Lindboom Elsa, Nidiffer Aaron, Carney Laurel H, Lalor Edmund
bioRxiv. 2023 Jan 2:2023.01.02.522438. doi: 10.1101/2023.01.02.522438.
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 analyses 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.x.
几十年来，描述人类大脑如何对复杂听觉刺激做出反应这一目标推动了听觉神经科学的研究。通常采用基于系统的方法，即根据所呈现刺激的特征对神经生理反应进行建模。这包括大量对脑电图（EEG）对复杂听觉刺激（如语音）反应进行建模的工作。此类建模中使用的声学特征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征到皮层活动存在直接映射。然而，在现实中，声音的表征在通过听觉通路的早期阶段时会发生转变，以至于输入到皮层的信息与所呈现的原始音频信号有根本不同。因此，在预测皮层对复杂声音的反应时，考虑在较低级听觉区域（如听神经、耳蜗核和下丘（IC））发生的转变可能是有价值的。具体而言，由于IC的反应比直接从音频信号得出的声学特征更类似于皮层输入，我们假设拟合到IC模型输出的线性映射（时间响应函数；TRF）将能更好地预测EEG对语音刺激的反应。为此，我们在将TRF拟合到建模的IC反应输出之前，对声学刺激通过听神经、耳蜗核和下丘时的反应进行了建模。结果表明，在传统系统分析中使用模型IC反应比使用语音刺激的包络或频谱图能更好地预测EEG活动。此外，研究发现从模型IC得出的TRF与声学特征TRF预测EEG的不同方面，并且将这两种类型的TRF模型结合起来能更准确地预测EEG反应。
