Dynamic neural representations of memory and space during human ambulatory navigation

ABSTRACT Our ability to recall memories of personal experiences is an essential part of daily life. These episodic memories often involve movement through space and thus require continuous

encoding of one’s position relative to the surrounding environment. The medial temporal lobe (MTL) is thought to be critically involved, based on studies in freely moving rodents and

stationary humans. However, it remains unclear if and how the MTL represents both space and memory especially during physical navigation, given challenges associated with deep brain

recordings in humans during movement. We recorded intracranial electroencephalographic (iEEG) activity while participants completed an ambulatory spatial memory task within an immersive

virtual reality environment. MTL theta activity was modulated by successful memory retrieval or spatial positions within the environment, depending on dynamically changing behavioral goals.

Altogether, these results demonstrate how human MTL oscillations can represent both memory and space in a temporally flexible manner during freely moving navigation. SIMILAR CONTENT BEING

VIEWED BY OTHERS HUMAN NEURAL DYNAMICS OF REAL-WORLD AND IMAGINED NAVIGATION Article Open access 10 March 2025 SCALP RECORDED THETA ACTIVITY IS MODULATED BY REWARD, DIRECTION, AND SPEED

DURING VIRTUAL NAVIGATION IN FREELY MOVING HUMANS Article Open access 07 February 2022 CORTICAL REACTIVATION OF SPATIAL AND NON-SPATIAL FEATURES COORDINATES WITH HIPPOCAMPUS TO FORM A MEMORY

DIALOGUE Article Open access 27 November 2023 INTRODUCTION The ability to learn and recall personal experiences, or episodic memories, is critical for everyday life and guiding of future

behaviors. Encoding of the environmental (spatial) context in which an episode takes place is important for its successful subsequent recall. The medial temporal lobe (MTL) has long been

identified as a brain region essential for successful episodic memory formation within a spatiotemporal context across rodents, non-human primates, and humans alike1,2,3,4. Current evidence

from rodent studies suggests that oscillatory activity in the theta frequency band (~6–12 Hz)5 in the MTL supports spatial navigation6,7 and successful memory function2,8 through its ability

to temporally organize neural activity locally and across brain regions2,8. However, studies in humans show mixed results9,10 regarding the presence of theta activity and its temporal

dynamics during retrieval and encoding of subsequently recalled items11,12,13,14. Specifically, a majority of human memory studies identify that lower frequency theta (~3 Hz) activity

increases/decreases during encoding/retrieval, thereby also calling into question the role of higher-frequency theta oscillations, analogous to those found in rodents, in human memory9,10.

Given the difficulty of recording human deep brain activity during physical movement, it is currently unknown if and how MTL theta oscillations flexibly support memory during ambulatory

spatial navigation and/or during complex experiences that involve dynamically changing cognitive demands. Human neuroimaging studies of spatial memory during navigation have traditionally

used view-based virtual reality (VR) to simulate movement through an environment while participants remained immobile and restricted due to large recording equipment that is susceptible to

motion artifacts. Recent technological advancements in human mobile neuroimaging15, however, have enabled the discovery of higher-frequency (~7 Hz) MTL theta oscillations that are modulated

by physical movement (e.g., walking)16,17,18 and proximity to environmental boundaries17. Nonetheless, it remains unclear if and how these theta oscillations support successful memory

retrieval during ambulatory spatial navigation, and further, how to reconcile their role in flexibly representing both memory and space during a complex behavioral experience. The current

study capitalized on a recently developed mobile neuroimaging platform15 that enables wireless recording of intracranial electroencephalographic (iEEG) activity from the MTL during

unrestricted ambulatory movement in humans. Freely moving participants performed a spatial memory task in immersive VR environments while movement was simultaneously tracked to examine how

memory-related processes and spatial features within the environment dynamically modulated MTL activity. Our results suggest that MTL theta activity reflects both successful memory retrieval

and spatial environmental features in a temporally dynamic and flexible manner that can remap based on environmental context and momentary task goals. RESULTS MEASURING SPATIAL MEMORY USING

AMBULATORY VR AND MOTION TRACKING We developed an ambulatory VR spatial memory task which six participants completed while MTL iEEG activity was recorded (Fig. 1a) from a chronically

implanted responsive neurostimulator (RNS) system (Fig. 1b, see detailed information in Supplementary Table 1). The spatial memory task was carried out in an immersive room-scale VR

environment (5.84 × 5.84 m, Fig. 1c–h) during which participants interactively navigated to, learned, and later recalled the position of uniquely colored visible translucent cylinders

(halos). The physical movement of participants in the real room was mapped to body position in VR space such that the scene was updated according to each participants’ motion in a

one-to-one-manner. The spatial memory task consisted of learning (encoding) trials, visually guided navigation (“arrow”) trials, and memory recall (retrieval) trials (Fig. 1c–f). During

encoding trials, participants were instructed to navigate to a halo (Fig. 1c, Supplementary Movie 1) and learn its spatial location, which was fixed over the course of the task. During arrow

(search) trials, participants were instructed to navigate to an arrow (Fig. 1d) located in the perimeter of the room, which appeared at a new randomized position in each trial. The task

began with encoding trials (each repeated with unique halo colors and positions, Fig. 1g, h) interleaved with arrow trials. After one encoding and arrow trial was completed for each halo, in

a one-by-one and sequential manner, participants began retrieval trials, during which they were instructed to navigate to a previously learned halo position from memory and indicate their

arrival using a button press on a wireless handheld VR controller (Fig. 1e). After each retrieval trial, visual feedback (“correct” or “incorrect”) appeared specifying whether the

participant responded correctly or incorrectly. At the end of this feedback and regardless of performance, the halo became visible (visible halos, Fig. 1f) in its correct position until the

participant navigated to its center, providing an opportunity to re-learn the halo position. Arrow trials were also interleaved in between retrieval trials similar to encoding trials. See

Supplementary Movie 2 for example retrieval, feedback, and arrow trials. Participants completed the task with 15 retrieval and arrow trials (constituting one retrieval block) and alternated

between two environmental contexts (stone room: Fig. 1g, wooden room: Fig. 1h) each of which contained three halos with unique colors and positions that differed between the two contexts and

were fixed over the duration of the task. For further details see “Methods”. Memory performance during retrieval trials was measured by computing the distance (error) between the recalled

position (button press) and the actual halo position (Supplementary Movie 2). After retrieval block #1, mean error across participants was 0.56 m (±0.01, standard error of the mean, s.e.m.)

