Exploring neuro-glymphatic coupling during sleep using wearable technology

In recent years, it has been demonstrated that sleep not only plays a crucial role in cognitive, emotional, and immunological functions but also helps to clear the brain from metabolic waste products via the recently discovered glymphatic system.

A physiological marker closely associated with glymphatic clearance is electroencephalographic (EEG) slow-wave activity (SWA, 1-4 Hz).

An increase in SWA, driven by high sleep pressure accumulated during the day, is related to a boost of glymphatic clearance.

In humans, such temporal neuro-glymphatic coupling has been confirmed by using a combination of sleep EEG and functional magnetic resonance imaging (fMRI), which, however, is challenging to use in sleeping subjects.

Recently, a first wearable solution has been developed that allows simultaneous assessment of brain electrophysiology and glymphatic system activity by using a combination of the sleep EEG and non-invasive functional near-infrared spectroscopy (fNIRS).

This unique technological solution provides an opportunity for the investigation of neuro-glymphatic coupling in sleep without disturbing natural sleep.SWA can be boosted by the well-timed presentation of brief auditory stimuli during sleep without otherwise altering natural sleep architecture.

This approach is referred to as closed-loop auditory stimulation (CLAS) and has gained a lot of attention from both sleep researchers and the industry.

Given that deep sleep is a primary driver of glymphatic clearance, it is of great importance to establish if CLAS can boost glymphatic activity along with SWA enhancement.

Evidence for the modulatory effect of CLAS on glymphatic clearance will open new avenues for future implementations of CLAS for prevention and/or therapy of neurodegenerative disorders.

For instance, the regulation of glymphatic clearance might be of high clinical relevance in Alzheimers disease, characterised by reduced glymphatic convection causing harmful protein aggregation.