Reinforcement learning during adolescence in rats

Neema Moin Afshar, Alex J. Keip, Jane R. Taylor, Daeyeol Lee, Stephanie M. Groman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The most dynamic period of postnatal brain development occurs during adolescence, the period between childhood and adulthood. Neuroimaging studies have observed morphologic and functional changes during adolescence, and it is believed that these changes serve to improve the functions of circuits that underlie decision-making. Direct evidence in support of this hypothesis, however, has been limited because most preclinical decision-making paradigms are not readily translated to humans. Here, we developed a reversal-learning protocol for the rapid assessment of adaptive choice behavior in dynamic environments in rats as young as postnatal day 30. A computational framework was used to elucidate the reinforcementlearning mechanisms that change in adolescence and into adulthood. Using a cross-sectional and longitudinal design, we provide the first evidence that value-based choice behavior in a reversal-learning task improves during adolescence in male and female Long-Evans rats and demonstrate that the increase in reversal performance is due to alterations in value updating for positive outcomes. Furthermore, we report that reversal-learning trajectories in adolescence reliably predicted reversal performance in adulthood. This novel behavioral protocol provides a unique platform for conducting biological and systems-level analyses of the neurodevelopmental mechanisms of decision-making.

Original languageEnglish (US)
Pages (from-to)5857-5870
Number of pages14
JournalJournal of Neuroscience
Volume40
Issue number30
DOIs
StatePublished - Jul 22 2020
Externally publishedYes

Bibliographical note

Funding Information:
Received Apr. 17, 2020; revised June 7, 2020; accepted June 17, 2020. Author contributions: S.M.G. designed research; N.M.A., A.J.K., and S.M.G. performed research; D.L. contributed unpublished reagents/analytic tools; S.M.G. analyzed data; J.R.T. and S.M.G. wrote the paper. *N.M.A. and S.M.G. contributed equally to this work. This work was supported by a Yale/ National Institute on Drug Abuse (NIDA) Neuroproteomics Center Pilot Research Project Grant (to S.M.G.) through a Public Health Service grant from NIDA (Grant P30-DA-018343), a Public Health Service grant from NIDA (Grant DA-041480 to S.M.G., D.L., and J.R.T.), a Public Health Service grant from the National Institute of Mental Health (Grant R21-MH-120615 to S.M.G.), NARSAD Young Investigator Award from the Brain and Behavior Research Foundation (to S.M.G.), and funding provided by the State of Connecticut. D.L. is a cofounder of Neurogazer Inc. The authors declare no other competing financial interests. Correspondence should be addressed to Stephanie M. Groman at stephanie.groman@yale.edu. https://doi.org/10.1523/JNEUROSCI.0910-20.2020 Copyright © 2020 the authors

Keywords

  • Computational Psychiatry
  • Meta-Learning
  • Neurodevelopment
  • Reversal Learning
  • Reward

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