Computational modeling of threat learning reveals links with anxiety and neuroanatomy in humans

  1. Rany Abend  Is a corresponding author
  2. Diana Burk
  3. Sonia G Ruiz
  4. Andrea L Gold
  5. Julia L Napoli
  6. Jennifer C Britton
  7. Kalina J Michalska
  8. Tomer Shechner
  9. Anderson M Winkler
  10. Ellen Leibenluft
  11. Daniel S Pine
  12. Bruno B Averbeck
  1. National Institute of Mental Health, United States
  2. Brown University, United States
  3. University of Miami, United States
  4. University of California, Riverside, United States
  5. University of Haifa, Israel

Abstract

Influential theories implicate variations in the mechanisms supporting threat learning in the severity of anxiety symptoms. We use computational models of associative learning in conjunction with structural imaging to explicate links among the mechanisms underlying threat learning, their neuroanatomical substrates, and anxiety severity in humans. We recorded skin-conductance data during a threat-learning task from individuals with and without anxiety disorders (N=251; 8-50 years; 116 females). Reinforcement-learning model variants quantified processes hypothesized to relate to anxiety: threat conditioning, threat generalization, safety learning, and threat extinction. We identified the best-fitting models for these processes and tested associations among latent learning parameters, whole-brain anatomy, and anxiety severity. Results indicate that greater anxiety severity related specifically to slower safety learning and slower extinction of response to safe stimuli. Nucleus accumbens gray-matter volume moderated learning-anxiety associations. Using a modeling approach, we identify computational mechanisms linking threat learning and anxiety severity and their neuroanatomical substrates.

Data availability

We cannot share the full dataset due to the NIH IRB requirements, which require participants to explicitly consent to their data being shared publicly. An important element in that is to protect patients who agree to participate in studies that relate to their psychopathology. Such consent was not acquired from most participants; as such, we cannot upload our complete dataset in its raw or deidentified form, or derivatives of the data, since we will be violating IRB protocols. Still, a subset of participants did consent to data sharing and we have uploaded their data as noted in the revised manuscript (https://github.com/rany-abend/threat_learning_eLife). Researchers interested in potentially acquiring access to the data could contact Dr. Daniel Pine (pined@mail.nih.gov), Chief of the Emotion and Development Branch at NIH, with a research proposal; as per IRB rules, the IRB may approve adding such researchers as Associate Investigators if a formal collaboration is initiated. No commercial use of the data is a lowed. The modeling and imaging analyses have now been uploaded in full as source code files.

Article and author information

Author details

  1. Rany Abend

    Emotion and Development Branch, National Institute of Mental Health, Bethesda, United States
    For correspondence
    rany.abend@nih.gov
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0022-3418
  2. Diana Burk

    Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Sonia G Ruiz

    Emotion and Development Branch, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Andrea L Gold

    Department of Psychiatry and Human Behavior, Brown University, Providence, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Julia L Napoli

    Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Jennifer C Britton

    Department of Psychology, University of Miami, Coral Gables, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Kalina J Michalska

    Department of Psychology, University of California, Riverside, Riverside, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Tomer Shechner

    Psychology Department, University of Haifa, Haifa, Israel
    Competing interests
    The authors declare that no competing interests exist.
  9. Anderson M Winkler

    Emotion and Development Branch, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Ellen Leibenluft

    Emotion and Development Branch, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Daniel S Pine

    Emotion and Development Branch, National Institute of Mental Health, Besthesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Bruno B Averbeck

    Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3976-8565

Funding

National Institutes of Health (ZIAMH002781-15)

  • Daniel S Pine

National Institutes of Health (R00MH091183)

  • Jennifer C Britton

Brain and Behavior Research Foundation (28239)

  • Rany Abend

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

Ethics

Human subjects: Human Subjects: Yes Ethics Statement: Written informed consent was obtained from adult (greater than or equal to 18 years) participants as we l as parents, and written assent was obtained from youth. Procedures were approved by the NIMH Institutional Review Board (protocol 01-M-0192).

Reviewing Editor

  1. Alexander Shackman, University of Maryland, United States

Publication history

  1. Received: December 31, 2020
  2. Accepted: April 25, 2022
  3. Accepted Manuscript published: April 27, 2022 (version 1)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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  1. Rany Abend
  2. Diana Burk
  3. Sonia G Ruiz
  4. Andrea L Gold
  5. Julia L Napoli
  6. Jennifer C Britton
  7. Kalina J Michalska
  8. Tomer Shechner
  9. Anderson M Winkler
  10. Ellen Leibenluft
  11. Daniel S Pine
  12. Bruno B Averbeck
(2022)
Computational modeling of threat learning reveals links with anxiety and neuroanatomy in humans
eLife 11:e66169.
https://doi.org/10.7554/eLife.66169

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