Evidence map›Paper›PMID 38612321›Full record

ArticleAnimals : an open access journal from MDPI2024

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

Gopikrishna Deshpande, Sinan Zhao, Paul Waggoner, Ronald Beyers, Edward Morrison, Nguyen Huynh, Vitaly Vodyanoy, Thomas S Denney, Jeffrey S Katz

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Gopikrishna DeshpandeAuburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0001-7471-5357
Sinan ZhaoAuburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.
Paul WaggonerCanine Performance Sciences Program, College of Veterinary Medicine, Auburn University, Auburn, AL 36849, USA.
Ronald BeyersAuburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.
Edward MorrisonDepartment of Anatomy, Physiology & Pharmacology, Auburn University, Auburn, AL 36849, USA.
Nguyen HuynhAuburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.
Vitaly VodyanoyDepartment of Anatomy, Physiology & Pharmacology, Auburn University, Auburn, AL 36849, USA.
Thomas S DenneyAuburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.
Jeffrey S KatzAuburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0001-9966-1033

Funding

Auburn University's Intramural Grant; the Defense Advanced 661 Research Projects Agency W911QX-13-C-0123
6 · The paper itself

Abstract

Functional brain connectivity based on resting-state functional magnetic resonance imaging (fMRI) has been shown to be correlated with human personality and behavior. In this study, we sought to know whether capabilities and traits in dogs can be predicted from their resting-state connectivity, as in humans. We trained awake dogs to keep their head still inside a 3T MRI scanner while resting-state fMRI data was acquired. Canine behavior was characterized by an integrated behavioral score capturing their hunting, retrieving, and environmental soundness. Functional scans and behavioral measures were acquired at three different time points across detector dog training. The first time point (TP1) was prior to the dogs entering formal working detector dog training. The second time point (TP2) was soon after formal detector dog training. The third time point (TP3) was three months' post detector dog training while the dogs were engaged in a program of maintenance training for detection work. We hypothesized that the correlation between resting-state FC in the dog brain and behavior measures would significantly change during their detection training process (from TP1 to TP2) and would maintain for the subsequent several months of detection work (from TP2 to TP3). To further study the resting-state FC features that can predict the success of training, dogs at TP1 were divided into a successful group and a non-successful group. We observed a core brain network which showed relatively stable (with respect to time) patterns of interaction that were significantly stronger in successful detector dogs compared to failures and whose connectivity strength at the first time point predicted whether a given dog was eventually successful in becoming a detector dog. A second ontologically based flexible peripheral network was observed whose changes in connectivity strength with detection training tracked corresponding changes in behavior over the training program. Comparing dog and human brains, the functional connectivity between the brain stem and the frontal cortex in dogs corresponded to that between the locus coeruleus and left middle frontal gyrus in humans, suggestive of a shared mechanism for learning and retrieval of odors. Overall, the findings point toward the influence of phylogeny and ontogeny in dogs producing two dissociable functional neural networks.

Indexed as

caninecomparative biologydogfunctional connectivityfunctional MRIresting state

Identifiers

PMID38612321
PMCPMC11010877

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.