The trajectory of cortical GABA across the lifespan, an individual participant data meta-analysis of edited MRS studies

  1. Eric C Porges  Is a corresponding author
  2. Greg Jensen
  3. Brent Foster
  4. Richard AE Edden PhD
  5. Nicolaas AJ Puts
  1. University of Florida, United States
  2. Columbia University, United States
  3. Johns Hopkins University School of Medicine, United States
  4. King's College London, United Kingdom

Abstract

GABA is the principal inhibitory neurotransmitter in the human brain and can be measured with Magnetic Resonance Spectroscopy (MRS). Conflicting accounts report decreases and increases in cortical GABA levels across the lifespan. This incompatibility may be an artifact of the size and age-range of the samples utilized in these studies. No single study to date has included the entire lifespan. In this study, 8 suitable datasets were integrated to generate a model of the trajectory of frontal GABA estimates (as reported through edited MRS; both expressed as ratios and in institutional units). across the lifespan. Data were fit using both a log-normal curve and a nonparametric spline as regression models using a multi-level Bayesian model utilizing the Stan language. Integrated data show the lifespan trajectory of frontal GABA measures involves an early period of increase, followed by a period of stability during early adulthood, with a gradual decrease during adulthood and aging that is described well by both spline and log-normal models. The information gained will provide a general framework to inform expectations of future studies based on the age of the population being studied.

Data availability

All data and code used in this manuscript can be found here https://osf.io/rmhwc/

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Eric C Porges

    Clinical and Health Psychology, University of Florida, Gainesville, United States
    For correspondence
    eporges@phhp.ufl.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3885-5859
  2. Greg Jensen

    Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Brent Foster

    Clinical and Health Psychology, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Richard AE Edden PhD

    Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Nicolaas AJ Puts

    King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (KO1AA025306)

  • Eric C Porges

Evelyn F. McKnight Brain Research Foundation

  • Eric C Porges

Center for Cognitive Aging and Memory at the University of Florida

  • Eric C Porges

National Institutes of Health (R00MH107719)

  • Nicolaas AJ Puts

National Institutes of Health (R01EB016089)

  • Richard AE Edden PhD

National Institutes of Health (R01MH106564)

  • Richard AE Edden PhD

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

Copyright

© 2021, Porges 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

  • 3,053
    views
  • 330
    downloads
  • 69
    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. Eric C Porges
  2. Greg Jensen
  3. Brent Foster
  4. Richard AE Edden PhD
  5. Nicolaas AJ Puts
(2021)
The trajectory of cortical GABA across the lifespan, an individual participant data meta-analysis of edited MRS studies
eLife 10:e62575.
https://doi.org/10.7554/eLife.62575

Share this article

https://doi.org/10.7554/eLife.62575

Further reading

    1. Biochemistry and Chemical Biology
    2. Neuroscience
    Silvia Galli, Marco Di Antonio
    Insight

    The buildup of knot-like RNA structures in brain cells may be the key to understanding how uncontrolled protein aggregation drives Alzheimer’s disease.

    1. Neuroscience
    Paul I Jaffe, Gustavo X Santiago-Reyes ... Russell A Poldrack
    Research Article

    Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.