GeroScienceAnimal Behavior & CognitionPeer-reviewed journal article
Functional assessments of short-term spatial memory in the Dog Aging Project identify strong associations with age that are not moderated by body mass
Stephanie H. Hargrave, Amber J. KeyseriD, Emma Kristal, Gene E. AlexanderiD, Theadora A. BlockiD, Emily E. BrayiD, Laura E.L.C. DouglasiD, Brenda S. KennedyiD, Daniel PromislowiD, David A. RaichleniD, Evan L. MacLeaniD
Abstract
Companion dogs have emerged as a valuable model in the study of cognitive aging, but assessments of cognitive function in large, diverse, and geographically distributed samples of dogs are challenging to obtain. We developed two novel functional assessments of short-term spatial memory that were administered by community science participants in a sample of 6,753 dogs through the Dog Aging Project. We compared data generated by community scientists to those gathered by research professionals, estimated relationships between age and task performance, and tested the hypothesis that associations between age and cognitive performance vary by dog body mass, as a proxy for expected lifespan. Community scientists generated similar data to research professionals and both cognitive tasks were sensitive to age- related deficits, beginning in midlife. Relationships between age and cognitive function were highly similar across small and large dogs and, for both tasks, comparison of models with and without an interaction between age and body mass yielded decisive evidence for the model without the interaction. Large dogs exhibit accelerated aging across many traits, and so the lack of evidence for accelerated cognitive aging raises the possibility that their large size confers a neuroprotective advantage. We consider possible mechanisms underlying this effect and address how experimental studies of dog cognition using community science methods can support future research on mechanisms of brain and cognitive aging.
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 2
- FWCI
- 1.18
- Field percentile
- top 22%
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