Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics
Year: 2018
Type: article
Study design: Observational Study
Abstract: Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year-outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 88% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first reported implementation of a multidomain prognostic model that is based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness, which we suggest is accurate, robust, and interpretable.
Source: eLife
DOI: 10.7554/elife.36173
Institutions: Chinese Academy of Sciences, Institute of Automation, Chinese PLA General Hospital, General Hospital of Guangzhou Military Command, University of Electronic Science and Technology of China, The University of Queensland, Center for Excellence in Brain Science and Intelligence Technology
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,500
Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Beijing Municipal Science and Technology Commission, National Key Research and Development Program of China, Youth Innovation Promotion Association
OpenAlex ID: W2788817014