RT Journal Article SR Electronic T1 Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms JF bioRxiv FD Cold Spring Harbor Laboratory SP 147215 DO 10.1101/147215 A1 James J Bonaiuto A1 Holly E Rossiter A1 Sofie S Meyer A1 Natalie Adams A1 Simon Little A1 Martina F Callaghan A1 Fred Dick A1 Sven Bestmann A1 Gareth R Barnes YR 2017 UL http://biorxiv.org/content/early/2017/06/07/147215.abstract AB 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 methodsClassifications Source localization: inverse problem; Source localization: Other