Ongoing neural oscillations predict the post-stimulus outcome of closed loop auditory stimulation during slow-wave sleep.

The large slow oscillation (SO, 0.5-2Hz) that characterises slow-wave sleep is crucial to memory consolidation and other physiological functions. Manipulating slow oscillations can enhance sleep and memory, as well as benefitting the immune system. Closed-loop auditory stimulation (CLAS) has been demonstrated to increase the SO amplitude and to boost fast sleep spindle activity (11-16Hz). Nevertheless, not all such stimuli are effective in evoking SOs, even if they are precisely phase-locked. Here, we studied whether it is possible to use ongoing activity patterns to determine which oscillations to stimulate in order to effectively enhance SOs or SO-locked spindle activity. To this end, we trained classifiers using the morphological characteristics of the ongoing SO, as measured by electroencephalography (EEG), to predict whether stimulation would lead to a benefit in terms of the resulting SO and spindle amplitude. Separate classifiers were trained using trials from spontaneous control and stimulated datasets, and we evaluated their performance by applying them to held-out data both within and across conditions. We were able to predict both when large SOs will occur spontaneously, and whether a phase-locked auditory click will effectively enlarge them with an accuracy of ~70%. We were also able to predict when stimulation would elicit spindle activity with an accuracy of ~60%. Finally, we evaluate the importance of the various SO features used to make these predictions. Our results offer new insight into SO and spindle dynamics and provide a new method for online optimisation of stimulation.