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| Home > Publications Database > Prediction of Stroke Outcome in Mice Based on Non-Invasive MRI and Behavioral Testing > print |
| 001 | 169345 | ||
| 005 | 20240308121405.0 | ||
| 024 | 7 | _ | |a 10.1101/2022.05.13.491869 |2 doi |
| 024 | 7 | _ | |a altmetric:128290830 |2 altmetric |
| 037 | _ | _ | |a DZNE-2023-00120 |
| 100 | 1 | _ | |a Knab, Felix |0 0000-0002-7240-5926 |b 0 |
| 245 | _ | _ | |a Prediction of Stroke Outcome in Mice Based on Non-Invasive MRI and Behavioral Testing |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1709896395_26561 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Prediction of post-stroke outcome using the degree of subacute deficit or magnetic resonance imaging metrics is well studied in humans. While mice are the most commonly used animals in pre-clinical stroke research, systematic analysis of outcome predictors is lacking.Methods Data from a total of 13 studies that included 45 minutes 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.Results We detected both a subacute and residual motor-functional deficit after stroke in mice. Different functional severity grades and recovery trajectories could be observed. We found that lesion volume is the best predictor of subacute deficit. The residual deficit can 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 increases prediction accuracy. A subset of anatomical regions within the ischemic lesion have an outstanding impact on the prediction of long-term outcome. Prediction accuracy depends on the degree of functional impairment.Conclusions For 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. In the future, using outcome prediction can improve the design of pre-clinical studies and guide intervention decisions. |
| 536 | _ | _ | |a 353 - Clinical and Health Care Research (POF4-353) |0 G:(DE-HGF)POF4-353 |c POF4-353 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 700 | 1 | _ | |a Koch, Stefan Paul |0 0000-0001-6606-6369 |b 1 |
| 700 | 1 | _ | |a Major, Sebastian |0 0000-0003-0970-1308 |b 2 |
| 700 | 1 | _ | |a Farr, Tracy D. |0 0000-0002-6781-5226 |b 3 |
| 700 | 1 | _ | |a Mueller, Susanne |0 0000-0002-5053-2211 |b 4 |
| 700 | 1 | _ | |a Euskirchen, Philipp |0 0000-0002-9138-805X |b 5 |
| 700 | 1 | _ | |a Eggers, Moritz |0 0000-0001-6018-409X |b 6 |
| 700 | 1 | _ | |a Kuffner, Melanie T. C. |0 0000-0001-7932-470X |b 7 |
| 700 | 1 | _ | |a Walter, Josefine |0 0000-0002-7755-7531 |b 8 |
| 700 | 1 | _ | |a Dreier, Jens P. |0 0000-0001-7459-2828 |b 9 |
| 700 | 1 | _ | |a Endres, Matthias |0 P:(DE-2719)2811033 |b 10 |u dzne |
| 700 | 1 | _ | |a Dirnagl, Ulrich |0 P:(DE-2719)2810838 |b 11 |
| 700 | 1 | _ | |a Wenger, Nikolaus |0 0000-0002-0965-7530 |b 12 |
| 700 | 1 | _ | |a Hoffmann, Christian J. |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Boehm-Sturm, Philipp |0 0000-0001-8777-4823 |b 14 |
| 700 | 1 | _ | |a Harms, Christoph |0 0000-0002-2063-2860 |b 15 |
| 773 | _ | _ | |a 10.1101/2022.05.13.491869 |
| 787 | 0 | _ | |a Knab, Felix et.al. |d Zenodo, 2022 |i RelatedTo |0 DZNE-2022-01804 |r |t Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing |
| 856 | 4 | _ | |u https://pub.dzne.de/record/169345/files/DZNE-2023-00120_Restricted.pdf |
| 856 | 4 | _ | |u https://pub.dzne.de/record/169345/files/DZNE-2023-00120_Restricted.pdf?subformat=pdfa |x pdfa |
| 909 | C | O | |p VDB |o oai:pub.dzne.de:169345 |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 10 |6 P:(DE-2719)2811033 |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 11 |6 P:(DE-2719)2810838 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Neurodegenerative Diseases |1 G:(DE-HGF)POF4-350 |0 G:(DE-HGF)POF4-353 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Clinical and Health Care Research |x 0 |
| 914 | 1 | _ | |y 2022 |
| 920 | 1 | _ | |0 I:(DE-2719)1811005 |k AG Endres |l Interdisciplinary Dementia Research |x 0 |
| 920 | 1 | _ | |0 I:(DE-2719)1810002 |k AG Dirnagl |l Vascular Pathology |x 1 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-2719)1811005 |
| 980 | _ | _ | |a I:(DE-2719)1810002 |
| 980 | _ | _ | |a UNRESTRICTED |
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