Everyday tasks in social settings require humans to encode neural representations of not only their own spatial location, but also the location of other individuals within an environment. At present, the vast majority of what is known about neural representations of space for self and others stems from research in rodents and other non-human animals1,2,3. However, it is largely unknown how the human brain represents the location of others, and how aspects of human cognition may affect these location-encoding mechanisms. To address these questions, we examined individuals with chronically implanted electrodes while they carried out real-world spatial navigation and observation tasks. We report boundary-anchored neural representations in the medial temporal lobe that are modulated by one’s own as well as another individual’s spatial location. These representations depend on one’s momentary cognitive state, and are strengthened when encoding of location is of higher behavioural relevance. Together, these results provide evidence for a common encoding mechanism in the human brain that represents the location of oneself and others in shared environments, and shed new light on the neural mechanisms that underlie spatial navigation and awareness of others in real-world scenarios.
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A wearable platform for closed-loop stimulation and recording of single-neuron and local field potential activity in freely moving humans
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The data that support the findings of this study are available from the corresponding authors upon reasonable request.
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Moser, M.-B., Rowland, D. C. & Moser, E. I. Place cells, grid cells, and memory. Cold Spring Harb. Perspect. Biol. 7, a021808 (2015).
Omer, D. B., Maimon, S. R., Las, L. & Ulanovsky, N. Social place-cells in the bat hippocampus. Science 359, 218–224 (2018).
Danjo, T., Toyoizumi, T. & Fujisawa, S. Spatial representations of self and other in the hippocampus. Science 359, 213–218 (2018).
Julian, J. B., Keinath, A. T., Marchette, S. A. & Epstein, R. A. The neurocognitive basis of spatial reorientation. Curr. Biol. 28, R1059–R1073 (2018).
Hardcastle, K., Ganguli, S. & Giocomo, L. M. Environmental boundaries as an error correction mechanism for grid cells. Neuron 86, 827–839 (2015).
Doeller, C. F. & Burgess, N. Distinct error-correcting and incidental learning of location relative to landmarks and boundaries. Proc. Natl Acad. Sci. USA 105, 5909–5914 (2008).
Horner, A. J., Bisby, J. A., Wang, A., Bogus, K. & Burgess, N. The role of spatial boundaries in shaping long-term event representations. Cognition 154, 151–164 (2016).
Meilinger, T., Strickrodt, M. & Bülthoff, H. H. Qualitative differences in memory for vista and environmental spaces are caused by opaque borders, not movement or successive presentation. Cognition 155, 77–95 (2016).
Bellmund, J. L. S. et al. Deforming the metric of cognitive maps distorts memory. Nat. Hum. Behav. 4, 177–188 (2020).
Solstad, T., Boccara, C. N., Kropff, E., Moser, M.-B. & Moser, E. I. Representation of geometric borders in the entorhinal cortex. Science 322, 1865–1868 (2008).
Lever, C., Burton, S., Jeewajee, A., O’Keefe, J. & Burgess, N. Boundary vector cells in the subiculum of the hippocampal formation. J. Neurosci. 29, 9771–9777 (2009).
Savelli, F., Yoganarasimha, D. & Knierim, J. J. Influence of boundary removal on the spatial representations of the medial entorhinal cortex. Hippocampus 18, 1270–1282 (2008).
O’Keefe, J. & Burgess, N. Geometric determinants of the place fields of hippocampal neurons. Nature 381, 425–428 (1996).
Lever, C., Wills, T., Cacucci, F., Burgess, N. & O’Keefe, J. Long-term plasticity in hippocampal place-cell representation of environmental geometry. Nature 416, 90–94 (2002).
Krupic, J., Bauza, M., Burton, S., Barry, C. & O’Keefe, J. Grid cell symmetry is shaped by environmental geometry. Nature 518, 232–235 (2015).
Stensola, T., Stensola, H., Moser, M.-B. & Moser, E. I. Shearing-induced asymmetry in entorhinal grid cells. Nature 518, 207–212 (2015).
Lee, S. A. et al. Electrophysiological signatures of spatial boundaries in the human subiculum. J. Neurosci. 38, 3265–3272 (2018).
Shine, J. P., Valdés-Herrera, J. P., Tempelmann, C. & Wolbers, T. Evidence for allocentric boundary and goal direction information in the human entorhinal cortex and subiculum. Nat. Commun. 10, 4004 (2019).
