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ERIC Number: EJ1477738
Record Type: Journal
Publication Date: 2025-Dec
Pages: 12
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2056-7936
Available Date: 2025-07-22
Personalized Targeted Memory Reactivation Enhances Consolidation of Challenging Memories via Slow Wave and Spindle Dynamics
Gi-Hwan Shin1; Young-Seok Kweon1; Seungwon Oh2; Seong-Whan Lee3
npj Science of Learning, v10 Article 47 2025
Sleep is crucial for memory consolidation, underpinning effective learning. Targeted memory reactivation (TMR) can strengthen neural representations by re-engaging learning circuits during sleep. However, TMR protocols overlook individual differences in learning capacity and memory trace strength, limiting efficacy for difficult-to-recall memories. Here, we present a personalized TMR protocol that adjusts stimulation frequency based on individual retrieval performance and task difficulty during a word-pair memory task. In an experiment comparing personalized TMR, TMR, and control groups, the personalized protocol significantly reduced memory decay and improved error correction under challenging recall. Electroencephalogram (EEG) analyses revealed enhanced synchronization of slow waves and spindles, with a significant positive correlation between behavioral and EEG features for challenging memories. Multivariate classification identified distinct neural signatures linked to the personalized approach, highlighting its ability to target memory-specific circuits. These findings provide novel insights into sleep-dependent memory consolidation and support personalized TMR interventions to optimize learning outcomes.
Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Data File: URL: https://osf.io/3g8rm
Author Affiliations: 1Korea University, Department of Brain and Cognitive Engineering, Seoul, Republic of Korea; 2Kongju National University, Department of Artificial Intelligence, Cheonan, Republic of Korea; 3Korea University, Department of Artificial Intelligence, Seoul, Republic of Korea