85
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns.

          Related collections

          Most cited references 50

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Information-based functional brain mapping.

            The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Neural correlations, population coding and computation.

              How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                06 January 2015
                2014
                : 8
                Affiliations
                [1] 1Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf Hamburg, Germany
                [2] 2Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin Berlin, Germany
                [3] 3Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Germany
                [4] 4Berlin School of Mind and Brain, Humboldt-Universität zu Berlin Berlin, Germany
                [5] 5Fachgebiet Neurotechnologie, Technische Universität Berlin Berlin, Germany
                Author notes

                Edited by: Arjen Van Ooyen, VU University Amsterdam, Netherlands

                Reviewed by: Julien Dubois, California Institute of Technology, USA; Qiyong Gong, West China Hospital of Sichuan University, China

                *Correspondence: Martin N. Hebart, Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, W34, Martinistraße 52, 20251 Hamburg, Germany e-mail: m.hebart@ 123456uke.de ;
                Kai Görgen, Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin, Haus 6, Philippstr. 13, 10115 Berlin, Germany e-mail: kai.goergen@ 123456bccn-berlin.de

                This article was submitted to the journal Frontiers in Neuroinformatics.

                †These authors have contributed equally to this work.

                Article
                10.3389/fninf.2014.00088
                4285115
                d8ae01b7-ec0b-4a57-98ec-d37c821b3656
                Copyright © 2015 Hebart, Görgen and Haynes.

                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) or licensor 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.

                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 70, Pages: 18, Words: 14099
                Categories
                Neuroscience
                Methods Article

                Comments

                Comment on this article