Evidence map›Paper›PMID 41656193›Full record

ArticleJournal of neuroengineering and rehabilitation2026

Prognosis prediction of patients with disorders of consciousness based on digital twin brain models.

Shaoting Yan, Qing Li, Ruiqi Li, Lipeng Zhang, Rui Zhang, Mingming Chen, Meng Li, Runtao Li, Hui Zhang, Li Shi and 1 more

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In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Shaoting YanSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.ORCID http://orcid.org/0000-0002-6054-5024
Qing LiSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.
Ruiqi LiSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.ORCID http://orcid.org/0009-0002-1397-9484
Lipeng ZhangSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.ORCID http://orcid.org/0000-0001-6215-8792
Rui ZhangSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.ORCID http://orcid.org/0000-0002-9815-5101
Mingming ChenSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.ORCID http://orcid.org/0000-0003-3525-0088
Meng LiZhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
Runtao LiZhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
Hui ZhangZhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
Li ShiDepartment of Automation, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0003-0882-8243
Yuxia HuSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China. huyuxia@zzu.edu.cn.ORCID http://orcid.org/0000-0002-7645-5920

Funding

the National Natural Science Foundation of China 62173310the STI 2030-Major Project 2022ZD0208500
6 · The paper itself

Abstract

backgroundThe accurate prediction of prognosis in patients with disorders of consciousness (DOC) is a significant challenge in clinical practice. Some studies based on traditional electroencephalography (EEG) features have shown potential for DOC prognosis. However, the underlying mechanisms behind the recovery of patients with DOC still lack in-depth research.

methodsIn this study, we used mathematical tools to construct digital twin brain models (DTBM) for DOC patients with different outcomes. Then, we trained a support vector machine classifier using model parameters and modal controllability features to distinguish between DOC patients with different outcomes, and assessed the importance of these features. Finally, we used a support vector machine regressor to predict the Coma Recovery Scale-Revised (CRS-R) score at 6-month follow-up.

resultsThe results showed that the prognosis model based on local model parameters and modal controllability features achieved better performance (AUC = 90.22%, F-score = 86.00%, SEN = 84.31%, SPE = 91.43%) than the prognosis models based on some traditional EEG features. Additionally, a positive prognosis is associated with lower levels of inhibitory gain, higher levels of excitatory gain and modal controllability, particularly in brain regions within the frontoparietal network. In 74% and 70% of UWS and MCS patients, the MAE between the predicted CRS-R score and the actual CRS-R score was less than 5.

conclusionsOverall, our study contributes to enriching the neuromarkers associated with DOC prognosis and further elucidates the neural mechanisms of consciousness recovery.

Indexed as

BrainConsciousness DisordersModels, NeurologicalAdultElectroencephalographyFemaleHumansMaleMiddle AgedPrognosisSupport Vector MachineDigital twin brain modelsDisorders of consciousnessModal controllabilityPrognosis prediction

Identifiers

PMID41656193
PMCPMC12983701

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.