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Neuroimage. 2018 Aug 1;176:203-214. doi: 10.1016/j.neuroimage.2018.04.029. Epub 2018 Apr 17.

Identification of memory reactivation during sleep by EEG classification.

Author information

1
School of Biological Sciences, Division of Neuroscience and Experimental Psychology, Manchester University, Zochonis Building, Brunswick Street, Manchester, M13 9PT, UK; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
2
Cognitive Neuroscience Laboratory, Duke-NUS Graduate Medical School, 8 College Road, Level 6, 169857, Singapore.
3
School of Biological Sciences, Division of Neuroscience and Experimental Psychology, Manchester University, Zochonis Building, Brunswick Street, Manchester, M13 9PT, UK.
4
Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
5
School of Biological Sciences, Division of Neuroscience and Experimental Psychology, Manchester University, Zochonis Building, Brunswick Street, Manchester, M13 9PT, UK; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK. Electronic address: lewisp8@cardiff.ac.uk.

Abstract

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Neural reactivation was classified above chance in all participants when TMR was applied in SWS, and in 5 of the 14 participants to whom TMR was applied in N2. Classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be valuable for future investigations of memory consolidation.

KEYWORDS:

Consolidation; Machine learning; Memory reactivation; Pattern recognition; Sleep

PMID:
29678758
PMCID:
PMC5988689
DOI:
10.1016/j.neuroimage.2018.04.029
[Indexed for MEDLINE]
Free PMC Article

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