Marstaller, L., Burianová, H. and Reutens, D.C., 2016. Adaptive contextualization: A new role for the default mode network in affective learning. Human Brain Mapping, 38 (2), 1082 - 1091.
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DOI: 10.1002/hbm.23442
Abstract
Safety learning describes the ability to learn that certain cues predict the absence of a dangerous or threatening event. Although incidental observations of activity within the default mode network (DMN) during the processing of safety cues have been reported previously, there is as yet no evidence demonstrating that the DMN plays a functional rather than a corollary role in safety learning. Using functional magnetic resonance imaging and a Pavlovian fear conditioning and extinction paradigm, we investigated the neural correlates of danger and safety learning. Our results provide evidence for a functional role of the DMN by showing that (i) the DMN is activated by safety but not danger cues, (ii) the DMN is anti-correlated with a fear-processing network, and (iii) DMN activation increases with safety learning. Based on our results, we formulate a novel proposal, arguing that activity within the DMN supports the contextualization of safety memories, constrains the generalization of fear, and supports adaptive fear learning. Our findings have important implications for our understanding of affective and stress disorders, which are characterized by aberrant DMN activity, as they suggest that therapies targeting the DMN through mindfulness practice or brain stimulation might help prevent pathological over-generalization of fear associations. Hum Brain Mapp 38:1082-1091, 2017. © 2016 Wiley Periodicals, Inc.
Item Type: | Article |
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ISSN: | 1065-9471 |
Uncontrolled Keywords: | context; fMRI; fear; generalization; safety learning; adult; brain; conditioning, psychological; fear; female; galvanic skin response; generalization, psychological; humans; image processing, computer-assisted; magnetic resonance imaging; male; models, neurological; reaction time; reinforcement, psychology; young adult |
Group: | Faculty of Science & Technology |
ID Code: | 34385 |
Deposited By: | Symplectic RT2 |
Deposited On: | 05 Aug 2020 09:31 |
Last Modified: | 14 Mar 2022 14:23 |
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