Statistical decision theory and multiscale analyses of human brain data

Background In the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often contain information at a single spatiotemporal scale only. New method We discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain responses at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data. Results We provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6. Comparison with existing methods This is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed. Conclusions We discussed a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it also allows us to validate macroscale models by exploiting animal data