Morrell, M. J. & RNS System in Epilepsy Study Group. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology 77, 1295–1304 (2011).
Aghajan, Z. M. et al. Theta oscillations in the human medial temporal lobe during real-world ambulatory movement. Curr. Biol. 27, 3743–3751 (2017).
Caplan, J. B., Madsen, J. R., Raghavachari, S. & Kahana, M. J. Distinct patterns of brain oscillations underlie two basic parameters of human maze learning. J. Neurophysiol. 86, 368–380 (2001).
Caplan, J. B. et al. Human θ oscillations related to sensorimotor integration and spatial learning. J. Neurosci. 23, 4726–4736 (2003).
Ekstrom, A. D. et al. Human hippocampal theta activity during virtual navigation. Hippocampus 15, 881–889 (2005).
Kahana, M. J., Sekuler, R., Caplan, J. B., Kirschen, M. & Madsen, J. R. Human theta oscillations exhibit task dependence during virtual maze navigation. Nature 399, 781–784 (1999).
Mizuhara, H., Wang, L.-Q., Kobayashi, K. & Yamaguchi, Y. A long-range cortical network emerging with theta oscillation in a mental task. Neuroreport 15, 1233–1238 (2004).
Jutras, M. J., Fries, P. & Buffalo, E. A. Oscillatory activity in the monkey hippocampus during visual exploration and memory formation. Proc. Natl Acad. Sci. USA 110, 13144–13149 (2013).
Jeewajee, A., Barry, C., O’Keefe, J. & Burgess, N. Grid cells and theta as oscillatory interference: electrophysiological data from freely moving rats. Hippocampus 18, 1175–1185 (2008).
McFarland, W. L., Teitelbaum, H. & Hedges, E. K. Relationship between hippocampal theta activity and running speed in the rat. J. Comp. Physiol. Psychol. 88, 324–328 (1975).
Høydal, Ø. A., Skytøen, E. R., Andersson, S. O., Moser, M.-B. & Moser, E. I. Object-vector coding in the medial entorhinal cortex. Nature 568, 400–404 (2019).
Barry, C. et al. The boundary vector cell model of place cell firing and spatial memory. Rev. Neurosci. 17, 71–97 (2006).
Hollup, S. A., Molden, S., Donnett, J. G., Moser, M.-B. & Moser, E. I. Accumulation of hippocampal place fields at the goal location in an annular watermaze task. J. Neurosci. 21, 1635–1644 (2001).
Dupret, D., O’Neill, J., Pleydell-Bouverie, B. & Csicsvari, J. The reorganization and reactivation of hippocampal maps predict spatial memory performance. Nat. Neurosci. 13, 995–1002 (2010).
Bourboulou, R. et al. Dynamic control of hippocampal spatial coding resolution by local visual cues. eLife 8, e44487 (2019).
Tort, A. B. L., Komorowski, R., Eichenbaum, H. & Kopell, N. Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. J. Neurophysiol. 104, 1195–1210 (2010).
Canolty, R. T. & Knight, R. T. The functional role of cross-frequency coupling. Trends Cogn. Sci. 14, 506–515 (2010).
Kunz, L. et al. Mesoscopic neural representations in spatial navigation. Trends Cogn. Sci. 23, 615–630 (2019).
Bellmund, J. L. S., Gärdenfors, P., Moser, E. I. & Doeller, C. F. Navigating cognition: spatial codes for human thinking. Science 362, eaat6766 (2018).
Topalovic, U. et al. Wireless programmable recording and stimulation of deep brain activity in freely moving humans. Neuron 108, 322–334 (2020).
König, S. D. & Buffalo, E. A. A nonparametric method for detecting fixations and saccades using cluster analysis: removing the need for arbitrary thresholds. J. Neurosci. Methods 227, 121–131 (2014).
Gelinas, J. N., Khodagholy, D., Thesen, T., Devinsky, O. & Buzsáki, G. Interictal epileptiform discharges induce hippocampal-cortical coupling in temporal lobe epilepsy. Nat. Med. 22, 641–648 (2016).
Aghajan, Z. M. et al. Modulation of human intracranial theta oscillations during freely moving spatial navigation and memory. Preprint at https://doi.org/10.1101/738807 (2019).
