Deep learning-based behavioral profiling of rodent stroke recovery.

ABSTRACT:

Background

Stroke research heavily relies on rodent behavior when assessing underlying disease mechanisms and treatment efficacy. Although functional motor recovery is considered the primary targeted outcome, tests in rodents are still poorly reproducible and often unsuitable for unraveling the complex behavior after injury.

Results

Here, we provide a comprehensive 3D gait analysis of mice after focal cerebral ischemia based on the new deep learning-based software (DeepLabCut, DLC) that only requires basic behavioral equipment. We demonstrate a high precision 3D tracking of 10 body parts (including all relevant joints and reference landmarks) in several mouse strains. Building on this rigor motion tracking, a comprehensive post-analysis (with >100 parameters) unveils biologi

SUBMITTER: Weber RZ 

PROVIDER: S-EPMC9571460 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature