Decoding Odor Mixtures in the Dog Brain: An Awake fMRI Study

Author:

Prichard Ashley1,Chhibber Raveena1,King Jon1,Athanassiades Kate1,Spivak Mark23,Berns Gregory S1ORCID

Affiliation:

1. Psychology Department, Emory University, Atlanta, GA, USA

2. Comprehensive Pet Therapy, Inc., Sandy Springs, GA, USA

3. Dog Star Technologies, LLC, Sandy Springs, GA, USA

Abstract

Abstract In working and practical contexts, dogs rely upon their ability to discriminate a target odor from distracting odors and other sensory stimuli. Using awake functional magnetic resonance imaging (fMRI) in 18 dogs, we examined the neural mechanisms underlying odor discrimination between 2 odors and a mixture of the odors. Neural activation was measured during the presentation of a target odor (A) associated with a food reward, a distractor odor (B) associated with nothing, and a mixture of the two odors (A+B). Changes in neural activation during the presentations of the odor stimuli in individual dogs were measured over time within three regions known to be involved with odor processing: the caudate nucleus, the amygdala, and the olfactory bulbs. Average activation within the amygdala showed that dogs maximally differentiated between odor stimuli based on the stimulus-reward associations by the first run, while activation to the mixture (A+B) was most similar to the no-reward (B) stimulus. To clarify the neural representation of odor mixtures in the dog brain, we used a random forest classifier to compare multilabel (elemental) versus multiclass (configural) models. The multiclass model performed much better than the multilabel (weighted-F1 0.44 vs. 0.14), suggesting the odor mixture was processed configurally. Analysis of the subset of high-performing dogs’ brain classification metrics revealed a network of olfactory information-carrying brain regions that included the amygdala, piriform cortex, and posterior cingulate. These results add further evidence for the configural processing of odor mixtures in dogs and suggest a novel way to identify high-performers based on brain classification metrics.

Funder

Office of Naval Research

Publisher

Oxford University Press (OUP)

Subject

Behavioral Neuroscience,Physiology (medical),Sensory Systems,Physiology

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