Rapid and independent memory formation in the parietal cortex Svenja Brodta, Dorothee Pöhlchena, Virginia L. Flanaginb,c, Stefan Glasauerb,c,d, Steffen Gaisa,c,1, and Monika Schönauera,c a Medical Psychology and Behavioral Neurobiology, Eberhard-Karls-Universität Tübingen, 72076 Tuebingen, Germany; bGerman Center for Vertigo and Balance Disorders, University Hospital Munich, 81377 Munich, Germany; cBernstein Center for Computational Neuroscience, Ludwig-MaximiliansUniversität München, 82152 Planegg-Martinsried, Germany; and dCenter for Sensorimotor Research, Department of Neurology, University Hospital Munich, 81377 Munich, Germany

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.

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long-term memory posterior parietal cortex consolidation virtual reality

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and neocortex, as well as increased intracortical connectivity of representational areas (6). A region that is currently receiving increased attention in memory research is the posterior parietal cortex (PPC) (9). Although many fMRI studies have shown activation of the PPC during memory tasks (10), it has only recently been confirmed that there is a genuine contribution of the PPC to episodic memory that goes beyond attentional processes (11). Studies on functional connectivity identifying a hippocampal-parietal memory network (12), together with findings that posterior parietal areas can maintain material-specific working memory representations (13), suggest that the PPC might be involved in hippocampalneocortical interactions during memory formation. The domain of spatial navigation offers great possibilities to investigate the gradual process of building a complex memory representation. Similar to other domains of memory, lesion and neuroimaging studies in animals and humans indicate that spatial learning and memory primarily rely on the hippocampus (14, 15) but can, at some point, become independent (16). In a longitudinal study, the hippocampus was only activated during mental navigation when subjects had just moved to a new city, but not when they had already lived there for 1 y. In the latter case, the subjects relied instead on neocortical areas like the retrosplenial cortex (RSC) and the PPC (17). Indeed, in their metaanalysis, Boccia et al. (18) found activation in the hippocampus only for studies investigating recently learned environments but not for highly familiar surroundings. Studies in rodents support these findings by showing a decrease in metabolic activity and expression of immediate early genes in the hippocampus following retrieval after 4 wk, whereas, at the same time, these measures increased in several neocortical areas (5). Together, these findings indicate that Significance

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earning enables adaptive and effective interaction with the environment based on past experience. How this essential capability of the brain to encode, store, and later retrieve new information is implemented on the systems level has been the focus of many studies. However, although there is consistent evidence that specific brain regions are involved in learning and memory, the interactions between these regions and their temporal dynamics remain unclear. For declarative memory, one influential model proposes complementary roles of the hippocampus and neocortex in supporting memory representations (1–3). It assumes that the highly plastic hippocampus serves as a fast learner, transiently storing newly encountered information. Later on, this information is gradually integrated into more stable neocortical networks (4). Many experiments in animals and humans have confirmed decreased hippocampal but increased neocortical contributions to memory retrieval with longer consolidation intervals (5–7). Concerning the time frame during which hippocampal independence of a memory is established, accounts diverge widely. In the case of patients with medial temporal lobe (MTL) damage, retrograde amnesia is reported to have a temporal gradient spanning years or decades (8). However, other studies describe a decrease in hippocampal activation as early as 1 d after learning (6, 7). These changes were paralleled by decreasing connectivity between the hippocampus

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When it comes to memory, our brain needs to deal with two opposing demands: remaining plastic to acquire new information rapidly and maintaining old information faithfully over long periods. It is assumed that two distinct brain systems are in charge of these functions: the fast-learning hippocampus and the slowlearning neocortex. The interaction between these two systems has traditionally been seen as very slow, requiring weeks or months to build a neocortical memory trace. In this study, brain activity during virtual-reality navigation shows that contributions of hippocampus and parietal neocortex to memory are changing substantially already at the time a spatial memory representation is built. Notably, we show that the posterior parietal cortex fulfills all criteria for a hippocampus-independent memory representation. Author contributions: S.B., V.L.F., S. Glasauer, S. Gais, and M.S. designed research; S.B., D.P., and M.S. performed research; S.B., D.P., and M.S. analyzed data; and S.B., V.L.F., S. Glasauer, S. Gais, and M.S. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. 1

To whom correspondence should be addressed. Email: [email protected].

