Journal
ELIFE
Volume 11, Issue -, Pages -Publisher
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.66169
Keywords
computational modeling; neuroanatomy; anxiety; learning; fear; Human
Categories
Funding
- National Institute of Mental Health (IRP, NIMH), National Institutes of Health
- (NIMH)
- National Institutes of Health
- NIMH IRP [ZIAMH002781-15]
- NIH [K99/R00MH091183]
- NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation
Ask authors/readers for more resources
This study used computational models and structural imaging to investigate the links between threat learning, its neuroanatomical substrates, and anxiety severity. The results suggest that anxiety severity is specifically related to slower safety learning and slower extinction of response to safe stimuli.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available