Lauflumide (NLS-4) Is a New Potent Wake-Promoting Compound.

Psychostimulants are used for the treatment of excessive daytime sleepiness in a wide range of sleep disorders as well as in attention deficit hyperactivity disorder or cognitive impairment in neuropsychiatric disorders. Here, we tested in mice the wake-promoting properties of NLS-4 and its effects on the following sleep as compared with those of modafinil and vehicle. C57BL/6J mice were intraperitoneally injected with vehicle, NLS-4 (64 mg/kg), or modafinil (150 mg/kg) at light onset. EEG and EMG were recorded continuously for 24 h after injections and vigilance states as well as EEG power densities were analyzed. NLS-4 at 64 mg/kg induced significantly longer wakefulness duration than modafinil at 150 mg/kg. Although no significant sleep rebound was observed after sleep onset for both treatments as compared with their vehicles, modafinil-treated mice showed significantly more NREM sleep when compared to NLS-4. Spectral analysis of the NREM EEG after NLS-4 treatment indicated an increased power density in delta activity (0.75-3.5 Hz) and a decreased power in theta frequency range (6.25-7.25 Hz), while there was no differences after modafinil treatment. Also, time course analysis of the delta activity showed a significant increase only during the first 2 time intervals of sleep after NLS-4 treatment, while delta power was increased during the first 9 time intervals after modafinil. Our results indicate that NLS-4 is a highly potent wake-promoting drug with no sign of hypersomnia rebound. As opposed to modafinil, recovery sleep after NLS-4 treatment is characterized by less NREM amount and delta activity, suggesting a lower need for recovery despite longer drug-induced wakefulness

EEG synchronization measures are early outcome predictors in comatose patients after cardiac arrest.

Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures. 94 comatose patients with EEG within 24h after CA were included. Clinical outcome was assessed at 3months using the Cerebral Performance Categories (CPC). EEG synchronization between the left and right parasagittal, and between the frontal and parietal brain regions was assessed with 4 different quantitative measures (delta power asymmetry, cross-correlation, mutual information, and transfer entropy). 2/3 of patients were used to assess the predictive power of all possible combinations of these eight features (4 measures×2 directions) using cross-validation. The predictive power of the best combination was tested on the remaining 1/3 of patients. The best combination for prognostication consisted of 4 of the 8 features, and contained linear and non-linear measures. Predictive power for poor outcome (CPC 3-5), measured with the area under the ROC curve, was 0.84 during cross-validation, and 0.81 on the test set. At specificity of 1.0 the sensitivity was 0.54, and the accuracy 0.81. Combinations of EEG synchronization measures can contribute to early prognostication after CA. In particular, combining linear and non-linear measures is important for good predictive power. Quantitative methods might increase the prognostic yield of currently used multi-modal approaches

Inactivation of hypocretin receptor-2 signaling in dopaminergic neurons induces hyperarousal and enhanced cognition but impaired inhibitory control.