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1605719113/-/DCSupplemental.

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Edited by Larry R. Squire, Veterans Affairs San Diego Healthcare System, San Diego, CA, and approved September 30, 2016 (received for review April 22, 2016)

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.

independent neocortical memory representations may be formed more rapidly than previously expected. Although many studies focus on the retrieval of an already learned environment, in this study, we targeted memory processes during the acquisition period. We aimed to identify areas involved during the establishment of a new memory representation and to examine the changes in brain activity over the course of the initial encounters with this information. In an MRI scanner, we let participants navigate through a completely unknown, complex virtual environment and find specific objects over an extended amount of time. Because we were particularly interested in those brain regions specifically involved in the storage of spatial memory, and not in navigating through an environment per se, we designed two conditions. In the experimental condition, participants were able to encode new information continuously about the spatial layout of the maze (“static maze”; Fig. 1). In the control condition, no information could be learned because the maze layout changed constantly outside the field of view (“random maze”). In all other respects (e.g., construction of the maze, optic flow, parameters of movement), the conditions were identical (Movie S1). Our main interest in this study was to elucidate the dynamics of hippocampal and neocortical contributions during spatial learning, with a special focus on the relation between both systems. We hypothesized that the hippocampus would be required mainly for the initial encoding of new information, and that its influence should diminish over the course of learning. Instead, neocortical, probably parietal, regions should gradually support the spatial representation. Results Activation Changes over Repeated Encounters. The main goal of this study was to track brain activity during the development of a spatial representation. Subjects were navigating alternately through a learnable virtual environment (static maze) and nonlearnable virtual environment (random maze). We first looked for brain regions that 13252 | www.pnas.org/cgi/doi/10.1073/pnas.1605719113

show a gradual increase of activity over trials in the static environment that did not similarly occur in the random environment. The interaction between the factors environment (static/random) and trial should therefore represent those brain areas that develop a spatial representation, whereas it controls for changes related to moving through the maze and to the passage of time. We found significant clusters of activity predominantly in the bilateral precuneus, as well as a few smaller activations in other parietal and frontal areas (Fig. 2A and Table S1). The mean beta values for the precuneus clusters increase steadily over learning trials in the static condition. No such increase could be observed over repeated navigation trials in the random maze (Fig. 2B). This increase in precuneus activity is mirrored by the overall increase in performance over trials [Fig. 2B, orange line; r(58) = 0.55, P < 0.001]. The beginning of session 3, on the second day, reveals a drop in precuneus activity as well as in navigation performance. However, when comparing the first trials of sessions 1 and 3 in the static and random conditions, it becomes obvious that the precuneus response to the first trial of the static maze on the second experimental day is already higher than on the first day (t26 = −2.19, P = 0.037) and also higher than the precuneus response of the corresponding random condition (t26 = 2.33, P = 0.028). Learning-dependent changes in precuneus response therefore persist between experimental days (additional analysis is provided in SI Results and Discussion) (see also Fig. S1 and Table S2). The time-based analyses above implicate the precuneus in the development of a memory representation in the static maze condition. Because junctions in a maze are the locations where memory-based decisions have to be made, we compared brain activity at these decision points with activity while traveling along a corridor without junctions. Again, we found a very similar network of brain areas as in the comparison between static and variable conditions, featuring the precuneus and some other parietal and frontal areas (Table S3). To assess the dynamics underlying learning-related changes in more detail, we investigated the effect of repeated encounters with specific spatial locations. For this analysis, we compared brain responses during initial and later encounters with a given location and modeled a linear change over repetitions. To avoid bias, these preplanned analyses were based on the anatomically defined regions (Fig. S2). We found higher activation in the precuneus and RSC during late compared with initial encounters (Fig. 3A and Table S4). The mean beta values over all precuneus voxels increased linearly (β = 0.96, t16 = 13.32, P < 0.001, R2 =

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 (PFWE) < 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).

