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This "toolbox" is no longer maintained, updated and may not even work anymore.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:

• spm, for file loading, for now
• load_nii, from Nifti Tools

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

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