Time-dependent consolidation mechanisms of durable memory in spaced learning
Poster No:
842
Submission Type:
Abstract Submission
Authors:
yifeixue yang1, Ziyi Huang1, Yun Yang1, Mingxia Fan2, Dazhi Yin1
Institutions:
1Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal, Shanghai, Shanghai, 2Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China, Shanghai, Shanghai
First Author:
yifeixue yang
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal
Shanghai, Shanghai
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal
Shanghai, Shanghai
Co-Author(s):
Ziyi Huang
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal
Shanghai, Shanghai
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal
Shanghai, Shanghai
Yun Yang
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal
Shanghai, Shanghai
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal
Shanghai, Shanghai
Mingxia Fan
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China
Shanghai, Shanghai
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China
Shanghai, Shanghai
Dazhi Yin
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal
Shanghai, Shanghai
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Heal
Shanghai, Shanghai
Introduction:
Compared to massed learning, spaced learning can enhance memory performance, especially for memory durability, (Cepeda et al., 2008; Xue et al., 2011). Spacing effect can last months with the day-based learning design but not the trial-based learning design, which might reflect the effect of time-dependent consolidation (Cepeda et al., 2008). Time-dependent consolidation fosters effective stabilization and reinforcement of memory trace, leading to a transition from detailed to gist-like memory (Alvarez & Squire, 1994; Cowan et al., 2020; Dandolo & Schwabe, 2018). This integrative processing leads to higher retrieval-related neural pattern similarity between different items and makes memory more durable (Liu et al., 2016; Tompary & Davachi, 2017). Recent studies revealed the leading mechanism underlying memory consolidation as the spontaneous activities within hippocampal and across hippocampal-cortical networks after learning which also facilitating the transfer of memory storage from hippocampus to distributed cortical networks (Huang et al., 2024; Tambini & Davachi, 2019). Specifically, the emerging model emphasizes that the default mode network (DMN) may serve as a hub for igniting replay cascades and support the reactivation of older memories(Kaefer et al., 2022). However, how time-dependent consolidation contributes to forming durable memory and what neural signatures predict durable memory in spaced learning remain unclear.
Methods:
We recruited 48 participants who underwent either 3-day spaced learning or 1-day massed learning. Both resting-state and task-based fMRI data were collected in multiple delayed tests (i.e., immediate, 1-week, and 1-month), while baseline resting-state fMRI data were collected before learning.
Retention rate was the percentage of durable memories that successfully retrieved in both the immediate test and delayed test.
Intertrial similarity was calculated in the hippocampus and DMN subsystems (Fig. 1A) as averaged pair-wised Pearson's correlation between the multivoxel neural activity of each correct trials in the immediate test (Fig. 1B).
For replay analysis, templates for durable and weak memories were obtained based on the retrieval-related multivoxel neural activity in the hippocampus and DMN subsystems. Replay was defined as a frame-wise resting activity pattern that showed high similarity (above the 90th percentile) with the retrieval-evoked neural activity template. (Fig. 1C-D).
Retention rate was the percentage of durable memories that successfully retrieved in both the immediate test and delayed test.
Intertrial similarity was calculated in the hippocampus and DMN subsystems (Fig. 1A) as averaged pair-wised Pearson's correlation between the multivoxel neural activity of each correct trials in the immediate test (Fig. 1B).
For replay analysis, templates for durable and weak memories were obtained based on the retrieval-related multivoxel neural activity in the hippocampus and DMN subsystems. Replay was defined as a frame-wise resting activity pattern that showed high similarity (above the 90th percentile) with the retrieval-evoked neural activity template. (Fig. 1C-D).
·Fig. 1 The regions of interest and methods.
Results:
Spaced learning led to higher memory retention for both 1-week (t(67) = 2.87, p = 0.006) and 1-month (t(67) = 2.06, p = 0.043) delays, compared to massed learning.
