Environmental deformations dynamically shift the grid cell spatial metric

  1. Alexandra T Keinath  Is a corresponding author
  2. Russell A Epstein
  3. Vijay Balasubramanian  Is a corresponding author
  1. University of Pennsylvania, United States

Abstract

In familiar environments, the firing fields of entorhinal grid cells form regular triangular lattices. However, when the geometric shape of the environment is deformed, these time-averaged grid patterns are distorted in a grid scale-dependent and local manner. We hypothesized that this distortion in part reflects dynamic anchoring of the grid code to displaced boundaries, possibly through border cell-grid cell interactions. To test this hypothesis, we first reanalyzed two existing rodent grid rescaling datasets to identify previously unrecognized boundary-tethered shifts in grid phase that contribute to the appearance of rescaling. We then demonstrated in a computational model that boundary-tethered phase shifts, as well as scale-dependent and local distortions of the time-averaged grid pattern, could emerge from border-grid interactions without altering inherent grid scale. Together, these results demonstrate that environmental deformations induce history-dependent shifts in grid phase, and implicate border-grid interactions as a potential mechanism underlying these dynamics.

Data availability

All simulations were conducted with custom-written MATLAB scripts. These scripts and the simulation results presented here are available on Github at: https://github.com/akeinath/Keinath_BoundaryTetheredModel. All values generated during our reanalysis are included as source data files. All original reanalyzed data were originally reported in the following papers:1)Barry et al., 2007. Experience-dependent rescaling of entorhinal grids. https://doi.org/10.1038/nn1905;2)Stensola et al., 2012. The entorhinal map is descritized. https://doi.org/10.1038/nature11649.These data are available upon request from the corresponding authors of these papers.

Article and author information

Author details

  1. Alexandra T Keinath

    Department of Psychology, University of Pennsylvania, Philadelphia, United States
    For correspondence
    atkeinath@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1622-7835
  2. Russell A Epstein

    Department of Psychology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Vijay Balasubramanian

    Department of Physics, University of Pennsylvania, Philadelphia, United States
    For correspondence
    vijay@physics.upenn.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6497-3819

Funding

National Science Foundation (PHY-1734030)

  • Vijay Balasubramanian

National Institutes of Health (EY022350)

  • Russell A Epstein

National Institutes of Health (EY022350)

  • Russell A Epstein

National Science Foundation (966142)

  • Alexandra T Keinath

National Science Foundation (PHY-584 1607611))

  • Vijay Balasubramanian

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Laura Colgin, The University of Texas at Austin, Center for Learning and Memory, United States

Version history

  1. Received: May 8, 2018
  2. Accepted: October 21, 2018
  3. Accepted Manuscript published: October 22, 2018 (version 1)
  4. Version of Record published: October 26, 2018 (version 2)

Copyright

© 2018, Keinath et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,926
    Page views
  • 280
    Downloads
  • 32
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Alexandra T Keinath
  2. Russell A Epstein
  3. Vijay Balasubramanian
(2018)
Environmental deformations dynamically shift the grid cell spatial metric
eLife 7:e38169.
https://doi.org/10.7554/eLife.38169

Share this article

https://doi.org/10.7554/eLife.38169

Further reading

    1. Neuroscience
    Kiwamu Kudo, Kamalini G Ranasinghe ... Srikantan S Nagarajan
    Research Article

    Alzheimer’s disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

    1. Medicine
    2. Neuroscience
    Luisa Fassi, Shachar Hochman ... Roi Cohen Kadosh
    Research Article

    In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual’s subjective experience of the type of treatment she or he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment - the thought of receiving the active or placebo condition during an experiment - can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N = 387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Studies 1 and 2; transcranial direct current stimulation: Studies 3 and 4). Our findings show that the inclusion of subjective treatment can provide a better model fit either alone or in interaction with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive, and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants’ subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.