Spectral pattern similarity analysis: Tutorial and application in developmental cognitive neuroscience

The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational cont...

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Published inDevelopmental cognitive neuroscience Vol. 54; p. 101071
Main Authors Sommer, Verena R., Mount, Luzie, Weigelt, Sarah, Werkle-Bergner, Markus, Sander, Myriam C.
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier Ltd 01.04.2022
Elsevier
Subjects
Adult
Brain - physiology
Brain Mapping - methods
Child
Cognition - physiology
Cognitive Neuroscience
Electroencephalography
Electroencephalography (EEG)
Humans
Magnetic Resonance Imaging
Neural distinctiveness
Neural stability
Original Research
Representational pattern similarity analysis
Time-frequency representations (TFR)
Neural distinctiveness
Neural stability
Electroencephalography (EEG)
Time-frequency representations (TFR)
Representational pattern similarity analysis
Online AccessGet full text
ISSN1878-9293
1878-9307
1878-9307
DOI10.1016/j.dcn.2022.101071

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Abstract The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.
AbstractList The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.
The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.
ArticleNumber 101071
Author Weigelt, Sarah
Sander, Myriam C.
Mount, Luzie
Werkle-Bergner, Markus
Sommer, Verena R.
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Keywords Neural distinctiveness
Neural stability
Electroencephalography (EEG)
Time-frequency representations (TFR)
Representational pattern similarity analysis
Language English
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Snippet The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g.,...
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StartPage 101071
SubjectTerms Adult
Brain - physiology
Brain Mapping - methods
Child
Cognition - physiology
Cognitive Neuroscience
Electroencephalography
Electroencephalography (EEG)
Humans
Magnetic Resonance Imaging
Neural distinctiveness
Neural stability
Original Research
Representational pattern similarity analysis
Time-frequency representations (TFR)
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Title Spectral pattern similarity analysis: Tutorial and application in developmental cognitive neuroscience
URI https://dx.doi.org/10.1016/j.dcn.2022.101071
https://www.ncbi.nlm.nih.gov/pubmed/35063811
https://www.proquest.com/docview/2622282727
https://pubmed.ncbi.nlm.nih.gov/PMC8784303
https://doaj.org/article/43f4fc37ec2640efb4fe78ff1da43dc6
Volume 54
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