Beyond deep versus superficial: true laminar inference with MEG

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 demonstrate that under certain conditions, magnetoencephalography (MEG) can achieve laminar inference by localizing sources at the level of individual cortical laminae. Using a multilayer source reconstruction approach, we systematically assess the limits of MEG depth resolution, and show that laminar precision is achievable under realistic signal-to-noise ratios and co-registration accuracy. We show that accurate laminar inference depends critically on aligning forward model dipole orientations with true cortical column orientations, and that regional variations in cortical anatomy influence reconstruction fidelity. We then apply this approach to empirical data from three independent datasets, revealing the expected laminar patterns of activity during event-related fields in primary visual, somatosensory, and motor cortices. These findings position MEG as a powerful tool for investigating lamina-specific neural dynamics in cognition and behavior, and offer new opportunities to bridge invasive electrophysiology and human neuroimaging. TeaserMultilayer MEG enables depth-resolved localization across cortical laminae under achievable conditions.