TY - JOUR
T1 - Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli
T2 - Methodological Considerations for Applied Research
AU - Crosse, Michael J.
AU - Zuk, Nathaniel J.
AU - Di Liberto, Giovanni M.
AU - Nidiffer, Aaron R.
AU - Molholm, Sophie
AU - Lalor, Edmund C.
N1 - Publisher Copyright:
Copyright © 2021 Crosse, Zuk, Di Liberto, Nidiffer, Molholm and Lalor.
PY - 2021/11/22
Y1 - 2021/11/22
N2 - 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.
AB - 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.
KW - EEG
KW - MEG
KW - TRF
KW - clinical and translational neurophysiology
KW - electrophysiology
KW - neural decoding
KW - neural encoding
KW - temporal response function
UR - http://www.scopus.com/inward/record.url?scp=85120730396&partnerID=8YFLogxK
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U2 - 10.3389/fnins.2021.705621
DO - 10.3389/fnins.2021.705621
M3 - Review article
AN - SCOPUS:85120730396
SN - 1662-4548
VL - 15
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 705621
ER -