Whitten, T. A., Hughes, A. M., Dickson, C. T. & Caplan, J. B. A better oscillation detection method robustly extracts EEG rhythms across brain state changes: the human alpha rhythm as a test case. Neuroimage 54, 860–874 (2011).
Seager, M. A., Johnson, L. D., Chabot, E. S., Asaka, Y. & Berry, S. D. Oscillatory brain states and learning: impact of hippocampal theta-contingent training. Proc. Natl Acad. Sci. USA 99, 1616–1620 (2002).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).
This work was supported by the National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS; grant NS103802), 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. We also thank A. Lin for support with statistical analyses, J. Schneiders and T. Wishard for help with illustrations and manuscript preparation, and J. Gill for methodological support.
N.R.H. is an employee of NeuroPace Inc., Mountain View, CA, USA.
Peer review information Nature thanks Jack Lin, Hugo Spiers and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
Extended Data Fig. 1 Target locations, participants’ seating location, ‘boundary’ versus ‘inner’ distance thresholds, and prevalence of theta bouts.
a, Target locations (red dots) during the self-navigation task and the participants’ seating location (green squares) during the observation task. There were three target locations in each task block, except in the case of participant 1, who performed a slightly modified version of the task with four target locations. Target locations remained unchanged throughout the first three task blocks. For those participants who took part in more than three task blocks, new target locations were introduced in task block four. All units are in metres. b, Distance thresholds for ‘boundary’ versus ‘inner’ room area. Tested thresholds range between 0.8 m and 1.6 m. Both theta (θ) and gamma (γ) bandpowers were significantly increased near boundaries, independent of the selected boundary width threshold to distinguish ‘boundary’ versus ‘inner’ room areas (self, P < 0.001 for all thresholds and both frequency bands; other, P = 0.001 for theta at thresholds 0.8 m and 1.0 m, P < 0.001 for all other boundary widths and both frequency bands). θ denotes the theta frequency band (self, 3–12 Hz; other, 5–8 Hz); γ denotes the gamma frequency band (self/other, 60–80 Hz). Data show means ± s.e.m. from nCh = 16. Asterisks denote significant differences (P < 0.05, uncorrected) in a one-sided permutation test. ‘Boundary’ versus ‘inner’ comparisons and plots were corrected for differences in data amount between room areas (see Methods). c, Prevalence of theta bouts. Episodes of significant theta oscillations were detected using the BOSC toolbox42,43 for individual frequency steps between 3 Hz and 20 Hz with a step width of 0.25 Hz, separately for self-navigation (self) and observation (other). The prevalence of an oscillation was defined as the percentage of time (relative to the total duration of the experiment) when such oscillations could be observed at a given frequency (see Methods). Data show means ± s.e.m. from nCh = 16. Oscillation prevalence was not significantly different between ‘boundary’ and ‘inner’ room area for any of the tested frequencies in a one-sided permutation test for nCh = 16 (all P > 0.05, uncorrected).
a, Theta bandpower was significantly higher for ‘boundary’ versus ‘inner’ locations (‘Main effect’; self, P < 0.001; other, P < 0.001), shown also during periods of immobility (when movement periods were excluded from data analysis; ‘Standing periods’; self, P = 0.021; other, P = 0.008), when circular areas with a diameter of 1 m around target locations were excluded from data analysis (‘No target locations’; self, P < 0.001; other, P < 0.001), for movements towards a boundary (‘Towards boundary’; self, P < 0.001; other, P < 0.001), and for movements away from a boundary (‘Away from boundary’; self, P < 0.001; other, P = 0.019). b, Theta bandpower was significantly higher for ‘boundary’ versus ‘inner’ locations, after excluding start and end periods per trial (where each trial is a ‘target search’ period during self-navigation or an individual trajectory of the experimenter during the observation task) from analysis. Time windows of 2 s and 5 s were excluded for self-navigation (‘Without trial start period (5s)’, P = 0.008; ‘Without trial start period (2s)’, P = 0.003; ‘Without trial end period (2s), P = 0.012; ‘Without trial start & end period (2s each)’, P = 0.020). For observation task trials, which were considerably shorter and did not have long breaks in between individual trajectories, only 2-s windows were excluded (‘Without trial start period’, P < 0.001; ‘Without trial end period’, P < 0.001; ‘Without trial start & end period’, P < 0.001). c, The strength of the main effect, quantified as each channel’s average ‘boundary’ minus ‘inner’ bandpower, did not significantly differ between different task blocks (P > 0.05 for all pairwise comparisons, separately for ‘self’ and ‘other’ task blocks). d, The strength of the main effect did not significantly differ between trials with high versus low performance (self, P = 0.210; other, P = 0.151). Performance was classified as ‘high’ or ‘low’ on the basis of a median split of performance during all trials (see Methods). e, Theta power was significantly increased near boundaries during high-performance as well as low-performance trials, both for self-navigation (self, high performance P = 0.003; low performance P = 0.021) and during observation (other, high performance P < 0.001; low performance P = 0.012). All plots (a–e) show means ± s.e.m. from nCh = 16. Asterisks denote a significant difference (P < 0.05, uncorrected) in a one-sided permutation test; ns, non-significant. ‘Boundary’ versus ‘inner’ comparisons and plots were corrected for differences in data amount between room areas (see Methods).
