Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

• DOI: 10.1016/j.neuroimage.2017.11.068
• Publication Date: 2017-12-01T20:30:19Z
• Authors: James J. Bonaiuto; Holly E. Rossiter; Sofie S. Meyer; Natalie Adams; Simon Little; Martina F. Callaghan; Fred Dick; Sven Bestmann; Gareth R. Barnes
• Keywords: Magnetoencephalography; Robustness

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution therefore not constrained by physiology but rather data quality and the models used to explain these data. Recent simulation work has shown that it possible distinguish between signals arising in deep superficial cortical laminae given accurate knowledge surfaces with respect MEG sensors. This previous focused around single inversion scheme (multiple sparse priors) global parametric fit metric (free energy). In this paper we use several different source algorithms both local global, as well non-parametric metrics order demonstrate robustness discrimination layers. We find only some sparsity constraint can successfully be make laminar discrimination. Importantly, t-statistics, cross-validation free energy all provide robust mutually corroborating fit. show accuracy affected patch size estimates, surface features, lead field strength, which suggests future improvements technique. study demonstrates possibility determining origin sensor activity, thus directly testing theories human cognition involve laminar- frequency-specific mechanisms. now achieved using recent developments high precision MEG, most notably subject-specific head-casts, allow for significant increases anatomically precise recordings.Analysis methods.Source localization: inverse problem; Source other.