The intertrial similarity of the DMNdm (t(43) = 2.05, p = 0.046), the DMNcore (t(43) = 2.30, p = 0.027) and the DMNmt (t(44) = 2.33, p = 0.024) was significantly higher in the spaced learning group than in the massed learning group (Fig. 2A). The intertrial similarity of DMNdm (Fig. 2B) and DMNmt (Fig. 2C) was significantly correlated with the retention rate for spaced but not massed learning.
The increasements of replays for durable memory were significantly higher than 0 in the DMNdm (t(22) = 2.81, p = 0.010) and hippocampus (t(21) = 2.54, p = 0.019) after spaced learning, while only within the hippocampus after massed learning (t(24) = 2.69, p = 0.013). The increasement of replay for durable memory in the DMNdm after spaced learning was significantly higher than that after massed learning (t(46) = 2.10, p = 0.041) (Fig. 2D). The increasements of replays for weak memory were significantly higher than 0 only in the hippocampus after both spaced (t(21) = 3.46, p = 0.002) and massed (t(24) = 2.24, p = 0.035) learning (Fig. 2E).
The intertrial similarity of the DMNdm (t(43) = 2.05, p = 0.046), the DMNcore (t(43) = 2.30, p = 0.027) and the DMNmt (t(44) = 2.33, p = 0.024) was significantly higher in the spaced learning group than in the massed learning group (Fig. 2A). The intertrial similarity of DMNdm (Fig. 2B) and DMNmt (Fig. 2C) was significantly correlated with the retention rate for spaced but not massed learning.
The increasements of replays for durable memory were significantly higher than 0 in the DMNdm (t(22) = 2.81, p = 0.010) and hippocampus (t(21) = 2.54, p = 0.019) after spaced learning, while only within the hippocampus after massed learning (t(24) = 2.69, p = 0.013). The increasement of replay for durable memory in the DMNdm after spaced learning was significantly higher than that after massed learning (t(46) = 2.10, p = 0.041) (Fig. 2D). The increasements of replays for weak memory were significantly higher than 0 only in the hippocampus after both spaced (t(21) = 3.46, p = 0.002) and massed (t(24) = 2.24, p = 0.035) learning (Fig. 2E).
·Fig. 2 Intertrial similarity analysis and replay analysis.
Conclusions:
Time-dependent consolidation promotes neural integration and replay in the cortex rather than in the hippocampus, which may underlie the formation of durable memory after spaced learning.
Learning and Memory:
Long-Term Memory (Episodic and Semantic) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Multivariate Approaches 2
Task-Independent and Resting-State Analysis
Keywords:
FUNCTIONAL MRI
Learning
Memory
Multivariate
NORMAL HUMAN
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cepeda, N. J. (2008). Spacing effects in learning: a temporal ridgeline of optimal retention. Psychological Science, 19(11), 1095-1102.
Cowan, E. (2020). Sleep spindles promote the restructuring of memory representations in ventromedial prefrontal cortex through enhanced hippocampal-cortical functional connectivity. Journal of Neuroscience, 40(9), 1909-1919.
Dandolo, L. C. (2018). Time-dependent memory transformation along the hippocampal anterior-posterior axis. Nature Communications, 9, 1205.
Huang, Q. (2024). Replay-triggered brain-wide activation in humans. Nature Communications, 15, 7185.
Kaefer, K. (2022). Replay, the default mode network and the cascaded memory systems model. Nature Reviews Neuroscience, 23(10), 628-640.
Liu, Y., (2016). Memory consolidation reconfigures neural pathways involved in the suppression of emotional memories. Nature Communications, 7, 13375.
Tambini, A. (2019). Awake reactivation of prior experiences consolidates memories and biases cognition. Trends in Cognitive Sciences, 23(10), 876-890.
Tompary, A. (2017). Consolidation promotes the emergence of representational overlap in the hippocampus and medial prefrontal cortex. Neuron, 96(1), 228-241.
Xue, G. (2011). Spaced learning enhances subsequent recognition memory by reducing neural repetition suppression. Journal of Cognitive Neuroscience, 23(7), 1624-1633.