Typical brain networks consist of many peripheral regions and a few highly central ones, i.e. hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain’s most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches.

The “non-specific” ventromedial thalamic nucleus (VM) has long been considered a candidate for mediating cortical arousal due to its diffuse, superficial projections, but direct evidence was lacking. Here, we show in mice that the activity of VM calbindin1-positive matrix cells is high in wake and REM sleep and low in NREM sleep, and increases before cortical activity at the sleep-to-wake transition. Optogenetic stimulation of VM cells rapidly awoke all mice from NREM sleep and consistently caused EEG activation during slow wave anesthesia, while arousal did not occur from REM sleep. Conversely, chemogenetic inhibition of VM decreased wake duration. Optogenetic activation of the “specific” ventral posteromedial nucleus (VPM) did not cause arousal from either NREM or REM sleep. Thus, matrix cells in VM produce arousal and broad cortical activation during NREM sleep and slow wave anesthesia in a way that accounts for the effects classically attributed to “non-specific” thalamic nuclei.

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of ‘TV noise’ frames, triggers always the same experience—of seeing ‘TV noise’—even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical responses underlying the subject’s experiences, as measured by Lempel-Ziv complexity (incompressibility) of functional MRI images, should reflect the overall meaningfulness of a set of stimuli for the subject, rather than differences among the stimuli. We tested this hypothesis by quantifying the differentiation of brain activity patterns in response to a movie sequence, to the same movie scrambled in time, and to ‘TV noise’, where the pixels from each movie frame were scrambled in space. While overall cortical activation was strong and widespread in all conditions, the differentiation (Lempel-Ziv complexity) of brain activation patterns was correlated with the meaningfulness of the stimulus set, being highest in the movie condition, intermediate in the scrambled movie condition, and minimal for ‘TV noise’. Stimulus set meaningfulness was also associated with higher information integration among cortical regions. These results suggest that the differentiation of neural responses can be used to assess the meaningfulness of a given set of stimuli for a given subject, without the need to identify the features and categories that are relevant to the subject, nor the precise location of selective neural responses.

Significance: When one drives on an easy route and listens to a radio, it is at times as if one’s brain splits into two separate entities: one that drives and one that listens. When, instead, one drives while listening to a global positioning system, there is only one functional stream. Here, using measures of information integration, we show that a brain may functionally split into two separate “driving” and “listening” systems when the listening task is unrelated to concurrent driving, but not when the two systems are related. This finding raises the possibility that under certain conditions of daily life, a single brain may support two independent functional streams, a “functional split brain” similar to what is observed in patients with an anatomical split.