Estimates of cortical column orientation improve MEG source inversion

James J. Bonaiuto, Fardin Afdideh, Maxime Ferez, Konrad Wagstyl, Jérémie Mattout, Mathilde Bonnefond, Gareth R. Barnes, Sven Bestmann

NeuroImage · 2020 · DOI 10.1016/j.neuroimage.2020.116862

Lamina-specific cortical dynamics in human visual and sensorimotor cortices

James J. Bonaiuto, Sofie S. Meyer, Simon Little, Holly E. Rossiter, Martina F. Callaghan, Frederic Dick, Gareth R. Barnes, Sven Bestmann

eLife · 2018 · DOI 10.7554/eLife.33977

Laminar dynamics of high amplitude beta bursts in human motor cortex

James J. Bonaiuto, Simon Little, Samuel A. Neymotin, Stephanie R. Jones, Gareth R. Barnes, Sven Bestmann

NeuroImage · 2021 · DOI 10.1016/j.neuroimage.2021.118479

Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

James J. Bonaiuto, Holly E. Rossiter, Sofie S. Meyer, Natalie Adams, Simon Little, Martina F. Callaghan, Fred Dick, Sven Bestmann, Gareth R. Barnes

NeuroImage · 2018 · DOI 10.1016/j.neuroimage.2017.11.068

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings. Section Analysis methods. Classifications Source localization: inverse problem; Source localization: other.

Inferring laminar origins of MEG signals with optically pumped magnetometers (OPMs): A simulation study

Saskia Helbling

Imaging Neuroscience · 2025 · DOI 10.1162/imag_a_00410

Flexible head-casts for high spatial precision MEG

Sofie S. Meyer, James J. Bonaiuto, Mark Lim, Holly E. Rossiter, Sheena Waters, David Bradbury, Sven Bestmann, Matthew Brookes, Martina F. Callaghan, Nikolaus Weiskopf, Gareth R. Barnes

Journal of Neuroscience Methods · 2017 · DOI 10.1016/j.jneumeth.2016.11.009

Multilayer MEG source modelling enables depth-resolved laminar inference in humans

Maciej J. Szul, Matteo G. Maspoli, Danila Shelepenkov, Ishita Agarwal, Quentin Moreau, Solène Gailhard, Maxime Ferez, Carolina Fernandez Pujol, Yunkai Zhu, Bassem Hiba, Sébastien Daligault, Franck Lamberton, Denis Schwartz, Jérémie Mattout, Mathilde Bonnefond, Andrew R. Dykstra, Sven Bestmann, Gareth R. Barnes, James J. Bonaiuto

Nature Communications · 2026 · DOI 10.1038/s41467-026-77782-9

Discrimination of cortical laminae using MEG

Luzia Troebinger, José David López, Antoine Lutti, Sven Bestmann, Gareth R. Barnes

NeuroImage · 2014 · DOI 10.1016/j.neuroimage.2014.07.015

High precision anatomy for MEG

Luzia Troebinger, José David López, Antoine Lutti, David Bradbury, Sven Bestmann, Gareth R. Barnes

NeuroImage · 2014 · DOI 10.1016/j.neuroimage.2013.07.065

LaMEG package

Package by DANClab for laminar inference

https://danclab.github.io/laMEG/