Abstract

Schemas can facilitate memory consolidation. Studies have suggested that interactions between the hippocampus and the ventromedial prefrontal cortex (vmPFC) are important for schema-related memory consolidation. However, in humans, how schema accelerates the consolidation of new information and relates to durable memory remains unclear. To address these knowledge gaps, we used a human analogue of the rodent spatial schema task and resting-state fMRI to investigate how post-encoding brain networks can predict long-term memory performance in different schema conditions. After participants were trained to obtain schema-consistent or schema-inconsistent object-location associations, they learned new object-location associations. The new associations were tested after the post-encoding rest in the scanner and 24 h later outside the scanner. The Bayesian multilevel modelling was applied to analyse the post-encoding brain networks. The results showed that during the post-encoding, stronger vmPFC- anterior hippocampal connectivity was associated with durable memory in the schema-consistent condition, whereas stronger object-selective lateral occipital cortex (LOC)-ventromedial prefrontal connectivity and weaker connectivity inside the default mode network were associated with durable memory in the schema inconsistent condition. In addition, stronger LOC-anterior hippocampal connectivity was associated with memory in both schema conditions. These results shed light on how schemas reconfigure early brain networks, especially the prefrontal-hippocampal and stimuli-relevant cortical networks and influence long-term memory performance.

Details

Title
Effects of schema on the relationship between post-encoding brain connectivity and subsequent durable memory
Author
Guo, Dingrong 1 ; Chen, Gang 2 ; Yang, Jiongjiong 1 

 Peking University, School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behaviour and Mental Health, Beijing, People’s Republic of China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319) 
 National Institute of Mental Health, Scientific and Statistical Computing Core, Bethesda, USA (GRID:grid.416868.5) (ISNI:0000 0004 0464 0574) 
Pages
8736
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2820836894
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.