A recent study published in Nature Neuroscience demonstrated that selective retrosplenial cortex activation encodes and regulates two-stage rapid eye movement sleep (‘REM sleep’). This work was performed by researchers from Dr. Liu Danqian’s Lab at the Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences (CAS). This work has successfully revealed the cortical activity patterns using large-scale wide-field imaging and discovered a selectively activated cortical region during REM sleep — the retrosplenial cortex (‘RSC’), which is also the main source of cortical waves during REM sleep. At the same time the research also discovered and characterized two distinct substages of REM sleep using an unsupervised algorithm. Moreover, combining two-photon imaging and optogenetic inactivation experiments, it confirmed that RSC neuronal activity encodes and regulates the two substage transition. The research lays a foundation for understanding the cognitive process and regulatory mechanism of spontaneous cortical activation during REM sleep.
REM sleep, first discovered and defined in 1951 by American scientist Eugene Aserinsky, who recorded the Electroencephalogram (‘EEG’) and electro-oculogram (‘EOG’) of his eight-year-old son during sleep, discovering a special sleep state with bursts of eye movements and high brain activation. In following studies he confirmed that this state with enriched dreaming is different from normal sleep states, and named it ‘Rapid Eye Movement sleep’, or REM sleep, and also ‘Dream sleep’. Until now, the mechanism underlying REM sleep regulation, including how the brain can gate external sensory stimulation during REM sleep with such a high brain activation level, the biological meaning of the state’s existence and its role in brain development, were elusive. From the view of evolution, REM sleep appears nearly at the same time with the evolution of the cortex. To this end, studying the brain activity pattern and regulatory mechanism of REM sleep could advance our understanding of the evolution of the central nervous system.
In order to investigate whether the cortex participates in REM sleep regulation, researchers implanted a chronic window on Thy1-GCaMP6s mice covering the entire dorsal cortex. They used a two-channel (470 nm and 405 nm) wide-field imaging system to capture the calcium and hemodynamic signals, with EEG and concurrent electromyogram (‘EMG’) and video recording (Fig. A). After spatial independent component analysis (‘sICA’), eleven function modules, named after their corresponding anatomical structures, were separated. Among them, RSC exhibited the highest activation (Fig. A). Moreover, using Granger causality and convolutional non-negative matrix factorization analysis, the researchers found that cortical waves mostly originated from RSC and specifically happened in REM sleep but not in other brain states (Fig. B). Furthermore, they discovered that RSC neuronal activation is layer-specific, with selective activation of layer 2/3 instead of layer 5 (Fig. C).
Interestingly, by inspection of video recordings, the researchers found that during REM sleep, mice showed enriched facial movements besides bursts of rapid eye movement (Fig. D). By analyzing the facial movements using an unsupervised method to extract the features and cluster, they discovered two substages of REM sleep: with (“aREM”) or without (“qREM”) enriched facial movement (Fig. D). These two substages have distinct autonomic activity and differential EEG spectra in both head-fixed and freely-moving mice, and REM sleep always transits from qREM to aREM. Strikingly, researchers found that the RSC L2/3 neuron exhibited two distinct temporal patterns, which highly matched with the two REM substages (Fig. E). Using closed-loop optogenetics, researchers finally found that RSC inactivation during REM sleep largely abolishes qREM to aREM transition, suggesting that RSC is essential for substage transition. In sum, this study found that the RSC L2/3 neuronal activity encodes the two REM substages, and is crucial for REM sleep substage transition (Fig. F).
In this work, Liu’s Lab first discovered and defined the two REM substages, depicted spatio-temporal cortical dynamics during REM sleep in mice, and uncovered a critical role of the RSC in REM sleep substage transition. This work for the first time shows a role for the cortex in REM sleep regulation and paves a way for understanding the complex role of cortical activation during REM sleep. The discovery of two substages is sure to provide a new direction for studying the complex function of REM sleep.
This work, entitled “Cortical regulation of two-stage rapid eye movement sleep”, was published online in Nature Neuroscience on November 18, 2022. It was completed by Dong Yufan, under the supervision of Dr. Liu Danqian, with help from Li Jiaqi, Zhou Min, and Du Yihui.
Fig. legend: (A) Upper, scheme of two-channel wide-field imaging system. Bottom, activity difference between REM sleep and wake states, with statistical significance (two-sided paired t test) plotted against difference in average Ca2+ ΔF/F. Inset shows one independent module representing “RSC”. (B) Left, three main cortical waves during REM sleep. Right, summary of causality between RSC and other cortical modules. (C) Calcium activity of two types of neurons during REM sleep. (D) REM substages characterization. (E) Normalized calcium activity of two types of neuron in RSC aligned with qREM to aREM transitions. (F) Upper, scheme of close-loop optogenetics inactivation during REM sleep. Bottom, RSCREM inactivation-induced changes in transition probability. The increase transition from Wake to NREM was not shown in the summary plot (middle). */# Increase/Decrease, P<0.05; **/##, Increase/decrease, P < 0.0005, unpaired t test. [IMAGE: CEBSIT]
For more information, please contact:
Dr. Liu Danqian
E-mail: dqliu@ion.ac.cn
Center for Excellence in Brain Science and Intelligence Technology,
Chinese Academy of Sciences
Source: Center for Excellence in Brain Science and Intelligence Technology,
Chinese Academy of Sciences