Medial Prefrontal Cortex Reduces Memory Interference by Modifying Hippocampal Encoding

Abstract

The prefrontal cortex (PFC) is crucial for accurate memory performance when prior knowledge interferes with new learning, but the mechanisms that minimize proactive interference are unknown. To investigate these, we assessed the influence of medial PFC (mPFC) activity on spatial learning and hippocampal coding in a plus maze task that requires both structures. mPFC inactivation did not impair spatial learning or retrieval per se, but impaired the ability to follow changing spatial rules. mPFC and CA1 ensembles recorded simultaneously predicted goal choices and tracked changing rules; inactivating mPFC attenuated CA1 prospective coding. mPFC activity modified CA1 codes during learning, which in turn predicted how quickly rats adapted to subsequent rule changes. The results suggest that task rules signaled by the mPFC become incorporated into hippocampal representations and support prospective coding. By this mechanism, mPFC activity prevents interference by "teaching" the hippocampus to retrieve distinct representations of similar circumstances.

Keywords: CA1; hippocampus; learning; memory; neuronal representation; prefrontal cortex; proactive interference; recall; retrieval.

Copyright © 2017 Elsevier Inc. All rights reserved.

Figures

Figure 1

mPFC inactivation impairs serial reversal learning in a hippocampus-dependent spatial memory task. (A) Rats are put on one of two pseudorandomly chosen start arms (North or South) in each trial and learn to associate one of two possible goal arms (East or West) with food reward. The same goal is rewarded during a block of trials until a rat performs well (10 of 12 correct choices), and then the other goal is rewarded in a new block of trials. Well-trained animals learned the initial discrimination (ID) and each spatial reversal in ~5 min. (B) The mPFC of each rat was infused bilaterally with muscimol or saline 25 or 50 minutes before testing. (C) mPFC inactivation had no effect on initial learning but profoundly impaired subsequent reversal learning (R1, R2). Similar effects were observed using other behavioral metrics, as illustrated in Figure S1 and Table S1A. (D) Additional experiments included a sham infusion and a 25 min delay between ID and R1. One experiment gave the real infusion before the ID and the sham infusion before R1 (upper timeline); a second experiment reversed this order so that the sham preceded the ID and the actual infusion preceded R1 (lower timeline). (E) Inactivating the mPFC before ID impaired R1 despite the additional delay (cf. panel C). Inactivating the mPFC after the ID and before R1 had no effect on R1, but impaired learning a second reversal (R2). Similar findings were obtained using other behavioral metrics, as illustrated in Tables S1B, S2A, and S2B. *above columns indicate significant differences between drug conditions within each learning epoch.

Figure 2

Theta oscillations were coordinated in the mPFC and CA1 as rats perform the spatial reversal task. (A) Typical examples of LFPs recorded in CA1 (top traces) and mPFC (bottom traces) as rats progress through a single trial, each filtered in either low (lower trace) or high (upper trace) frequency bands. CA1 LFPs oscillate persistently in theta while mPFC LFPs revealed a different pattern in which oscillations increased in frequency and decreased in amplitude across the trial. (B) Power spectrograms quantified group averaged LFP data in 0–14, 14–30, and 30–80 Hz bands, and show that CA1 theta power was greatest on the start arm (left). Spectrograms verified the more dynamic mPFC power spectra (middle). Trials began with prominent 4 Hz oscillations that increased in frequency as animals approached the goal arm. Theta oscillations became prominent as animals entered the goal arm, and increased in frequency just before animals reached the reward point. Theta coherence was strongest before a trial was initiated, and after animals crossed into the goal arm (right). Negative values on the horizontal axis indicate time before initiation of the trial, and positive values indicate position on the maze in cm. The dotted line marks the boundary between the start arm and the maze choice point. Different panels represent wavelet transformations carried out using different parameters to optimize wavelet time-frequency resolution. (C) Unit activity in both structures is coordinated by hippocampal theta rhythm. A significant portion of units in both mPFC (20.2%) and CA1 (75.8%) showed greater than chance modulation of spiking activity by theta phase. Top: the magnitude of theta locking expressed in terms of the resultant vector length (see methods). Bottom: the proportion of CA1 and mPFC units passing significance at various alpha values. The dotted line represents alpha = 0.05. (D) mPFC and CA1 units fired at different preferred theta phases. Most CA1 units tended to fire near the trough of the hippocampal theta cycle, while mPFC units tended to prefer to fire early in the descending phase (Beta2 in (Lansink et al., 2016)). The different phase preferences correspond to a temporal offset in spiking between the two structures of about 51 ms. (E) Theta trough-triggered averages of the firing rate of mPFC and CA1 units verified the phase modulation of firing rates in both regions and the temporal offset of activity between the two structures.

Figure 3

Ensemble activity in both CA1 and mPFC distinguish rules, and mPFC activity predicts CA1 dynamics. All activity was recorded in the start arm before the rat entered the choice point. Population coding dynamics across testing sessions are illustrated qualitatively as 2D projections of CA1 (A) and mPFC (B) ensemble states analyzed by MDS. Trials in each contingency block were averaged and subdivided into 7 points to span learning curves, shown here by arrow heads. The colors show the progression of neuronal activity from the start of a new contingency (red) through the learning curve (brightening colors, goal 1 in shades of green, and goal 2 in shades of blue) to criterion performance and the end of the block (white). Ensemble states distinguish time (or distance) and goals (X and Y axes, respectively; goal 1 top, goal 2 bottom). (C) Ensemble codes recorded in the start arms were quantified by SVMs that decoded pending goals in single trials, showing rule coding in both brain regions. Each dot represents a single ensemble, blue indicates better than chance decoding. (D) The history of mPFC activity helped predict changes in CA1 activity early in learning (EL), but not during stable performance (SP), when CA1 activity has a small but significant influence on mPFC states. * Above columns indicate significantly different than chance defined by permutation tests.

Figure 4

mPFC inactivation reduces the separation of rule, but not place representations in CA1 ensembles recorded in the start arms. (A) mPFC inactivation did not impair prediction accuracy coding by CA1 ensembles, but (B) did reduced the separation between prospective codes as measured by the distance between activity patterns in each trial and the SVM goal-decoding margin. (C) mPFC inactivation did not affect start arm decoding accuracy by CA1 ensembles, (D) and did not alter the separation between spatial codes recorded in the start arm.

Figure 5

mPFC activity during learning influenced CA1 activity most strongly when the subsequent reversal is learned most quickly. Granger values were calculated during early learning (A) and stable performance (B) for trials that preceded reversals that were learned at different rates. Significant influence of mPFC on CA1 was only observed when animals learned the subsequent reversal quickly, in ≤ 3 trials (see main text). * above columns indicate significantly different than chance calculated by permutation tests.