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This content refers to the previous stable release of PyMVPA. Please visit for the most recent version of PyMVPA and its documentation.


The literature concerning the application of multivariate pattern analysis procedures to neuro-scientific datasets contains a lot of specific terms to refer to procedures or types of data, that are of particular importance. Unfortunately, sometimes various terms refer to the same construct and even worse these terms do not necessarily match the terminology used in the machine learning literature. The following glossary is an attempt to map the various terms found in the literature to the terminology used in this manual.

Averaging all samples recorded during a block of continuous stimulation in a block-design fMRI experiment. The rationale behind this technique is, that a averaging might lead to an improved signal-to-noise ratio. However, averaging further decreases the number of samples in a dataset, which is already very low in typical fMRI datasets, especially in comparison to the number of features/voxels. Block-averaging might nevertheless improve the classifier performance, if it indeed improves signal-to-noise and the respective classifier benefits more from few high-quality samples than from a larger set of lower-quality samples.
A chunk is a group of samples. In PyMVPA chunks define independent groups of samples (note: the groups are independent from each other, not the samples in each particular group). This information is important in the context of a cross-validation procedure, as it is required to measure the classifier performance on independent test datasets to be able to compute unbiased generalization estimates. This is of particular importance in the case of fMRI data, where two successively recorded volumes cannot be considered as independent measurements. This is due to the significant temporal forward contamination of the hemodynamic response whose correlate is measured by the MR scanner.
In PyMVPA a dataset is the combination of samples, their ...
This term is usually used to refer to the application of machine learning or pattern recognition techniques to brainimaging datasets, and therefore is another term for MVPA. Sometimes also ‘brain-reading’ is used as another alternative.
Sometimes used to refer to a group of successively acquired samples, and, thus, related to a chunk.
Another term for sample.
This is a name for a variable in the dataset.
This abbrevation stands for functional magnetic resonance imaging.
A label associates each sample in the dataset with a certain category, experimental condition or, in case of a regression problem, with some metric variable. The label therefore defines the model that a classifier has to learn. The labels also provide the “true” model value when computing classifier errors.
This term originally stems from the authors of the Matlab MVPA toolbox, and in that context stands for multi-voxel pattern analysis (see Norman et al., 2006). PyMVPA obviously adopted this acronym. However, as PyMVPA is explicitly designed to operate on non-fMRI data as well, the ‘voxel’ term is not appropriate and therefore MVPA in this context stands for the more general term multivariate pattern analysis.
Processing object
Most objects dealing with data are implemented as processing objects. Such objects are instantiated once, with all appropriate parameters configured as desired. When created, they can be used multiple time by simply calling them with new data.
A sample a vector with observations for all feature variables.
The sensitivity is a score assigned to a particular feature with respect to its impact on a classifier’s decision. The sensitivity is often available from a classifier’s weight vector. There are some more scores which are similar to a sensitivity in terms of indicating the “importance” of a particular feature – examples are a univariate ANOVA score or a Noise Perturbation measure.
Sensitivity Map
A vector of several sensitivity scores – one for each feature in a dataset.
Spatial Discrimination Map (SDM)
This is another term for a sensitivity map, used in e.g. Wang et al. (2007).
Statistical Discrimination Map (SDM)
This is another term for a sensitivity map, used in e.g. Sato et al. (2008), where instead of raw sensitivity significance testing result is assigned.
This usually refers to the block-averaging of samples from a block-design fMRI dataset.
Weight Vector
See sensitivity.