Post-learning Hippocampal Dynamics Promote Preferential Retention of Rewarding Events

Abstract

Reward motivation is known to modulate memory encoding, and this effect depends on interactions between the substantia nigra/ventral tegmental area complex (SN/VTA) and the hippocampus. It is unknown, however, whether these interactions influence offline neural activity in the human brain that is thought to promote memory consolidation. Here we used fMRI to test the effect of reward motivation on post-learning neural dynamics and subsequent memory for objects that were learned in high- and low-reward motivation contexts. We found that post-learning increases in resting-state functional connectivity between the SN/VTA and hippocampus predicted preferential retention of objects that were learned in high-reward contexts. In addition, multivariate pattern classification revealed that hippocampal representations of high-reward contexts were preferentially reactivated during post-learning rest, and the number of hippocampal reactivations was predictive of preferential retention of items learned in high-reward contexts. These findings indicate that reward motivation alters offline post-learning dynamics between the SN/VTA and hippocampus, providing novel evidence for a potential mechanism by which reward could influence memory consolidation.

Figure 1

Overview of study design. (A) fMRI data were collected during a pre-learning rest phase (10 min), a reward-motivated learning task (48 min), and a post-learning rest phase (10 min). After scanning, participants performed a surprise recognition memory test (on average, the interval between initial learning of an item and presentation of that item at test was around 83 min). (B) During learning, participants encoded objects in 30s trial blocks, with each block associated with a particular scene background and encoding task. Each block started with a high ($2.00) or low ($0.02) reward cue and instructions about which encoding task should be performed for the next four consecutively presented objects. Reward was contingent on accurate judgment performance, and feedback about the accumulated reward was given at the end of the block. To maintain the continuity of each encoding context, the background scene semantically matched the relevant task (e.g. For the task: “Does this item weigh more than a basketball?”, a basketball court remained on the screen). (C) During the memory test, participants indicated (i) whether they could remember an object and how confident they are about their response and (ii) whether they could remember the associated encoding context (i.e. particular task semantically matched with background scene). (D) Participants showed enhanced memory for object-context associations that were learned in high-reward compared to low-reward contexts (i.e. HR>LR object-context memory advantage).

Figure 2

Increases in SN/VTA-hippocampal resting-state functional connectivity (RSFC) correlate with the HR>LR object-context memory advantage. (A) We asked whether RSFC changes following learning (i.e. Pre-learning rest < Post-learning rest) between the SN/VTA and hippocampus ROI predicted later the reward-related memory advantage. (B) Across subjects, changes in SN/VTA-hippocampal RSFC positively correlated with the HR>LR object-context memory advantage.

Figure 3

Hippocampal reactivation of high-reward contexts predict the HR>LR object-context memory advantage. (A) A 2-way classifier was trained on hippocampal activity patterns to dissociate between high- and low-reward contexts and then ‘tested’ on all time points during the pre- and post-learning rest period. We counted the number of time points that were classified as for high-reward context. A schematic is shown how timepoints might be labeled for one participant’s pre- and post-learning rest period. (B) Classifier results showing that the hippocampal activity patterns were more likely than chance-level (dotted line) to be associated with a high-reward context during the post-learning rest, but not during the pre-learning rest period. (C) The ‘high-reward reactivation index’, which is the difference in reactivation of high-reward contexts in post-learning rest compared to pre-learning rest, correlated with the HR>LR object-context memory advantage.