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Midbrain signaling of identity prediction errors depends on orbitofrontal cortex networks

Author

Listed:
  • Qingfang Liu

    (National Institute on Drug Abuse Intramural Research Program)

  • Yao Zhao

    (National Institute on Drug Abuse Intramural Research Program)

  • Sumedha Attanti

    (Mayo Clinic Alix School of Medicine)

  • Joel L. Voss

    (The University of Chicago)

  • Geoffrey Schoenbaum

    (National Institute on Drug Abuse Intramural Research Program)

  • Thorsten Kahnt

    (National Institute on Drug Abuse Intramural Research Program)

Abstract

Outcome-guided behavior requires knowledge about the identity of future rewards. Previous work across species has shown that the dopaminergic midbrain responds to violations in expected reward identity and that the lateral orbitofrontal cortex (OFC) represents reward identity expectations. Here we used network-targeted transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) during a trans-reinforcer reversal learning task to test the hypothesis that outcome expectations in the lateral OFC contribute to the computation of identity prediction errors (iPE) in the midbrain. Network-targeted TMS aiming at lateral OFC reduced the global connectedness of the lateral OFC and impaired reward identity learning in the first block of trials. Critically, TMS disrupted neural representations of expected reward identity in the OFC and modulated iPE responses in the midbrain. These results support the idea that iPE signals in the dopaminergic midbrain are computed based on outcome expectations represented in the lateral OFC.

Suggested Citation

  • Qingfang Liu & Yao Zhao & Sumedha Attanti & Joel L. Voss & Geoffrey Schoenbaum & Thorsten Kahnt, 2024. "Midbrain signaling of identity prediction errors depends on orbitofrontal cortex networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45880-1
    DOI: 10.1038/s41467-024-45880-1
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    References listed on IDEAS

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    1. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    2. Thomas A. Stalnaker & Nisha K. Cooch & Michael A. McDannald & Tzu-Lan Liu & Heather Wied & Geoffrey Schoenbaum, 2014. "Orbitofrontal neurons infer the value and identity of predicted outcomes," Nature Communications, Nature, vol. 5(1), pages 1-13, September.
    3. Nikos K. Logothetis & Jon Pauls & Mark Augath & Torsten Trinath & Axel Oeltermann, 2001. "Neurophysiological investigation of the basis of the fMRI signal," Nature, Nature, vol. 412(6843), pages 150-157, July.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. James D. Howard & Thorsten Kahnt, 2018. "Identity prediction errors in the human midbrain update reward-identity expectations in the orbitofrontal cortex," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    6. Wolfgang M. Pauli & Giovanni Gentile & Sven Collette & Julian M. Tyszka & John P. O’Doherty, 2019. "Evidence for model-based encoding of Pavlovian contingencies in the human brain," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
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