Deep brain stimulation of hypothalamus for narcolepsy-cataplexy in mice.

BACKGROUND: Narcolepsy type 1 (NT1, narcolepsy with cataplexy) is a disabling neurological disorder caused by loss of excitatory orexin neurons from the hypothalamus and is characterized by decreased motivation, sleep-wake fragmentation, intrusion of rapid-eye-movement sleep (REMS) during wake, and abrupt loss of muscle tone, called cataplexy, in response to sudden emotions. OBJECTIVE: We investigated whether subcortical stimulation, analogous to clinical deep brain stimulation (DBS), would ameliorate NT1 using a validated transgenic mouse model with postnatal orexin neuron degeneration. METHODS: Using implanted electrodes in freely behaving mice, the immediate and prolonged effects of DBS were determined upon behavior using continuous video-electroencephalogram-electromyogram (video/EEG/EMG) and locomotor activity, and neural activation in brain sections, using immunohistochemical labeling of the immediate early gene product c-Fos. RESULTS: Brief 10-s stimulation to the region of the lateral hypothalamus and zona incerta (LH/ZI) dose-responsively reversed established sleep and cataplexy episodes without negative sequelae. Continuous 3-h stimulation increased ambulation, improved sleep-wake consolidation, and ameliorated cataplexy. Brain c-Fos from mice sacrificed after 90 min of DBS revealed dose-responsive neural activation within wake-active nuclei of the basal forebrain, hypothalamus, thalamus, and ventral midbrain. CONCLUSION: Acute and continuous LH/ZI DBS enhanced behavioral state control in a mouse model of NT1, supporting the feasibility of clinical DBS for NT1 and other sleep-wake disorders.

Supervised and unsupervised machine learning for automated scoring of sleep-wake and cataplexy in a mouse model of narcolepsy.

Despite commercial availability of software to facilitate sleep-wake scoring of electroencephalography (EEG) and electromyography (EMG) in animals, automated scoring of rodent models of abnormal sleep, such as narcolepsy with cataplexy, has remained elusive. We optimize two machine-learning approaches, supervised and unsupervised, for automated scoring of behavioral states in orexin/ataxin-3 transgenic mice, a validated model of narcolepsy type 1, and additionally test them on wild-type mice. The supervised learning approach uses previously labeled data to facilitate training of a classifier for sleep states, whereas the unsupervised approach aims to discover latent structure and similarities in unlabeled data from which sleep stages are inferred. For the supervised approach, we employ a deep convolutional neural network architecture that is trained on expert-labeled segments of wake, non-REM sleep, and REM sleep in EEG/EMG time series data. The resulting trained classifier is then used to infer on the labels of previously unseen data. For the unsupervised approach, we leverage data dimensionality reduction and clustering techniques. Both approaches successfully score EEG/EMG data, achieving mean accuracies of 95% and 91%, respectively, in narcoleptic mice, and accuracies of 93% and 89%, respectively, in wild-type mice. Notably, the supervised approach generalized well on previously unseen data from the same animals on which it was trained but exhibited lower performance on animals not present in the training data due to inter-subject variability. Cataplexy is scored with a sensitivity of 85% and 57% using the supervised and unsupervised approaches, respectively, when compared to manual scoring, and the specificity exceeds 99% in both cases.