Evidence map›Paper›PMID 37702075›Full record

ArticleEuropean psychiatry : the journal of the Association of European Psychiatrists2023

Dissociation between neuroanatomical and symptomatic subtypes in schizophrenia.

Chao Chai, Hao Ding, Xiaotong Du, Yingying Xie, Weiqi Man, Yu Zhang, Yi Ji, Meng Liang, Bin Zhang, Yuping Ning and 3 more

Abstract read
In one paragraph

Article in European psychiatry : the journal of the Association of European Psychiatrists, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
–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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

13 authors.

Chao ChaiDepartment of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.ORCID 0000-0001-6267-9109
Hao DingSchool of Medical Imaging, Tianjin Medical University, Tianjin, China.
Xiaotong DuDepartment of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.ORCID 0000-0003-2092-0448
Yingying XieDepartment of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
Weiqi ManDepartment of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
Yu ZhangDepartment of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
Yi JiDepartment of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.ORCID 0000-0002-5125-1163
Meng LiangSchool of Medical Imaging, Tianjin Medical University, Tianjin, China.ORCID 0000-0003-0916-520X
Bin ZhangDepartment of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.ORCID 0000-0002-9280-8247
Yuping NingDepartment of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
Chuanjun ZhuoDepartment of Psychiatry, Tianjin Fourth Center Hospital, Tianjin, China.
Chunshui YuDepartment of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.ORCID 0000-0001-5648-5199
Wen QinDepartment of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.ORCID 0000-0002-9121-8296

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSchizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated.

methodsA total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using

resultsPatients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either

conclusionsThese findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.

Indexed as

SchizophreniaBrainGray MatterHumansMachine LearningMagnetic Resonance Imaginggray matter volumeheterogeneitymagnetic resonance imagingPositive and Negative Syndrome Scaleschizophreniasubtypes

Identifiers

PMID37702075
PMCPMC10594537

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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.