ArticleImaging neuroscience (Cambridge, Mass.)2025
Inferring laminar origins of MEG signals with optically pumped magnetometers (OPMs): A simulation study.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multilayer MEG source modelling enables depth-resolved laminar inference in humans.Nature communications · 2026Article
- Optically pumped magnetometers enhance neuroimaging performance-An EEG, OPM, and SQUID-MEG study.iScience · 2026Article
- Testing circuit-level theories of consciousness in humans.Trends in cognitive sciences · 2026Review
- Cortical-layer EEG-fMRI at 7T: experimental setup and analysis pipeline to elucidate generating mechanisms of alpha oscillations.bioRxiv : the preprint server for biology · 2025Article
- SPM-30 years and beyond.Cerebral cortex (New York, N.Y. : 1991) · 2025Review
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Abstract
We explore the potential of optically pumped magnetometers (OPMs) to non-invasively infer the laminar origins of neural activity. OPM sensors can be positioned closer to the scalp than conventional cryogenic magnetoencephalography (MEG) sensors, opening an avenue to higher spatial resolution when combined with high-precision source space modelling. By simulating the forward model projection of single dipole sources at deep and superficial cortical surfaces onto OPM sensor arrays with varying sensor densities and measurement axes, and employing sparse source reconstruction approaches, we find that laminar inference with OPM arrays is possible at relatively low sensor counts under moderate-to-high signal-to-noise ratios (SNR). We observe improvements in laminar inference with increasing spatial sampling densities and measurement axes and demonstrate the advantage of placing the sensors closer to the scalp for inferring the laminar origins of cortical sources. However, challenges remain, such as biases towards both the superficial and deep surfaces at very low SNRs and a notable bias towards the deep surface when combining empirical Bayesian beamformer (EBB) source reconstruction with a whole-brain analysis. Adequate SNR through appropriate trial numbers and shielding, as well as precise co-registration, is crucial for reliable laminar inference with OPMs.
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