Linear mixed-effects models were calculated to predict each participant’s normalized theta power timeseries (response variable) by a range of predictor variables (fixed effects) that can simultaneously affect theta power variability over time. Proximity to boundary was specified as a continuous numerical variable (instead of a threshold-based ‘boundary’ versus ‘inner’ categorization). Cognitive state was specified as a categorical variable with two states (‘target search’ versus ‘no target search’ for self-navigation, and ‘target ahead’ versus ‘no target ahead’ during observation). Movement direction was quantified as a categorical variable in 12 equally sized directional bins. Eye movements were specified as the magnitude of eye movement per time point. ‘Proximity to other’ indicates the distance between the participant and the experimenter during the observation task. See Methods section ‘Linear mixed-effect model analysis’ for more details on how predictor variables were specified. All non-categorical predictor variables were standardized (z-scored) before model fitting. Each variable’s impact on theta power is reflected by the variable’s beta weight (standardized effect size; grey bars) and corresponding t-statistic (tStat; orange bars). The frequency range for theta was identical to the range in the main ‘boundary’ versus ‘inner’ analyses (3–12 Hz for self-navigation, 5–8 Hz for observation). The upper panels show results from a ‘full model’ including all predictor variables. Interactions and correlations between predictor variables (see also correlation matrices in bottom panels) can lead to enhancement or suppression of some variables’ beta weights (that is, overestimation or underestimation of the variables’ impact). Therefore, each variable’s impact was also calculated in ‘independent models’ (separate models for each of the predictor variables, thus showing each variable’s impact independently of interactions and correlations with the other variables). Asterisks denote a significant impact of a variable on theta power (P < 0.05, uncorrected). Significance was determined for each variable by testing whether the variable’s beta weight was significantly different from zero in a one-sided permutation test across participants. Diamonds denote P < 0.1 (non-significant). Data are mean beta/tStat values ± s.e.m. across n = 5 participants.
Extended Data Fig. 4 Impact of eye movements, and relationship between movement speed and theta power.
a, Participants’ eye movements were quantified throughout the experiment by measuring the pupil position on a normalized two-dimensional recording plane. Epochs of saccades and fixations were classified through algorithms based on a k-means cluster analysis39. Reported data for fixations and saccades indicate their prevalence over time (percentage of time throughout all task blocks). Theta power was higher during saccadic compared with fixation periods, for both self-navigation (P = 0.029) and observation (P < 0.001). b, Theta power was increased significantly near boundaries during both saccadic and fixation periods, for both self-navigation (saccades, P < 0.001; fixations, P < 0.001) and observation (saccades, P = 0.007; fixations, P = 0.002). c, The magnitude of eye movements (in normalized units per time point), fixation prevalence and saccade prevalence were not significantly different between ‘boundary’ and ‘inner’ room areas, neither during self-navigation (eye movement magnitude, P = 0.190; fixations, P = 0.189; saccades, P = 0.191) nor during observation (eye movement magnitude, P = 0.183; fixations, P = 0.372; saccades, P = 0.502). d, Theta power during epochs of high movement speed and low movement speed, based on a median split of movement speed values across all data from the self-navigation (self) or observation (other) tasks. Plots show the average power during movements with low speed (below median) subtracted from high speed (above median); thus, positive values indicate higher power for fast compared with slow movements. High–low speed power was not significantly different after multiple comparisons