Study of EEG alpha rhythm in insomnia by combining clinical data and neural computational model

Insomnia is a typical sleep disorder, which is characterized by difficulty falling asleep, short duration of deep sleep and early awakening etc. Clinically, some patients with insomnia exhibit increased EEG alpha activity during rapid eye movement (REM) sleep stage, but the relevant biophysical mechanisms still remain unclear. Therefore, by combining Liley neural computational model and clinical EEG data from insomnia patients, this paper focuses on exploring the underlying mechanisms of the typical rhythms during REM stage of insomnia patient from a computational neuroscience perspective. Firstly, an automatic key parameter selection and estimation method for Liley model is designed based on Markov chain Monte Carlo algorithm. Secondly, by combining EEG data from insomnia patients, the key model parameters are identified and a mechanistic hypothesis of increased EEG alpha activity during REM stage is developed for insomnia patients: The decrease in the inhibitory synaptic activity leads to the enhanced alpha activity. The obtained results are helpful in improving the sleep quality and controlling the progression of insomnia patients.