Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research.
Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research.
复制标题
神经生理学对语音和其他连续刺激的响应的线性模型：应用研究的方法学考虑因素。
DOI：
10.3389/fnins.2021.705621
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发表时间：
2021
影响因子：
4.3
通讯作者：
Lalor EC
中科院分区：
文献类型：
--
作者：
Crosse MJ;Zuk NJ;Di Liberto GM;Nidiffer AR;Molholm S;Lalor EC
认知神经科学，特别是对言语和语言的研究，发现越来越多地使用线性建模技术来研究自然、环境刺激的处理。这些计算工具的出现促进了许多临床领域的类似研究，促进了在更自然的条件下对认知和感觉缺陷的研究。然而，研究临床(通常是高度异质性的)队列给这种建模过程增加了一层复杂性，可能导致这种技术的不稳定，结果是不一致的结果。在这里，我们概述了一些关键的方法论考虑的应用研究，参考了一个假想的临床实验涉及语音处理和模拟电生理(EEG)数据的工作例子。重点介绍了实验设计、数据预处理、刺激特征提取、模型设计、模型训练与评价以及模型权重的解释。在整篇文章中，我们使用MTRF工具箱演示了在MatLab中实现每个步骤，并讨论了如何解决在应用研究中可能出现的问题。通过这样做，我们希望在这些更多的技术点上提供更好的直觉，并为应用和临床研究人员使用丰富的生态刺激来研究感觉和认知过程提供资源。
Cognitive neuroscience, in particular research on speech and language, has seen an increase in the use of linear modeling techniques for studying the processing of natural, environmental stimuli. The availability of such computational tools has prompted similar investigations in many clinical domains, facilitating the study of cognitive and sensory deficits under more naturalistic conditions. However, studying clinical (and often highly heterogeneous) cohorts introduces an added layer of complexity to such modeling procedures, potentially leading to instability of such techniques and, as a result, inconsistent findings. Here, we outline some key methodological considerations for applied research, referring to a hypothetical clinical experiment involving speech processing and worked examples of simulated electrophysiological (EEG) data. In particular, we focus on experimental design, data preprocessing, stimulus feature extraction, model design, model training and evaluation, and interpretation of model weights. Throughout the paper, we demonstrate the implementation of each step in MATLAB using the mTRF-Toolbox and discuss how to address issues that could arise in applied research. In doing so, we hope to provide better intuition on these more technical points and provide a resource for applied and clinical researchers investigating sensory and cognitive processing using ecologically rich stimuli.
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