and significantly reduced during the last compared to the first retrieval block (see “Methods” for further details, _p_ = 0.021, Fig. 2a). Accuracy (calculated as % correct) was also

computed during the same retrieval blocks based on a 0.75 meter (m) radial distance threshold (from the center of the halo), which was used to provide visual feedback to the participant

(“correct” or “incorrect”). Mean accuracy was 65% (±8.5% s.e.m.) across participants and improved significantly during the last compared to the first retrieval block (see “Methods” for

further details, _p_ = 0.036, Fig. 2b, c). Furthermore, the complete trajectory of an example participant over the course of the entire task in each VR environment is shown in Fig. 2d,

illustrating adequate and evenly distributed sampling of positions across the room as was seen in all participants. Altogether, these behavioral findings showcase the ability of ambulatory

immersive VR combined with motion tracking to be used to precisely assess spatial memory performance in freely moving human participants with simultaneous iEEG recordings. SUCCESSFUL MEMORY

RETRIEVAL IS ASSOCIATED WITH INCREASED MTL THETA BAND POWER We next investigated whether MTL oscillatory activity was modulated by successful memory retrieval. To do this, we examined power

across a range of oscillatory frequencies (3–120 Hz) during time periods around the instances of recall. Given the experimental task was predominantly self-paced in nature and participants

were freely moving, pinpointing the precise moment of recall required additional consideration. We hypothesized that the temporal windows most likely to contain critical data indicative of

memory retrieval would be either prior to button press upon reaching the recollected position and/or subsequent to retrieval cue (trial) onset. Prior to reaching the recollected position

(button press), MTL oscillatory power significantly increased only at 6–8 Hz theta frequencies (6–8 Hz: all individual frequencies _p_ < 0.05, after correcting for multiple comparisons

using the false discovery rate [FDR]19,20, _n_channels = 19, Fig. 3a–d). Specifically, this theta (6–8 Hz) band power was significantly elevated during correct but not incorrect retrieval

trials, arrival at visible halos during feedback, or arrival at arrows during arrow trials and this increase occurred during the 0.5 s prior to arrival at the recalled position (Fig. 3i, j;

correct vs. incorrect, _p_ = 0.003; correct vs. visible halo, _p_ < 0.001; correct vs. arrow, _p_ = 0.047; incorrect vs. visible halo, _p_ = 0.190; arrow vs. incorrect, _p_ = 0.280; arrow

vs. visible, _p_ = 0.022; FDR corrected, _n_channels = 19, Supplementary Movie 3). However, this finding of increased theta power for correct versus incorrect trials was not dependent on

the specific temporal window (0.5 s, Supplementary Fig. 1a), was numerically present across participants (Supplementary Fig. 1b), persisted when averaging over channels for each participant

(_p_ = 0.031, _n_participants = 6, Supplementary Fig. 1c), and remained after a leave-one-out approach when each participant’s data was excluded one at a time (Supplementary Fig. 1d),

suggesting that findings were not driven by individual subjects. Increases in MTL theta band power prior to arrival at the correctly recalled position only occurred in MTL not non-MTL

channels (Supplementary Table 1). Specifically, in non-MTL channels (_n_channels = 5), there were no significant theta band power changes during successful (6–8 Hz: all individual

frequencies _p_ > 0.05, FDR corrected) or unsuccessful memory retrieval trials (6–8 Hz: all individual frequencies _p_ > 0.05, FDR corrected), or for arrival at visible halos during

feedback (6–8 Hz: all individual frequencies _p_ > 0.05, FDR corrected). MTL memory-related theta band power increases could not be explained by the presence of a virtual object since

halos were not visible during retrieval trials (Fig. 3a, b, e, f). An example channel illustrating the effect is shown in Fig. 3, where MTL theta band power peaked near correctly (Fig. 3e)

but not incorrectly (Fig. 3f) recalled halo positions, visible halo positions (Fig. 3g), or near halo locations during arrow trials (Fig. 3h). Given prior studies showing that movement speed

modulates the prevalence of theta oscillations16,21,22, we evaluated whether there were differences in speed profiles during correct versus incorrect retrieval trials. We found no

significant differences in movement speed during correct compared to incorrect retrieval trials nor between retrieval trials and visible feedback during the same 0.5 s prior to arrival at a

visible halo, (_p_ = 0.115, _p_ = 0.845, _n_participants = 6, Supplementary Fig. 2), suggesting that the observed memory-related effects were not driven by differences in movement speed

between conditions. Additionally, there were no significant differences in movement speed between navigation in the stone and wooden context nor between the first-encountered and

second-encountered context (since starting context was counterbalanced across participants, stone vs. wooden: _p_ = 0.293, first vs. second: _p_ = 0.998, _n_participants = 6). Furthermore,

we quantified the impact of movement speed and correct relative to incorrect memory performance on changes in theta band power during the last 0.5 s prior to reaching the recalled position,

evaluated on a trial-by-trial basis using a linear mixed-effects model and found that only correct performance but not movement speed significantly predicted increases in theta band power

during this temporal window (correct vs. incorrect, _p_ = 0.028; movement speed, _p_ = 0.337; _n_participants = 6, Supplementary Fig. 3a). Next, we examined the simultaneous contribution of

multiple behavioral variables (distance to recalled position, distance to boundary, correct vs. incorrect performance, distance error) and movement-related variables (movement speed, angular

velocity, movement direction) on fluctuations in theta band power during the entire duration of retrieval trials, up until arrival at the recalled position (when no cues were present;

Supplementary Fig. 3b). Specifically, distance to the recalled position (button press) and distance error (distance between button press and target location) were significant predictors of

theta band power fluctuations (movement speed, _p_ = 0.370; angular velocity, _p_ = 0.998; proximity to recalled position, _p_ = 0.044; proximity to boundary, _p_ = 0.741; correct vs.

incorrect, _p_ = 0.340; distance error, _p_ = 0.046; movement direction = 0.290; _n_participants = 6, Supplementary Fig. 3b). We also explored successful memory-related theta band power

changes during other time periods (Supplementary Fig. 4) and found that while theta band power (6–8 Hz) initially appeared to be similar between correct and incorrect trials after initial

cue presentation during retrieval trials, a difference was detected around 1.5 s after cue onset (Supplementary Fig. 4a). Previous work has suggested that, within a broader theta frequency

range, low-frequency theta oscillations (e.g. type II theta) are related to episodic memory and higher-frequency theta oscillations (e.g. type I theta) are movement-related23,24. As such, we

also investigated differences between correct and incorrect trials in low-frequency oscillations (3–6 Hz) after cue presentation, and before the onset of movement (Supplementary Fig. 4c–f).