correction (FDR44,45) for any of the tested frequencies, in a one-sided permutation test for nCh = 16 (all P > 0.05, uncorrected). e, Theta power during epochs of high movement speed versus low movement speed, separately for ‘target search’ versus ‘no target search’ periods during self-navigation, and for ‘target ahead’ versus ‘no target ahead’ periods during the observation task. Green horizontal bars indicate significant high–low speed power differences (uncorrected) between ‘target search’ versus ‘no target search’ periods (self) or ‘target ahead’ versus ‘no target ahead’ (other) periods, and the inset red bars indicate significance after multiple comparisons correction (FDR44,45), in a one-sided permutation test for nCh = 16. The power difference between ‘target search/ahead’ and ‘no target search/ahead’ was not significantly different between self-navigation and observation ([NoTargetSearchself – TargetSearchself] versus [NoTargetAheadother – TargetAheadother]; P > 0.05 for all frequencies; P < 0.1 for frequencies between 7 Hz and 14 Hz; FDR-corrected44,45; one-sided permutation test for each frequency in steps of 1 Hz; nCh = 16). Data in all plots show means ± s.e.m. from nCh = 16 (a, b, d, e) or n = 5 participants (c). Asterisks denote a significant difference (P < 0.05, uncorrected) in a one-sided permutation test; ns, non-significant. Analyses and plots of bandpower differences were corrected for differences in data amount between conditions (see Methods).
Linear mixed-effect models were used to quantify the impact of wall-mounted signs and proximity to the boundary on theta power during self-navigation (self) and observation (other). The theta frequency range was identical to the main ‘boundary’ versus ‘inner’ analyses (self, 3–12 Hz; other, 5–8 Hz). a, Diagram of the room from the top-down perspective, with yellow rectangles indicating wall-mounted signs (which provided strong visual cues for the participants), purple dots indicating locations in between signs (which did not correspond to visible landmarks but were defined for post hoc analysis only), and the grey rectangle indicating the room boundary. The red rectangle frame indicates the next wall-mounted sign that the participant (during self-navigation) or the experimenter (during observation) was walking towards (shown here is one example; the specific sign changed between trials). b, Comparison between the impact of proximity to sign locations versus proximity to locations in between signs. The impact of proximity to sign locations on theta power was not significantly different from the impact of locations in between signs, either during self-navigation (self, P = 0.815) or during observation (other, P = 0.176). c, Comparison between the impact of proximity to boundary versus the next wall-mounted sign that the participant or experimenter was walking towards. The impact of proximity to boundary was numerically higher in both conditions, but the difference between the variables’ impact was statistically significant only during observation (other, P = 0.028) and not during self-navigation (self, P = 0.059). Note that periods of walking towards the next wall-mounted sign during self-navigation are equivalent to ‘no target search’ periods, and that the proximity of the experimenter to the next wall-mounted sign during the observation task is generally highest during ‘no target ahead’ periods. This explains the low or negative relationship between theta power and proximity to the next wall sign, as theta power is generally decreased and there is no significant effect of boundaries on theta power during these periods (Fig. 4). The difference between the impact of ‘boundary’ and ‘next sign’ was not significantly different between self-navigation and observation ([Boundaryself – NextSignself] versus [Boundaryother – NextSignother], P = 0.493). Data in all plots (b, c) show mean beta values ± s.e.m. from n = 5 participants. The asterisk denotes a significant difference (P < 0.05, uncorrected) in a one-sided permutation test; the diamond denotes P < 0.1 (non-significant); ns, P > 0.1 (non-significant).
