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

Abstract

BackgroundPrediction of post-stroke outcome using the degree of subacute deficit or magnetic resonance imaging is well studied in humans. While mice are frequently used animals in preclinical stroke research, systematic analysis of outcome predictors is lacking. MethodsWe introduced heterogeneity into our study to broaden the applicability of our prediction tools. We analyzed the effect of 30, 45 and 60 minutes of arterial occlusion on the variance of stroke volumes. Next, we built a heterogeneous cohort of 215 mice using data from 15 studies that included 45 minutes of middle cerebral artery occlusion and various genotypes. Motor function was measured using 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. ResultsVariance of stroke volumes was increased by 45 minutes of arterial occlusion compared to 60 minutes and including various genotypes. We detected both a subacute and residual motor-functional deficit after stroke and different recovery trajectories. In mice with small cortical lesions, lesion volume was the best predictor of the subacute deficit. The residual deficit was most accurately predicted 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. ConclusionsWe developed and validated a robust tool for the prediction of functional outcome after stroke in mice using a large heterogeneous cohort. Study design and imaging limitations are discussed. In the future, using outcome prediction can improve the design of preclinical studies and guide intervention decisions.