Evidence map›Paper›PMID 39761216›Full record

ArticleSchizophrenia bulletin2025

Aberrant Cortical Morphological Networks in First-Episode Schizophrenia.

Fengmei Fan, Suhui Jin, Yating Lv, Shuping Tan, Yuqing Liao, Zhenzhen Luo, Jingxuan Ruan, Zhiren Wang, Hongzhen Fan, Xiaole Han and 6 more

Abstract read
In one paragraph

Article in Schizophrenia bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
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  5. 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

16 authors.

Fengmei FanBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China.
Suhui JinInstitute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
Yating LvCenter for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou 311121, Zhejiang, China.
Shuping TanBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China.
Yuqing LiaoInstitute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
Zhenzhen LuoInstitute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
Jingxuan RuanSchool of Electronics and Information Technology, South China Normal University, Foshan 528200, China.
Zhiren WangBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China.
Hongzhen FanBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China.
Xiaole HanBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China.
Qihong ZouCenter for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Hong XiangChongqing Three Gorges Central Hospital, Chongqing 404000, China.
Hua GuoZhumadian Psychiatry Hospital, Henan Province, China.
Fude YangBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China.
Yunlong TanBeijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China.ORCID 0000-0002-3522-3912
Jinhui WangInstitute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.

Funding

National Natural Science Foundation of China 82171507National Natural Science Foundation of China 82472092Research Center for Brain Cognition and Human Development, Guangdong, China 2024B0303390003STI 2030-Major Projects 2021ZD0200500
6 · The paper itself

Abstract

background and hypothesisPopulation-based morphological covariance networks are widely reported to be altered in schizophrenia. Individualized morphological brain network approaches have emerged recently. We hypothesize that individualized morphological brain networks are disrupted in schizophrenia. STUDY

designWe constructed single-subject morphological brain networks for 203 patients with first-episode schizophrenia (FES) and 131 healthy controls separately based on regional cortical thickness (CT), fractal dimension (FD), gyrification index, and sulcal depth (SD) by dividing the cerebral cortex into 360 regions in terms of the Human Connectome Project Multi-Modal Parcellation atlas.

resultsCompared with the controls, the patients exhibited morphological similarity reductions in all types of networks while increases in FD- and SD-based networks. The altered morphological similarities were commonly involved in cingulo-opercular and default mode networks. Interestingly, the altered morphological similarities accounted for clinical symptoms and cognitive dysfunction in the patients and distinguished the patients from controls, with better performance than altered local morphology. In addition, graph-based analysis revealed that global organization was intact while nodal centrality was altered in the patients as characterized by decreased degree and efficiency in the left inferior parietal cortex and increased efficiency in left area superior temporal gyrus for the CT-based networks, decreased degree and efficiency in the left Posterior Insular Area 2 for the FD-based networks, and decreased betweenness in the left Area 52 for the SD-based networks.

conclusionsThese findings indicate that FES is accompanied by characteristic disruptions in single-subject cortical morphological networks, which provide novel insights into neurobiological mechanisms underlying schizophrenia.

Indexed as

Cerebral CortexConnectomeNerve NetSchizophreniaAdolescentAdultFemaleHumansMagnetic Resonance ImagingMaleYoung Adultbrain networkcortical surfacefirst-episode schizophreniamorphologystructural MRI

Identifiers

PMID39761216
PMCPMC12414567

What Socratic holds

Textmetadata
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Registered trials

None linked

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 Socratic graph.