A new paper led by Luke Tait is published on Neuroimage. The paper gave a detailed overview of the +microstate toolbox that can calculate MEG/EEG sensor-level/source-level microstates, simulation, functional connectivity and many relevent statistics.

+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 statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and 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.

The paper is now available online.

+microstate toolbox

A new paper led by Dominik is published.

We combined cognitive modelling and neural-mass modelling to characterise the neurocognitive process underlying perceptual decision-making with single or double information sources. Ninety-four human participants performed binary decisions to discriminate the coherent motion direction averaged across two independent apertures. Regardless of the angular distance of the apertures, separating motion information into two apertures resulted in a reduction in accuracy. Our cognitive and neural-mass modelling results are consistent with the hypotheses that the addition of the second information source led to a lower signal-to-noise ratio of evidence accumulation with two congruent information sources, and a change in the decision strategy of speed–accuracy trade-off with two incongruent sources. Thus, our findings support a robust behavioural change in relation to multiple information sources, which have congruency-dependent impacts on selective decision-making subcomponents.

The paper is now available online.

A new paper led by Luke Tait is published on Neuroimage.

We generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.

The paper is now available online.

Everyone has been busy in the last few weeks with various projects. We however managed to have a face-to-face get-together. The first is to welcome our new Research Fellow Sabina Baltruschat. Sabina will work on MRI/MEG decision-making projects.

We also say goodbye to Marinho. Marinho joined us in 2019 and has made substantial achievements in his work on the mathematical modelling of epileptic seizures with MEG and stereo-EEG data. Marinho will still collaborate closely on ongoing projects, and we wish Marinho all the best for his future adventures.

A new paper led by Ruoguang Si is published on Neuroimage. This meta-analysis study identified 35 fMRI/PET experiments using various free-choice paradigms, in which participants choose among options with identical values or outcomes.

An Activation Likelihood Estimate (ALE) meta-analysis showed that, compared with external instructions, intentional decisions consistently activate the medial and dorsolateral prefrontal cortex, the left insula and the inferior parietal lobule. We then categorized the studies into four different types according to their experimental designs: reactive motor intention, perceptual intention, inhibitory intention, and cognitive intention. We conducted conjunction and contrast meta-analyses to identify consistent and selective spatial convergence of brain activation within each specific category of intentional decision. Finally, we used meta-analytic decoding to probe cognitive processes underlying free choices. Our findings suggest that the neurocognitive process underlying intentional decision incorporates anatomically separated components subserving distinct cognitive and computational roles.

The paper is now available online.