ArticleFrontiers in human neuroscience2024
A classification-based generative approach to selective targeting of global slow oscillations during sleep.
Article in Frontiers in human neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 5 citations in OpenAlex.
- Predictability of sleep slow oscillation emergence and spatial extent from pre-onset neural dynamics.Scientific reports · 2026Article
- Slow wave canonical activity deviation concept: Toward a slow wave-based EEG-fMRI reference map for health-associated network function.iScience · 2026Article
- Classifying Sleep Slow Oscillations in Low Density EEG.Neuroinformatics · 2026Article
- A user's introduction to an algorithmic method to identify space-time profiles of sleep slow oscillations: dataset constraints, case-use examples, and open-source code.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Closed-loop acoustic stimulation modified by cathodal tDCS is beneficial for retention.bioRxiv : the preprint server for biology · 2025Article
- Stimulating the Stimulated Cortex-Frontocortical Anodal Electric Stimulation Combined With Closed-Loop Acoustic Stimulation During Sleep Impairs Memory in Subjects With High Cognitive Ability.The European journal of neuroscience · 2025Article
- The Space-Time Organisation of Sleep Slow Oscillations as Potential Biomarker for Hypersomnolence.Journal of sleep research · 2025Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Given sleep's crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. Objective/Hypothesis: This study targets a specific type of SOs, widespread on the electrode manifold in a short delay ("global SOs"), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. Methods: We introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. Results: Our study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. Conclusion: Our research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.
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