Refined movement analysis in the Staircase test reveals differential motor deficits in mouse models of stroke

Abstract

AbstractAccurate assessment of post-stroke deficits is vital in translational research. Recent advances in machine learning provide unprecedented precision in quantifying rodent motor behavior post-stroke. However, the extent to which these tools can detect lesion-specific upper extremity deficits remains unclear. Using proximal middle cerebral artery occlusion (MCAO) and cortical photothrombosis (PT), we assessed post-stroke impairments in mice through the Staircase test. Lesion locations were identified using 7T-MRI. Machine learning was applied to reconstruct kinematic trajectories usingMouseReach, a data-processing toolbox. This yielded 30 refined outcome parameters effectively capturing motor deficits. Lesion reconstructions located ischemic centers in the striatum (MCAO) and sensorimotor cortex (PT). Pellet retrieval was altered in both cases but did not correlate with stroke volume or ischemia extent. Instead, cortical ischemia was characterized by increased hand slips and modified reaching success. Striatal ischemia led to progressively prolonged reach durations, mirroring delayed symptom onset in basal ganglia strokes. In summary, refined machine learning-based movement analysis revealed specific deficits in mice after cortical and striatal ischemia. These findings emphasize the importance of thorough behavioral profiling in preclinical stroke research to increase translational validity of behavioral assessments.