A joint research group including Team Director Masanori Murayama and Special Postdoctoral Researcher Ikumi Oomoto from the RIKEN Center for Brain Science; Associate Professor Masafumi Oizumi and Doctoral Student Daiki Kiyooka from the Graduate School of Arts and Sciences at the University of Tokyo; Associate Professor Jun Kitazono from the School of Data Science at Yokohama City University; and Associate Professor Kenta Kobayashi from the Center for Genetic Analysis of Behavior, National Institute for Physiological Sciences used a wide-field two-photon microscope independently developed by Murayama and his colleagues to clarify that the single-cell-level functional networks in the mouse cerebral cortex differ between unconscious states (sleep and anesthesia) and the conscious state (wakefulness). Their findings were published in Cell Reports.
A: Using a microscope with a 3 mm × 3 mm field of view, the group simultaneously recorded the activity of more than 10,000 neurons from over 10 brain regions in the cerebral cortex. Each brain region is shown in a different color.
B: Examples of brain states in mice cycling between wakefulness and sleep, along with the corresponding neuronal activity. In this experiment, REM sleep (a shallow sleep state characterized by small brain waves similar to those during wakefulness) was rarely observed and was therefore not included in the analysis.
C: Examples of neuronal activity during wakefulness and under anesthesia.
Provided by RIKEN
Even in unconscious states, neurons fire spontaneously and respond to external stimuli, just as they do during wakefulness. Yet we are unable to consciously perceive the information carried by this neural activity. The mechanism behind this inability to perceive anything during unconscious states had not been well understood.
Studies using fMRI had reported that the inter-regional brain network structure in the unconscious state is separated into subnetworks, groups of neurons that exhibit similar activity patterns, which differs from wakefulness. However, because fMRI lacks cellular-level resolution, it has not been possible to examine how individual neurons form functional networks in the brain or how they contribute to changes in functional network structure.
The wide-field two-photon microscope independently developed by Murayama and his colleagues can simultaneously record the activity of over 10,000 neurons across more than 10 brain regions. The microscope enables analysis of large-scale functional network structures while maintaining single-cell-level microscopic resolution.
The joint research group used calcium imaging with the wide-field two-photon microscope to observe neuronal activity in the mouse cerebral cortex on a large scale during both the unconscious state (sleep and anesthesia) and the conscious state (wakefulness), and used the resulting data to analyze the functional network structure at the cellular level. Murayama noted: "We repeatedly trained the mice to fall asleep naturally under the two-photon microscope. However, even minor stimuli would prevent them from sleeping, which made this the most challenging part of the experiments."
The results showed that while individual neurons did fire during unconscious states, the overall level of neuronal activity was lower than during wakefulness. The coordinated activity among neurons was extracted from these observations through mathematical analysis, allowing estimation of the functional network structure in both conscious and unconscious states.
To understand the large-scale functional network structure at the cellular level, the researchers focused on "modularity," which is a measure of network integration and segregation that indicates how a network is divided into subnetworks, and investigated how modularity changes between conscious and unconscious states. Higher modularity means the network is more divided into separate subnetworks; lower modularity means the network operates as a more unified whole.
The analysis revealed differences in functional network modularity between conscious and unconscious states. Compared with the conscious state, the unconscious state showed stronger intra-subnetwork connections and weaker inter-subnetwork connections, resulting in higher modularity, meaning the overall functional network was in a more segregated state. This weakening of connections between subnetworks is thought to impair the efficient transfer of information. The result is consistent with findings from fMRI studies.
When subnetwork stability was examined at the level of individual neurons, it was found that the subnetwork to which each neuron belonged changed over time, but not in a completely random manner—there was a certain degree of temporal stability. This suggests that subnetworks represent structured populations that carry some form of brain function.
To investigate the properties of neurons that contribute to the segregation or integration of functional networks, the researchers calculated the contribution to modularity and its degree (a measure of the extent of connections with other neurons) for every neuron in the functional network. A higher degree indicates stronger connections. The analysis found that high-degree hub cells contributed to network formation itself, but not to the modularity differences associated with changes in conscious state. By contrast, neurons with an intermediate degree were responsible for generating differences in modularity.
Further examination of the spatial distribution of subnetworks in the brain revealed that, in both conscious and unconscious states, the neurons making up each subnetwork were intermixed across multiple brain regions. This cross-regional connectivity between neurons is thought to be involved in the expression of brain function.
Finally, to explain the discrepancy between the functionally segregated picture from fMRI and the spatially intermixed picture from the current study, the researchers performed spatial coarse-graining by averaging the activities of multiple neighboring neurons of a certain neuron and then re-examined the distribution of subnetworks. The result showed that coarse-graining caused the distribution of neurons constituting each subnetwork to shift from intermixed to localized.
Oizumi commented: "The sheer number of cells to be analyzed made this extremely challenging, but we were able to bridge the micro and macro scales for the first time."
Because this method enables network recording at the single-cell level, it becomes possible to analyze network structure across scales from micro to macro. Changes in brain functional network structure have been reported in a wide range of conditions including dementia and mental disorders. Applying this method to animal models of disease could help explain at the cellular level how the macro-scale network changes observed clinically arise from changes in cell-to-cell connectivity. In the future, this is expected to contribute to the early detection of disease and to the development of cell-focused therapeutic strategies.
Journal Information
Publication: Cell Reports
Title: Single-cell resolution functional networks during unconsciousness are segregated into spatially intermixed modules
DOI: 10.1016/j.celrep.2025.116902
This article has been translated by JST with permission from The Science News Ltd. (https://sci-news.co.jp/). Unauthorized reproduction of the article and photographs is prohibited.