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      A Toolbox for Representational Similarity Analysis

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          Abstract

          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/).

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          Most cited references36

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Individual Comparisons by Ranking Methods

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              Distributed and overlapping representations of faces and objects in ventral temporal cortex.

              The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                April 2014
                17 April 2014
                : 10
                : 4
                Affiliations
                [1 ]MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom
                [2 ]Department of Computer Science, University of Bath, Bath, United Kingdom
                [3 ]Department of Experimental Psychology, University of Cambridge, Cambridge, United Kingdom
                UCSD, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Contributed reagents/materials/analysis tools: HN NK CW AW LS WMW. Wrote the paper: HN NK.

                Article
                PCOMPBIOL-D-13-00048
                10.1371/journal.pcbi.1003553
                3990488
                24743308
                fd385777-564e-4286-985c-1a528c5dea77

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 11
                Funding
                This work was funded by the Medical Research Council of the UK (programme MC-A060-5PR20) and by a European Research Council Starting Grant (ERC-2010-StG 261352) to NK. Additional funding was provided by European Research Council Advanced Grant (230570-NEUROLEX) to WMW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Neuroscience
                Cognitive Neuroscience
                Neuroimaging
                Sensory Perception
                Computer and Information Sciences
                Neural Networks

                Quantitative & Systems biology
                Quantitative & Systems biology

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