Memory recall involves a transient break in excitatory-inhibitory balance

  1. Renée S Koolschijn  Is a corresponding author
  2. Anna Shpektor
  3. William T Clarke
  4. I Betina Ip
  5. David Dupret
  6. Uzay E Emir
  7. Helen C Barron  Is a corresponding author
  1. University of Oxford, United Kingdom
  2. Purdue University, United States

Abstract

The brain has a remarkable capacity to acquire and store memories that can later be selectively recalled. These processes are supported by the hippocampus which is thought to index memory recall by reinstating information stored across distributed neocortical circuits. However, the mechanism that supports this interaction remains unclear. Here, in humans, we show that recall of a visual cue from a paired associate is accompanied by a transient increase in the ratio between glutamate and GABA in visual cortex. Moreover, these excitatory-inhibitory fluctuations are predicted by activity in the hippocampus. These data suggest the hippocampus gates memory recall by indexing information stored across neocortical circuits using a disinhibitory mechanism.

Data availability

The data and code used in this study are available via the MRC BNDU Data Sharing PlatformThe data is available here:https://data.mrc.ox.ac.uk/data-set/fmri-fmrs-inferenceDOI: https://doi.org/10.5287/bodleian:vmJOOm7KDThe code is available here:https://data.mrc.ox.ac.uk/data-set/frms-codeDOI: https://doi.org/10.5287/bodleian:8JwYayQmD

Article and author information

Author details

  1. Renée S Koolschijn

    University of Oxford, Oxford, United Kingdom
    For correspondence
    renee.koolschijn@keble.ox.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9553-4213
  2. Anna Shpektor

    University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. William T Clarke

    University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. I Betina Ip

    University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3544-0711
  5. David Dupret

    University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Uzay E Emir

    school of health science, Purdue University, West Lafayette, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5376-0431
  7. Helen C Barron

    University of Oxford, Oxford, United Kingdom
    For correspondence
    helen.barron@merton.ox.ac.uk
    Competing interests
    The authors declare that no competing interests exist.

Funding

Engineering and Physical Sciences Research Council (EP/L016052/1)

  • Renée S Koolschijn

Royal Society Dorothy Hodgkin Research Fellowship

  • I Betina Ip

Wellcome Trust (203836/Z/16/Z)

  • Anna Shpektor

Biotechnology and Biological Sciences Research Council (BB/N0059TX/1)

  • David Dupret

Medical Research Council (MC_UU_12024/3)

  • David Dupret

John Fell Fund, University of Oxford (153/046)

  • Helen C Barron

Wellcome Centre for Integrative Neuroimaging (Seed grant)

  • Helen C Barron

Merton College, University of Oxford (JRF)

  • Helen C Barron

Medical Research Council (MC_UU_12024/3)

  • Helen C Barron

Wellcome Trust (203139/Z/16/Z)

  • Renée S Koolschijn
  • Anna Shpektor
  • William T Clarke
  • I Betina Ip
  • Helen C Barron

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

Reviewing Editor

  1. Muireann Irish, University of Sydney, Australia

Ethics

Human subjects: All participants gave informed written consent.All experiments were approved by the University of Oxford ethics committee (reference number R43594/RE001).

Version history

  1. Preprint posted: November 27, 2020 (view preprint)
  2. Received: May 5, 2021
  3. Accepted: September 7, 2021
  4. Accepted Manuscript published: October 8, 2021 (version 1)
  5. Version of Record published: October 14, 2021 (version 2)

Copyright

© 2021, Koolschijn 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

  • 3,436
    views
  • 452
    downloads
  • 17
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Renée S Koolschijn
  2. Anna Shpektor
  3. William T Clarke
  4. I Betina Ip
  5. David Dupret
  6. Uzay E Emir
  7. Helen C Barron
(2021)
Memory recall involves a transient break in excitatory-inhibitory balance
eLife 10:e70071.
https://doi.org/10.7554/eLife.70071

Share this article

https://doi.org/10.7554/eLife.70071

Further reading

    1. Neuroscience
    Zilu Liang, Simeng Wu ... Chao Liu
    Research Article

    People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a ‘social cognitive map’ organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being.

    1. Neuroscience
    Alina Tetereva, Narun Pat
    Research Article

    One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36–100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.