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Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model
Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is know...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5019407/ https://www.ncbi.nlm.nih.gov/pubmed/27617747 http://dx.doi.org/10.1371/journal.pcbi.1005099 |
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author | Li, Yuzhe Nakae, Ken Ishii, Shin Naoki, Honda |
author_facet | Li, Yuzhe Nakae, Ken Ishii, Shin Naoki, Honda |
author_sort | Li, Yuzhe |
collection | PubMed |
description | Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction. |
format | Online Article Text |
id | pubmed-5019407 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50194072016-09-27 Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model Li, Yuzhe Nakae, Ken Ishii, Shin Naoki, Honda PLoS Comput Biol Research Article Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction. Public Library of Science 2016-09-12 /pmc/articles/PMC5019407/ /pubmed/27617747 http://dx.doi.org/10.1371/journal.pcbi.1005099 Text en © 2016 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Yuzhe Nakae, Ken Ishii, Shin Naoki, Honda Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model |
title | Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model |
title_full | Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model |
title_fullStr | Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model |
title_full_unstemmed | Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model |
title_short | Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model |
title_sort | uncertainty-dependent extinction of fear memory in an amygdala-mpfc neural circuit model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5019407/ https://www.ncbi.nlm.nih.gov/pubmed/27617747 http://dx.doi.org/10.1371/journal.pcbi.1005099 |
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