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Related Concept Videos

Neuroplasticity01:01

Neuroplasticity

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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Updated: Jun 28, 2025

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

Gopikrishna Deshpande1,2,3,4,5,6, Sinan Zhao1, Paul Waggoner7

  • 1Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.

Animals : an Open Access Journal From MDPI
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

Resting-state functional connectivity in dogs predicts their success in detection training. Brain networks identified show stable patterns in successful dogs and dynamic changes correlating with behavioral improvements during training.

Keywords:
caninecomparative biologydogfunctional MRIfunctional connectivityresting state

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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.