Bonaiuto, J. J. | Search bioRxiv

bioRxiv2017

Laminar-specific cortical dynamics in human visual and sensorimotor cortices

Lower frequency, feedback, activity in the alpha and beta range is thought to predominantly originate from infragranular cortical layers, whereas feedforward signals in the gamma range stem largely from supragranular layers. Distinct anatomical and spectral channels may therefore play specialized roles in communication within hierarchical cortical networks; however, empirical evidence for this organization in humans is limited. We leverage high precision MEG to test this proposal, directly and non-invasively, in human participants during visually guided actions. Visual alpha activity mapped onto deep cortical laminae, whereas visual gamma activity predominantly arose from superficial laminae. This laminar-specificity was echoed in sensorimotor beta and gamma activity. Visual gamma activity scaled with task demands in a way compatible with feedforward signaling. For sensorimotor activity, we observed a more complex relationship with feedback and feedforward processes. Distinct frequency channels thus operate in a laminar-specific manner, but with dissociable functional roles across sensory and motor cortices.

neuroscience↗

bioRxiv2017

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 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.

neuroscience↗