Evidence map›Paper›PMID 27617747›Full record

ArticlePLoS computational biology2016

Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model.

Yuzhe Li, Ken Nakae, Shin Ishii, Honda Naoki

Abstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Role of Human Ventromedial Prefrontal Cortex in Learning and Recall of Enhanced Extinction.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019
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  8. Neurocognitive Mechanism of Human Resilience: A Conceptual Framework and Empirical Review.International journal of environmental research and public health · 2019
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuzhe LiGraduate School of Biostudies, Kyoto University, Kyoto, Japan.
Ken NakaeGraduate School of Informatics, Kyoto University, Kyoto, Japan.
Shin IshiiGraduate School of Informatics, Kyoto University, Kyoto, Japan.
Honda NaokiImaging Platform of Spatio-temporal Information, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Models, NeurologicalAmygdalaConditioning, PsychologicalFearHumansMemoryNeural PathwaysNeuronsPrefrontal CortexReinforcement, PsychologyUncertainty

Identifiers

PMID27617747
PMCPMC5019407

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.