Intrinsic functional connectivity in the default mode network predicts mnemonic discrimination: A connectome-based modeling approach.
Hippocampus
; 32(1): 21-37, 2022 01.
Article
en En
| MEDLINE
| ID: mdl-34821439
The ability to distinguish existing memories from similar perceptual experiences is a core feature of episodic memory. This ability is often examined using the mnemonic similarity task in which people discriminate memories of studied objects from perceptually similar lures. Studies of the neural basis of such mnemonic discrimination have mostly focused on hippocampal function and connectivity. However, default mode network (DMN) connectivity may also support such discrimination, given that the DMN includes the hippocampus, and its connectivity supports many aspects of episodic memory. Here, we used connectome-based predictive modeling to identify associations between intrinsic DMN connectivity and mnemonic discrimination. We leveraged a wide range of abilities across healthy younger and older adults to facilitate this predictive approach. Resting-state functional connectivity in the DMN predicted mnemonic discrimination outside the MRI scanner, especially among prefrontal and temporal regions and including several hippocampal regions. This predictive relationship was stronger for younger than older adults, primarily for temporal-prefrontal connectivity. The novel associations established here are consistent with mounting evidence that broader cortical networks including the hippocampus support mnemonic discrimination. They also suggest that age-related network disruptions undermine the extent that the DMN supports this ability. This study provides the first indication of how intrinsic functional properties of the DMN support mnemonic discrimination.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Memoria Episódica
/
Conectoma
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Humans
Idioma:
En
Revista:
Hippocampus
Asunto de la revista:
CEREBRO
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Estados Unidos