Sense of self impacts spatial navigation and hexadirectional coding in human entorhinal cortex

Abstract

Grid cells in entorhinal cortex (EC) encode an individual's location in space and rely on environmental cues and self-motion cues derived from the individual's body. Body-derived signals are also primary signals for the sense of self as located in space (i.e. bodily self-consciousness, BSC). However, it is currently unknown whether BSC impacts grid cell activity and how such changes relate to experimental modulations of BSC. Integrating BSC with a spatial navigation task and an fMRI measure to detect grid cell-like representation (GCLR) in humans, we report a robust GCLR modulation in EC when participants navigated during an enhanced BSC state. These changes were further associated with improved spatial navigation performance and increased activity in posterior parietal and retrosplenial cortex. These data link entorhinal grid cell activity with BSC and show that BSC modulates ego- versus allocentric spatial processes about an individual's location in space in a distributed spatial navigation system.

The brain mechanisms of self-identification & self-location in neurosurgical patients using virtual reality and lesion network mapping

“…In animals, spatial representations of self and other are encoded in the hippocampus 33 . In humans, some evidence suggests the involvement the hippocampus in spatial navigation and memory but also in the construction of our sense of bodily self-location 17 , in line with recent findings showing a relationship between experimental self-location manipulations and changes in grid-cell activities in medial temporal regions 34 . Also, the manipulation of the first-person body view induces changes in episodic memory performances 35 and also led to changes in connectivity between brain areas involved episodic memory and autonoetic consciousness such as the right hippocampus and the right parahippocampal region 36 .…”

The role of bodily self-consciousness in episodic memory of naturalistic events: An immersive virtual reality study

“…Mixed models are well suited for repeated measure designs as they enable dependencies in the data to be considered. Models were computed using the lmer function from the lme4 package in R studio version 4.0.4 [45] . When data residuals were not normally distributed (as assessed by a Shapiro-Wilk test), we used a robust version of the lmer function named rlmer using the robustlmm package [46] .…”

“…At retrieval, participants were again placed in a virtual environment and asked to navigate to the position of the object memorized. It was found that they committed smaller distance errors and navigated shorter paths when seeing the avatar associated with synchronous sensorimotor stimulation 45 . Furthermore, it has been shown that discrepancies between physical and seen body position during memory encoding may disturb binding processes in the hippocampus and impair EM for spatial, temporal, and emotional information 44 .…”

“…Similarly, Moon and colleagues found that seeing one's body in rst-person perspective during navigation in a virtual environment decreases entorhinal cortex activity and increases retrosplenial cortex activity. Moreover, changes in activity in these regions were correlated with participants' spatial navigation and spatial memory performances [45] . Moreover, Bergouignan, Nyberg, and Ehrsson found reduced hippocampal activity during retrieval in the out-of-body condition.…”

Spatialization of Time in the Entorhinal-Hippocampal System

“…Like the hippocampus, however, grid cells are also not specifically tuned for space, as their properties have been described with computational principles ( Dordek et al, 2016 ; Stachenfeld et al, 2017 ; Baram et al, 2018 ; Behrens et al, 2018 ; Gershman, 2018 ; Momennejad and Howard, 2018 ; Bicanski and Burgess, 2019 ; Mark et al, 2020 ; Momennejad, 2020 ; Rueckemann et al, 2021 ), specifically, as a (basis) set of vectors that can linearly combine to represent any point in two dimensions. This “grid code” has since been found to facilitate non-spatial representation, such as for virtual ( Doeller et al, 2010 ), imagined ( Bellmund et al, 2016 ; Horner et al, 2016 ), conceptual ( Constantinescu et al, 2016 ), visual ( Julian et al, 2018 ; Nau et al, 2018 ), odor ( Bao et al, 2019 ), egocentric ( Moon et al, 2020 ), social ( Park et al, 2021 ), semantic ( Viganò et al, 2021 ), and contextual ( Julian and Doeller, 2021 ) information in humans.…”