Since participants often had multiple movement onset periods within a single trial, we specifically examined the last movement onset before button press, which we hypothesized would better

capture the temporal window when participants initiated memory retrieval to determine their final recalled position for the indicated target halo on any given trial. Indeed, we found that

low-frequency theta band power (3–6 Hz) was increased already around 0.5 s after cue onset (Supplementary Fig. 4c) and that this elevation could only be explained by distance from the cue

itself and not other movement or behavioral variables (Supplementary Fig. 3c). Furthermore, both low-frequency (3–6 Hz) and high-frequency (6–8 Hz) theta band power was also increased prior

to the last movement onset during retrieval trials (Supplementary Fig. 4b,d), another time period likely to capture moments of memory recall. Given prior results illustrating that MTL theta

oscillations occur in non-continuous bouts in freely ambulating humans16,17, and that these bouts are modulated by behavioral variables (e.g., movement speed), we examined whether

differences in the prevalence of theta bouts could explain memory-related effects on MTL theta band power (Supplementary Fig. 5). We found that MTL theta band power increases did occur in

transient bouts and occurred at similar rates compared to previous studies16,17, however, the prevalence of these bouts did not significantly differ between task conditions either during the

entire retrieval trial period (retrieval vs. arrow vs. visible halo trials, _p_ > 0.05; correct vs. incorrect, _p_ > 0.05; across all individual frequencies between 3–25 Hz,

_n_channels = 19, Supplementary Fig. 5a–c) or the last 0.5 s prior to arrival at the recalled position (retrieval vs. arrow vs. visible halo trials, _p_ < 0.05; correct vs. incorrect, _p_

 > 0.05; across all individual frequencies between 3–25 Hz, _n_channels = 19, Supplementary Fig. 5e, f), suggesting successful memory retrieval results in increased MTL theta band power

in the absence of changes in its prevalence. MTL THETA BAND POWER IS MODULATED BY SPATIAL POSITION Next, we investigated whether MTL theta oscillations were modulated by one’s location in

the environment. To do this, we used data from both contexts (stone and wooden) and computed MTL theta band power across positions, separately in each room, during retrieval (when halos were

not visible) and arrow (search) trials (when arrival positions at arrows were excluded). We first excluded iEEG data from retrieval periods immediately (0.5 s) preceding the button press.

In this way, we could determine whether MTL theta band power was modulated by spatial position, independent of reaching a designated target goal halo position during retrieval. Thus, this

analytic approach retained instances when participants incidentally traversed non-visible previously learned (non-target) halo positions along the participants’ trajectory to the goal halo

location. We examined MTL theta band power when participants were in positions that were classified as “close” to or “far” from the non-visible non-target halos during participants’

trajectories to the target halo (of which the 0.5 s prior to target halo arrival was excluded). MTL theta (6–8 Hz) band power was significantly increased at “close” compared to “far”

distances relative to the non-visible non-target halo positions. The difference in MTL theta band power between “close” and “far” positions peaked at a distance threshold of 2 m from

non-target halo positions (distance thresholds of 1, 1.25, 1.5, and 2 m: all _p_ < 0.05, _n_channels × conditions = 38, Fig. 4a, and _p_ < 0.05 at distance thresholds of 1.5 and 2 m

after FDR correction; illustration of 2 m threshold: close vs. far, _p_ = 0.008, Fig. 4b). Interestingly, we did not observe such a pattern of results during arrow trials (Supplementary Fig.

 6a), indicating that proximity to (non-target) halo locations modulated theta power only during memory retrieval but not when participants walked towards a visible cue. The spatial

distribution of theta (6–8 Hz) band power increases was specific to relevant positions within each context separately (stone: _p_ = 0.012; wooden: _p_ = 0.041, _n_channels = 19,

Supplementary Fig. 6b), suggesting that MTL spatial representations can remap based on the perceived environment (see example channel showing theta activity in the stone (Fig. 4c) and wooden

(Fig. 4d) context). Furthermore, the difference in theta band power between “close” and “far” positions was strongest in later blocks, likely after the participants developed robust spatial

representations (Supplementary Fig. 6c). Together, these results suggest that MTL theta band power increased incidentally at meaningful spatial positions within a familiar environmental

context. We next examined how MTL oscillatory power was modulated by spatial positions near room boundaries (e.g., walls), based on evidence of boundary-related representations identified in

a prior ambulatory spatial navigation study in humans17. Since the VR room dimensions in our study were identical to those in this previous navigation study17, we used the same

boundary-inner room area cutoff of 1.2 m from the wall (although, see Supplementary Fig. 7a for additional cutoffs used). Across widespread (3–120 Hz) oscillatory frequencies examined, mean

power significantly increased at boundary compared to inner room positions only for lower theta frequencies (4–6 Hz) during arrow search trials (excluding 0.5 m prior to arrow arrival: 4–6 

Hz: all individual frequencies _p_ < 0.05, FDR corrected, _n_channels = 19, Fig. 5a; boundary versus inner: _p_ < 0.001, Fig. 5b; _n_channels = 19). Conversely, there were no

significant differences in mean theta band power between boundary and inner room positions during memory retrieval trials (4–6 Hz: all individual frequencies _p_ > 0.05, FDR corrected,

_n_channels = 19). Boundary-related power increases were also observed at higher frequencies during the _entire_ duration of arrow trials, including the last 0.5 m prior to arrival at the

arrow, (12–14, 31–35 Hz, all individual frequencies _p_ < 0.05, FDR corrected, _n_channels = 19) similar to a previous study17. The boundary-related theta band power increase was also

present when looking at data over the _entire_ task (again, excluding data from positions within 0.5 m of arrow arrival, 4–6 Hz band power, _p_ < 0.001) and can be seen in an example

channel in Fig. 5c. It is unlikely that differences in movement speed were driving boundary modulation of theta band power since speed was significantly elevated in inner relative to

boundary positions, in direct opposition to the increased theta band power in boundary positions, given prior work in humans and non-human animals showing higher theta band power associated

with faster movement speeds16,18,25 (_p_ = 0.048, _n_participants = 6, Supplementary Fig. 2f). Further, after accounting for movement variables (speed, angular velocity, movement direction)

in addition to behavioral variables in the previously described linear mixed-effects model approach, we found that movement speed was not a significant predictor of theta band power

fluctuations (movement speed, _p_ = 0.966; angular velocity, _p_ = 0.617; movement direction, _p_ = 0.507; _n_participants = 6, Supplementary Fig. 3d). Moreover, while proximity to boundary

was not a significant predictor of theta band power in the previously described linear mixed-effects model during memory retrieval (Supplementary Fig. 3b), we found that during arrow search

periods only distance to (nearest) boundary was a significant predictor of elevated theta band power, whereas proximity to the visible arrow cue (distance to arrow) were not (proximity to

arrow, _p_ = 0.267; proximity to boundary, _p_ = 0.048; _n_participants = 6, Supplementary Fig. 3d), suggesting that there is a linear relationship between boundary proximity and theta band

power. Together, these results suggest that boundary-related theta increases are not driven by movement speed, movement-related variables, nor visible cues (arrows) and that theta is

dynamically modulated by boundary proximity based on ongoing task demands. Also, the prevalence of theta bouts was not significantly different between “boundary” and “inner” positions (_p_ 

