Recent developments in automatic scoring of rodent sleep

Validation of PiezoSleep Scoring Against EEG/EMG Sleep Scoring in Rats

INTRODUCTION

Current methods of sleep research in rodents involve invasive surgical procedures of EEG and EMG electrodes implantation. Recently, a new method of measuring sleep, PiezoSleep, has been validated against implanted electrodes in mice and rats. PiezoSleep uses a piezoelectric film transducer to detect the rodent's movements and respiration and employs an algorithm to automatically score sleep. Here, we validate PiezoSleep scoring versus EEG/EMG implanted electrodes sleep scoring in rats.

METHODS

Adult male Brown Norway and Wistar Kyoto rats were implanted with bilateral stainless-steel screws into the skull for EEG recording and bilateral wire electrodes into the nuchal muscles for EMG assessment. In Brown Norway rats, the EEG/EMG electrode leads were soldered to a miniature connector plug and fixed to the skull. In Wistar Kyoto rats, the EEG/EMG leads were tunneled subcutaneously to a telemetry transmitter implanted in the flank. Rats were allowed to recover from surgery for one week. Brown Norway rats were placed in PiezoSleep cages, and had their headsets connected to cable for recording EEG/EMG signals, which were then manually scored by a human scorer in 10-sec epochs. Wistar Kyoto rats were placed in PiezoSleep cages, and EEG/EMG signals were recorded using a telemetry system (DSI). Sleep was scored automatically in 4-sec epochs using NeuroScore software. PiezoSleep software recorded and scored sleep in the rats.

RESULTS

Rats implanted with corded EEG/EMG headsets had 85.6% concurrence of sleep-wake scoring with PiezoSleep. Rats implanted with EEG/EMG telemetry had 80.8% concurrence sleep-wake scoring with PiezoSleep. Sensitivity and specificity rates were similar between the EEG/EMG recording systems. Total sleep time and hourly sleep times did not differ in all three systems. However, automatic sleep detection by NeuroScore classified more sleep during the light period compared to the PiezoSleep.

CONCLUSION

We showed that PiezoSleep system can be a reliable alternative to both automatic and visual EEG/EMG- based sleep-wake scoring in rat.

Automated scoring of pre-REM sleep in mice with deep learning

Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.

Spike-Based Functional Connectivity in Cerebral Cortex and Hippocampus: Loss of Global Connectivity Is Coupled to Preservation of Local Connectivity During Non-REM Sleep

Behavioral states are commonly considered global phenomena with homogeneous neural determinants. However, recent studies indicate that behavioral states modulate spiking activity with neuron-level specificity as a function of brain area, neuronal subtype, and preceding history. Although functional connectivity also strongly depends on behavioral state at a mesoscopic level and is globally weaker in non-REM (NREM) sleep and anesthesia than wakefulness, it is unknown how neuronal communication is modulated at the cellular level. We hypothesize that, as for neuronal activity, the influence of behavioral states on neuronal coupling strongly depends on type, location, and preceding history of involved neurons. Here, we applied nonlinear, information-theoretical measures of functional connectivity to ensemble recordings with single-cell resolution to quantify neuronal communication in the neocortex and hippocampus of rats during wakefulness and sleep. Although functional connectivity (measured in terms of coordination between firing rate fluctuations) was globally stronger in wakefulness than in NREM sleep (with distinct traits for cortical and hippocampal areas), the drop observed during NREM sleep was mainly determined by a loss of inter-areal connectivity between excitatory neurons. Conversely, local (intra-area) connectivity and long-range (inter-areal) coupling between interneurons were preserved during NREM sleep. Furthermore, neuronal networks that were either modulated or not by a behavioral task remained segregated during quiet wakefulness and NREM sleep. These results show that the drop in functional connectivity during wake-sleep transitions globally holds true at the cellular level, but confine this change mainly to long-range coupling between excitatory neurons.

SIGNIFICANCE STATEMENT

Studies performed at a mesoscopic level of analysis have shown that communication between cortical areas is disrupted in non-REM sleep and anesthesia. However, the neuronal determinants of this phenomenon are not known. Here, we applied nonlinear, information-theoretical measures of functional coupling to multi-area tetrode recordings from freely moving rats to investigate whether and how brain state modulates coordination between individual neurons. We found that the previously observed drop in functional connectivity during non-REM (NREM) sleep can be explained by a decrease in coupling between excitatory neurons located in distinct brain areas. Conversely, intra-area communication and coupling between interneurons are preserved. Our results provide significant new insights into the neuron-level mechanisms responsible for the loss of consciousness occurring in NREM sleep.

Accurate discrimination of the wake-sleep states of mice using non-invasive whole-body plethysmography

A major limitation in the study of sleep breathing disorders in mouse models of pathology is the need to combine whole-body plethysmography (WBP) to measure respiration with electroencephalography/electromyography (EEG/EMG) to discriminate wake-sleep states. However, murine wake-sleep states may be discriminated from breathing and body movements registered by the WBP signal alone. Our goal was to compare the EEG/EMG-based and the WBP-based scoring of wake-sleep states of mice, and provide formal guidelines for the latter. EEG, EMG, blood pressure and WBP signals were simultaneously recorded from 20 mice. Wake-sleep states were scored based either on EEG/EMG or on WBP signals and sleep-dependent respiratory and cardiovascular estimates were calculated. We found that the overall agreement between the 2 methods was 90%, with a high Cohen's Kappa index (0.82). The inter-rater agreement between 2 experts and between 1 expert and 1 naïve sleep investigators gave similar results. Sleep-dependent respiratory and cardiovascular estimates did not depend on the scoring method. We show that non-invasive discrimination of the wake-sleep states of mice based on visual inspection of the WBP signal is accurate, reliable and reproducible. This work may set the stage for non-invasive high-throughput experiments evaluating sleep and breathing patterns on mouse models of pathophysiology.