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0.92; Fig. 3B). Most strikingly, in contrast to this increase in activity with repeated encounters, the opposite pattern emerges in the hippocampus. Comparing the initial with later encounters yielded higher activity in the left hippocampus [uncorrected P value (Puncorr) < 0.001; Fig. 3C and Table S4]. Opposing the trend of precuneus activity, the mean beta values over the left hippocampus decreased linearly with encounters (β = −0.70, t16 = −3.78, P = 0.002, R2 = 0.49; Fig. 3D). Comparing the course of hippocampal and precuneus activity on a single-subject level yielded a significant negative average correlation over all subjects (r = −0.23, t26 = −2.66, P = 0.013), indicating that engagement of these two regions follows an opposing course. Finally, to assess the interaction between the precuneus and hippocampus over repeated encounters with a location, we computed a multilevel psychophysiological interaction (PPI) analysis, which represents the activity common to both areas during navigation. Linear regression on the results of this PPI analysis revealed that functional connectivity declines linearly with repeated encounters (β = −0.65, t16 = −3.30, P = 0.005, R2 = 0.42; Fig. 4). Together, our data speak for the rapid build-up of an independent spatial representation in the precuneus over the first encounters with a novel location. The hippocampus, although initially active and functionally connected to the precuneus, shows a gradually diminished contribution over encounters (additional analyses on the role of the hippocampus and other regions are provided in SI Results and Discussion and Tables S5 and S6). Overall, our analyses confirm that precuneus activity increases during the development of a spatial representation, initially supported by the hippocampus. Behavioral Results and Performance-Dependent Effects. Because the present study aimed at finding correlates of spatial memory representations, we tested whether brain activity was related to spatial Brodt et al.

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).

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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 PFWE < 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 (Puncorr) < 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.

memory performance during navigation. We used four different analyses to test for a relation between memory performance and brain activity. First, we considered the percentage of target objects found within a session. We find that participants form a memory of the static maze over time: performance in the static condition increased linearly from session 1 to session 4 (F1,26 = 10.39, P = 0.003; Fig. 5A and Table 1). Learning occurred continuously over all sessions because learning rates did not differ between the two experimental days (t26 = −3.96, P = 0.695). The random condition showed no such trend (F1,26 = 0.73, P = 0.400; interaction static/ random × session: F1,26 = 4.36, P = 0.047; Table 1). Although both good and bad learners show similar activity in the static maze compared with the random maze (Table S7), comparing brain activity between good and bad learners based on a median split revealed significantly higher activity in the precuneus in good learners in the interaction good/bad × static/random (Fig. 5C and Table S8). Analysis of corresponding beta values showed higher precuneus activation in the static condition in good performers compared with bad performers (t25 = 3.14, P = 0.004; Fig. S3A). For a second analysis, we calculated a route bias score in the static condition. This score shows whether the preference for certain routes in the maze changes over learning. Over repeated encounters, some routes should be preferred over others if a spatial representation is built. This preference changes the distribution of path use frequencies. Behaviorally, route preference indicated significant learning over sessions in the static condition in an ANOVA (F3,78 = 5.01, P = 0.003), with route bias increasing linearly over the sessions (F1,26 = 11.35, P = 0.002; Fig. 5B and Table 1). Performance was related to increased blood oxygen level-dependent signal intensities in the good/bad × static/random interaction, again bilaterally in the same region of the precuneus (Fig. 5C, Fig. S3B, and Table S8). There was no difference in connectivity between the precuneus and hippocampus over encounters between good and bad learners (based on percentage of targets found: t25 = −0.14, P = 0.887; based on route bias: t25 = −0.07, P = 0.995). Above, we reported higher precuneus activity for junctions vs. corridors. This activity can be due to memory retrieval while making navigational decisions or to higher familiarity with the junctions, which are encountered more often than the corridors. Similarly, subjects with a higher route bias score, based on their recall of the maze, choose certain routes more and more often over sessions, but they also encounter certain corridors more often, which increases familiarity. Therefore, as a third measure of performance, we looked at brain activity when participants reached a junction and made a correct choice compared with an

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 (F1,26 = 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 (F1,26 = 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 PFWE < 0.05 full-volume corrected and are displayed at Puncorr < 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.

