Detecting Representations of Recent and Remote Autobiographical Memories in vmPFC and Hippocampus

Abstract

How autobiographical memories are represented in the human brain and whether this changes with time are questions central to memory neuroscience. Two regions in particular have been consistently implicated, the ventromedial prefrontal cortex (vmPFC) and the hippocampus, although their precise contributions are still contested. The key question in this debate, when reduced to its simplest form, concerns where information about specific autobiographical memories is located. Here, we availed ourselves of the opportunity afforded by multivoxel pattern analysis to provide an alternative to conventional neuropsychological and fMRI approaches, by detecting representations of individual autobiographical memories in patterns of fMRI activity. We examined whether information about specific recent (two weeks old) and remote (10 years old) autobiographical memories was represented in vmPFC and hippocampus, and other medial temporal and neocortical regions. vmPFC contained information about recent and remote autobiographical memories, although remote memories were more readily detected there, indicating that consolidation or a change of some kind had occurred. Information about both types of memory was also present in the hippocampus, suggesting it plays a role in the retrieval of vivid autobiographical memories regardless of remoteness. Interestingly, we also found that while recent and remote memories were both represented within anterior and posterior hippocampus, the latter nevertheless contained more information about remote memories. Thus, like vmPFC, the hippocampus too respected the distinction between recent and remote memories. Overall, these findings clarify and extend our view of vmPFC and hippocampus while also informing systems-level consolidation and providing clear targets for future studies.

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

“…In particular, left anterior HPC showed greater functional connectivity during the early phase of learning with regions of mPFC, a finding predicted by SUSTAIN's neural framework (11). These results are also consistent with episodic memory findings that PFC biases encoding and retrieval of mnemonic information in HPC (9,27,28) and a recent proposal (29) that memories for individual experiences are updated through HPC-mPFC interactions to create generalized knowledge that supports complex behaviors like inference (8,30). We also found that early-learning HPC activation was coupled with regions implicated in a neural hierarchy of cognitive control along the rostral-caudal axis of lateral PFC (26,57,58).…”

“…Prefrontal cortex (PFC) is proposed to tune selective attention to features (24)(25)(26), as well as direct encoding and retrieval of HPC cluster representations (9,(27)(28)(29)(30). In particular, PFC monitors the similarity between the current stimulus information and existing conceptual knowledge and biases HPC functions in reorganizing clusters to reflect goal-relevant features.…”

Memory Consolidation

“…In one typical design, a prescan interview is conducted to elicit autobiographical memories from the recent and remote past, and these memories are then queried during scanning (e.g., Niki and Luo 2002;Piefke et al 2003;Bonnici et al 2012Bonnici et al , 2013Söderlund et al 2012). Studies using this design sometimes found greater medial temporal lobe activity in association with recent recollections compared with remote recollections (i.e., evidence of a temporal gradient), but sometimes there was no evidence for a temporal gradient.…”

Why musical memory can be preserved in advanced Alzheimer’s disease

“…Recent methodological developments of functional MRI analysis employing pattern classification provide a novel approach for investigating memory (Bonnici et al, 2012), by determining the coding of information in distributed patterns rather than by comparing brain activity levels in a voxel-wise fashion. This methodology is particularly effective in the analysis of ultra high-field (7 T) functional imaging data (Bode et al, 2011).…”