Effects of dopamine on reinforcement learning and consolidation in Parkinson’s disease

  1. John P Grogan  Is a corresponding author
  2. Demitra Tsivos
  3. Laura Smith
  4. Brogan E Knight
  5. Rafal Bogacz
  6. Alan Whone
  7. Elizabeth J Coulthard  Is a corresponding author
  1. University of Bristol, United Kingdom
  2. North Bristol NHS Trust, United Kingdom
  3. University of Oxford, United Kingdom

Abstract

Emerging evidence suggests that dopamine may modulate learning and memory with important implications for understanding the neurobiology of memory and future therapeutic targeting. An influential hypothesis posits that dopamine biases reinforcement learning. More recent data also suggest an influence during both consolidation and retrieval. Eighteen Parkinson’s disease patients learned through feedback ON or OFF medication with memory tested 24 hours later ON or OFF medication (4 conditions, within-subjects design with matched healthy control group). Patients OFF medication during learning decreased in memory accuracy over the following 24 hours. In contrast to previous studies, however, dopaminergic medication during learning and testing did not affect expression of positive or negative reinforcement. Two further experiments were run without the 24-hour delay, but they too failed to reproduce effects of dopaminergic medication on reinforcement learning. While supportive of a dopaminergic role in consolidation, this study failed to replicate previous findings on reinforcement learning.

Article and author information

Author details

  1. John P Grogan

    Institute of Clinical Neurosciences, School of Clinical Sciences, University of Bristol, Bristol, United Kingdom
    For correspondence
    john.grogan@bristol.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0463-8904
  2. Demitra Tsivos

    Clinical Neurosciences, North Bristol NHS Trust, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Laura Smith

    Institute of Clinical Neurosciences, School of Clinical Sciences, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Brogan E Knight

    Clinical Neurosciences, North Bristol NHS Trust, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Rafal Bogacz

    Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Alan Whone

    Institute of Clinical Neurosciences, School of Clinical Sciences, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Elizabeth J Coulthard

    Institute of Clinical Neurosciences, School of Clinical Sciences, University of Bristol, Bristol, United Kingdom
    For correspondence
    elizabeth.coulthard@bristol.ac.uk
    Competing interests
    The authors declare that no competing interests exist.

Funding

Wellcome (PhD Studentshipt SJ1102)

  • John P Grogan

BRACE (Project grant)

  • John P Grogan
  • Elizabeth J Coulthard

Medical Research Council (MC UU 12024/5)

  • Rafal Bogacz

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

Ethics

Human subjects: Ethical approval was obtained from the NHS Research Ethics Committee at Frenchay, Bristol (09/H0107/18). All participants gave written consent, in accordance with the Declaration of Helsinki.

Copyright

© 2017, Grogan 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

  • 4,671
    views
  • 443
    downloads
  • 56
    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. John P Grogan
  2. Demitra Tsivos
  3. Laura Smith
  4. Brogan E Knight
  5. Rafal Bogacz
  6. Alan Whone
  7. Elizabeth J Coulthard
(2017)
Effects of dopamine on reinforcement learning and consolidation in Parkinson’s disease
eLife 6:e26801.
https://doi.org/10.7554/eLife.26801

Share this article

https://doi.org/10.7554/eLife.26801

Further reading

    1. Developmental Biology
    2. Neuroscience
    Odessa R Yabut, Jessica Arela ... Samuel J Pleasure
    Research Article

    Mutations in Sonic Hedgehog (SHH) signaling pathway genes, for example, Suppressor of Fused (SUFU), drive granule neuron precursors (GNP) to form medulloblastomas (MBSHH). However, how different molecular lesions in the Shh pathway drive transformation is frequently unclear, and SUFU mutations in the cerebellum seem distinct. In this study, we show that fibroblast growth factor 5 (FGF5) signaling is integral for many infantile MBSHH cases and that FGF5 expression is uniquely upregulated in infantile MBSHH tumors. Similarly, mice lacking SUFU (Sufu-cKO) ectopically express Fgf5 specifically along the secondary fissure where GNPs harbor preneoplastic lesions and show that FGFR signaling is also ectopically activated in this region. Treatment with an FGFR antagonist rescues the severe GNP hyperplasia and restores cerebellar architecture. Thus, direct inhibition of FGF signaling may be a promising and novel therapeutic candidate for infantile MBSHH.

    1. Neuroscience
    Lanfang Liu, Jiahao Jiang ... Guosheng Ding
    Research Article

    Speech comprehension involves the dynamic interplay of multiple cognitive processes, from basic sound perception, to linguistic encoding, and finally to complex semantic-conceptual interpretations. How the brain handles the diverse streams of information processing remains poorly understood. Applying Hidden Markov Modeling to fMRI data obtained during spoken narrative comprehension, we reveal that the whole brain networks predominantly oscillate within a tripartite latent state space. These states are, respectively, characterized by high activities in the sensory-motor (State #1), bilateral temporal (State #2), and default mode networks (DMN; State #3) regions, with State #2 acting as a transitional hub. The three states are selectively modulated by the acoustic, word-level semantic, and clause-level semantic properties of the narrative. Moreover, the alignment with both the best performer and the group-mean in brain state expression can predict participants’ narrative comprehension scores measured from the post-scan recall. These results are reproducible with different brain network atlas and generalizable to two datasets consisting of young and older adults. Our study suggests that the brain underlies narrative comprehension by switching through a tripartite state space, with each state probably dedicated to a specific component of language faculty, and effective narrative comprehension relies on engaging those states in a timely manner.