+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

Abstract

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for microstate analysis for other neuroimaging modalities such as sensor-space MEG and source-space data. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as microstate-segmented functional connectivity analysis, cluster-permutation topographic ANOVAs, and χ2 analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.

MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

“…Interestingly, the well-studied auditory evoked response has been localized to the auditory cortex and first peaks around 100ms following the stimulus ( Picton, 2010 ), known as the N100 response, in line with our microstate results. An alternative approach to study states associated with a stimulus in greater temporal detail involves averaging over trials and performing microstate analysis on the grand average evoked response ( Murray et al., 2008 ), an approach which is possible in source-space using our generalized microstate algorithm ( Tait and Zhang, 2021 ). The states derived in such an approach would, by definition, be associated with the response elicited from the stimulus and allow for plotting a group-level time course of states along the evoked response, but would likely represent very different states to those from resting-state activity.…”

“…The states derived in such an approach would, by definition, be associated with the response elicited from the stimulus and allow for plotting a group-level time course of states along the evoked response, but would likely represent very different states to those from resting-state activity. Hence, such an approach is useful for segmenting the evoked response into stable segments and potentially give insight into phenomena such as mismatch negativity ( Tait and Zhang, 2021 ), while the approach presented here is useful for understanding how resting microstates vary with cognitive state. Future work should build upon these approaches to examine the functional significance of these states.…”

“…Data preprocessing and source reconstruction used Fieldtrip version 2019-07-16 (Oostenveld et al., 2011). Code for performing microstate analysis in sensor- or source-space MEG/EEG, simulations as described in this manuscript, statistics and visualisation have been compiled in the +microstate toolbox ( Tait and Zhang, 2021 ) ( https://plus-microstate.github.io ), a freely-available open-source toolbox written in MATLAB R2017b. Codes for running the analyses presented in this script are available at https://github.com/plus-microstate/manuscript_codes/tree/main/MEG-cortical-microstates .…”

The temporal dynamics of Large‐Scale brain network changes in disorders of consciousness: A Microstate‐Based study

“…From the T1‐weighted MRI images, we extracted the scalp, brain, and cortical surfaces using FreeSurfer. Vertices of the cortical surface were labeled based on the AAL78 atlas in the +microstate toolbox by taking the time course of the first principal component of all voxels within a region of interest 21 . The 78 region AAL atlas was the same as in Fieldtrip just excluding subcortical regions such as the thalamus and hippocampus.…”

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

“…Data preprocessing and source reconstruction used Fieldtrip version 2019-07-16 80 . Code for performing microstate analysis in sensor-or source-space MEG/EEG, simulations as described in this manuscript, statistics and visualisation have been compiled in the +microstate toolbox 92 (https://plus-microstate.github.io), a…”