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000279507 0247_ $$2doi$$a10.5281/ZENODO.14709930
000279507 0247_ $$2doi$$a10.5281/zenodo.14709930
000279507 037__ $$aDZNE-2025-00834
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000279507 1001_ $$00000-0002-7240-5926$$aKnab, Felix$$b0
000279507 245__ $$aDataset: Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing, v3.2
000279507 260__ $$bZenodo$$c2025
000279507 3367_ $$2BibTeX$$aMISC
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000279507 520__ $$aOpen data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip) Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing Open code and documentation of prediction models available via https://github.com/major-s/mouse-mcao-outcome-predictor Content: README.txt This information dat Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/Files used for the manuscript:t2.nii: t2 weighted image acquired 24 h post strokemasklesion.nii: manually delineated lesionx_masklesion.nii: lesion in atlas spaceix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.niiOverlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region prediction_models Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.The files have following sturcture:Column 1: animal IDColumns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)Column 538: lesion volumeColumn 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficitColumn 541: residual performance/deficitColumn 542: test or training groupConsecutive rows contain data for each animal specified by the animal id The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigmFolder 'subacute deficit prediction' contains: - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mriFolder 'long-term outcome prediction' contains: - lesion_volume: prediction of residual deficit using lesion volume - segmented_mri: prediction of residual deficit using segmented_mri - initial_performance: prediction of residual deficit using subacute deficitFolder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders. Files with equal file name and different extension always contain the same data templatesAllen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2
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000279507 650_7 $$2Other$$astroke
000279507 650_7 $$2Other$$amice
000279507 650_7 $$2Other$$aoutcome prediction
000279507 650_7 $$2Other$$amotor-function
000279507 650_7 $$2Other$$astaircase
000279507 7001_ $$00000-0001-6606-6369$$aKoch, Stefan Paul$$b1
000279507 7001_ $$00000-0003-0970-1308$$aMajor, Sebastian$$b2
000279507 7001_ $$00000-0002-6781-5226$$aFarr, Tracy D.$$b3
000279507 7001_ $$00000-0002-5053-2211$$aMueller, Susanne$$b4
000279507 7001_ $$aEuskirchen, Philipp$$b5
000279507 7001_ $$aEggers, Moritz$$b6
000279507 7001_ $$aKuffner, Melanie T. C.$$b7
000279507 7001_ $$aWalter, Josefine$$b8
000279507 7001_ $$00000-0001-7459-2828$$aDreier, Jens P.$$b9
000279507 7001_ $$0P:(DE-2719)2811033$$aEndres, Matthias$$b10
000279507 7001_ $$0P:(DE-2719)2810838$$aDirnagl, Ulrich$$b11
000279507 7001_ $$00000-0002-0965-7530$$aWenger, Nikolaus$$b12
000279507 7001_ $$0P:(DE-2719)9000582$$aHoffmann, Christian$$b13$$udzne
000279507 7001_ $$00000-0001-8777-4823$$aBoehm-Sturm, Philipp$$b14
000279507 7001_ $$00000-0002-2063-2860$$aHarms, Christoph$$b15
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000279507 7870_ $$0DZNE-2022-01804$$aKnab, Felix et.al.$$dZenodo, 2022$$iRelatedTo$$r$$tDataset: Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing
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000279507 9101_ $$0I:(DE-HGF)0$$6P:(DE-2719)9000582$$aExternal Institute$$b13$$kExtern
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