Chemical SensesNeurosciencePeer-reviewed journal article
Decoding Odor Mixtures in the Dog Brain: An Awake fMRI Study
Ashley PrichardiD, Raveena ChhibberiD, Jon A King, Kate Athanassiades, Mark SpivakiD, Gregory S. BernsiD
Abstract
In working and practical contexts, dogs rely upon their ability to discriminate a target odor from distracting odors and other sensory stimuli. Few studies have examined odor discrimination using non-behavioral methods or have approached odor discrimination from the dogs perspective. Using awake fMRI in 18 dogs, we examined the neural mechanisms underlying odor discrimination between two 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 identify the neural representation of odor mixtures in the dog brain, we used a random forest classifier to compare multilabel (elemental) vs. 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 based on their 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.
- assay: imaging
- research focus: benchmarking
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
- Citations
- 17
- FWCI
- 0.88
- Field percentile
- top 24%
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