8Remembering is a reconstructive process. Surprisingly little is known about how the reconstruction 9 of a memory unfolds in time in the human brain. We used reaction times and EEG time-series 10 decoding to test the hypothesis that the information flow is reversed when an event is reconstructed 11 from memory, compared to when the same event is initially being perceived. Across three 12 experiments, we found highly consistent evidence supporting such a reversed stream. When seeing 13 an object, low-level perceptual features were discriminated faster behaviourally, and could be 14 decoded from brain activity earlier, than high-level conceptual features. This pattern reversed during 15 associative memory recall, with reaction times and brain activity patterns now indicating that 16conceptual information was reconstructed more rapidly than perceptual details. Our findings 17 support a neurobiologically plausible model of human memory, suggesting that memory retrieval is 18 a hierarchical, multi-layered process that prioritizes semantically meaningful information over 19 perceptual detail.
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.
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