> 0.05, across all individual frequencies 3–25 Hz, _n_channels = 19, Supplementary Fig. 5d) similar to a previous study17. To examine whether encoding of visual information on walls was

contributing to boundary modulation of theta power, we examined theta band power fluctuations in two separate conditions: when participants were moving towards the (nearest) wall and when

participants were moving away from the (nearest) wall. We observed that boundary modulation of theta band power persisted in both conditions (towards: _p_ < 0.001, away: _p_ < 0.001,

_n_channels = 19, Supplementary Fig. 7d). Notably, boundary-related modulation of MTL theta band power was not present during retrieval trials, with or without including the 0.5 s of data

preceding arrival at the recalled location (all retrieval trials: _p_ = 0.175; excluding 0.5 s preceding recall: _p_ = 0.202 boundary versus inner, 4–6 Hz band power, _n_channels = 19),

potentially due to competing modulation of theta activity by non-visible non-target halo positions during retrieval search periods as discussed previously. Also, recall-related theta

increases during correct trials persisted when excluding data from boundary positions, suggesting that theta band power differences between correct and incorrect retrieval trials were not

driven by boundary-related theta effects (correct vs. incorrect, _p_ = 0.033, correct vs. visible, _p_ = 0.003, incorrect vs. visible, _p_ = 0.401, _n_channels = 19, Supplementary Fig. 1e).

Similarly, non-target modulation of theta band power further persisted when examined only in the boundary region of the environment (Supplementary Fig. 6d, e), suggesting that this effect

was also not driven by boundary modulation of theta band power. Lastly, boundary-related increases in theta power were not dependent on the specific 1.2 m boundary vs. inner cutoff

(Supplementary Fig. 7a) and occurred within individual participants (Supplementary Fig. 7b), when averaged over individual channels of participants (_p_ = 0.049, _n_participants = 6,

Supplementary Fig. 7c), and persisted during a leave-one-out approach when each participant’s data was excluded one at a time (Supplementary Fig. 7e), suggesting that findings were

consistent across participants and not driven by individual subjects. Taken together, these results demonstrate that MTL theta band power can be dynamically modulated by critical positions

(e.g., that previously contained relevant objects or proximity to walls) depending on environmental context or task goal. DISCUSSION We have shown that human MTL theta band power is

modulated dynamically by successful memory retrieval and spatial position depending on environmental context and momentary behavioral demands. While previous studies have used simultaneous

ambulatory iEEG recordings and immersive VR15, this is the first to collect empirical data to investigate human spatial memory. In this way, we were able to investigate how MTL oscillations

represent memory and space flexibly during an ambulatory spatial navigation task that involves changes in context and behavioral demands. Our findings highlight two phenomena, one related to

memory recall and the other to spatial position. First, we find that MTL theta band power is elevated during memory recall. This increase is particularly pronounced around 0.5 s after cue

onset and 0.5 s before reaching the retrieved location, when the recalled item (halo) is not visible, and only when it is recalled correctly. This pattern of results echoes previous findings

in stationary humans, showing hippocampal reinstatement of low-frequency theta oscillations during early retrieval time windows (specifically within the first 0.5 s after a retrieval cue

was presented)26, stronger representational similarity of iEEG activity in the 1 s prior to recall during remembered relative to forgotten trials27, and increased theta power during spatial

memory retrieval in view-based navigation tasks28,29,30,31. Additionally, our finding that theta band power was elevated only during successfully recalled trials is in line with prior

reports from human iEEG studies that identify low-frequency theta activity being modulated by memory performance during stationary view-based spatial memory tasks28,29,30,32. It is possible

that the higher-frequency theta effects seen prior to reaching the retrieved location are due to the fact that participants were physically navigating. In line with this hypothesis, we found

elevated low-frequency theta band power (e.g. memory-related type II theta) in two time windows associated with less movement: around (1) 0.5 s after cue presentation and (2) 0.5 s prior to

movement onset, while there was elevated higher-frequency theta band power (e.g. movement-related type I theta) in two time windows associated with more movement: (1) around 1.5 s after cue

presentation and (2) in the 0.5 s prior to arrival at the recalled position23,24. Moreover, we also found that a continuous metric of memory performance (distance error) was linearly

related to changes in theta band power over the entire duration of retrieval trials, suggesting that memory retrieval success modulated theta power fluctuations throughout the retrieval

period. Importantly, we found no significant differences in speed profiles during correct versus incorrect memory retrieval trials or task conditions (memory retrieval, or arrival at arrows

or visible cues) suggesting MTL high-frequency memory-related theta band power changes were not driven by changes in movement speed, nor was there any significant contribution of

movement-related variables to theta band power fluctuations during retrieval trials. Further, while prior work in ambulatory humans has shown that theta prevalence (not power) is modulated

by movement speed during a non-mnemonic walking task16 our findings here suggest successful memory retrieval modulates theta band power in the absence of changes in its prevalence. Thus, our

results emphasize the importance of investigating memory and spatial representations during ambulation, while highlighting the need for future studies to determine how high versus

low-frequency memory-related theta changes differ between ambulatory compared to stationary (virtual) navigation33. Second, we found that MTL theta band power increased near previously

learned object (halo) positions or environmental boundaries depending on context and momentary task goals. Specifically, MTL theta band power increased near non-target halos when

participants were actively searching for and recalling a separate non-visible target halo. This neural representation of relevant halo positions was specific to each context (stone or

wooden) and alternated as participants switched between environments, but did not persist during the interleaved arrow trials (visual search period that lacked a memory demand), consistent

