Evidence map›Paper›PMID 35849250›Full record

ArticleBrain topography2022

Recognition of the Multi-class Schizophrenia Based on the Resting-State EEG Network Topology.

Fali Li, Lin Jiang, Yuanyuan Liao, Cunbo Li, Qi Zhang, Shu Zhang, Yangsong Zhang, Li Kang, Rong Li, Dezhong Yao and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in Brain topography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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

1 citing paper in PubMed.

  1. Article
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

13 authors.

Fali LiThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Lin JiangSchool of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Yuanyuan LiaoSchool of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Cunbo LiSchool of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Qi ZhangThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Shu ZhangSchool of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Yangsong ZhangSchool of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Li KangThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Rong LiThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Dezhong YaoThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Gang YinDepartment of Equipment, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Peng XuThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Jing DaiThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China. jing.dai@uestc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The clinical therapy of schizophrenia (SCZ) replies on the corresponding accurate and reliable recognition. Although efforts have been paid, the diagnosis of SCZ is still roughly subjective, it is thus urgent to search for related objective physiological parameters. Motivated by the great potential of resting-state networks in underling the brain deficits among different SCZ groups, in this study, we then developed a multi-class feature extraction approach that could effectively extract the spatial network topology and facilitate the recognition of the SCZ, by combining a network structure based supervised learning with an ensemble co-decision strategy. The results demonstrated that the multi-class spatial pattern of the network (MSPN) features outperformed the other conventional electrophysiological features, such as relative power spectrums and network properties, and achieved the highest classification accuracy of 71.58% in the alpha band. These findings did validate that the resting-state MSPN is a promising tool for the clinical assessment of the SCZ.

Indexed as

SchizophreniaBrainElectroencephalographyHumansMagnetic Resonance ImagingRecognition, PsychologyFunctional connectivityMulti-class spatial pattern of the networkResting-state EEGSchizophrenia

Identifiers

PMID35849250

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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.