A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies—Related Issues and Future Directions

Abstract

Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep’s effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with ‘sleep and stimulation’ and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems.

Neuronal-spiking-based closed-loop stimulation during cortical ON and OFF states in freely moving mice

“…The possibility of non-invasive modulation of sleep oscillations has recently attracted significant attention (Bellesi, Riedner, Garcia-Molina, Cirelli, & Tononi, 2014;Choi, Kwon, & paradigms likely leads to many instances when stimulation is delivered during a suboptimal or even undesirable phase of the network oscillation. Arguably, this could influence the outcome of modulation.…”

“…The possibility of non-invasive modulation of sleep oscillations has recently attracted significant attention (Bellesi, Riedner, Garcia-Molina, Cirelli, & Tononi, 2014; Choi, Kwon, & Jun, 2020; Fattinger et al, 2019; Frase et al, 2019; Geiser et al, 2020; Krugliakova et al, 2022; Malkani & Zee, 2020; Marshall, Helgadottir, Molle, & Born, 2006; Ngo, Martinetz, Born, & Molle, 2013; Schneider, Lewis, Koester, Born, & Ngo, 2020). Slow waves are a predominant type of sleep oscillatory activity during NREM sleep, but can also occur during REM sleep and wakefulness (Andrillon, Burns, Mackay, Windt, & Tsuchiya, 2021; Bernardi et al, 2019; Borbely, Tobler, & Hanagasioglu, 1984; Funk, Honjoh, Rodriguez, Cirelli, & Tononi, 2016; Vyazovskiy et al, 2014; Vyazovskiy et al, 2011).…”

Updated Review of the Acoustic Modulation of Sleep: Current Perspectives and Emerging Concepts

“…49 Their similarity to slow oscillations (SO), which are supposed to be critically involved in memory consolidation, has provoked researchers to manipulate their occurrence with acoustic stimulation. Recently, Choi et al 50 51 This study demonstrated for the first time that this approach can be used to investigate sleep functions. One important conclusion, however, was that this effect critically depends on the prevailing brain state during presentation.…”

The Portiloop: a deep learning-based open science tool for closed-loop brain stimulation

“…Namely, we train the model on subjects drawn only from phase 1 on the one hand, and on subjects drawn only from phase 2 on the other hand. The results of these experiments are presented in Table I, rows (8,9). We observe that the ANN trained on phase 1 performs almost as well as the ANN trained on the whole cohort (6) on all subsets, including phase 2, whereas the ANN trained on phase 2 is noticeably worse on all subsets, even including phase 2.…”

“…The ability to interact with brain oscillations in a precisely-timed fashion to enhance or inhibit endogenous processes -using sensory [1], [2], [3], [4], electrical [5] or magnetic [6] stimulation -allows for their functional roles to be determined [7], and potentially for restoration of processes deteriorated by aging or pathology [8]. While there is a great deal of interest in closed-loop stimulation [7], [9], researchers lack flexible, powerful tools that are easily accessible. Research efforts are also limited by the portability of systems, and by their complexity and expense.…”