a, Gamma (60–80 Hz) bandpower at ‘boundary’ versus ‘inner’ locations (‘Main effect’; self, P < 0.001; other, P < 0.001), shown also during periods of immobility (when movement periods were excluded from data analysis; ‘Standing periods’; self, P = 0.004; other, P = 0.464), when circular areas with a diameter of 1 m around target locations were excluded from data analysis (‘No target locations’; self, P = 0.003; other, P = 0.001), for movements towards a boundary (‘Towards boundary’; self, P = 0.031; other, P = 0.010), and for movements away from a boundary (‘Away from boundary’; self, P = 0.022; other, P < 0.001). b, Numerically higher gamma bandpower at ‘boundary’ versus ‘inner’ locations, shown for each participant separately. Plots show mean values across channels per participant (participants 1 and 5, nCh = 3; participants 2 and 4, nCh = 4; participant 3: nCh = 2). c, During self-navigation (self), gamma bandpower was significantly lower for ‘target search’ versus ‘no target search’ periods (P < 0.001). During the observation task (other), gamma power was significantly higher for ‘target ahead’ versus ‘no target ahead’ periods (P < 0.001). d, For self-navigation, gamma bandpower was significantly different between ‘boundary’ versus ‘inner’ areas during ‘target search’ (P < 0.001) but not during ‘no target search’ periods (P = 0.113). During the observation task, gamma bandpower was not significantly different between ‘boundary’ versus ‘inner’ areas of the room, both for ‘target ahead’ (P = 0.137) and for ‘no target ahead’ (P = 0.255) periods. The difference between ‘boundary’ and ‘inner’ bandpower was not significantly different between ‘target search/ahead’ and ‘no target search/ahead’ periods ([BoundaryTargetSearch/Ahead – InnerTargetSearch/Ahead] versus [BoundaryNoTargetSearch/Ahead – InnerNoTargetSearch/Ahead]; self, P = 0.087; other, P = 0.347). Data in a, c, d show means ± s.e.m. from nCh = 16. Asterisks denote a significant difference (P < 0.05, uncorrected) in a one-sided permutation test; ns, non-significant. Statistical comparisons and plots were corrected for differences in data amount between conditions (see Methods).
a, Phase–amplitude coupling was calculated between the phase of low-frequency (theta) oscillations and the amplitude of high-frequency (gamma) oscillations, following a procedure used previously34. The intensity of phase–amplitude coupling was quantified by the ‘modulation index’, calculated for each theta–gamma frequency pair individually. The low-frequency (theta) range was between 4 Hz and 16 Hz (in 1 Hz steps, with a frequency window of ±1 Hz; that is, 3–5 Hz, 4–6 Hz, and so on), and the high-frequency (gamma) range was between 40 Hz and 90 Hz (in 2 Hz steps, with a frequency window of ±2 Hz; that is, 38–42 Hz, 40–44 Hz, and so on). The modulation index was normalized relative to a surrogate distribution (determined for each frequency pair by 100 iterations of calculating the coupling of gamma amplitudes with randomly shuffled 1-s theta-phase segments). Warmer colours indicate stronger coupling of high-frequency amplitudes to a specific low-frequency phase. Strong phase–amplitude coupling is evident at a low-frequency (phase) peak between 6 Hz and 10 Hz. b, Illustration of average gamma amplitude height for different segments of the 0 to 360 degrees theta-phase range (segmented into 18 phase bins of 20 degrees each). Theta frequency was defined as 6–10 Hz. Warmer colours indicate higher (normalized) high-frequency amplitude. Strong coupling of gamma amplitude to theta oscillations is evident at a theta phase of about 90 degrees (indicated by the black dashed line, for visualization purpose only), during both self-navigation (self) and observation (other). Heat maps (a, b) were smoothed for display purposes.
Extended Data Fig. 8 Main effects after exclusion of an extended 3-s time window around epileptic events.
Control analysis confirming the main effects of increased theta (top two rows) and gamma (bottom two rows) bandpower near boundaries, as compared with the inner area of the room, during both self-navigation (self) and observation (other), after excluding data within a time window of 3 s around each epileptic event (1.5 s before and after each detected IED sample, respectively). Data in the left panel show means ± s.e.m. from nCh = 16. Data in the right panels show mean values across channels per participant (participants 1 and 5, nCh = 3; participants 2 and 4, nCh = 4; participant 3, nCh = 2).
First, interictal epileptiform discharges were identified in the iEEG raw data and excluded from further analyses. Then, time–frequency analyses of the iEEG data were performed. Resulting power and bandpower timeseries were analysed and compared for different conditions, which were derived from behavioural data (motion-tracking data, eye-tracking data, or task-related events). See Methods for more details.
The Supplementary Information file contains two Supplementary Discussion sections: Supplementary Discussion 1, about the impact of task demands and cognitive factors on theta oscillations and neural representations of space. Supplementary Discussion 2, about the localization of boundary-related oscillatory effects in the human brain.
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Stangl, M., Topalovic, U., Inman, C.S. et al. Boundary-anchored neural mechanisms of location-encoding for self and others. Nature 589, 420–425 (2021). https://doi.org/10.1038/s41586-020-03073-y
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