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

Tait, Luke; Zhang, Jiaxiang

doi:10.1016/j.neuroimage.2022.119346

Cited by 20 publications

(21 citation statements)

References 67 publications

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“…eLORETA was chosen based on a systematic evaluation of source reconstruction of resting‐state EEG, where it demonstrated high performance in a range of metrics, especially with parcellated data, which is similar to our present study. 20 The entire T1 volume was resliced onto a 1 × 1 × 1 mm 3 grid aligned with the coordinate axes. From the T1‐weighted MRI images, we extracted the scalp, brain, and cortical surfaces using FreeSurfer.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…For all MEG analyses, we therefore used data from those 28 participants. The MEG dataset was used in previous studies for different analyses 17 , 58 , 59 .…”

Section: Methodsmentioning

confidence: 99%

The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure

Karahan

Tait

et al. 2022

Commun Biol

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Humans differ from each other in a wide range of biometrics, but to what extent brain connectivity varies between individuals remains largely unknown. By combining diffusion-weighted imaging (DWI) and magnetoencephalography (MEG), this study characterizes the inter-subject variability (ISV) of multimodal brain connectivity. Structural connectivity is characterized by higher ISV in association cortices including the core multiple-demand network and lower ISV in the sensorimotor cortex. MEG ISV exhibits frequency-dependent signatures, and the extent of MEG ISV is consistent with that of structural connectivity ISV in selective macroscopic cortical clusters. Across the cortex, the ISVs of structural connectivity and beta-band MEG functional connectivity are negatively associated with cortical myelin content indexed by the quantitative T1 relaxation rate measured by high-resolution 7 T MRI. Furthermore, MEG ISV from alpha to gamma bands relates to the hindrance and restriction of the white-matter tissue estimated by DWI microstructural models. Our findings depict the inter-relationship between the ISV of brain connectivity from multiple modalities, and highlight the role of tissue microstructure underpinning the ISV.

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“…Microstate analysis was conducted in the same procedures as some former microstate studies [ 8 , 21 , 22 ]: global field power (GFP) calculation, microstates clustering, back-fitting, labeling, and microstate parameters calculation. Microstate and MSFC analyses were conducted with a MATLAB toolbox: +microstate ([ 25 ]; ).…”

Section: Methodsmentioning

confidence: 99%

“…GFP of EEG signals was calculated according to Equation (1) [ 25 ]. GFP is used to quantify the amount of activity [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

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Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study

Wang

Chen

2023

Bioengineering

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Motor imagery-based brain–computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.

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

Cited by 20 publications

References 67 publications

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

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

The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure

Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study

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