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.

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.

Metrics

  • 4,518
    views
  • 734
    downloads
  • 69
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Cell Biology
    2. Medicine
    Pengbo Chen, Bo Li ... Xinfeng Zheng
    Research Article

    Background:

    It has been reported that loss of PCBP2 led to increased reactive oxygen species (ROS) production and accelerated cell aging. Knockdown of PCBP2 in HCT116 cells leads to significant downregulation of fibroblast growth factor 2 (FGF2). Here, we tried to elucidate the intrinsic factors and potential mechanisms of bone marrow mesenchymal stromal cells (BMSCs) aging from the interactions among PCBP2, ROS, and FGF2.

    Methods:

    Unlabeled quantitative proteomics were performed to show differentially expressed proteins in the replicative senescent human bone marrow mesenchymal stromal cells (RS-hBMSCs). ROS and FGF2 were detected in the loss-and-gain cell function experiments of PCBP2. The functional recovery experiments were performed to verify whether PCBP2 regulates cell function through ROS/FGF2-dependent ways.

    Results:

    PCBP2 expression was significantly lower in P10-hBMSCs. Knocking down the expression of PCBP2 inhibited the proliferation while accentuated the apoptosis and cell arrest of RS-hBMSCs. PCBP2 silence could increase the production of ROS. On the contrary, overexpression of PCBP2 increased the viability of both P3-hBMSCs and P10-hBMSCs significantly. Meanwhile, overexpression of PCBP2 led to significantly reduced expression of FGF2. Overexpression of FGF2 significantly offset the effect of PCBP2 overexpression in P10-hBMSCs, leading to decreased cell proliferation, increased apoptosis, and reduced G0/G1 phase ratio of the cells.

    Conclusions:

    This study initially elucidates that PCBP2 as an intrinsic aging factor regulates the replicative senescence of hBMSCs through the ROS-FGF2 signaling axis.

    Funding:

    This study was supported by the National Natural Science Foundation of China (82172474).

    1. Immunology and Inflammation
    2. Medicine
    Ole Bæk, Tik Muk ... Duc Ninh Nguyen
    Research Article

    Preterm infants are susceptible to neonatal sepsis, a syndrome of pro-inflammatory activity, organ damage, and altered metabolism following infection. Given the unique metabolic challenges and poor glucose regulatory capacity of preterm infants, their glucose intake during infection may have a high impact on the degree of metabolism dysregulation and organ damage. Using a preterm pig model of neonatal sepsis, we previously showed that a drastic restriction in glucose supply during infection protects against sepsis via suppression of glycolysis-induced inflammation, but results in severe hypoglycemia. Now we explored clinically relevant options for reducing glucose intake to decrease sepsis risk, without causing hypoglycemia and further explore the involvement of the liver in these protective effects. We found that a reduced glucose regime during infection increased survival via reduced pro-inflammatory response, while maintaining normoglycemia. Mechanistically, this intervention enhanced hepatic oxidative phosphorylation and possibly gluconeogenesis, and dampened both circulating and hepatic inflammation. However, switching from a high to a reduced glucose supply after the debut of clinical symptoms did not prevent sepsis, suggesting metabolic conditions at the start of infection are key in driving the outcome. Finally, an early therapy with purified human inter-alpha inhibitor protein, a liver-derived anti-inflammatory protein, partially reversed the effects of low parenteral glucose provision, likely by inhibiting neutrophil functions that mediate pathogen clearance. Our findings suggest a clinically relevant regime of reduced glucose supply for infected preterm infants could prevent or delay the development of sepsis in vulnerable neonates.