incorrect choice. We found higher bilateral precuneus activation at junctions in the static maze when the optimal path was chosen (Fig. 5C and Table S9). Finally, to investigate brain regions that predict the subsequent outcome of a trial, we analyzed brain activity only at the beginning of each trial, when the subjects were presented with their starting location and target. Again, the interaction analysis correct/ incorrect × static/random yielded a significant effect bilaterally in the precuneus (Fig. 5C and Table S10). This effect stems from a greater activation of the precuneus during the initial 5 s of a trial for correct (target found) compared with incorrect (target not found) trials specifically in the static condition (t26 = 4.47, P < 0.001; Fig. S3C). Therefore, precuneus activity predicts successful navigation already at the very beginning of a trial. In all four analyses, overlapping areas of the precuneus were found in relation to performance (Fig. 5C). Together, these four measures of performance in conjunction with the finding of a gradual increase of precuneus activity over successive encounters, provide strong evidence that the precuneus is involved in the encoding and retrieval of spatial representations, and thus has a clear memoryrelated function that emerges already early during learning. Discussion In this virtual navigation fMRI study, we followed the development of the memory representation of a previously unknown, complex environment. We investigated specifically memory-related processes by comparing navigation in a static, and thus learnable, environment with navigation in a continuously changing environment. We were mainly interested in how the involvement of regions that have previously been implicated in spatial processing (i.e., PPC, including the precuneus and RSC, as well as the hippocampus) develops over multiple encounters with a specific location. Our analyses point toward a role of the hippocampus during early encoding, whereas the relevance of the precuneus increases when the memory representation gets stronger. The dissociated activation gradients of the hippocampus and precuneus, together with the decreasing connectivity between both areas, shed light on the possible location of extrahippocampal spatial memory representations and suggest that neocortical representations can develop rapidly during memory acquisition. Our findings are in good agreement with the standard model of memory consolidation, which posits that the role of the hippocampus in memory diminishes with time, whereas the neocortical trace gains in importance (19). We show that this process can occur at a much faster time scale than previously thought. Activation of the PPC, and especially the precuneus, has only infrequently been discussed in terms of memory-related processing. Only recently, more studies on memory have focused their attention on the precuneus. Harvey et al. (20) have found choice-specific firing patterns of PPC neurons during memory-based navigation in the rat, which can be interpreted as the representation of a memory. 13254 | www.pnas.org/cgi/doi/10.1073/pnas.1605719113

Furthermore, activity in the PPC is able to represent category-specific information during a delay period after category information has been learned (21). It is also one of very few regions in the brain that represents memory accuracy in human fMRI when memory confidence is held constant (22). Moreover, GABAergic and glutamatergic signatures of memory exist in the PPC, which speak to its role in episodic memory (23). Together, these and other recent findings led to the proposal of a parietal memory network, which is anatomically distinct from the spatially adjacent default-mode network (9, 24). This network increases its activity with stimulus repetition and represents familiarity. Even though it is therefore a good candidate for long-term memory storage, the mnemonic nature of activity in the parietal cortex is heavily discussed. Although several other brain regions also elicited activity in some of the analyses, only posterior parietal regions, and particularly the precuneus, were consistently engaged in all memoryrelated analyses (other regions are discussed in SI Results and Discussion). Our findings strongly support previous suggestions that these areas in the parietal memory network have a genuine role in memory representation (9). In line with previous evidence (11), a purely attentional account of PPC function (25) does not sufficiently explain our data, because posterior parietal regions showed increased activation over time and with every additional encounter with a location. Regarding the question as to which memory processes are most likely supported by the PPC, the increase in activity with repeated encounters suggests that it is Table 1. Performance indices over the four sessions Session (M ± SEM) Cond

1

Percentage of targets found S 32.6 ± 2.8 38.5 R 46.1 ± 2.8 45.7 Time to target S 37.3 ± 1.5 38.2 R 26.5 ± 0.6 26.0 Route bias S −0.63 ± 0.02 −0.70

2

3

4

± 3.6 ± 3.0

41.0 ± 4.1 50.6 ± 2.3

49.1 ± 4.5 48.4 ± 3.0

± 1.0 ± 0.9

37.4 ± 1.5 27.6 ± 0.8

39.1 ± 1.4 26.2 ± 0.9

± 0.03

−0.72 ± 0.03

−0.76 ± 0.04

Session mean and SE values for the percentage of correct trials, for the time needed to find the target for the static (S) and random (R) conditions (Cond), and for the route bias score in the static condition are displayed. In the static condition, but not in the random condition, participants found an increasing number of target objects within the trial time limit in each session. The static condition also shows a steady increase over the sessions in the strength of preference for certain corridors, which is indicated by more negative values.

