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.

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