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These images are now produced in tedana, which can be found here: https://github.com/ME-ICA/tedana

This "toolbox" is no longer maintained, updated and may not even work anymore. The figures are produced by default in the much improved multi-echo denoising package tedana.

meica_tool

I've created a handy matlab script that works with meica.py (https://bitbucket.org/prantikk/me-ica) v3 - from the experimental branch.

It creates a series of figures that are useful for visualizing the output in a quick manner, including component timeseries from meica.py, color coded on whether they were:

  • BOLD-like - green
  • Non-BOLD - red
  • r2 weighted - pink
  • Ignored - black.

2017/09/22 update - now more 4ier - enjoy a fft plot.

Each plot includes brain slices of the component beta values (from TED/betas_OC.nii)

  • motion parameters and framewise displacement
  • kappa vs rho scatter plot, where size is proportaional to variance, colors as above
  • kappa vs rho line plot
  • Bar plot of variance explained
  • tSNR figures, with histograms

It then creates a bar plot showing the relative variance of each of those categories.

Its (still) ugly code, but effective...for now.

Current dependencies include:

But these few functions will eventually be packaged together and included.

Usage

  • Add to matlab path
  • run meica_component_displayer(tr), where tr is the repitition of your EPI timeseries in seconds.
  • select the meica.py output folder, ex. meica_nback_e1.label
  • wait a bit

Example Figures Kappa vs Rho plot Kappa vs Rho Scatter Timeseries and brains Noise even!

Thanks to bramila framewise displacement and detrend code (from https://git.becs.aalto.fi/bml/bramila/tree/master) for dvars calculation

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