Shin Gi-Hwan, Kweon Young-Seok, Oh Seungwon, Lee Seong-Whan
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Department of Artificial Intelligence, Kongju National University, Cheonan, Republic of Korea.
NPJ Sci Learn. 2025 Jul 22;10(1):47. doi: 10.1038/s41539-025-00340-3.
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.
睡眠对于记忆巩固至关重要，是有效学习的基础。靶向记忆再激活（TMR）可通过在睡眠期间重新激活学习回路来强化神经表征。然而，TMR方案忽略了学习能力和记忆痕迹强度的个体差异，限制了对难以回忆的记忆的效果。在此，我们提出一种个性化TMR方案，该方案在单词对记忆任务中根据个体检索表现和任务难度调整刺激频率。在一项比较个性化TMR、TMR和对照组的实验中，个性化方案显著减少了记忆衰退，并在具有挑战性的回忆中改善了错误纠正。脑电图（EEG）分析显示慢波和纺锤波的同步增强，对于具有挑战性的记忆，行为特征和EEG特征之间存在显著正相关。多变量分类识别出与个性化方法相关的独特神经特征，突出了其针对特定记忆回路的能力。这些发现为依赖睡眠的记忆巩固提供了新见解，并支持个性化TMR干预以优化学习成果。
