Evidence map›Paper›PMID 31474882›Full record

ArticleFrontiers in psychiatry2019

Occipital Alpha Connectivity During Resting-State Electroencephalography in Patients With Ultra-High Risk for Psychosis and Schizophrenia.

Tiantian Liu, Jian Zhang, Xiaonan Dong, Zhucheng Li, Xiaorui Shi, Yizhou Tong, Ruobing Yang, Jinglong Wu, Changming Wang, Tianyi Yan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 16% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 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

10 authors at 3 institutions in 1 country.

Tiantian LiuSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Jian ZhangIntelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.
Xiaonan DongSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Zhucheng LiCollege of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China.
Xiaorui ShiSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Yizhou TongSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Ruobing YangSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Jinglong WuIntelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.
Changming WangBeijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Tianyi YanSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Beijing Institute of Technology · CNBeijing Anding Hospital · CNJiangsu University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Schizophrenia patients always show cognitive impairment, which is proved to be related to hypo-connectivity or hyper-connectivity. Further, individuals with an ultra-high risk for psychosis also show abnormal functional connectivity-related cognitive impairment, especially in the alpha rhythm. Thus, the identification of functional networks is essential to our understanding of the disorder. We investigated the resting-state functional connectivity of the alpha rhythm measured by electroencephalography (EEG) to reveal the relation between functional network and clinical symptoms. The participants included 28 patients with first-episode schizophrenia (FES), 28 individuals with ultra-high risk for psychosis (UHR), and 28 healthy controls (HC). After the professional clinical symptoms evaluation, all the participants were instructed to keep eyes closed for 3-min resting-state EEG recording. The 3-min EEG data were segmented into artefact-free epochs (the length was 3 s), and the functional connectivity of the alpha phase was estimated using the phase lag index (PLI), which measures the phase differences of EEG signals. The FES and UHR groups displayed increased resting-state PLI connectivity compared with the HC group [F(2,74) = 10.804, p < 0.001]. Significant increases in the global efficiency, the local efficiency, and the path length were found in the FES and UHR groups compared with those of the HC group. FES and UHR showed an increased degree of connectivity compared with HC. The degree of the left occipital lobe area was higher in the UHR group than in the FES group. The hypothesis of disconnection is confirmed. Furthermore, differences between the UHR and FES group were found, which is valuable for producing clinical significance before the onset of schizophrenia.

Indexed as

alpha rhythmfunctional connectivityoccipital lobeschizophreniaultra-high risk for psychosis

Identifiers

PMID31474882
PMCPMC6706463
OpenAlexW2960049973

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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.