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plus offline reactivation, our experiment involved up to 17 encounters with the learning material. Our findings suggest that both can result in a similar extent of systems consolidation. The speed with which consolidation integrates the memory representation into neocortical networks is supposed to be very slow (2). Can the neocortex support an independent memory representation already after only a few learning trials? Indeed, outside declarative memory, a rapid establishment of a hippocampus-independent neocortical memory trace within a few trials has already been shown, for example, in the domains of prism adaptation and priming (38, 39). Additionally, patients with hippocampal damage can rapidly acquire lasting relational memories via a fast-mapping strategy, which is supposed to be mainly reliant on a neocortical network (40). Also, when incorporating new information into preexisting knowledge networks in the spatial domain, hippocampus-independent neocortical memory traces can be successfully established within 3–48 h in rats (41). Thus, our data are in line with previous literature and support the idea that under certain circumstances, the neocortex is able to store new information within a very short time frame. By tracking the initial development of a complex memory representation over an extended amount of training, we were able to uncover early dynamical changes in the relative contribution of and interaction between the hippocampus and the PPC during memory formation. Our data implicate the hippocampus mainly in early encoding processes, whereas memory-related processing in the PPC, especially in the precuneus, is gradually strengthened with each encounter of a location, indicating the immediate establishment of a memory representation upon exposure to new information. We show an early decline in connectivity between both regions, indicating a rapid independence of parietal memoryrelated processing from hippocampal function. The observed activity in the precuneus was specific for individual episodes of the learning experience, predicted behavior, and remained stable over offline periods, which attests to its mnemonic function. Our study provides a general lead for further investigation on the role of the PPC in a parietal memory network and its putative function as an independent store for episodic memory. Materials and Methods Participants. Twenty-eight healthy, right-handed subjects [13 female and 15 male; mean (M) age: 23.04 ± 2.37 y (M ± SD)] participated in this study. Experimental procedures were approved by the Ethics Committee of the Ludwig-Maximilians-Universität München, and participants gave written informed consent. Data from one participant was excluded because of excessive movement (>10 mm in translational parameters), and another participant felt nauseous and aborted the experiment, resulting in a smaller number of trials for one session. Self-rated motivation on a scale of 0–10 was consistently high for all participants (day 1: 8.22 ± 1.60, day 2: 8.15 ± 1.79). Virtual Maze Task. A discussion of the virtual maze task is provided in SI Methods. Procedure. Before the experimental scanning sessions, subjects attended a screening and pretraining session to even out interindividual differences in experience with moving in virtual realities and to assess proneness to cyber sickness. Pretraining consisted of walking around in a virtual test maze, using the same finger-to-movement direction mapping as in the MRI scanner later on. Maze layout and wall textures differed from the actual maze, and no hidden objects were placed in the training maze. Several female subjects dropped out during this stage because of nausea. Participants who had successfully completed the training were handed a questionnaire concerning their experience and preferences in computer games and a sleep diary to keep track of their sleeping behavior before and during scanning days. Participants were scanned on two consecutive days at approximately the same time of day. On each day, the experiment consisted of two scanning sessions of 30 min each and a rest period of 10 min in-between. Each session had a blocked design and consisted of 30 blocks with one trial per block and alternating conditions. The starting condition was counterbalanced between participants and sessions. Trials in the static maze lasted either until the target object was found or maximally for 60 s. The length of the random trials was jittered between 20 and 40 s. On each experimental day, participants filled out pre- and postscan questionnaires assessing their fatigue, motivation, and nausea.

