Evidence map›Paper›PMID 40275324›Full record

ArticleBMC medicine2025

Neurocognitive resilience as a predictor of psychosis onset and functional outcomes in individuals at high risk.

TianHong Zhang, XiaoChen Tang, YanYan Wei, LiHua Xu, HuiRu Cui, HaiChun Liu, ZiXuan Wang, Tao Chen, LingYun Zeng, YingYing Tang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
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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.

TianHong Zhang *Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China. zhang_tianhong@126.com.ORCID http://orcid.org/0000-0002-5379-7119
XiaoChen Tang *Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
YanYan Wei *Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
LiHua XuShanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
HuiRu CuiShanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
HaiChun LiuDepartment of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China.
ZiXuan WangShanghai Xinlianxin Psychological Counseling Center, Shanghai, China.
Tao ChenBig Data Research Lab, University of Waterloo, Waterloo, ON, Canada.
LingYun ZengDepartment of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, ShenZhen, GuangDong, China.
YingYing TangShanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
ZhengHui YiShanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
ChunBo LiShanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
JiJun WangShanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China. jijunwang27@163.com.

Funding

Ministry of Science and Technology of the People's Republic of China 2023YFC2506800National Natural Science Foundation of China 82171544, 82371505, 82151314, 82101623Shenzhen Medical and Health Three Project SZSM202011014Shenzhen Science and Technology Plan Project JCYJ20220530165009020STI 2030-Major Projects 2022ZD0208500
6 · The paper itself

Abstract

backgroundNeurocognitive resilience (NCR) refers to the ability of individuals to maintain cognitive function despite the presence of risk factors for psychosis. Investigating NCR is important as it may help predict the onset of psychosis and functional outcomes in individuals at clinical high risk (CHR) for psychosis.

methodsThis study employed a multi-group prospective design with a 3-year follow-up as part of the ShangHai At Risk for Psychosis-Extended project. Neurocognitive performance was assessed using the Chinese version of the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery. The study focused on two primary outcomes: conversion/non-conversion to psychosis (CHR-C/CHR-NC) and non-remission/remission (CHR-NR/CHR-R). NCR was defined based on the adjusted cognitive variable relative to the healthy control(HC) group's mean, with three categories: NCR (NCR = 0) for scores within one standard deviation, NCR + (NCR = 1) for scores more than one standard deviation above, and NCR - (NCR = - 1) for scores more than one standard deviation below.

resultsThe study included 771 individuals at CHR (346 males, mean age 18.8 years) and 764 HCs (359 males, mean age 22.5 years). Among the CHR participants, 540 (70.0%) completed the 3-year follow-up, with 106 (19.6%) converting to psychosis (CHR-C) and 277 (51.3%) classified as non-remission (CHR-NR). Significant negative correlations were found between the total NCR score and various clinical symptoms. Comparing CHR-C and non-converters (CHR-NC), there were notable differences in NCR distributions across four cognitive measures, with a higher proportion of CHR-C individuals categorized as NCR - . For CHR-NR versus remission (CHR-R), CHR-NR individuals were more likely to be classified as NCR - across nearly all cognitive domains. The receiver operating characteristic (ROC) curve for predicting conversion to psychosis yielded an area under the curve (AUC) of 0.621 (95% CI (0.561-0.681), p = 0.0001), while the ROC for predicting non-remission demonstrated a higher AUC of 0.826 (95% CI (0.790-0.861), p < 0.0001).

conclusionsNCR was associated with both conversion to psychosis and non-remission outcomes in CHR individuals, showing notable predictive accuracy, particularly for non-remission.

Indexed as

CognitionPsychotic DisordersResilience, PsychologicalAdolescentAdultChinaFemaleHumansMaleNeuropsychological TestsProspective StudiesRisk FactorsYoung AdultCognitionFunctional outcomeProdromal psychosisRemissionTransitionUltra high risk

Identifiers

PMID40275324
PMCPMC12023670

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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.