。这些模型隐含地假设从刺激表示到皮质活动的直接映射。然而，实际上，声音的表示在通过听觉通路的早期阶段时会发生转换，使得输入到皮质的信号与呈现的原始音频信号根本不同。因此，在预测复杂声音对皮质的反应时，考虑到较低阶听觉区域（如听神经、耳蜗核和下丘）中发生的转换可能是有价值的。具体来说，因为下丘的反应比直接从音频信号中得出的声学特征更类似于皮质输入，所以我们假设拟合到下丘模型输出的线性映射（时间响应函数；TRFs）将更好地预测语音刺激的 EEG 反应。为此，我们在拟合模型下丘的 TRF 之前，对听觉神经、耳蜗核和下丘中通过的声音刺激的反应进行建模。结果表明，在传统的系统分析中使用模型下丘的反应比使用语音刺激的包络或频谱可以更好地预测 EEG 活动。此外，揭示了模型下丘衍生的 TRFs 比声学特征 TRFs 预测 EEG 的不同方面，并且结合这两种类型的 TRF 模型可以更准确地预测 EEG 反应。
