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

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 ar...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Jg. 167; S. 372 - 383
Hauptverfasser: Bonaiuto, James J., Rossiter, Holly E., Meyer, Sofie S., Adams, Natalie, Little, Simon, Callaghan, Martina F., Dick, Fred, Bestmann, Sven, Barnes, Gareth R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 15.02.2018
Elsevier Limited
Academic Press
Schlagworte:
Adult
Algorithms
Cognition
Computer Simulation
Cortex
Cortical laminae
Cross validation
Free energy
Generative model
Humans
Localization
Magnetic Resonance Imaging - methods
Magnetoencephalography
Magnetoencephalography - methods
Magnetoencephalography - standards
MEG
Models, Theoretical
Neocortex - physiology
NMR
Nuclear magnetic resonance
Sensors
Statistical analysis
Washington DC
United States > US
Cross validation
Cortical laminae
MEG
Free energy
Generative model
ISSN:1053-8119, 1095-9572, 1095-9572
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Zusammenfassung: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. Analysis methods. Source localization: inverse problem; Source localization: other. •Laminar inferences can be made with MEG using both local and global fit metrics.•Source inversion algorithms with sparsity constraints performed best.•Classification is affected by patch size estimates, anatomy, and lead field strength.
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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2017.11.068