Dynamic neural networks supporting memory retrieval

• Published in: NeuroImage (Orlando, Fla.) Vol. 57; no. 2; pp. 608 - 616
• Main Authors: St. Jacques, Peggy L., Kragel, Philip A., Rubin, David C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15-07-2011; Elsevier Limited
• Subjects: Adolescent; Adult; Autobiographical memory; Brain; Brain Mapping; Construction; Episodic memory; Female; fMRI; Functional neuroimaging; Humans; Image Interpretation, Computer-Assisted; Large-scale brain network; Magnetic Resonance Imaging; Male; Memory; Memory - physiology; Mental Recall - physiology; Neural networks; Prefrontal Cortex - physiology; Retrieval; Studies; Young Adult; Retrieval; Large-scale brain network; fMRI; Functional neuroimaging; Episodic memory; Autobiographical memory
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.04.039

How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) medial prefrontal cortex (PFC) network, associated with self-referential processes, 2) medial temporal lobe (MTL) network, associated with memory, 3) frontoparietal network, associated with strategic search, and 4) cingulooperculum network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior.
► Autobiographical memory retrieval is supported by interacting large-scale networks. ► The medial PFC network drives neuronal activation. ► Connectivity with this network increases when memories are accessible and recollected.