Two Separate Brain Networks for Predicting Trainability and Tracking Training-Related Plasticity in Working Dogs

Area of Science:

• Neuroscience
• Animal Behavior
• Comparative Cognition

Background:

• Resting-state functional magnetic resonance imaging (fMRI) reveals correlations between brain connectivity and human behavior.
• Investigating canine functional brain connectivity may predict working dog capabilities and traits.

Purpose of the Study:

• To determine if resting-state functional connectivity (FC) in dogs predicts their success in detector training.
• To examine how FC changes during and after detection training and correlates with behavioral measures.

Main Methods:

• Acquired resting-state fMRI data from awake dogs during three time points of detector training.
• Assessed canine behavior using an integrated score for hunting, retrieving, and soundness.
• Compared FC patterns between successful and unsuccessful detector dogs and analyzed changes over time.

Main Results:

• A core brain network showed stronger connectivity in successful detector dogs, predicting training success.
• A peripheral network's connectivity changes tracked behavioral improvements during training.
• Identified homologous brain regions and pathways between dogs and humans involved in odor learning.

Conclusions:

• Resting-state FC is a significant predictor of detector dog training success.
• Two distinct functional neural networks in dogs are influenced by phylogeny and ontogeny.
• Findings suggest shared neural mechanisms for learning and odor retrieval in dogs and humans.