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involved in the retrieval of an existing memory trace. This view is also supported by studies on spatial (26) and episodic memory, where precuneus activation has been shown during recognition as well as cued recall (10, 27) and parietal lesions disrupt recall performance (28, 29). Although the MTL, and especially the hippocampus, has been strongly implicated in the representation of space, a number of previous studies show no hippocampal activity in spatial memory tasks (16, 30). In line with these studies, we did not observe differences in hippocampal activity during memory-related navigation over time on the level of sessions or individual trials (SI Results and Discussion). However, we did find hippocampal activity when a corridor was encountered for the first time (i.e., during early memory encoding). Similar to a study by Wolbers and Büchel (31) showing that the hippocampus is most active when encoding is strongest, this activation decreased gradually with every additional encounter. First, this pattern is consistent with the finding that the hippocampus is especially active when perceptually new stimuli with high entropy are encoded (32). Second, our study shows that decreasing reliance on the hippocampus can already occur at very early stages of memory formation when a complete representation has not yet been established. Further analyses (SI Results and Discussion) support the idea that the hippocampus acts as a novelty detector (33) or as an encoder that supports information storage in other areas. It has been proposed that the primary function of parietal regions lies in integrating (egocentric) sensory inputs, which cue recall of (allocentric) the long-term spatial representations residing in the MTL. These representations, in turn, elicit associated mental imagery in the PPC (34). Our results go beyond this view in several important aspects. Increasing activity in the PPC over the course of learning, concurrently decreasing hippocampal activation as well as decreasing connectivity between both areas with every additional encounter with a location, speaks against stronger integration of hippocampal with parietal memory systems over learning. If the PPC only functions as a retrieval hub for long-term spatial information, this spatial representation does not seem to be retrieved from within the hippocampus. Instead, we believe that it is more likely that the increasing parietal response with growing familiarity is due to the build-up of a memory representation in the PPC itself. The PPC has often been implicated in the context of working memory (20, 35). Most recently, it has been shown that activity changes in subregions of the PPC during working memory reflect not only task-general but also feature-specific information (13), arguing for a transient memory representation in these areas. Our data support that the PPC stores a feature-specific representation. Moreover, evidence for short-term, map-like representations in the PPC, especially involving the precuneus, has already been reported in human navigation studies (36). Here, we show that the enhanced response of the precuneus to the learned spatial representation is preserved between experimental days. This finding can be best explained by a representation of the static environment within the precuneus, which is stable for at least 1 d. Correspondingly, a recent study showed that working memory signatures can be traced in the PPC of monkeys during the delay period of a categorization task, but only after the corresponding task has been learned over a long-term period (21). Therefore, both short-term working memory and long-term episodic representations seem to exist in the precuneus. Models of systems consolidation postulate that a neocortical memory representation may be strengthened by neuronal reactivation between the hippocampus and neocortex, as observed, for example, during resting wakefulness and sleep (1, 2). It is therefore surprising that the effect of additional learning repetitions on memory systems consolidation has so far received little attention. Previous studies assessing systems consolidation in humans have mainly investigated how the memory trace develops over offline periods, and their main focus was on the time between learning and recall (6, 7, 37). We suggest that it is mainly the amount of neuronal activation, not time or level of performance, that determines the progress of systems consolidation. Whereas previous studies have mainly used one to three learning repetitions

Behavioral Data and Performance Measures. Three different parameters were obtained as performance indicators. The first was the percentage of trials in which the target object was found within the time limit. The second was based on the assumption that the development of a spatial representation is accompanied by a preference for those corridors that lead to target objects. Thus, the distribution of frequencies with which every corridor was visited provides information about learning-induced changes in navigation behavior. For each session, we standardized the frequencies to the total number of corridor encounters and fitted a natural logarithmic curve to the frequency distribution sorted in descending order (Fig. 5B). The time constant of the fitted curve describes its steepness and basically represents how many corridors make up 63.2% of all encounters within a session. Thus, this measure reflects the strength of bias or memory for individual routes within the static maze. A steeper distribution has a lower time constant and indicates that fewer corridors were visited a higher number of times, and thus a higher route bias. In this way, we can assess overall memory for routes within the

maze independent of the particular objects participants were navigating between. The third approach to analyze behavioral performance looked at the decision taken at each junction. Here, we compared junctions where subjects chose the corridor that led to the target on the optimal path with the ones where a suboptimal path was chosen in an event-related fMRI analysis. Statistical testing relied on univariate ANOVAs. All tests were twotailed with an α-level of 0.05. If not indicated otherwise, means are reported together with their SEs (M ± SEM).

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MRI Data Acquisition and Analysis. A discussion of MRI data acquisition and analysis is provided in SI Methods. ACKNOWLEDGMENTS. This study was supported by the Deutsche Forschungsgemeinschaft (Grant GA730/3-1), the German Center for Vertigo and Balance Disorders [Bundesministerium für Bildung und Forschung (BMBF) Grant 01EO0901], and the Bernstein Center Munich.

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