Closed-Loop tACS Delivered during Slow-Wave Sleep Reduces Retroactive Interference on a Paired-Associates Learning Task

Recent Evidence of Non-Rapid Eye Movement Sleep Oscillation Abnormalities in Psychiatric Disorders

PURPOSE OF REVIEW

We review recent studies published from 2019 to 2024 examining slow waves and sleep spindles abnormalities across neurodevelopmental, mood, trauma-related, and psychotic disorders using polysomnography and Electroencephalogram (EEG).

RECENT FINDINGS

Individuals with attention-deficit/hyperactivity disorder (ADHD) showed higher slow-spindle activity, while findings on slow-wave activity were mixed. Individuals with autism spectrum disorder (ASD) showed inconsistent results with some evidence of lower spindle chirp and slow-wave amplitude. Individuals with depression displayed lower slow-wave and spindle parameters mostly in medicated patients. Individuals with post-traumatic stress disorder (PTSD) showed higher spindle frequency and activity, which were associated with their clinical symptoms. Psychotic disorders demonstrated the most consistent alterations, with lower spindle density, amplitude, and duration across illness stages that correlated with patients' symptom severity and cognitive deficits, whereas lower slow-wave measures were present in the early phases of the disorders. Sleep spindle and slow-wave abnormalities are present across psychiatric populations, with the most consistent alterations observed in psychotic disorders. Larger studies with standardized methodologies and longitudinal assessments are needed to establish the potential of these oscillations as neurophysiological biomarkers and/or treatment targets.

A classification-based generative approach to selective targeting of global slow oscillations during sleep

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.