The brain is composed of networks of interacting brain regions that support higher-order cognition. Among these, a core network of regions has been associated with recollection and other forms of episodic construction. Past research has focused largely on the roles of individual brain regions in recollection or on their mutual engagement as part of an integrated network. However, the relationship between these region- and network-level contributions remains poorly understood. Here, we applied multilevel structural equation modeling to examine the functional organization of the posterior medial (PM) network and its relationship to episodic memory outcomes. We evaluated two aspects of functional heterogeneity in the PM network: first, the organization of individual regions into subnetworks, and second, the presence of regionally specific contributions while accounting for network-level effects. Our results suggest that the PM network is composed of ventral and dorsal subnetworks, with the ventral subnetwork making a unique contribution to recollection, especially to recollection of spatial information, and that memory-related activity in individual regions is well accounted for by these network-level effects. These findings highlight the importance of considering the functions of individual brain regions within the context of their affiliated networks.

Neurobiological memory models assume memory traces are stored in neocortex, with pointers in the hippocampus, and are then reactivated during retrieval, yielding the experience of remembering. Whereas most prior neuroimaging studies on reactivation have focused on the reactivation of sets or categories of items, the current study sought to identify cortical patterns pertaining to memory for individual scenes. During encoding, participants viewed pictures of scenes paired with matching labels (e.g., “barn,” “tunnel”), and, during retrieval, they recalled the scenes in response to the labels and rated the quality of their visual memories. Using representational similarity analyses, we interrogated the similarity between activation patterns during encoding and retrieval both at the item level (individual scenes) and the set level (all scenes). The study yielded four main findings. First, in occipitotemporal cortex, memory success increased with encoding-retrieval similarity (ERS) at the item level but not at the set level, indicating the reactivation of individual scenes. Second, in ventrolateral pFC, memory increased with ERS for both item and set levels, indicating the recapitulation of memory processes that benefit encoding and retrieval of all scenes. Third, in retrosplenial/posterior cingulate cortex, ERS was sensitive to individual scene information irrespective of memory success, suggesting automatic activation of scene contexts. Finally, consistent with neurobiological models, hippocampal activity during encoding predicted the subsequent reactivation of individual items. These findings show the promise of studying memory with greater specificity by isolating individual mnemonic representations and determining their relationship to factors like the detail with which past events are remembered.

Neural systems may be characterized by measuring functional interactions in the healthy brain, but it is unclear whether components of systems defined in this way share functional properties. For instance, within the medial temporal lobes (MTL), different subregions show different patterns of cortical connectivity. It is unknown, however, whether these intrinsic connections predict similarities in how these regions respond during memory encoding. Here, we defined brain networks using resting state functional connectivity (RSFC) then quantified the functional similarity of regions within each network during an associative memory encoding task. Results showed that anterior MTL regions affiliated with a network of anterior temporal cortical regions, whereas posterior MTL regions affiliated with a network of posterior medial cortical regions. Importantly, these connectivity relationships also predicted similarities among regions during the associative memory task. Both in terms of task-evoked activation and trial-specific information carried in multivoxel patterns, regions within each network were more similar to one another than were regions in different networks. These findings suggest that functional heterogeneity among MTL subregions may be related to their participation in distinct large-scale cortical systems involved in memory. At a more general level, the results suggest that components of neural systems defined on the basis of RSFC share similar functional properties in terms of recruitment during cognitive tasks and information carried in voxel patterns.

Emotion is known to influence multiple aspects of memory formation, including the initial encoding of the memory trace and its consolidation over time. However, the neural mechanisms whereby emotion impacts memory encoding remain largely unexplored. The present study used a levels-of-processing manipulation to characterize the impact of emotion on encoding with and without the influence of elaborative processes. Participants viewed emotionally negative, neutral, and positive scenes under two conditions: a shallow condition focused on the perceptual features of the scenes and a deep condition that queried their semantic meaning. Recognition memory was tested 2 days later. Results showed that emotional memory enhancements were greatest in the shallow condition. fMRI analyses revealed that the right amygdala predicted subsequent emotional memory in the shallow more than deep condition, whereas the right ventrolateral PFC demonstrated the reverse pattern. Furthermore, the association of these regions with the hippocampus was modulated by valence: the amygdala–hippocampal link was strongest for negative stimuli, whereas the prefrontal–hippocampal link was strongest for positive stimuli. Taken together, these results suggest two distinct activation patterns underlying emotional memory formation: an amygdala component that promotes memory during shallow encoding, especially for negative information, and a prefrontal component that provides extra benefits during deep encoding, especially for positive information.