Multilayer MEG source modelling enables depth-resolved laminar inference in humans
Abstract
Neural dynamics at the laminar level are critical for cortical computation. However, in humans, non-invasive methods to probe such dynamics have been limited to coarse distinctions between deep and superficial layers. Here, we present a multilayer magnetoencephalography source reconstruction framework and evaluate the conditions under which depth-resolved laminar inference may be feasible. Using simulations, we systematically assess the limits of magnetoencephalography depth resolution, showing that laminar discrimination depends on sufficiently high signal-to-noise ratio, precise co-registration, and accurate specification of cortical column orientation. We demonstrate that regional variations in cortical anatomy influence reconstruction fidelity, with lead-field separability emerging as a key determinant. We then apply this framework to empirical data from three independent datasets and find laminar activation patterns that align with canonical feedforward and feedback motifs in visual and sensorimotor circuits, supporting the plausibility of laminar inference under favorable conditions and offering opportunities to bridge invasive electrophysiology and human neuroimaging.
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BibTeX
@article{szul2026multilayer,
title = {{Multilayer MEG source modelling enables depth-resolved laminar inference in humans}},
author = {Maciej J. Szul and Matteo G. Maspoli and Danila Shelepenkov and Ishita Agarwal and Quentin Moreau and Solène Gailhard and Maxime Ferez and Carolina Fernandez Pujol and Yunkai Zhu and Bassem Hiba and Sebastien Daligault and Franck Lamberton and Denis Schwartz and Jérémie Mattout and Mathilde Bonnefond and Andrew R. Dykstra and Sven Bestmann and Gareth R. Barnes and James J. Bonaiuto},
journal = {Nature Communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77782-9},
url = {https://www.nature.com/articles/s41467-026-77782-9},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multilayer MEG source modelling enables depth-resolved laminar inference in humans
AU - Maciej J. Szul
AU - Matteo G. Maspoli
AU - Danila Shelepenkov
AU - Ishita Agarwal
AU - Quentin Moreau
AU - Solène Gailhard
AU - Maxime Ferez
AU - Carolina Fernandez Pujol
AU - Yunkai Zhu
AU - Bassem Hiba
AU - Sebastien Daligault
AU - Franck Lamberton
AU - Denis Schwartz
AU - Jérémie Mattout
AU - Mathilde Bonnefond
AU - Andrew R. Dykstra
AU - Sven Bestmann
AU - Gareth R. Barnes
AU - James J. Bonaiuto
PY - 2026
DA - 2026/09/30
JO - Nature Communications
DO - 10.1038/s41467-026-77782-9
UR - https://www.nature.com/articles/s41467-026-77782-9
AB - Neural dynamics at the laminar level are critical for cortical computation. However, in humans, non-invasive methods to probe such dynamics have been limited to coarse distinctions between deep and superficial layers. Here, we present a multilayer magnetoencephalography source reconstruction framework and evaluate the conditions under which depth-resolved laminar inference may be feasible. Using simulations, we systematically assess the limits of magnetoencephalography depth resolution, showing that laminar discrimination depends on sufficiently high signal-to-noise ratio, precise co-registration, and accurate specification of cortical column orientation. We demonstrate that regional variations in cortical anatomy influence reconstruction fidelity, with lead-field separability emerging as a key determinant. We then apply this framework to empirical data from three independent datasets and find laminar activation patterns that align with canonical feedforward and feedback motifs in visual and sensorimotor circuits, supporting the plausibility of laminar inference under favorable conditions and offering opportunities to bridge invasive electrophysiology and human neuroimaging.
SN - 2041-1723
ER -