A toolbox for representational similarity analysis.

Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis...

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Autores principales:	Hamed Nili, Cai Wingfield, Alexander Walther, Li Su, William Marslen-Wilson, Nikolaus Kriegeskorte
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4ebbf56d50b346ecbbed2b048c094b52
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id	oai:doaj.org-article:4ebbf56d50b346ecbbed2b048c094b52
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spelling	oai:doaj.org-article:4ebbf56d50b346ecbbed2b048c094b522021-11-18T05:52:58ZA toolbox for representational similarity analysis.1553-734X1553-735810.1371/journal.pcbi.1003553https://doaj.org/article/4ebbf56d50b346ecbbed2b048c094b522014-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24743308/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).Hamed NiliCai WingfieldAlexander WaltherLi SuWilliam Marslen-WilsonNikolaus KriegeskortePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 4, p e1003553 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Hamed Nili Cai Wingfield Alexander Walther Li Su William Marslen-Wilson Nikolaus Kriegeskorte A toolbox for representational similarity analysis.
description	Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).
format	article
author	Hamed Nili Cai Wingfield Alexander Walther Li Su William Marslen-Wilson Nikolaus Kriegeskorte
author_facet	Hamed Nili Cai Wingfield Alexander Walther Li Su William Marslen-Wilson Nikolaus Kriegeskorte
author_sort	Hamed Nili
title	A toolbox for representational similarity analysis.
title_short	A toolbox for representational similarity analysis.
title_full	A toolbox for representational similarity analysis.
title_fullStr	A toolbox for representational similarity analysis.
title_full_unstemmed	A toolbox for representational similarity analysis.
title_sort	toolbox for representational similarity analysis.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/4ebbf56d50b346ecbbed2b048c094b52
work_keys_str_mv	AT hamednili atoolboxforrepresentationalsimilarityanalysis AT caiwingfield atoolboxforrepresentationalsimilarityanalysis AT alexanderwalther atoolboxforrepresentationalsimilarityanalysis AT lisu atoolboxforrepresentationalsimilarityanalysis AT williammarslenwilson atoolboxforrepresentationalsimilarityanalysis AT nikolauskriegeskorte atoolboxforrepresentationalsimilarityanalysis AT hamednili toolboxforrepresentationalsimilarityanalysis AT caiwingfield toolboxforrepresentationalsimilarityanalysis AT alexanderwalther toolboxforrepresentationalsimilarityanalysis AT lisu toolboxforrepresentationalsimilarityanalysis AT williammarslenwilson toolboxforrepresentationalsimilarityanalysis AT nikolauskriegeskorte toolboxforrepresentationalsimilarityanalysis
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A toolbox for representational similarity analysis.

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