Evidence map›Paper›PMID 38385681›Full record

ArticleCNS neuroscience & therapeutics2024

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness.

Yaqian Li, Junfeng Gao, Ying Yang, Yutong Zhuang, Qianruo Kang, Xiang Li, Min Tian, Haoan Lv, Jianghong He

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in CNS neuroscience & therapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
6.5field-weighted citation impact, top 3% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

9 citing papers in PubMed, 19 citations in OpenAlex.

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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

9 authors at 3 institutions in 1 country.

Yaqian LiKey Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Wuhan, China.
Junfeng GaoKey Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Wuhan, China.
Ying YangCollege of Foreign Languages, Wuhan University of Technology, Wuhan, China.
Yutong ZhuangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Qianruo KangKey Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Wuhan, China.
Xiang LiKey Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Wuhan, China.
Min TianKey Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Wuhan, China.
Haoan LvKey Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Wuhan, China.
Jianghong HeDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.ORCID 0000-0001-9470-9966
State Ethnic Affairs Commission · CNCapital Medical University · CNWuhan University of Technology · CN

Funding

Beijing Natural Science Foundation 23G10455National Natural Science Foundation of China 61773408National Natural Science Foundation of China 82272118
6 · The paper itself

Abstract

backgroundAccurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%.

methodsA method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex.

resultsThe group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%.

conclusionConstructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.

Indexed as

Consciousness DisordersElectroencephalographyBrainCerebral CortexHumansdisorders of consciousnesselectroencephalographymicrostatesmutual information

Identifiers

PMID38385681
PMCPMC10883110
OpenAlexW4392059652

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.