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@ARTICLE{Cheng:916977,
      author       = {Cheng, Bastian and Chen, Ji and Königsberg, Alina and
                      Mayer, Carola and Rimmele, Leander and Patil, Kaustubh R.
                      and Gerloff, Christian and Thomalla, Götz and Eickhoff,
                      Simon B.},
      title        = {{M}apping the deficit dimension structure of the {N}ational
                      {I}nstitutes of {H}ealth {S}troke {S}cale},
      journal      = {EBioMedicine},
      volume       = {87},
      issn         = {2352-3964},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2023-00243},
      pages        = {104425 -},
      year         = {2023},
      abstract     = {Background: The National Institutes of Health Stroke Scale
                      (NIHSS) is the most frequently applied clinical rating scale
                      for standardized assessment of neurological deficits in
                      acute stroke in both clinical and research settings.
                      Notwithstanding this prominent role, important questions
                      regarding its validity remain insufficiently addressed:
                      Investigations of the underlying dimensional structure of
                      the NIHSS yielded inconsistent results that are largely not
                      generalizable across studies. Neurobiological validations by
                      linking measured deficit dimensions to brain anatomy and
                      function are missing.Methods: We, therefore, employ advanced
                      machine learning to identify an optimal representation of
                      the dimensional structure of the NIHSS across two
                      independent and heterogeneous stroke datasets (N = 503 and N
                      = 690). Associated lesion locations are identified by
                      multivariate lesion-deficit mapping (LDM) and their
                      functional relevance is profiled based on a-priori task
                      activation meta-data analysis, to provide an independent
                      link to the behavioural level.Findings: A five-factor
                      structure of the NIHSS was identified as the most robust and
                      generalizable representation of stroke deficit dimensions
                      across study populations, settings, and clinical phenotypes.
                      Specifically, the identified dimensions comprised NIHSS
                      items for (F1) left motor deficits, (F2) right motor
                      deficits, (F3) dysarthria and facial palsy, (F4) language,
                      and (F5) deficits in spatial attention and gaze. LDM linked
                      four of these factors to differentially localized, eloquent
                      neuroanatomical areas. Functional characterization of LDM
                      results aligned with detected deficit dimensions, revealing
                      associations with motor functions, language processing, and
                      various functions in the perception domain.Interpretation:
                      By cross-validating machine learning in heterogeneous
                      multi-site stroke cohorts, we report evidence on the
                      validity of the NIHSS: We identified an overarching
                      structure of the NISHS containing a five-dimensional
                      representation of stroke deficits. We provide an anatomical
                      map of the NIHSS that is of value for future applications of
                      individualized stroke treatment and rehabilitation.Funding:
                      This research was supported by the National Key $R\&D$
                      Program of China (Grant No. 2021YFC2502200), the National
                      Human Brain Project of China (Grant No. 2022ZD0214000)", the
                      German Research Foundation (Deutsche
                      Forschungsgemeinschaft), Project 178316478 (A1, C1, C2), and
                      Project 454012190 of the SPP 2041, the Helmholtz Portfolio
                      Theme "Supercomputing and Modelling for the Human Brain" and
                      Helmholtz Imaging Platform grant NimRLS (ZT-I-PF-4-010).},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36563488},
      UT           = {WOS:000905154300007},
      doi          = {10.1016/j.ebiom.2022.104425},
      url          = {https://juser.fz-juelich.de/record/916977},
}