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The brain activity of human subjects in a “resting” state is often interpreted in terms of background conscious processes. However, human neuroimaging lacks the cellular resolution required to relate such activity to the content of subjective experience. To overcome this limitation, we started a project on imaging neuronal resting state networks of a conscious mouse and relating their activity to animal’s past experiences. At 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 plotted networks with correlation coefficients varying from 0.6 to 0.9 and compared these experimentally identified networks with model networks (random, scale free and small world) using clustering, global efficiency 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 the same 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 a scale free network with local clusters. In addition, we identified several major groups of functionally connected areas in the resting state network of awake mouse brain: a cluster of medial prefrontal cortex and other medial associative neocortical areas, a cluster of visual areas, a tightly connected cluster of sensorimotor areas and basal nuclei, and a fully isolated cluster of auditory areas. Importantly, activity of structures known for their relationship to fear and threat learning (such as hippocampus, amygdala, and prelimbic cortex) was not correlated and did not comprise any 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 resting state network activity in relation to prior threat experience.