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Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research

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 domain...

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Autores principales: Crosse, Michael J., Zuk, Nathaniel J., Di Liberto, Giovanni M., Nidiffer, Aaron R., Molholm, Sophie, Lalor, Edmund C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648261/
https://www.ncbi.nlm.nih.gov/pubmed/34880719
http://dx.doi.org/10.3389/fnins.2021.705621
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author Crosse, Michael J.
Zuk, Nathaniel J.
Di Liberto, Giovanni M.
Nidiffer, Aaron R.
Molholm, Sophie
Lalor, Edmund C.
author_facet Crosse, Michael J.
Zuk, Nathaniel J.
Di Liberto, Giovanni M.
Nidiffer, Aaron R.
Molholm, Sophie
Lalor, Edmund C.
author_sort Crosse, Michael J.
collection PubMed
description 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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spelling pubmed-86482612021-12-07 Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research Crosse, Michael J. Zuk, Nathaniel J. Di Liberto, Giovanni M. Nidiffer, Aaron R. Molholm, Sophie Lalor, Edmund C. Front Neurosci Neuroscience 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. Frontiers Media S.A. 2021-11-22 /pmc/articles/PMC8648261/ /pubmed/34880719 http://dx.doi.org/10.3389/fnins.2021.705621 Text en Copyright © 2021 Crosse, Zuk, Di Liberto, Nidiffer, Molholm and Lalor. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Crosse, Michael J.
Zuk, Nathaniel J.
Di Liberto, Giovanni M.
Nidiffer, Aaron R.
Molholm, Sophie
Lalor, Edmund C.
Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research
title Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research
title_full Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research
title_fullStr Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research
title_full_unstemmed Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research
title_short Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research
title_sort linear modeling of neurophysiological responses to speech and other continuous stimuli: methodological considerations for applied research
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648261/
https://www.ncbi.nlm.nih.gov/pubmed/34880719
http://dx.doi.org/10.3389/fnins.2021.705621
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