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1.
Cell Rep ; 38(2): 110231, 2022 01 11.
Artigo em Inglês | MEDLINE | ID: mdl-35021077

RESUMO

Gait and posture are often perturbed in many neurological, neuromuscular, and neuropsychiatric conditions. Rodents provide a tractable model for elucidating disease mechanisms and interventions. Here, we develop a neural-network-based assay that adopts the commonly used open field apparatus for mouse gait and posture analysis. We quantitate both with high precision across 62 strains of mice. We characterize four mutants with known gait deficits and demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. Mouse gait and posture measures are highly heritable and fall into three distinct classes. We conduct a genome-wide association study to define the genetic architecture of stride-level mouse movement in the open field. We provide a method for gait and posture extraction from the open field and one of the largest laboratory mouse gait and posture data resources for the research community.


Assuntos
Marcha/genética , Marcha/fisiologia , Equilíbrio Postural/fisiologia , Animais , Transtorno do Espectro Autista/genética , Transtorno do Espectro Autista/fisiopatologia , Aprendizado Profundo , Comportamento Exploratório , Estudo de Associação Genômica Ampla/métodos , Camundongos , Movimento/fisiologia , Rede Nervosa/fisiologia , Teste de Campo Aberto/fisiologia , Equilíbrio Postural/genética
2.
Nat Aging ; 2(8): 756-766, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-37091193

RESUMO

Heterogeneity in biological aging manifests itself in health status and mortality. Frailty indices (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. Here we introduce a machine-learning-based visual FI for mice that operates on video data from an open-field assay. We use machine vision to extract morphometric, gait and other behavioral features that correlate with FI score and age. We use these features to train a regression model that accurately predicts the normalized FI score within 0.04 ± 0.002 (mean absolute error). Unnormalized, this error is 1.08 ± 0.05, which is comparable to one FI item being mis-scored by 1 point or two FI items mis-scored by 0.5 points. This visual FI provides increased reproducibility and scalability that will enable large-scale mechanistic and interventional studies of aging in mice.


Assuntos
Fragilidade , Humanos , Camundongos , Animais , Idoso , Fragilidade/diagnóstico , Idoso Fragilizado , Reprodutibilidade dos Testes , Avaliação Geriátrica , Envelhecimento
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