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

Background Prediction 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. Methods We 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. Results Variance 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. Conclusions We 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. ### Competing Interest Statement Funding was provided by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) to C.J.H. and C.H. (Project number 417284923), to P.B.S. (Project number 428869206), to N.W., M.E., and C.H. (Project number 424778381-TRR 295) and NeuroCure (EXC-2049-390688087) and the German Federal Ministry of Education and Research (BMBF, Center for Stroke Research Berlin 01EO1301) to U.D., P.B.S., and C.H., and to P.B.S. by the BMBF under the ERA-NET NEURON scheme (01EW1811). M.Ku. received funding from the DFG Graduate School 203. This work was supported by Charite 3R| Replace-Reduce-Refine and partly by the Fondation Leducq to M.E., and C.H.. F.K. and M.Eg. received a scholarship from the Berlin Institute of Health, Berlin. P.E., N.W., and C.J.H. are participants in the Charite Clinical Scientist Program funded by the Charite-Universitaetsmedizin Berlin and the Berlin Institute of Health and N.W. is a Freigeist Fellow with support from the Volkswagen Foundation. In addition, this work was supported by DFG (Project number 73500270 and 413848220) and ERA-NET NEURON EBio2, with funds from BMBF 01EW2004 to J.P.D. * AMBA : Allen Mouse Brain Atlas CCA : Common Carotid Artery ECA : External Carotid Artery FOV : Field Of View ICA : Internal Carotid Artery IQR : Interquartile Range LSMD : Least Square Mean Difference MD : Mean difference MCA : Middle Cerebral Artery MCAO : Middle Cerebral Artery Occlusion MedAE : Median Absolute Error PA : Pterygopalatine Artery pp : percentage points PE : prediction error