with the idea of context reinstatement during memory retrieval and in a manner relating to the trial objective30. Further, MTL theta band power increased at positions close to environmental

boundaries (walls) but only when searching for boundary-positioned cues (arrows). Importantly, boundary modulation of theta band power persisted in conditions both when participants

approached and moved away from the wall, suggesting that the visual information available when facing a wall was not driving this spatial representation. Additional analyses using a linear

mixed-effects model approach further highlighted the dynamic nature of theta oscillations in that proximity to boundaries (and not proximity to visual cues) predicted theta band power

fluctuations during arrow trials, but not during retrieval trials. However, although proximity to the (nearest) boundary but not proximity to the visual cue (arrow) was linearly related to

theta band power, we cannot fully rule out the possibility that visible cues (arrows) contributed in some way to boundary modulation of theta band power. This spatial remapping of

oscillatory activity based on the behavioral goal (memory recall versus cue-driven navigation) suggests that MTL theta band power can dynamically reflect multiple spatial and mnemonic

variables in an on/off and flexible manner, where the presence of specific neural representations depends on the immediate cognitive requirements of the task at hand. For instance,

boundary-related increases in theta power could be present exclusively during periods when the proximity to the spatial boundary is relevant (as seen in arrow trials, where arrows are

positioned at room boundaries). On the other hand, during memory retrieval phases when spatial boundaries are momentarily less relevant, these boundary-related representations might be

notably absent. Similarly, it is possible that transient theta power increases may reflect relevant neural representations that are momentarily engaged during dynamic mind-wandering states

in humans. Specifically, a momentary increase in power of theta bouts may reflect the relevant neural representations (e.g. for memory or space) that are recruited for a particular

cognitive/behavioral goal, in contrast with rodents, where more continuous theta activity occurs during freely moving navigation. Thus, these findings provide a possible explanation for

non-continuous theta bouts in humans16 where behavioral/cognitive variables may play a more critical role in their modulation as compared to continuous movement-related theta oscillations in

rodents6,34. Future studies will be needed to better understand the exact role of low- (type II) versus high- (type I) frequency theta oscillations in this context. Mechanistically,

remapping of MTL theta band power across different cognitive tasks could reflect coordinated remapping of local single-neuron activity, although the relationship between oscillatory and

single-neuron remapping in humans requires further exploration. Rodent studies have shown that place cells, which encode particular positions in an environment, globally remap in different

contexts and environments35,36. It is thus plausible that nearby local theta band power remapping may reflect or organize place cell remapping, or that changes in theta band power may

reflect the summation of populations of nearby remapped place cells. Prior studies recording MTL single-neuron activity in stationary humans showed firing rate changes that dynamically

changed during free recall tasks37 when virtually approaching the position of a previously learned object38, and in relation to egocentric directions while navigating towards local reference

points39. Our results demonstrated a linear relationship between theta band power fluctuations and proximity to the recalled position (button press) during retrieval trials. Recently

reported object vector trace cells40 may represent a population of hippocampal neurons that could contribute to this effect by modulating firing rate patterns to create a vector field

pointing to a previously encountered object’s position. Our results also highlight that theta power is modulated by non-visible and non-target locations of previously learned halos in a

latent manner. Consistent with this finding, object trace cells in the entorhinal cortex selectively fire in the location of previously encountered object positions, even at long time

periods after the object has been removed from the environment41. Finally, we found robust boundary representations elicited in our task consistent with the existence of border cells and

boundary-vector cells42,43, which increase their firing rate when animals are near borders of an environment. Given that these MTL neuronal populations each exhibit characteristic tuning to

memory and spatial features, it is possible that their summative activity may be coordinated in relation to broader regional theta oscillations to support successful memory retrieval and

anchoring of the positions of spatial targets. Indeed, environmental (contextual) remapping of population-level neuronal signals identified with fMRI has also been shown in a stationary

view-based virtual navigation study in humans where hippocampal-entorhinal cortex activity “flickered” between two contexts during incorrect memory retrieval trials as the participant

struggled to identify the environment they were in44. Traditional human neuroimaging studies of memory retrieval and spatial navigation have been carried out in stationary participants

viewing stimuli on a computer screen. Many of these studies were also designed to evaluate neural activity changes during brief stimulus presentations (e.g., 1–2 s when a cue is presented),

which limits the ability to disentangle more complex neural dynamics related to multidimensional spatiotemporal experiences in an immersive environmental setting. In contrast, by utilizing

3D ambulatory VR, our study presents a critical advancement for future human behavioral studies measuring brain activity during freely moving behavior by creating a more ecologically valid

setting that enables participants to physically explore, learn, and recall experimentally controlled stimuli in their environment. Furthermore, the use of VR in this way still allows for

deliberate experimental control of the environmental context, as well as the timing and placement of stimuli. In summary, our results provide insights into how human MTL oscillatory dynamics

support cognitive representations that could dynamically reflect both memory and space in an ecologically valid setting that involves physical movement through distinct spatial

environments. These findings suggest that MTL theta oscillations contain memory- and spatial contextual-related information that may enable transient changes in cognitive states during

complex real-world experiences. Our combined deep brain recording and immersive VR approach also presents a unique opportunity for future cognitive and clinical neurosciences studies of

naturalistic behavior in humans to unravel underlying mechanisms during complex freely moving behaviors that may be further impaired in patients with neurologic and psychiatric disorders.

METHODS PARTICIPANTS There were 6 participants in the study (33–54 years of age, 4 female, mean = 43.3 ± s.e.m. = 3.1). All the participants had pharmacoresistant epilepsy treated with a

chronically implanted FDA-approved RNS System (Neuropace, Inc; 320 Model) that continuously records iEEG activity across 8 contacts (4 bipolar channels). Participants with at least 2 bipolar

channels in MTL regions (e.g., hippocampus or entorhinal cortex) were recruited for the study (example electrode placement shown in Fig. 1b). The sites of electrode implants were determined

by clinical criteria. Further, participants with low seizure activity and thus fewer average daily stimulation therapies were recruited for the study. Informed consent approved by the UCLA

Medical Institutional Review Board (IRB) was obtained from all participants. Participants received financial compensation for taking part in this study. The participants’ sex and gender was

determined based on self-report. Due to the limited sample size, the participants’ sex and gender was not considered in the study design. SPATIAL MEMORY TASK IN IMMERSIVE VIRTUAL REALITY

