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.
Highlights
Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level
Spiking and compartmental models describe single neurons and firing rates while neural mass models describe large brain networks and population activities. They are limited because neurological diseases and disorders involve interactions of many factors that are simultaneously expressed at multiple scales
We used statistical decision theory (SDT) to establish that the neural mass model we used to explain human MEG data could make the same laminar predictions as a compartmental model previously validated with animal data[11,12] (Supplementary Methods)
Summary
Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Spiking and compartmental models describe single neurons and firing rates while neural mass models describe large brain networks and population activities They are limited because neurological diseases and disorders involve interactions of many factors that are simultaneously expressed at multiple scales. They depend on both genetic variations and environmental factors spanning microscopic and macroscopic scales ranging from, for example, altered mitochondria and single neuron function to neuroinflammation of axons connecting different brain areas[4]. We used statistical decision theory (SDT)[9] to prove that the these two models can be combined to infer neural dynamics in different cortical layers (laminar dynamics) using non-invasive MEG data.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
