THE DIRTY CONSEQUENCES OF POOR SLEEP: MODELING GLYMPHATIC EFFICIENCY ACROSS DIVERSE SLEEP-WAKE CYCLES QUALITY.

The glymphatic system plays a vital role in maintaining brain health by facilitating the clearance of metabolic waste, a process most active during sleep. This clearance is facilitated by changes in extracellular space due to glial and neuronal shrinkage, enabling enhanced flow of interstitial and cerebrospinal fluid. The relation between the change in brain volume and the effectiveness of the glymphatic system has already been described. Despite the evidence linking sleep to brain clearance, the relationship between the quality of the sleep-wake cycle and glymphatic system efficiency remains unexplored. Thus impeding the understanding of how disrupted sleep may increase vulnerability to neurodegenerative diseases by impairing brain waste clearance. This study investigates the relationship between sleep-wake cycle quality and glymphatic system effectiveness by utilizing an existing computational model of neural dynamics. We calibrated the model to replicate real brain activity – matching frequencies and activity with data collected through EEG– during healthy NREM and REM sleep. These cycles were modeled and their response in brain volume change examined to assess the performance of the glymphatic system. Then, parameters – such as ionic conductance or vascular volume– were modified to simulate poor or high-quality sleep-wake cycles and the glymphatic system’s response examined. Early findings suggest that high-quality sleep cycles induce higher volume changes and therefore better glymphatic performance. Nevertheless, further analysis is required to more fully assess the system’s behavior across all sleep conditions.

Hello everyone. My name is Álvaro Olmo Jiménez, and today I’ll be presenting my research on THE DIRTY CONSEQUENCES OF POOR SLEEP: MODELING GLYMPHATIC EFFICIENCY ACROSS DIVERSE SLEEP-WAKE CYCLES QUALITY.
First, we will start by explaining why we are doing this research. Basically,we know that there are established links between sleep and brain clearance. The glymphatic system acts as the brain’s cleaning system and during sleep changes in glial and neuronal cell volume expand the extracellular space, which promotes convective fluid flow and waste clearance. Nevertheless, the specific impact of sleep quality on the glymphatic functions remains unexplored. This knowledge gap limits our understanding on how disrupted sleep may contribute to neurodegenerative disease risk
Thus, this study aims to explain how the quality of the sleep-wake cycles affect the glymphatic system during sleep.
To do so, we first established what was going to be our indicator for sleep quality → Brain volume change. This is because variations in extracellular and intracellular volumes during sleep enhance the glymphatic performance. Also, because the release of sleep-promoting molecules like prostaglandin induces blood vessel dilation and further volume changes.
At this point, we could state that our main focus was to study how the volume change is affected by varying the sleep-quality.
Once we had our indicator for good sleep, we used an existing model of neural dynamics implemented with glial dynamics whose behavior is determined by the concentration of ions.
This model was calibrated to replicate real brain activity – matching frequencies and activity with data collected through EEG. For example, a frequency of 2.8Hz was used to simulate NREM and 5.6Hz to simulate REM.
Moreover, we used an electrical and a volume stimulation as parameters to determine the sleep quality. The higher these parameters, the higher the simulated sleep quality. Therefore, from a bigger volume stimulation, a bigger volume change and thus the better glymphatic performance.
In order to replicate regular sleep, we did numerous simulations. However, just the most significant ones are going to be shown.
In this figure we can see 3 different simulations. In the three of them, the same electrical stimulation is used. The difference between the high and low volume stimulation is that the stimulation effort is halved. We can observe that over 20 cycles, there is not a significant brain volume change if we don’t stimulate the volume and that there is some difference in the final volume depending on the stimulation.
Now, we can see the transmembrane potential change for the high volume stimulation. One can see that the voltage deeply decreases with the volume stimulation. This makes sense because while the volumes vary, ion concentration varies too. Thus, we can state that the alterations of pump dynamics and diffusion result in a decrease in the transmembrane voltage.
In this figure, which again outputs high volume stimulation over the last cycle of the simulation, we can clearly appreciate the change in frequency from NREM to REM with the change in volume. Also, for further visualization, the right has been done and the change in frequency revealed.
From these figures, which show the change of concentration of intracellular sodium and extracellular potassium over the last cycle between electrical and non-electrical simulation outputs, we can see how electrical stimulation is fundamental for the correct simulation of sleep dynamics. Although it does not seem that important for volume change, we can see that in the simulation with electrical stimulation there is a balance between the intracellular and extracellular potassium and sodium. While in the non electrical-stimulated run, there is no apparent difference. This happens because the ATP-pump is shut-off due to the low extracellular potassium and thus cannot transport these two ions correctly. Although these ionic effects may not seem that important, they can be highly significant, as they can alter the signalling properties of the neuron.

This figure shows how the overall volume change varies if the sleep quality is disrupted. It is important to remark that in the microarousals simulation, 3 random intervals ranging from 1 and 5 seconds for each cycle were done and volume stimulation was stopped. Something similar was done for the less quality sleep simulation. In it 3 random intervals ranging from 5 and 15 seconds for each cycle were done and volume stimulation force was halved.

We can see the final values for each volume in this next figure.

Although it seems that the volume decrease is higher in the simulation with microarousals – suggesting that it has better glymphatic performance than varying sleep quality simulation – it is not. This is because the microarousals last less than the low stimulation stages. Thus, the simulation (with microarousals) would have less volume decrease if both periods– microarousals and low stimulation stages– lasted the same.

Now, from this data we can conclude that as sleep quality decreases, we observe a reduction in both overall volume changes and thus in glymphatic efficiency. This is consistent with previous findings that link slow-wave activity and stable sleep patterns with enhanced interstitial fluid movement and metabolic waste clearance.

Moreover, while volume stimulation contributes to mechanical shifts in brain tissue, electrical stimulation proves essential for preserving ionic balance. Without it, ATP-dependent pumps like the sodium-potassium pump become ineffective, leading to disrupted ion gradients and impaired homeostasis.

This underscores the critical role of electrical activity in maintaining proper cellular function, beyond just facilitating volume changes. The breakdown of ionic regulation in the absence of electrical stimulation highlights the interdependence of mechanical and electrophysiological processes in sleep. Together, these findings reinforce the complexity of accurately simulating sleep.

Ultimately, further research is needed in order to flawlessly replicate sleep, accounting not only for volumetric shifts and electrical rhythms, but also for how these elements dynamically interact over time. Accounting for the metabolic rate of the pumps.