Participants completed an ambulatory spatial memory task in two different immersive VR environments (room dimensions were 5.84 × 5.84 m) where they learned and retrieved various positions of

translucent colored cylinders (halos) as discussed in the main text. All VR environments were matched in size to the real-world environment and constructed using the Unity game engine. VR

headsets used included the Quest 2 VR headset (Meta, Inc., as seen in Fig. 1a) or the Pico Neo 1 and Pico Neo 2 VR headsets (Pico Immersive Pte. Ltd). Prior to performing the task,

participants completed a 5-min practice version of the task in a distinct virtual environment to provide them familiarity with the immersive VR headset and to engage them in normal walking

behavior. The first retrieval block included several repeated sets of retrieval and visible halos, until the participant met a learning criterion (completing 15 consecutive trials with error

<1.5 m). Retrieval block #1 was completed in an identical manner in the second context, immediately following completion in the first context. The starting context (stone or wooden) was

counterbalanced across participants. The total number of trials in retrieval block #1 varied across participants (15–30 trials in participants 1–5, Fig. 2c, see details in Supplementary

Table 1). P6 was unable to learn all halos to meet the learning criterion in retrieval block #1 in both contexts, and as such, was manually advanced to retrieval block #2 after 40 min in

each context (69 trials in the stone context, 60 trials in the wooden context). For retrieval block #2 and above there were a fixed number of trials (15 in each block), with the context per

block alternating until a total of 2–11 retrieval blocks were completed depending on the time available for each participant (see number of block details by participant in Supplementary

Table 1). Total task time took approximately 30–200 min across participants. “Earlier” and “later” blocks were defined using a median split across all blocks that each participant completed.

Location and orientation tracking of participants was collected throughout the experiment with submillimeter resolution through the Opitrack motion tracking system using twenty-two

high-resolution infrared wall-mounted cameras and MOTIVE application (Natural Point, Inc., see Fig. 1a). The cameras sampled the position of a collection of uniquely oriented rigid body

position markers located atop the participants head at 120 Hz (Fig. 1a). Positional data was compared across VR headsets and Opitrack data collection, and analysis proceeded using VR headset

data since positional accuracy was comparable. Movement speed was computed as the change in position between consecutive samples divided by the time lapse between samples. Angular velocity

was computed as the change in yaw rotational dimension (radians) of consecutive samples divided by the time lapse between samples. IEEG DATA ACQUISITION The RNS System continuously records

iEEG activity and delivers stimulation in a closed-loop fashion upon detection of abnormal (i.e., epileptic) activity patterns to prevent imminent seizure activity, and is implanted in the

skull to support two penetrating electrode leads, 1.27 mm in diameter, with up 4 platinum-iridium electrode contacts spaced either 3.5 mm or 10 mm apart. In each participant, 4 bipolar

channels were recorded at a sampling rate of 250 Hz. In accordance with the IRB protocol and with participant consent, closed-loop stimulation was turned off during the experimental

recordings in order to remove potential stimulation artifacts from the data. For the duration of the experiment, amplifier settings on the RNS System (320 model) were programmed to apply a 1

 Hz high pass filter and a 90 Hz low pass filter (see Supplementary Fig. 8 for filter response). Wireless iEEG data was recorded from the RNS System as previously described15. Briefly, a

“Wand” accessory wirelessly recorded iEEG from the implanted RNS System using near field telemetry. The Wand was positioned on the head, immediately above the implanted RNS System on the

patients’ head and secured in a custom-made Wand holder and attached to a backpack to allow for free movement (Fig. 1a). Data was stored as a continuous timeseries across channels and

storage was remotely triggered wirelessly at the end of each session of continuous blocks. Of note, since there was no wired connection between the implanted RNS System (the recording

apparatus), the VR headset, and an external power source, the iEEG data was free from power line noise. To synchronize iEEG with behavioral data, the Unity application executed on the VR

headset was programmed to trigger a signal (mark) wirelessly at specific time points inserted into the iEEG data. These synchronization marks were sent at specified times in the tasks,

specifically at the start of each block (see Topalovic et al.15 for synchronization details of the setup). ELECTRODE LOCALIZATION Precise localization of electrode contacts was performed by

co-registering post-operative head CT scans with pre-operative MRI scans (T1 and/or T2-weighted sequences). One example localization of the four contacts on one electrode lead can be seen in

Fig. 1b. Across the six participants, there were nineteen total channels localized to the MTL in regions including the hippocampus, parahippocampal cortex, perirhinal cortex, and entorhinal

cortex. No recording contacts were located in the amygdala. For list of electrode contact localizations of all participants, see Supplementary Table 1. DETECTION OF EPILEPTIC EVENTS

Inter-epileptic discharges (IEDs) are abnormal electrical distortions related to epilepsy that can occur intermittently and on an individual basis. IEDs were removed from all iEEG channels

prior to normalization of power and all additional neurophysiological analyses. We applied IED detection methods previously described16,17,45. Briefly, IED detection used a double

thresholding approach where for the first threshold, each sample was tested against two criteria to identify IEDs to be removed from analysis: (1) whether the envelope of the unfiltered

signal was 6.5 standard deviations away from baseline, and (2) whether the envelope of filtered signals (15–80 Hz band pass filtered after signal rectification) was 6.5 standard deviations

away from baseline activity. Once these IED samples were detected, a second threshold was applied to remove samples surrounding detected IED samples. Specifically, a smoothing gaussian

filter with a moving kernel range of 0.1 s was applied to a binary vector with 1’s denoting detected IEDs and a threshold of 0.01 was applied to the smoothed vector to identify all samples

around and including detected IEDs, all of which were excluded from analysis in order to remove potential residual epileptic activity. In order to remove a wider window around high-amplitude

IED events, this method was applied a second time with a higher 7.25 standard deviation cutoff for the first threshold and a wider 0.25 s smoothing window for the second threshold. Using

this method, 2–10% of samples were removed per channel (Supplementary Table 1), similar to previous results16,17. We specifically recruited participants with low baseline IED activity based

on their historical data from the RNS System (i.e., average daily number of stimulation events delivered in recent months). BEHAVIORAL ANALYSES Memory performance was computed as the

distance error, or the distance between the position at which the participant pressed the button during retrieval trials to indicate the recalled halo position and the center position of the

halo. Immediately after the button press, the participants received visual on-screen feedback of either “Correct!” (if they were within 0.75 m of the halo’s center) or “Incorrect”. To

determine whether participants successfully learned halo positions over each experimental session, learning was evaluated by comparing each participants’ mean error (e.g., memory

performance) in retrieval block #1 across both contexts (excluding the last 15 trials which met the learning criterion threshold necessary to advance past retrieval block #1 for P1–5) to

mean error during their last retrieval block in each context. The mean error performance across the last retrieval block compared to that during retrieval block #1 (before meeting the

learning criterion) was evaluated for significance using a pairwise permutation test across participants. For memory retrieval analyses, correct and incorrect trials were defined from when

the participant received instructions to retrieve a particular halo (no visible halo cue was present) until the instance at which they recalled the halo position (button press). Visible halo

(feedback) periods were defined from the instance of recall (button press), at which point the halo appeared in its correct location, until the instance at which the participant navigated

to the visible halo. Visible halos occurred immediately following both correct and incorrect trials; feedback appeared after correct trials even when participants were within 0.75 m of the

halo and thus participants were still required to navigate to the center of the visible halo. For boundary versus inner room area analyses, we used a method similar to a previous study17.

