Dear colleagues,

Apologies for the cross-posting. I'm extremely pleased to announce the first release of laMEG: a toolbox for performing non-invasive inference of laminar-specific activity with MEG. My collaborators and I have been working on methods for laminar MEG for several years now, and we've finally put them all together in an easy-to-install toolbox. It includes test data, and tutorial jupyter notebooks to reproduce the simulations and analyses in all of our key papers:

- Laminar inference using model comparison with either free energy or cross validation error, as well as power-based ROI analysis

Bonaiuto, James J., Holly E. Rossiter, Sofie S. Meyer, Natalie Adams, Simon Little, Martina F. Callaghan, Fred Dick, Sven Bestmann, and Gareth R. Barnes. "Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms." Neuroimage 167 (2018): 372-383.
Bonaiuto, James J., Sofie S. Meyer, Simon Little, Holly Rossiter, Martina F. Callaghan, Frederic Dick, Gareth R. Barnes, and Sven Bestmann. "Lamina-specific cortical dynamics in human visual and sensorimotor cortices." Elife 7 (2018): e33977.

- Free energy model comparison in a sliding time window with priors

Bonaiuto, James J., Simon Little, Samuel A. Neymotin, Stephanie R. Jones, Gareth R. Barnes, and Sven Bestmann. "Laminar dynamics of high amplitude beta bursts in human motor cortex." NeuroImage 242 (2021): 118479.

- Surface post-processing, following up from FreeSurfer recon-all, to downsample multiple surfaces and compute link vectors to constrain dipole orientations

Bonaiuto, James J., Fardin Afdideh, Maxime Ferez, Konrad Wagstyl, Jérémie Mattout, Mathilde Bonnefond, Gareth R. Barnes, and Sven Bestmann. "Estimates of cortical column orientation improve MEG source inversion." Neuroimage 216 (2020): 116862.

laMEG includes the ability to run any of these analyses on an arbitrary number of layer surfaces (not just pial versus white matter), interactive 3d surface visualization, an experimental "CSD" laminar analysis, and SPM compiled as a python library - no matlab license required.

Github: https://github.com/danclab/laMEG

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

PyPI: https://pypi.org/project/lameg/

Best wishes,

Jimmy Bonaiuto

---------------------

James Bonaiuto

Group leader | Institut des Sciences Cognitives | CNRS / Université Claude Bernard Lyon

DANC lab | @danc_labo