Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

Abstract

Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject’s hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.

Cytoelectric Coupling: Electric fields sculpt neural activity and “tune” the brain’s infrastructure.

“…The idea has a long history going back to the work of Semon 33 , Hebb 34 and Hopfield 35 . Studies by us [36][37][38][39][40][41][42] and others [43][44][45] exploiting multielectrode arrays support the conclusion that oscillations can form neural ensembles via synchronous activity at the LFP level. They seem to mediate the processing of incoming stimuli 46 , attention 47,48 , encoding of rules, memory encoding and recall 38 or the binding of sensory inputs to representations 49 .…”

Inferring laminar origins of MEG signals with optically pumped magnetometers (OPMs): a simulation study

“…Alternative approaches to laminar inferences have been discussed previously. Pinotsis et al (2017) 56 and Pinotsis & Miller (2020) 57 employed dynamical causal modelling (DCM), with modelling parameters set based on estimates from intracranial data, and statistical decision theory to infer the laminar sources of non-invasive electrophysiological signals. However, the authors note that applying laminar DCM to non-invasive data is challenging due to the high collinearity of these parameters, and their results could not be replicated across data sets 57 .…”

“…Pinotsis et al (2017) 56 and Pinotsis & Miller (2020) 57 employed dynamical causal modelling (DCM), with modelling parameters set based on estimates from intracranial data, and statistical decision theory to infer the laminar sources of non-invasive electrophysiological signals. However, the authors note that applying laminar DCM to non-invasive data is challenging due to the high collinearity of these parameters, and their results could not be replicated across data sets 57 . In a proof-of-principle study, Ihle et al (2020) 29 combined DCM and high-precision forward models to recover the laminar origin of a cortical current source.…”

Beyond dimension reduction: Stable electric fields emerge from and allow representational drift

“…In particular, a large body of work by us and others using Dynamic Causal Models (DCM) has shown that it is possible to infer E/I ratios assuming that LFPs arise as a result of certain synaptic currents, usually AMPA and GABAA currents, see e.g. [86][87][88][89][90][91] . In future work, we will use separate recordings (depolarization or spike rates) from excitatory and inhibitory populations, to reconstruct excitatory and inhibitory activity separately.…”