Sleep-Deep-Learner is taught sleep-wake scoring by the end-user to complete each record in their style.

Sleep-wake scoring is a time-consuming, tedious but essential component of clinical and preclinical sleep research. Sleep scoring is even more laborious and challenging in rodents due to the smaller EEG amplitude differences between states and the rapid state transitions which necessitate scoring in shorter epochs. Although many automated rodent sleep scoring methods exist, they do not perform as well when scoring new datasets, especially those which involve changes in the EEG/EMG profile. Thus, manual scoring by expert scorers remains the gold standard. Here we take a different approach to this problem by using a neural network to accelerate the scoring of expert scorers. Sleep-Deep-Learner creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential (LFP) records via transfer learning of GoogLeNet, by learning from a small subset of manual scores of each EEG/LFP record as provided by the end-user. Sleep-Deep-Learner then automates scoring of the remainder of the EEG/LFP record. A novel REM sleep scoring correction procedure further enhanced accuracy. Sleep-Deep-Learner reliably scores EEG and LFP data and retains sleep-wake architecture in wild-type mice, in sleep induced by the hypnotic zolpidem, in a mouse model of Alzheimer's disease and in a genetic knock-down study, when compared to manual scoring. Sleep-Deep-Learner reduced manual scoring time to 1/12. Since Sleep-Deep-Learner uses transfer learning on each independent recording, it is not biased by previously scored existing datasets. Thus, we find Sleep-Deep-Learner performs well when used on signals altered by a drug, disease model, or genetic modification.

Sleep-Deep-Net learns sleep wake scoring from the end-user and completes each record in their style.

Sleep-wake scoring is a time-consuming, tedious but essential component of clinical and pre-clinical sleep research. Sleep scoring is even more laborious and challenging in rodents due to the smaller EEG amplitude differences between states and the rapid state transitions which necessitate scoring in shorter epochs. Although many automated rodent sleep scoring methods exist, they do not perform as well when scoring new data sets, especially those which involve changes in the EEG/EMG profile. Thus, manual scoring by expert scorers remains the gold-standard. Here we take a different approach to this problem by using a neural network to accelerate the scoring of expert scorers. Sleep-Deep-Net (SDN) creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential records via transfer learning of GoogleNet, by learning from a small subset of manual scores of each EEG/LFP record as provided by the end-user. SDN then automates scoring of the remainder of the EEG/LFP record. A novel REM scoring correction procedure further enhanced accuracy. SDN reliably scores EEG and LFP data and retains sleep-wake architecture in wild-type mice, in sleep induced by the hypnotic zolpidem, in a mouse model of Alzheimer's disease and in a genetic knock-down study, when compared to manual scoring. SDN reduced manual scoring time to 1/12. Since SDN uses transfer learning on each independent recording, it is not biased by previously scored existing data sets. Thus, we find SDN performs well when used on signals altered by a drug, disease model or genetic modification.

Translational approaches to influence sleep and arousal.

Sleep disorders are widespread in society and are prevalent in military personnel and in Veterans. Disturbances of sleep and arousal mechanisms are common in neuropsychiatric disorders such as schizophrenia, post-traumatic stress disorder, anxiety and affective disorders, traumatic brain injury, dementia, and substance use disorders. Sleep disturbances exacerbate suicidal ideation, a major concern for Veterans and in the general population. These disturbances impair quality of life, affect interpersonal relationships, reduce work productivity, exacerbate clinical features of other disorders, and impair recovery. Thus, approaches to improve sleep and modulate arousal are needed. Basic science research on the brain circuitry controlling sleep and arousal led to the recent approval of new drugs targeting the orexin/hypocretin and histamine systems, complementing existing drugs which affect GABAA receptors and monoaminergic systems. Non-invasive brain stimulation techniques to modulate sleep and arousal are safe and show potential but require further development to be widely applicable. Invasive viral vector and deep brain stimulation approaches are also in their infancy but may be used to modulate sleep and arousal in severe neurological and psychiatric conditions. Behavioral, pharmacological, non-invasive brain stimulation and cell-specific invasive approaches covered here suggest the potential to selectively influence arousal, sleep initiation, sleep maintenance or sleep-stage specific phenomena such as sleep spindles or slow wave activity. These manipulations can positively impact the treatment of a wide range of neurological and psychiatric disorders by promoting the restorative effects of sleep on memory consolidation, clearance of toxic metabolites, metabolism, and immune function and by decreasing hyperarousal.