Crosse Michael J, Zuk Nathaniel J, Di Liberto Giovanni M, Nidiffer Aaron R, Molholm Sophie, Lalor Edmund C
Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland.
X, The Moonshot Factory, Mountain View, CA, United States.
Front Neurosci. 2021 Nov 22;15:705621. doi: 10.3389/fnins.2021.705621. eCollection 2021.
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.
认知神经科学，尤其是关于言语和语言的研究，在使用线性建模技术来研究自然环境刺激的处理方面有所增加。此类计算工具的可用性促使在许多临床领域开展了类似的研究，有助于在更自然的条件下研究认知和感觉缺陷。然而，研究临床（且通常高度异质的）队列给此类建模程序带来了额外的复杂性，可能导致此类技术的不稳定性，结果是研究结果不一致。在此，我们概述应用研究的一些关键方法学考量，参考一个涉及言语处理的假设临床实验以及模拟电生理（脑电图）数据的实例。特别是，我们关注实验设计、数据预处理、刺激特征提取、模型设计、模型训练与评估以及模型权重的解释。在整篇论文中，我们展示了使用mTRF工具箱在MATLAB中每个步骤的实现，并讨论如何解决应用研究中可能出现的问题。通过这样做，我们希望能在这些更具技术性的要点上提供更好的直观理解，并为使用丰富生态刺激来研究感觉和认知处理的应用和临床研究人员提供一种资源。
