Prediction of Stroke Outcome in Mice Based on Non-Invasive MRI and Behavioral Testing

Abstract

ABSTRACTBackgroundPrediction of post-stroke outcome using the degree of subacute deficit or magnetic resonance imaging is well studied in humans. While mice are the most commonly used animals in preclinical stroke research, systematic analysis of outcome predictors is lacking.MethodsData from 13 studies that included 45 min of middle cerebral artery occlusion on 148 mice were pooled. Motor function was measured using a modified protocol for the staircase test of skilled reaching. Phases of subacute and residual deficit were defined. Magnetic resonance images of stroke lesions were co-registered on the Allen Mouse Brain Atlas to characterize stroke topology. Different random forest prediction models that either used motor-functional deficit or imaging parameters were generated for the subacute and residual deficits.ResultsWe detected both a subacute and residual motor-functional deficit after stroke in mice. Different functional severity grades and recovery trajectories could be observed. In mice with small cortical lesions, lesion volume was the best predictor of the subacute deficit. The residual deficit could be predicted most accurately by the degree of the subacute deficit. When using imaging parameters for the prediction of the residual deficit, including information about the lesion topology increased prediction accuracy. A subset of anatomical regions within the ischemic lesion had particular impact on the prediction of long-term outcome. Prediction accuracy depended on the degree of functional impairment.ConclusionsFor the first time, we identified and characterized predictors of post-stroke outcome in a large cohort of mice and found strong concordance with clinical data. These results are discussed in light of study design and imaging limitations. In the future, using outcome prediction can improve the design of preclinical studies and guide intervention decisions.