Computational modeling of threat learning reveals links with anxiety and neuroanatomy in humans
- National Institute of Mental Health, United States
- Brown University, United States
- University of Miami, United States
- University of California, Riverside, United States
- 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
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.
Reviewing Editor
- Alexander Shackman, University of Maryland, United States
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).
Version history
- Received: December 31, 2020
- Accepted: April 25, 2022
- Accepted Manuscript published: April 27, 2022 (version 1)
- Version of Record published: June 14, 2022 (version 2)
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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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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