Arousal and vigilance are essential for survival and relevant regulatory neural circuits lie within the brainstem, hypothalamus and forebrain. The nucleus incertus (NI) is a distinct site within the pontine periventricular gray, containing a substantial population of GABAergic neurons with long-range, ascending projections. Existing neuroanatomical data and functional studies in anesthetized rats, suggest the NI is a central component of a midline behavioral control network well positioned to modulate arousal, vigilance and exploratory navigation, yet none of these roles have been established experimentally. We used a chemogenetic approach-clozapine-N-oxide (CNO) activation of virally delivered excitatory hM3Dq-DREADDs-to activate the NI in rats and examined the behavioral and physiological effects, relative to effects in naïve rats and appropriate viral-treated controls. hM3Dq activation by CNO resulted in long-lasting depolarization of NI neurons with action potentials, in vitro. Peripheral injection of CNO significantly increased c-Fos immunoreactivity in the NI and promoted cortical electroencephalograph (EEG) desynchronization. These brain changes were associated with heightened arousal, and increased locomotor activity in the homecage and in a novel environment. Furthermore, NI activation altered responses in a fear conditioning paradigm, reflected by increased head-scanning, vigilant behaviors during conditioned fear recall. These findings provide direct evidence that the NI promotes general arousal via a broad behavioral activation circuit and support early hypotheses, based on its connectivity, that the NI is a modulator of cognition and attention, and emotional and motivated behaviors.
Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake–sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1–2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies.
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