Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices

Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat

Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and data analysis. Citizen science has a long history in several fields of the natural sciences, and with recent developments in wearable technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the influx of minimal sensing systems in the consumer market, allowing more do-it-yourself (DIY) and quantified-self (QS) investigations of the human brain. While most subfields of neuroscience require sophisticated monitoring devices and laboratories, the study of sleep characteristics can be performed at home with relevant noninvasive consumer devices. The strong influence of sleep quality on waking life and the accessibility of devices to measure sleep are two primary reasons citizen scientists have widely embraced sleep research. Their involvement has evolved from solely contributing to data collection to engaging in more collaborative or autonomous approaches, such as instigating ideas, formulating research inquiries, designing research protocols and methodology, acting upon their findings, and disseminating results. In this article, we introduce the emerging field of citizen neuroscience, illustrating examples of such projects in sleep research. We then provide overviews of the wearable technologies for tracking human neurophysiology and various open-source software used to analyse them. Finally, we discuss the opportunities and challenges in citizen neuroscience projects and suggest how to improve the study of the human brain outside the laboratory.

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.