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GLEAN (Group Level Exploratory Analysis of Networks) is a MATLAB pipeline for identifying patterns of covariation from M/EEG band-limited power using a Hidden Markov Model (HMM) or Independent Component Analysis (ICA), written for SPM12. This analysis consists of 3 main stages:

1. Computing band-limited amplitude envelopes for single or multiple frequency bands.
2. Reducing the dimensionality of the data via projection to a low-dimensional subspace (via Principal Component Analysis (PCA) or using a parcellation).
3. Decomposition using the HMM or ICA model, from group-concatenated envelopes.

After the decomposition has been run, post-hoc analyses and results may be computed at the session and group-level.

Requirements

• MATLAB: http://www.mathworks.com/products/matlab/
• SPM12: http://www.fil.ion.ucl.ac.uk/spm/software/spm12/
• FSL: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/
• HMM-MAR: https://github.com/OHBA-analysis/HMM-MAR/
• ROI-nets: https://github.com/OHBA-analysis/MEG-ROI-nets

Installation

1. Set up all dependencies
2. Add GLEAN repository folder to the MATLAB path
3. Run startup_glean to initialize GLEAN

Basic usage

To view the contents of this toolbox type:

help glean

For details on how to set up a new GLEAN analysis type:

help glean.setup

For details on particular GLEAN settings type:

help glean.check

To run a GLEAN analysis type:

glean.run(GLEAN)

Example dataset and analysis

An example dataset and analysis scripts for running GLEAN is provided here

If using this toolbox please consider citing the following

1. "Fast transient networks in spontaneous human brain activity", Baker el al., eLife, 2014
2. "Spectrally resolved fast transient brain states in electrophysiological data", Vidaurre et al., NeuroImage, 2016
3. "A symmetric multivariate leakage correction for MEG connectomes", Colclough et al., NeuroImage, 2015