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An elevated plus-maze in mixed reality for studying human anxiety-related behavior

Overview of attention for article published in BMC Biology, December 2017
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Title
An elevated plus-maze in mixed reality for studying human anxiety-related behavior
Published in
BMC Biology, December 2017
DOI 10.1186/s12915-017-0463-6
Pubmed ID
Authors

Sarah V. Biedermann, Daniel G. Biedermann, Frederike Wenzlaff, Tim Kurjak, Sawis Nouri, Matthias K. Auer, Klaus Wiedemann, Peer Briken, Jan Haaker, Tina B. Lonsdorf, Johannes Fuss

Abstract

A dearth of laboratory tests to study actual human approach-avoidance behavior has complicated translational research on anxiety. The elevated plus-maze (EPM) is the gold standard to assess approach-avoidance behavior in rodents. Here, we translated the EPM to humans using mixed reality through a combination of virtual and real-world elements. In two validation studies, we observed participants' anxiety on a behavioral, physiological, and subjective level. Participants reported higher anxiety on open arms, avoided open arms, and showed an activation of endogenous stress systems. Participants' with high anxiety exhibited higher avoidance. Moreover, open arm avoidance was moderately predicted by participants' acrophobia and sensation seeking, with opposing influences. In a randomized, double blind, placebo controlled experiment, GABAergic stimulation decreased avoidance of open arms while alpha-2-adrenergic antagonism increased avoidance. These findings demonstrate cross-species validity of open arm avoidance as a translational measure of anxiety. We thus introduce the first ecologically valid assay to track actual human approach-avoidance behavior under laboratory conditions.

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Mendeley readers

The data shown below were compiled from readership statistics for 234 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 234 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 32 14%
Researcher 30 13%
Student > Ph. D. Student 29 12%
Student > Master 26 11%
Student > Doctoral Student 18 8%
Other 34 15%
Unknown 65 28%
Readers by discipline Count As %
Neuroscience 39 17%
Psychology 31 13%
Medicine and Dentistry 20 9%
Computer Science 13 6%
Agricultural and Biological Sciences 9 4%
Other 36 15%
Unknown 86 37%