Since the same room dimensions used in this study were identical to those used in a previous study, the same 1.2 m proximity to boundary (i.e., wall) cutoff was used to separate “boundary”

versus “inner” room areas (but see Supplementary Fig. 7a for alternative boundary-distance thresholds tested). Movement onset time points were defined as the moment when movement speed

changed from “no movement” (speeds of less than 0.2 m/s) to “movement” (speeds of 0.2 m/s or greater) and remained above 0.2 m/s for at least 1 s. To distinguish movements towards versus

away from boundaries, we first measured the distance to the nearest boundary for each sampling point, and then calculated whether this distance decreased (categorized as “towards boundary”)

or increased (categorized as “away from boundary”) between two adjacent sampling points. IEEG DATA ANALYSIS Time frequency analysis was performed by computing the oscillatory power at

individual frequency steps of 1 Hz between 3 and 120 Hz using the BOSC toolbox46,47 with a three cycle Morlet wavelet. We also repeated all analyses with a six cycle Morlet wavelet given

previous approaches16,17 and the results were qualitatively the same (i.e., none of the results in the manuscript text changed from “significant” to “non-significant” or vice-versa, and all

patterns of data were virtually identical between the two analysis approaches), suggesting the robustness of the results with regards to this analysis parameter. Next, each channel’s power

timeseries was normalized for each frequency step using the MATLAB “zscore” function (after excluding IED samples). This normalization procedure was performed for each recording channel

separately, and involved initially computing the mean and standard deviation across the complete timeseries for each recording session. Subsequently, each individual data sample within the

entirety of the recording session duration (with the exception of IED samples) was subjected to z-score transformation using the computed mean and standard deviation values. Recorded

timeseries that were separated by longer breaks (more than ~40 min; e.g. before/after a participant’s lunch break) were treated as independent recording sessions and normalized separately.

For bar graphs comparing mean power across a band power range (i.e. 6–8 Hz), normalized power was summed over frequency steps (1 Hz) for all samples that fell within a particular task

condition of interest (e.g., any sample that occurred during any correct or incorrect trial). Mean normalized power was then computed over the summed band power timeseries. To evaluate the

prevalence of significant theta oscillations, we used the BOSC toolbox to detect bouts of at least 2 cycles above 95% chance for 1 Hz frequency steps between 3–25 Hz as has been done

previously16,17. Theta prevalence was computed as the percentage of detected bouts out of all relevant task condition samples. LINEAR MIXED EFFECT MODEL ANALYSIS Linear mixed effect models

were calculated using each participant’s normalized low (3–6 Hz) or high (6–8 Hz) frequency theta band power timeseries as the response variable with multiple movement- and task-related

predictor variables. Movement-related predictor variables included speed and angular velocity as continuous variables, as well as movement direction as a categorical variable. The movement

direction variable consisted of twelve possible rotational bins and test statistics were averaged over bins, because movement direction as a numeric variable (due to its circular nature) is

not expected to have a linear relationship with theta band power (e.g., 0 and 2 pi radians are not expected to evoke substantially different theta band power fluctuations). Models were

constructed to describe theta band power fluctuations across four specific time periods during the task: (1) the final 0.5 s of retrieval trials prior to the button press, (2) the initial 1 

s of retrieval trials following cue onset, (3) the complete duration of retrieval trials, and (4) arrow trials. In addition to movement-related variables, these models incorporated several

task-related behavioral variables as follows: For model (1) and (3), distance to the recalled position when a button press was made (“distance to recalled position”, measured in meters),

distance to the closest boundary (“distance to boundary”, measured in meters), distance between the button press location and halo position (“distance error”, measured in meters) all as

continuous variables, and correct versus incorrect trial performance (“correct/incorrect”, treated as a categorical variable) were included. For model (2) all the variables present in (1)

and (3) were included, while also introducing an additional variable: the distance from the cue onset position (“distance from cue”, measured in meters). For model (4) the added behavioral

variables included were: “distance to boundary” and distance to the trial-specific arrow (“distance to error”, measured in meters), both as continuous variables. All predictor variables were

defined as fixed effects. To account for individual channel-based variation, the recording channel was used as the grouping variable for random effects. The impact of each predictor

variable on theta band power was evaluated by statistically comparing beta weights. DATA SUBSAMPLING For analyses comparing oscillatory power or theta prevalence between conditions that had

a differing number of samples, we performed all calculations on 500 iteratively generated, equally sized subsets of data. Specifically, we first compared the number of samples for all

conditions to be compared (e.g., correct, incorrect, and visible halo conditions). For the condition with the fewest number of samples, we applied no correction. For the other conditions, we

randomly selected the same number of samples for the fewest-sample condition from the longer timeseries and repeated this step iteratively 500 times, with replacement (using the MATLAB

“datasample” function). For each iteration, we computed the parameter of interest (e.g., band power), then averaged this parameter of interest across all 500 iterations. We did this on a

channel-by-channel basis and used the averaged result for all statistical comparisons and plotting of data. STATISTICAL COMPARISONS Statistical comparisons between two conditions were

performed using a paired-sample permutation test as follows: To compare two paired arrays of values (e.g., each of the recording channels’ average band power during “correct” versus

“incorrect” trials, or in the “boundary” versus the “inner” room area), the paired-sample permutation test calculates whether the mean difference between paired values is significantly

different from zero. It estimates the sampling distribution of the mean difference under the null hypothesis, which assumes that the mean difference between the two conditions (correct vs.

incorrect, or boundary vs. inner) is zero, by shuffling the condition assignments and recalculating the mean difference many times (_n_perm = 1000). The observed mean difference between

conditions was then compared to this null distribution as a test of significance. The key steps of this procedure are described in more detail below. Step 1: The observed difference between

conditions is calculated, by first calculating the difference between conditions for each value pair (condition 1 value – condition 2 value), and then calculating the average difference

across pairs. Step 2: Condition labels are randomly shuffled within each value pair and the difference between “shuffled conditions” is calculated, by first calculating the difference

between randomly labeled conditions for each value pair (value randomly labeled with condition 1—value randomly labeled with condition 2), and then calculating the average difference across

pairs. This step is repeated _n_perm times (in _n_perm permutations), to generate a distribution of _n_perm “random differences” between conditions. Step 3: The observed difference between

conditions (calculated in step 1) is compared with the distribution of random differences from shuffling condition labels (calculated in step 2). The _p_-value is calculated by the number of

random differences that are larger than the observed difference, divided by the total number of samples in the distribution. Two-sided permutations tests were used unless otherwise noted.

