Identification of memory reactivation during sleep by EEG classificationCitation formats

  • External authors:
  • James Cousins
  • Hikaru Tsujimura
  • Alexia Zoumpoulaki
  • Marta Perapoch
  • Lorena Santamaria Covariabus
  • Penelope Lewis

Standard

Identification of memory reactivation during sleep by EEG classification. / Belal, Suliman; Cousins, James; El-Deredy, Wael; Parkes, Laura; Schneider, Jules; Tsujimura, Hikaru; Zoumpoulaki, Alexia; Perapoch, Marta; Covariabus, Lorena Santamaria; Lewis, Penelope.

In: NeuroImage, Vol. 176, 01.08.2018, p. 203-214.

Research output: Contribution to journalArticle

Harvard

Belal, S, Cousins, J, El-Deredy, W, Parkes, L, Schneider, J, Tsujimura, H, Zoumpoulaki, A, Perapoch, M, Covariabus, LS & Lewis, P 2018, 'Identification of memory reactivation during sleep by EEG classification', NeuroImage, vol. 176, pp. 203-214. https://doi.org/10.1016/j.neuroimage.2018.04.029

APA

Vancouver

Author

Belal, Suliman ; Cousins, James ; El-Deredy, Wael ; Parkes, Laura ; Schneider, Jules ; Tsujimura, Hikaru ; Zoumpoulaki, Alexia ; Perapoch, Marta ; Covariabus, Lorena Santamaria ; Lewis, Penelope. / Identification of memory reactivation during sleep by EEG classification. In: NeuroImage. 2018 ; Vol. 176. pp. 203-214.

Bibtex

@article{9418346e35e647e08ac864ee7731f024,
title = "Identification of memory reactivation during sleep by EEG classification",
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 = "Journal Article",
author = "Suliman Belal and James Cousins and Wael El-Deredy and Laura Parkes and Jules Schneider and Hikaru Tsujimura and Alexia Zoumpoulaki and Marta Perapoch and Covariabus, {Lorena Santamaria} and Penelope Lewis",
note = "Copyright {\circledC} 2018. Published by Elsevier Inc.",
year = "2018",
month = "8",
day = "1",
doi = "10.1016/j.neuroimage.2018.04.029",
language = "English",
volume = "176",
pages = "203--214",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Identification of memory reactivation during sleep by EEG classification

AU - Belal, Suliman

AU - Cousins, James

AU - El-Deredy, Wael

AU - Parkes, Laura

AU - Schneider, Jules

AU - Tsujimura, Hikaru

AU - Zoumpoulaki, Alexia

AU - Perapoch, Marta

AU - Covariabus, Lorena Santamaria

AU - Lewis, Penelope

N1 - Copyright © 2018. Published by Elsevier Inc.

PY - 2018/8/1

Y1 - 2018/8/1

N2 - 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.

AB - 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.

KW - Journal Article

U2 - 10.1016/j.neuroimage.2018.04.029

DO - 10.1016/j.neuroimage.2018.04.029

M3 - Article

VL - 176

SP - 203

EP - 214

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -