Glossary
The literature concerning the application of multivariate pattern analysis
procedures to neuroscientific datasets contains a lot of specific terms to
refer to procedures or types of data, that are of particular importants.
Unfortunately, sometimes various terms refer to the same contruct and even
worse these terms do not necessarily match the terminology used in the machine
learning literature. The foloowing glossary is an attempt to map the various
terms found in the literature to the terminology used in this manual.
- Block-averaging
- Averaging all samples recorded during a block of continous 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 comparsion 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-quaility samples than
from a larger set of lower-quality samples.
- Chunk
- 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 measured 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 whos correlate
is measures by the MR scanner.
- Dataset
- In PyMVPA a dataset is the combination of samples, their ...
- Decoding
- 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.
- Epoch
- Sometimes used to refer to a group of successively aqcuired samples, and,
thus, related to a chunk.
- Example
- Another term for sample.
- Feature
- This is a name for a variable in the dataset.
- Label
- A label associates each sample 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.
- MVPA
- 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 explicitely 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.
- Sample
- A sample a vector with observations for all feature variables.
- Sensitivity
- 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 Discriminanckonq e 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).
- Time-compression
- This usually refers to the block-averaging of samples from a
block-design fMRI dataset.
- Weight Vector
- See sensitivity.