Multivariate pattern analysis (MVPA) is a relatively recent innovation in functional magnetic resonance imaging (fMRI) methods.
Fluorescence decay traces of the individual pixels were partitioned into classes (i.e., clusters) using a multivariate pattern recognition method.
Using multivariate pattern analysis we trained a classifier to decode numerosity in various regions of interest (ROI).
The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification.
To examine how neural representations evolved throughout movement planning, execution, and suppression, we performed time-resolved multivariate pattern analysis (MVPA).
This study was designed to replicate and extend psychophysiological research, which has used multivariate pattern classification analysis (PCA) in support of ANS specificity.
There has been growing interest recently in the use of multivariate pattern analysis (MVPA) to decode information from high-dimensional neuroimaging data.
We advocate a shift in emphasis within cognitive neuroscience from multivariate pattern analysis (MVPA) to the design and testing of explicit models of neural representation.
Multivariate pattern analysis can be used to decode the orientation of a viewed grating from fMRI signals in early visual areas.
Multivariate pattern analysis is often assumed to rely on signals that directly reflect differences in the distribution of particular neural populations.
One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data.
