Electroencephalographic markers of idiopathic hypersomnia : where we are and where we are going

Purpose of the review: Idiopathic hypersomnia is a poorly defined nosological entity and has important phenotype heterogeneity. Moreover, diagnosing idiopathic hypersomnia is challenging as patients can report significant symptoms but may not meet diagnostic criteria on standard objective tests. Advanced analyses of electroencephalographic (EEG) activity could provide objective markers of idiopathic hypersomnia and explain part of its pathophysiology and heterogeneity. In fact, the EEG signal recorded during wakefulness and sleep not only captures sleep macroarchitecture, but also homeostatic sleep process efficiency, strength of neural synchrony during sleep, sleep consolidation and changes in cognitive processes. In this review, we will present current and potential markers of idiopathic hypersomnia, with a focus on sleep propensity. We will also discuss EEG changes occurring in sleep deprivation condition and recovery nights, as well as those observed in other central hypersomnolence conditions, namely narcolepsy type-1 and type-2. Recent findings: Among EEG markers studied in sleep deprivation and narcolepsy are those derived from sleep macroarchitecture (sleep consolidation, sleep stage proportion, sleep-stage transitions, sleep onset in rapid-eye movement sleep) and microarchitecture (slow-wave activity, spindles), spectral analysis of the waking electroencephalographic signal and cognitive event-related potentials. To date, no clear objective EEG markers of idiopathic hypersomnia exist, as studies were generally performed on small samples, used heterogeneous protocols (including changes of diagnostic criteria that occurred over time) and most of them focused on more traditional macroarchitecture sleep analyses. Summary: Based on studies of sleep deprivation and narcolepsy, further studies are needed to understand the EEG signal in idiopathic hypersomnia. Future studies should take advantage of the richness and the complexity of the EEG signal and verify whether subgroups of hypersomniac patients exist