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.

中文翻译：

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解码狗大脑中的气味混合物：清醒的 fMRI 研究。

在工作和实际环境中，狗依赖于它们区分目标气味与分散注意力的气味和其他感官刺激的能力。我们在 18 只狗中使用清醒功能磁共振成像 (fMRI)，研究了区分 2 种气味和混合气味之间气味的神经机制。在呈现与食物奖励相关的目标气味 (A)、与无相关的干扰气味 (B) 以及两种气味的混合 (A+B) 期间测量神经激活。在已知与气味处理有关的三个区域内，随着时间的推移，测量了个体狗在出现气味刺激时神经激活的变化：尾状核、杏仁核和嗅球。杏仁核内的平均激活表明，狗在第一次运行时根据刺激-奖励关联最大程度地区分气味刺激，而对混合物的激活 (A+B) 与无奖励 (B) 刺激最相似。为了阐明狗大脑中气味混合物的神经表征，我们使用随机森林分类器来比较多标签（元素）与多类（配置）模型。多类别模型的表现比多标签模型好得多（加权 F1 0.44 与 0.14），表明气味混合物是经过配置处理的。对高性能狗的大脑分类指标子集的分析揭示了一个携带嗅觉信息的大脑区域网络，包括杏仁核、梨状皮层和后扣带回。