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Abstract Introduction Due to slow oscillations (SO; 0.5-1.5 Hz) known role in cognition and health, brain stimulation techniques targeting SOs in closed loop are being developed as potential clinical interventions, as they hold promise for improving cognitive functions. SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location. Enabling prediction of SO emergence across the electrode manifold could remove the need for online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep. Methods SOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), with 64 EEG channels. We study SO emergence at progressively more refined time scales. Modeling of SO occurrence was achieved for SOs in slow wave sleep (SWS), or in a combination of stage 2 and SWS (S2 representing skewness) in successive cycles. Results The analysis reveals an exponential decrease in SO count across sleep cycles, with a decay rate per cycle of 1.49 for SWS and 1.09 for S2&SWS. The SO timing model for SWS also demonstrates an exponential increase in model parameters (μ, λ) across cycles, with increase constants per cycle at 0.38 and 2.65, respectively. These findings imply that personalized model parameters can be estimated building on SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution. Conclusion This study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of stimulation paradigms. Support (if any)
Alipour et al. (Sat,) studied this question.