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.
A new paper led by Luke Tait is published on Human Brain mapping. This study systematically evaluated the performance of six commonly-used source reconstruction algorithms. Using human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Project’s multi-modal parcellation of the human cortex based on metrics such as MEG signal-to-noise-ratio and resting-state functional connectivity gradients. This procedure produced a reduced cortical atlas with 230 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners.
Our results show that there is no “one size fits all” algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses. Our comprehensive comparisons and recommendations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG.
A new paper led by Marinho Lopes is published on Clinical Neurophysiology. The study applied the brain network ictogenicity framework to quantify the inherent propensity to generate seizures from resting-state MEG recordings. We found that resting-state MEG functional networks from people with epilepsy are characterized by a higher propensity to generate seizures than those from healthy volunteers, with a classification accuracy of 73%. This sensitive computational modelling approach could in future aid diagnosis.