The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data
By Martin N. Hebart, Kai Görgen, John-Dylan Haynes. Front. Neuroinform., 2015
419 citations recorded by the source corpus.
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.
Subfields: Cognitive Neuroscience, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/f41564121d7272f09315b1b904a3bb05b1b166ed
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In Cognitive Neuroscience
- Andrew C. Connolly
- Nikolaas N. Oosterhof
- James V. Haxby
- Susan G. Wardle
- Tijl Grootswagers
- Thomas A. Carlson
- Alexander Todorov
- Nikolaus Kriegeskorte
In Molecular Biology
Citations in the corpus
- Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, background): “However, this is only viable if the goal of the analysis is statistical inference (Hebart et al., 2015), as it could result in more optimistic decoding accuracies that would not generalize to new data.”
- CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave (2016, methodology): “CoSMoMVPA is not the first MVPA toolbox for cognitive neuroscience; indeed, since the Princeton toolbox (Detre et al., 2006), several other toolboxes have been released, both for the Python lan-guage—PyMVPA (Hanke, Halchenko, Sederberg, Hanson, et al., 2009; Hanke, Halchenko, Sederberg, Olivetti, et”