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Chemical Senses Advance Access published online on November 23, 2005
Chemical Senses, doi:10.1093/chemse/bjj001
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Accepted October 24, 2005

Article

Evaluation of the Validity of a Maximum Likelihood Adaptive Staircase Procedure for Measurement of Olfactory Detection Threshold in Mice

Amy C. Clevenger 1 * and Diego Restrepo 1

1 Department of Cell and Developmental Biology, Neuroscience Program, and Rocky Mountain Taste and Smell Center, University of Colorado School of Medicine, University of Colorado at Denver and Health Sciences Center at Fitzsimons, Mail Stop 8108, PO Box 6511, Aurora, CO 80045, USA

* To whom correspondence should be addressed.
Amy C. Clevenger, E-mail: amy.clevenger{at}uchsc.edu


  Abstract

Threshold is defined as the stimulus intensity necessary for a subject to reach a specified percent correct on a detection test. MLPEST (maximum likelihood parameter estimation by sequential testing) is a method that is able to determine threshold accurately and more rapidly than many other methods. Originally developed for human auditory and visual tasks, it has been adapted for human olfactory and gustatory tests. In order to utilize this technique for olfactory testing in mice, we have adapted MLPEST methodology for use with computerized olfactometry as a tool to estimate odor detection thresholds. Here we present Monte Carlo simulations and operant conditioning data that demonstrate the potential utility of this technique in mice, we explore the ramifications of altering MLPEST test parameters on performance, and we discuss the advantages and disadvantages of using MLPEST compared to other methods for the estimation of thresholds in rodents. Using MLPEST, we find that olfactory detection thresholds in mice deficient for the cyclic nucleotide-gated channel subunit A2 are similar to those of wild-type animals for odorants the knockout animals are able to detect.

Keywords: CNGA2; knockout mice; maximum likelihood; odor detection; olfactometer; operant conditioning.
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