ИСТИНА

A resting state network is a correlated activity of many neural structures in absence of external stimulation or functional tasks, and it is a fundamental endogenous feature of the human and animal brain. However, the nature and function of such spontaneous activity remain poorly understood. One of the hypothesis suggest that they reflect hidden activation, replay and consolidation of individually acquired neural networks of prior experience, which prepares and predetermines an organism’s perception, awareness and reaction to the external events. However, classical non-invasive fMRI and EEG methods used for resting state network detection cannot tag specific cellular elements of the neural network at the time when individual experience is acquired so that their activity can be subsequently investigated in the resting state. To overcome this limitation, we started a project on cellular imaging neuronal resting state networks of the mouse brain and relating their activity to animal’s past experiences. As the first stage of the project we characterized resting-state activity of 104 mouse brain structures by means of large-scale c-Fos cellular mapping. We made an estimate of the expected level of activation for each of the analyzed brain areas based on amount of c-Fos positive cells in all the experimental animals. Based on the level of resting state activity the examined brain areas were divided into four groups: 59 brain areas were non-active, 14 – were low, 29 – medium, and 8 – were highly active. There was no direct relationship between anatomical attributes of examined areas and the level of their activity. We also analyzed individual variability of levels of activity for each group of brain areas and showed that resting state networks identified by c-Fos expression were stable and reproducible in all the animals. Next, we selected 38 areas (associative, sensory and motor neocortical areas, hippocampus, parahippocampal cortex, amygdala, basal nuclei, thalamus, olfactory areas and subcortical nuclei) to characterize the major components and analyze functional connectivity of the resting state network. Based on the activity of selected brain areas and using Pearson correlation we obtained seven networks with correlation coefficients varying from 0.6 to 0.9 (inclusive, step=0.05) and compared each experimentally identified networks with model networks (random, scale free and small world) of the same size using clustering and global efficiency coefficients and degree distribution. The experimental resting state network had substantially higher clustering than model networks. Global efficiency of the resting state network was relatively low and was at the same level as global efficiency of a random network. At correlation coefficients, higher than 0.7 the resting state network broke down into separate subnetworks. The comparison of experimental network to the model networks and the analysis of its degree distribution suggests that the resting state network of the mouse brain is highly clustered and has features of scale free networl with low global efficiency. In addition, we identified several major groups of functionally connected areas in the resting state network of an awake mouse brain: a cluster of medial prefrontal cortex and other medial associative neocortical areas, a cluster of visual areas, a tightly linked cluster of sensorimotor areas and basal nuclei, and an entirely isolated cluster of auditory brain areas. Importantly, activity of structures known for their relationship to fear and threat learning (such as amygdala, hippocampus, and prelimbic cortex) was not correlated and did not comprise any special functional group. This high variability in the activity of fear-related brain structures will be used at the next stage of the project to examine changes in the cellular activity of the resting state network in relation to a neuronal network of threat experience previously labeled in the same mouse brain. Supported by RSF 16-15-00300.