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
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