Features

Here is a list of feature of rfxplot:

Plot types: bar graphs of effect sizes, fitted responses, event-related BOLD responses (aka time courses or peri-stimulus time histograms).
2-stage voxel selection process
1. Define a search volume based on a significant cluster in a random effects analysis (second level analysis).
2. Select either all voxels in that search volume or find individual peak voxels in it (and define single-subject spheres around them).
Select data from different image types (e.g. beta, con-images) from a subject's 1st level analysis.
Select images based on image description strings, not filenames. This way rfxplot can cope with different first-level design matrices. It requires, however, that the same event must have identical names.
Split regressors and/or parametric modulators into different bins based on:
1. Value or percentile ranges of a parametric modulators (e.g. trials with short, medium, and long reaction time).
2. Trial number (split the effect sizes into first and second half).
Split the sample into different groups or exclude some (outlying) subjects.
Plot effect as bars, dots, or connected dots with error bars.
Data can be plotted as contrast or parameter estimates or as percent signal change.
Configure error bars as standard deviation (sd), standard error of the mean (sem), or 90% confidence interval (ci).
Many configuration options for filtering and adjusting the extracted time series (for event-related BOLD time courses or splitting regressors into different bins).
For fitted responses and event-related BOLD time courses, plot errors as lines or as semi-transparent areas.
Plot the data in the SPM Graphics window or in another MATLAB figure (useful for creating compound figures for publications).
Fit a simple regression model to the effect sizes extracted from the subjects (e.g. for testing and visualizing a linear increase in activation across different task conditions); currently available regression models: linear, quadratic, exponential, logarithmic.