Department of Psychology and Interdepartmental Neuroscience Program, Northwestern University, Evanston, Illinois, USA.
Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
J Sleep Res. 2022 Dec;31(6):e13731. doi: 10.1111/jsr.13731. Epub 2022 Sep 21.
A widely accepted view in memory research is that previously acquired information can be reactivated during sleep, leading to persistent memory storage. Targeted memory reactivation (TMR) was developed as a technique whereby specific memories can be reactivated during sleep using a sensory stimulus linked to prior learning. As a research tool, TMR can improve memory, raising the possibility that it may be useful for cognitive enhancement and clinical therapy. A major challenge for the expanded use of TMR is that a skilled operator must manually control stimulation, which is impractical in many settings. To address this limitation, we developed the SleepStim system for automated TMR in the home. SleepStim includes a smartwatch to collect movement and heart-rate data, plus a smartphone to emit auditory cues. A machine-learning model identifies periods of deep sleep and triggers TMR sounds within these periods. We tested whether this system could replicate the spatial-memory benefit of in-laboratory TMR. Participants learned locations of objects on a grid, and then half of the object locations were reactivated during sleep over 3 nights. Recall was tested each morning. In an experiment with 61 participants, the TMR effect was not significant but varied systematically with stimulus intensity; low-intensity but not high-intensity stimuli produced memory benefits. In a second experiment with 24 participants, we limited stimulus intensity and found that TMR reliably improved spatial memory, consistent with effects observed in laboratory studies. We conclude that SleepStim can effectively accomplish automated TMR, and that avoiding sleep disruption is critical for TMR benefits.
在记忆研究中，一个被广泛接受的观点是，先前获得的信息可以在睡眠中被重新激活，从而导致持久的记忆存储。靶向记忆再激活（TMR）是一种技术，通过使用与先前学习相关的感觉刺激，可以在睡眠期间重新激活特定的记忆。作为一种研究工具，TMR 可以提高记忆力，这增加了它在认知增强和临床治疗中可能有用的可能性。TMR 广泛应用的一个主要挑战是，熟练的操作员必须手动控制刺激，这在许多情况下是不切实际的。为了解决这个限制，我们开发了 SleepStim 系统，用于在家中进行自动 TMR。SleepStim 包括一个智能手表来收集运动和心率数据，以及一个智能手机来发出听觉提示。一个机器学习模型可以识别深度睡眠期，并在这些期间触发 TMR 声音。我们测试了这个系统是否可以复制实验室 TMR 的空间记忆益处。参与者学习网格上物体的位置，然后在 3 个晚上的睡眠期间重新激活一半的物体位置。每天早上进行回忆测试。在一项有 61 名参与者的实验中，TMR 效应并不显著，但与刺激强度系统地变化；低强度而不是高强度刺激产生了记忆益处。在第二项有 24 名参与者的实验中，我们限制了刺激强度，发现 TMR 可靠地提高了空间记忆，与实验室研究中观察到的效果一致。我们的结论是，SleepStim 可以有效地完成自动 TMR，避免睡眠中断对于 TMR 益处至关重要。
