Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics

  1. Ming Song
  2. Ying Yang
  3. Jianghong He
  4. Zhengyi Yang
  5. Shan Yu
  6. Qiuyou Xie
  7. Xiaoyu Xia
  8. Yuanyuan Dang
  9. Qiang Zhang
  10. Xinhuai Wu
  11. Yue Cui
  12. Bing Hou
  13. Ronghao Yu
  14. Ruxiang Xu  Is a corresponding author
  15. Tianzi Jiang  Is a corresponding author
  1. The Chinese Academy of Sciences, China
  2. PLA Army General Hospital, China
  3. 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

  1. Ming Song

    National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Ying Yang

    Department of Neurosurgery, PLA Army General Hospital, Bejing, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Jianghong He

    Department of Neurosurgery, PLA Army General Hospital, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Zhengyi Yang

    National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Shan Yu

    National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Qiuyou Xie

    Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Xiaoyu Xia

    Department of Neurosurgery, PLA Army General Hospital, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Yuanyuan Dang

    Department of Neurosurgery, PLA Army General Hospital, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  9. Qiang Zhang

    Department of Neurosurgery, PLA Army General Hospital, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  10. Xinhuai Wu

    Department of Radiology, PLA Army General Hospital, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  11. Yue Cui

    National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  12. Bing Hou

    National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  13. Ronghao Yu

    Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  14. Ruxiang Xu

    Department of Neurosurgery, PLA Army General Hospital, Beijing, China
    For correspondence
    zjxuruxiang@163.com
    Competing interests
    The authors declare that no competing interests exist.
  15. Tianzi Jiang

    National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
    For correspondence
    jiangtz@nlpr.ia.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9531-291X

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.

Reviewing Editor

  1. Klaas Enno Stephan, University of Zurich and ETH Zurich, Switzerland

Version history

  1. Received: February 23, 2018
  2. Accepted: August 3, 2018
  3. Accepted Manuscript published: August 14, 2018 (version 1)
  4. Version of Record published: September 19, 2018 (version 2)

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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  1. Ming Song
  2. Ying Yang
  3. Jianghong He
  4. Zhengyi Yang
  5. Shan Yu
  6. Qiuyou Xie
  7. Xiaoyu Xia
  8. Yuanyuan Dang
  9. Qiang Zhang
  10. Xinhuai Wu
  11. Yue Cui
  12. Bing Hou
  13. Ronghao Yu
  14. Ruxiang Xu
  15. Tianzi Jiang
(2018)
Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics
eLife 7:e36173.
https://doi.org/10.7554/eLife.36173

Share this article

https://doi.org/10.7554/eLife.36173

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