Hypocretin/Orexin (HCRT/OX) and dopamine (DA) are both key effectors of salience processing, reward and stress-related behaviors and motivational states, yet their respective roles and interactions are poorly delineated. We inactivated HCRT-to-DA connectivity by genetic disruption of Hypocretin receptor-1 (Hcrtr1), Hypocretin receptor-2 (Hcrtr2), or both receptors (Hcrtr1&2) in DA neurons and analyzed the consequences on vigilance states, brain oscillations and cognitive performance in freely behaving mice. Unexpectedly, loss of Hcrtr2, but not Hcrtr1 or Hcrtr1&2, induced a dramatic increase in theta (7-11 Hz) electroencephalographic (EEG) activity in both wakefulness and rapid-eye-movement sleep (REMS). DA <sup>Hcrtr2</sup> -deficient mice spent more time in an active (or theta activity-enriched) substate of wakefulness, and exhibited prolonged REMS. Additionally, both wake and REMS displayed enhanced theta-gamma phase-amplitude coupling. The baseline waking EEG of DA <sup>Hcrtr2</sup> -deficient mice exhibited diminished infra-theta, but increased theta power, two hallmarks of EEG hyperarousal, that were however uncoupled from locomotor activity. Upon exposure to novel, either rewarding or stress-inducing environments, DA <sup>Hcrtr2</sup> -deficient mice featured more pronounced waking theta and fast-gamma (52-80 Hz) EEG activity surges compared to littermate controls, further suggesting increased alertness. Cognitive performance was evaluated in an operant conditioning paradigm, which revealed that DA <sup>Hcrtr2</sup> -ablated mice manifest faster task acquisition and higher choice accuracy under increasingly demanding task contingencies. However, the mice concurrently displayed maladaptive patterns of reward-seeking, with behavioral indices of enhanced impulsivity and compulsivity. None of the EEG changes observed in DA <sup>Hcrtr2</sup> -deficient mice were seen in DA <sup>Hcrtr1</sup> -ablated mice, which tended to show opposite EEG phenotypes. Our findings establish a clear genetically-defined link between monosynaptic HCRT-to-DA neurotransmission and theta oscillations, with a differential and novel role of HCRTR2 in theta-gamma cross-frequency coupling, attentional processes, and executive functions, relevant to disorders including narcolepsy, attention-deficit/hyperactivity disorder, and Parkinson's disease

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species

Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) signatures. Traditionally, these states are classified by trained human experts by visual inspection of raw EEG recordings, which is a laborious task prone to inter-individual variability. Recently, machine learning approaches have been developed to automate this process, but their generalization capabilities are often insufficient, especially across animals from different experimental studies. To address this challenge, we crafted a convolutional neural network-based architecture to produce domain invariant predictions, and furthermore integrated a hidden Markov model to constrain state dynamics based upon known sleep physiology. Our method, which we named SPINDLE (Sleep Phase Identification with Neural networks for Domain-invariant LEearning) was validated using data of four animal cohorts from three independent sleep labs, and achieved average agreement rates of 99%, 98%, 93%, and 97% with scorings from five human experts from different labs, essentially duplicating human capability. It generalized across different genetic mutants, surgery procedures, recording setups and even different species, far exceeding state-of-the-art solutions that we tested in parallel on this task. Moreover, we show that these scored data can be processed for downstream analyzes identical to those from human-scored data, in particular by demonstrating the ability to detect mutation-induced sleep alteration. We provide to the scientific community free usage of SPINDLE and benchmarking datasets as an online server at https://sleeplearning.ethz.ch. Our aim is to catalyze high-throughput and well-standardized experimental studies in order to improve our understanding of sleep

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species.

Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) signatures. Traditionally, these states are classified by trained human experts by visual inspection of raw EEG recordings, which is a laborious task prone to inter-individual variability. Recently, machine learning approaches have been developed to automate this process, but their generalization capabilities are often insufficient, especially across animals from different experimental studies. To address this challenge, we crafted a convolutional neural network-based architecture to produce domain invariant predictions, and furthermore integrated a hidden Markov model to constrain state dynamics based upon known sleep physiology. Our method, which we named SPINDLE (Sleep Phase Identification with Neural networks for Domain-invariant LEearning) was validated using data of four animal cohorts from three independent sleep labs, and achieved average agreement rates of 99%, 98%, 93%, and 97% with scorings from five human experts from different labs, essentially duplicating human capability. It generalized across different genetic mutants, surgery procedures, recording setups and even different species, far exceeding state-of-the-art solutions that we tested in parallel on this task. Moreover, we show that these scored data can be processed for downstream analyzes identical to those from human-scored data, in particular by demonstrating the ability to detect mutation-induced sleep alteration. We provide to the scientific community free usage of SPINDLE and benchmarking datasets as an online server at https://sleeplearning.ethz.ch. Our aim is to catalyze high-throughput and well-standardized experimental studies in order to improve our understanding of sleep