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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-04-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1003553 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850162105683017728 |
|---|---|
| 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 |
| collection | DOAJ |
| 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 |
| id | doaj-art-4ebbf56d50b346ecbbed2b048c094b52 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2014-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-4ebbf56d50b346ecbbed2b048c094b522025-08-20T02:22:38ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582014-04-01104e100355310.1371/journal.pcbi.1003553A toolbox for representational similarity analysis.Hamed NiliCai WingfieldAlexander WaltherLi SuWilliam Marslen-WilsonNikolaus KriegeskorteNeuronal 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/).https://doi.org/10.1371/journal.pcbi.1003553 |
| spellingShingle | Hamed Nili Cai Wingfield Alexander Walther Li Su William Marslen-Wilson Nikolaus Kriegeskorte A toolbox for representational similarity analysis. PLoS Computational Biology |
| title | 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_short | A toolbox for representational similarity analysis. |
| title_sort | toolbox for representational similarity analysis |
| url | https://doi.org/10.1371/journal.pcbi.1003553 |
| 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 |