For multiple comparisons correction (e.g., when performing statistical tests for multiple frequency steps in a band power analysis, or across multiple conditions), p values were adjusted

using the false discovery rate (FDR)18,19. For top-down maps of theta band power (e.g., Figs. 3–5), the room was divided into 19 × 19 bins. Mean band power over condition was computed for

each bin, specifically the band power for all samples in which the participant was positioned in a bin was summed, then divided by the number of samples the participant occupied in that bin.

A gaussian smoothing kernel of 0.2 standard deviations was applied to this heatmap, normalized to the peak power and finally, interpolated (using MATLAB function “interp2” with _k_ = 7) for

visualization. Statistical tests for main effects were conducted separately across channels and across participants (averaged across each participant’s individual channels). Significant

effects were observed in both analyses, except for the following instances where significance was only observed across channels but not across participants: Fig. 4a, Supplementary Figs. 6b

and  1e. ANALYSIS SOFTWARE Data were analyzed using MATLAB 2020a (The MathWorks, Natick, MA). REPORTING SUMMARY Further information on research design is available in the Nature Portfolio

Reporting Summary linked to this article. DATA AVAILABILITY The data that support the findings of this study are available from the corresponding authors upon request. Source data are

provided with this paper. CODE AVAILABILITY The custom computer code used to generate results that are reported in the paper are available from the corresponding authors upon request.

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1616–1620 (2002). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS This work was supported by the National Institutes of Health (NIH), the

National Institute of Neurological Disorders and Stroke (NINDS), and the National Institute of Mental Health (NIMH), under award numbers U01NS103802 to N.S., U01NS117838 to N.S., K99NS126715

to M.S., F30MH125534 to S.L.L.M, by the McKnight Foundation (Technological Innovations Award in Neuroscience to N.S.) and a Keck Junior Faculty Award (to N.S.). We thank all participants

for taking part in the study, and all members of the Suthana laboratory for discussions. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Bioengineering, University of California,

Los Angeles, Los Angeles, CA, 90095, USA Sabrina L. L. Maoz & Nanthia Suthana * Medical Scientist Training Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA

Sabrina L. L. Maoz * Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los

Angeles, CA, 90024, USA Sabrina L. L. Maoz, Matthias Stangl, Uros Topalovic, Daniel Batista, Sonja Hiller, Zahra M. Aghajan, Itzhak Fried & Nanthia Suthana * Department of Psychology,

University of California, Los Angeles, Los Angeles, CA, 90095, USA Barbara Knowlton & Nanthia Suthana * Department of Neurology, David Geffen School of Medicine, University of

California, Los Angeles, Los Angeles, CA, 90095, USA John Stern & Dawn Eliashiv * Neurosurgery Service, Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles,

CA, 90073, USA Jean-Philippe Langevin * Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA Jean-Philippe

Langevin, Itzhak Fried & Nanthia Suthana * Faculty of Medicine, Tel-Aviv University, Tel-Aviv, 69978, Israel Itzhak Fried Authors * Sabrina L. L. Maoz View author publications You can

also search for this author inPubMed Google Scholar * Matthias Stangl View author publications You can also search for this author inPubMed Google Scholar * Uros Topalovic View author

publications You can also search for this author inPubMed Google Scholar * Daniel Batista View author publications You can also search for this author inPubMed Google Scholar * Sonja Hiller

View author publications You can also search for this author inPubMed Google Scholar * Zahra M. Aghajan View author publications You can also search for this author inPubMed Google Scholar *

Barbara Knowlton View author publications You can also search for this author inPubMed Google Scholar * John Stern View author publications You can also search for this author inPubMed 

Google Scholar * Jean-Philippe Langevin View author publications You can also search for this author inPubMed Google Scholar * Itzhak Fried View author publications You can also search for

this author inPubMed Google Scholar * Dawn Eliashiv View author publications You can also search for this author inPubMed Google Scholar * Nanthia Suthana View author publications You can

also search for this author inPubMed Google Scholar CONTRIBUTIONS Conceptualization: S.L.M., M.S., N.S.; Methodology: S.L.M., M.S., U.T., Z.M.A., B.K., N.S.; Software: S.L.M., M.S., U.T.,

D.B., Z.M.A.; Analysis: S.L.M., M.S.; Investigation: S.L.M., M.S., U.T., S.H., J.S., J.P.L., I.F., D.E., N.S.; Resources: J.S., J.P.L., I.F., D.E., N.S.; Data curation: S.L.M., M.S., U.T.;

Writing-original draft preparation: S.L.M., M.S., N.S.; Writing-review and editing: S.L.M., M.S., U.T., D.B., S.H., Z.M.A., B.K., J.S., J.P.L., I.F., D.E., N.S.; Visualization: SLM;

Supervision: M.S., J.S., J.P.L., I.F., D.E., N.S.; Project administration: S.L.M., S.H., J.S., J.P.L., I.F., D.E., N.S.; Funding acquisition: S.L.M., M.S., N.S. CORRESPONDING AUTHOR

Correspondence to Nanthia Suthana. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Communications_ thanks the

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licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Maoz, S.L.L., Stangl, M., Topalovic, U. _et al._ Dynamic neural

representations of memory and space during human ambulatory navigation. _Nat Commun_ 14, 6643 (2023). https://doi.org/10.1038/s41467-023-42231-4 Download citation * Received: 16 February

2023 * Accepted: 03 October 2023 * Published: 20 October 2023 * DOI: https://doi.org/10.1038/s41467-023-42231-4 SHARE THIS ARTICLE Anyone you share the following link with will be able to

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