Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics
- The Chinese Academy of Sciences, China
- PLA Army General Hospital, China
- Guangzhou General Hospital of Guangzhou Military Command, China
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 based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness, which we suggest is accurate, robust, and interpretable.
Data availability
We have provided anonymised demographic and clinical characteristics of the DOC patients in Appendix 1. We have made the analysis pipeline, including fMRI preprocessing, feature calculation and extraction, regression and classification, and the results visualization publicly available. Also, we have uploaded the fMRI signals in each of region of interest for every DOC patient and healthy control involved in this study. Anyone is welcome to download them from GitHub (https://github.com/realmsong504/pDOC).
Article and author information
Author details
Funding
National Natural Science Foundation of China (81471380)
- Ming Song
National Natural Science Foundation of China (91432302,31620103905)
- Tianzi Jiang
The Science Frontier Program of the Chinese academy of Sciences (QYZDJ-SSW-SMC019)
- Tianzi Jiang
National Key R&D Program of China (2017YFA0105203)
- Tianzi Jiang
Beijing Municipal Science and Technology Commission (Z161100000216139)
- Tianzi Jiang
Beijing Municipal Science and Technology Commission (Z161100000216152)
- Ming Song
Beijing Municipal Science and Technology Commission (Z161100000516165)
- Ying Yang
The Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team (2016ZT06S220)
- Tianzi Jiang
Youth Innovation Promotion Association of the Chinese Academy of Sciences
- Ming Song
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The study was approved by the Ethics Committee of the PLA Army General Hospital (protocol no: 2011-097) and the Ethics Committee of the Guangzhou General Hospital of Guangzhou Military Command (protocol no: jz20091287). Informed consent to participate in the study was obtained from the legal surrogates of the patients and from the normal controls.
Copyright
© 2018, Song et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics
eLife 7:e36173.
https://doi.org/10.7554/eLife.36173
Further reading
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Background: Several fields have described low reproducibility of scientific research and poor accessibility in research reporting practices. Although previous reports have investigated accessible reporting practices that lead to reproducible research in other fields, to date, no study has explored the extent of accessible and reproducible research practices in cardiovascular science literature.
Methods: To study accessibility and reproducibility in cardiovascular research reporting, we screened 639 randomly selected articles published in 2019 in three top cardiovascular science publications: Circulation, the European Heart Journal, and the Journal of the American College of Cardiology (JACC). Of those 639 articles, 393 were empirical research articles. We screened each paper for accessible and reproducible research practices using a set of accessibility criteria including protocol, materials, data, and analysis script availability, as well as accessibility of the publication itself. We also quantified the consistency of open research practices within and across cardiovascular study types and journal formats.
Results: We identified that fewer than 2% of cardiovascular research publications provide sufficient resources (materials, methods, data, and analysis scripts) to fully reproduce their studies. Of the 639 articles screened, 393 were empirical research studies for which reproducibility could be assessed using our protocol, as opposed to commentaries or reviews. After calculating an accessibility score as a measure of the extent to which an article makes its resources available, we also showed that the level of accessibility varies across study types with a score of 0.08 for Case Studies or Case Series and 0.39 for Clinical Trials (p = 5.500E-5) and across journals (0.19 through 0.34, p = 1.230E-2). We further showed that there are significant differences in which study types share which resources.
Conclusion: Although the degree to which reproducible reporting practices are present in publications varies significantly across journals and study types, current cardiovascular science reports frequently do not provide sufficient materials, protocols, data, or analysis information to reproduce a study. In the future, having higher standards of accessibility mandated by either journals or funding bodies will help increase the reproducibility of cardiovascular research.
Funding: Authors Gabriel Heckerman, Arely Campos-Melendez, and Chisomaga Ekwueme were supported by an NIH R25 grant from the National Heart, Lung and Blood Institute (R25HL147666). Eileen Tzng was supported by an AHA Institutional Training Award fellowship (18UFEL33960207).
