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Recovery after stroke: the severely impaired are a distinct group
  1. Anna K Bonkhoff1,
  2. Tom Hope2,
  3. Danilo Bzdok3,4,5,
  4. Adrian G Guggisberg6,
  5. Rachel L Hawe7,8,
  6. Sean P Dukelow8,
  7. François Chollet9,
  8. David J Lin10,
  9. Christian Grefkes11,12,
  10. Howard Bowman13,14
  1. 1J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
  2. 2Wellcome Centre for Human Neuroimaging, University College London, London, UK
  3. 3Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada
  4. 4Mila – Quebec Artificial Intelligence Institute, Montreal, Québec, Canada
  5. 5Canadian Institute for Advanced Research (CIFAR), Montreal, Québec, Canada
  6. 6Department of Clinical Neurosciences, Hopitaux Universitaires de Geneve Hopital de Beau-Sejour, Geneva, Switzerland
  7. 7School of Kinesiology, University of Minnesota, Minneapolis, Minnesota, USA
  8. 8Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
  9. 9Neurology, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
  10. 10Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
  11. 11Department of Neurology, University of Cologne, Cologne, Germany
  12. 12Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
  13. 13School of Psychology, University of Birmingham, Birmingham, UK
  14. 14School of Computing, University of Kent, Canterbury, UK
  1. Correspondence to Dr Anna K Bonkhoff, J. Philip Kistler Stroke Research Center, Boston, MA 02114, USA; abonkhoff{at}


Introduction Stroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently.

Methods We designed a Bayesian hierarchical model to estimate 3–6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5–30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range.

Results Recovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3–6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%).

Conclusions Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

  • stroke
  • cerebrovascular disease
  • statistics
  • rehabilitation

Data availability statement

Data are available upon reasonable request. Data is available from the authors on reasonable request. Jupyter notebook scripts (python 3.7, predominantly pymc3) is openly available:

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Data availability statement

Data are available upon reasonable request. Data is available from the authors on reasonable request. Jupyter notebook scripts (python 3.7, predominantly pymc3) is openly available:

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  • Twitter @annabonkhoff, @Francois Chollet

  • Contributors AKB, TH, DB, CG and HB conceived and designed the study. AKB and HB led data analysis, interpretation and prepared the manuscript and act as guarantors of this work. AGG, RLH, SPD, FC and DJL contributed to data acquisition, management and preprocessing. All authors contributed to interpretation of results and final manuscript preparation.

  • Funding DB was supported by the Brain Canada Foundation, through the Canada Brain Research Fund, with the financial support of Health Canada, National Institutes of Health (NIH R01 AG068563A), the Canadian Institute of Health Research (CIHR 438531), the Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund), Google (Research Award) and by the CIFAR Artificial Intelligence Chairs programme (Canada Institute for Advanced Research). AGG was supported by the Swiss National Science Foundation, CRSII5-170985. CG is in part funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 431549029—SFB 1451 projects B05 and C05.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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