Detection of central and obstructive sleep apneas in mice: A new surgical and recording protocol. - Gabriele Matteoli; Sara Alvente; Chiara Berteotti; Dario Coraci; Viviana Lo Martire; Martina Lops; Elena Miglioranza; Alessandro Silvani; Emilia Volino; Giovanna Zoccoli; Stefano Bastianini (PLoS ONE)
Sleep apnea is a common respiratory disorder in humans and consists of recurrent episodes of cessation of breathing or decrease in airflow during sleep. Sleep apnea can be classified as central or obstructive, based on its origin. Central sleep apnea results from an impaired transmission of the signal for inspiration from the brain to inspiratory muscles, while obstructive sleep apnea occurs in the presence of an obstruction of the upper airways during inspiration. This condition leads to repetitive episodes of reduced oxygen and elevated carbon dioxide levels in the bloodstream, which entail both direct and indirect adverse effects on vital organs, especially the brain and heart. Basic research on animal models has been instrumental in advancing the understanding of disease mechanisms and pathophysiology, and in expediting the development of targeted therapies in several medical fields. Among animal models, mice are the mammalian species of choice for functional genomics of integrative functions such as sleep. Mice have long been known to show sleep apneas, but the classification of sleep apneas as central or obstructive in mice is technically challenging due to the small size of these animals. Here we present a method aimed at identifying central and obstructive sleep apneas in mice. This method involves the surgical implantation of electrodes for recording the electroencephalogram and nuchal muscle electromyogram, which are the gold standard to study the wake-sleep cycle, and for recording the diaphragm electromyogram, which allows the detection of diaphragm contraction. The method also includes the simultaneous recording of the above-mentioned biological signals and breathing inside a whole-body plethysmograph and the data analysis allows to score wake-sleep states and to detect sleep apneas and categorize them into central and obstructive events.