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Front Neuroinform. 2015 Jan 6;8:88. doi: 10.3389/fninf.2014.00088. eCollection 2014.

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

Author information

1
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf Hamburg, Germany ; Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin Berlin, Germany ; Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Germany ; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin Berlin, Germany.
2
Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin Berlin, Germany ; Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Germany ; Fachgebiet Neurotechnologie, Technische Universität Berlin Berlin, Germany.
3
Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin Berlin, Germany ; Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Germany ; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin Berlin, Germany.

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.

KEYWORDS:

decoding; fMRI; multivariate pattern analysis; pattern classification; representational similarity analysis; searchlight

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