Rapid and independent memory formation in the parietal cortex

Abstract

Previous evidence indicates that the brain stores memory in two complementary systems, allowing both rapid plasticity and stable representations at different sites. For memory to be established in a long-lasting neocortical store, many learning repetitions are considered necessary after initial encoding into hippocampal circuits. To elucidate the dynamics of hippocampal and neocortical contributions to the early phases of memory formation, we closely followed changes in human functional brain activity while volunteers navigated through two different, initially unknown virtual environments. In one condition, they were able to encode new information continuously about the spatial layout of the maze. In the control condition, no information could be learned because the layout changed constantly. Our results show that the posterior parietal cortex (PPC) encodes memories for spatial locations rapidly, beginning already with the first visit to a location and steadily increasing activity with each additional encounter. Hippocampal activity and connectivity between the PPC and hippocampus, on the other hand, are strongest during initial encoding, and both decline with additional encounters. Importantly, stronger PPC activity related to higher memory-based performance. Compared with the nonlearnable control condition, PPC activity in the learned environment remained elevated after a 24-h interval, indicating a stable change. Our findings reflect the rapid creation of a memory representation in the PPC, which belongs to a recently proposed parietal memory network. The emerging parietal representation is specific for individual episodes of experience, predicts behavior, and remains stable over offline periods, and must therefore hold a mnemonic function.

Keywords: long-term memory; memory systems consolidation; posterior parietal cortex; precuneus; virtual reality.

Fig. 1.

Properties of the maze. (A) Layout of the virtual labyrinth in the static condition. Locations of the five objects are depicted as black dots, and junctions are marked with red triangles. (B) View of a junction in the static condition from the participant’s perspective. (C) Layout of the virtual labyrinth in the random condition. All pathways within a hexagonal structure were theoretically possible, but some were blocked at random. The experience for the participant in the random condition was identical to the experience in the static condition, because wall locations and wall textures were changed constantly but only outside the field of view of the subject (red circle). (D) Sample wall textures.

Fig. 2.

Effect of number of trials in the static condition vs. the random condition. (A) Precuneus shows a higher increase in activation with an increasing number of trials for the static condition compared with the random condition. Highlighted clusters exceed 10 voxels and exhibit significant peak-level effects at a family-wise error–corrected probability threshold (P_FWE) < 0.05. No masking was applied (also Table S1). (B) Precuneus activation increases with the number of trials only in the static condition, but not in the random condition. Precuneus activation increase in the static condition parallels the percentage of target objects found in each trial (orange line). Trials 1–30 occurred on day 1, and trials 31–60 occurred on day 2. Mean beta values of all voxels in an anatomical precuneus region of interest that were significantly activated in A are displayed. Error bars indicate SEM. Linear regression for the static condition was significant (β = 0.76, t₅₉ = 8.85, P < 0.001, R² = 0.58), whereas it was not significant for the random condition (β = −0.24, t₅₉ = −1.84, not significant, R² = 0.06).

Fig. S1.

Interaction effect of condition and day. (A) Precuneus shows greater activation in sessions 3 + 4 (day 2) in the static condition than in the random condition compared with sessions 1 + 2 (day 1) (Table S3). Highlighted clusters exceed 10 voxels and exhibit significant peak-level effects at a full-volume, family-wise error–corrected probability threshold (P_FWE) < 0.05. No masking was applied. (B) Mean beta values for the static and random conditions in sessions 1 + 2 and sessions 3 + 4 of the significant precuneus voxels show that the precuneus was more strongly activated in sessions 3 + 4 compared with sessions 1 + 2 in the static condition. *P < 0.05; ***P < 0.001.

Fig. 3.

Effect of repeated encounters with locations. (A) Precuneus shows greater activation in the last encounter compared with the first encounter with a location in the static condition. Highlighted clusters exhibit significant peak-level effects at P_FWE < 0.05 (also Table S4). (B) Precuneus activation increases with the number of encounters. Mean beta values of all voxels in an anatomical precuneus region of interest (ROI) over encounters are displayed. Error bars indicate SEM. Linear regression was significant (β = 0.96, t₁₆ = 13.32, P < 0.001, R² = 0.92). (C) Hippocampus shows greater activation in the first encounter compared with the last encounter with a location in the static condition. The highlighted cluster exhibits a significant peak-level effect at an uncorrected P value (P_uncorr) < 0.001 (also Table S4). (D) Hippocampus activation decreases with the number of encounters. Mean beta values of all voxels in an anatomical ROI of the left hippocampus over encounters are displayed. Error bars indicate SEM. Linear regression was significant (β = −0.70, t₁₆ = 3.78, P = 0.002, R² = 0.49). All highlighted clusters exceed 10 voxels, and no masking was applied.

Fig. S2.

Masks used for effect localization. (A) Precuneus. (B) RSC. (C) Hippocampus. All masks are anatomically defined.

Fig. 4.

Connectivity between precuneus and hippocampus with repeated encounters. Coactivation of precuneus and hippocampus decreases with increasing number of encounters. The beta values represent the average values over all anatomical hippocampal voxels in a PPI analysis with the precuneus as a seed region. Error bars indicate SEM. Linear regression was significant (β = −0.65, t₁₆ = 3.30, P = 0.005, R² = 0.42).

Fig. 5.

Performance and precuneus activation. (A) Mean percentage of correct trials in which the target object was found within the time limit increased linearly over the four experimental sessions (F_1,26 = 10.39, P = 0.003). Error bars indicate SEM (also Table 1). (B) Route bias based on the distribution of frequencies with which each of the 79 corridors was traveled in a session when sorted in descending order. Gray bars show the mean frequency for each corridor for session 1. Mean logarithmic fits for sessions 1 (solid line) and 4 (dashed line) and time constants for every session (vertical black bars) are displayed. Decreasing time constants on the x axis indicate that route bias increases with every session (F_1,26 = 11.35, P = 0.002). (C) Precuneus shows overlapping performance-dependent activation in four different analyses (white). Higher precuneus activation was found in good navigators compared with poor navigators based on the percentage of correct trials (red) as well as on the route bias score (blue). Similar precuneus activity was found at junctions in the static maze when the correct decision in terms of the optimal path leading to the target was made (green), as well as when comparing brain activity at the beginning of successful and unsuccessful trials (yellow) (also Fig. S3 and Tables S8–S10). All four analyses revealed peak activity in the precuneus of P_FWE < 0.05 full-volume corrected and are displayed at P_uncorr < 0.001. Dorsolateral prefrontal cortex activity, in contrast to activity in the precuneus, did not reach family-wise error–corrected thresholds (also SI Results and Discussion). All highlighted clusters exceed 10 voxels. No masking was applied.

Fig. S3.

Performance × condition interactions. (A and B) Mean beta values for the static and random conditions of good and bad navigators of the significant precuneus voxels show that the interaction effects were due to a greater activation of the precuneus in the static condition compared with the random condition in good navigators. Performance was measured as the percentage of correct trials (A) and route bias score (B). (C) Similarly, effects in the interaction analysis of condition and trial success were driven by a greater activation of the precuneus for correct compared with incorrect trials, specifically in the static condition. Beta values are based on all significant precuneus voxels in the corresponding interaction analysis. **P < 0.01;***P < 0.001.