1
|
Cheng B, Chen J, Königsberg A, Mayer C, Rimmele L, Patil KR, Gerloff C, Thomalla G, Eickhoff SB. Mapping the deficit dimension structure of the National Institutes of Health Stroke Scale. EBioMedicine 2022; 87:104425. [PMID: 36563488 PMCID: PMC9800288 DOI: 10.1016/j.ebiom.2022.104425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
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).
Collapse
Affiliation(s)
- Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,Corresponding author.
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Corresponding author.
| | - Alina Königsberg
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Leander Rimmele
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Gerloff
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| |
Collapse
|