Evidence map›Paper›PMID 39975949›Full record

ArticleFrontiers in psychiatry2025

Differences in EEG complexity of cognitive activities among subtypes of schizophrenia.

Hang Qi, Jilin Zou, Zhenzhen Yao, Gaofeng Zhao, Jing Zhang, Chunlei Liu, Min Chen

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Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hang Qi *School of Psychology, Qufu Normal University, Qufu, China.
Jilin Zou *Department of Psychology, School of Education, Linyi University, Linyi, Shandong, China.
Zhenzhen YaoClinical Psychology Department, Shandong Mental Health Center, Jinan, China.
Gaofeng ZhaoGeriatrics Department, Shandong Daizhuang Hospital, Jining, China.
Jing ZhangGeriatrics Department, Shandong Daizhuang Hospital, Jining, China.
Chunlei LiuSchool of Psychology, Qufu Normal University, Qufu, China.
Min ChenSchool of Mental Health, Jining Medical University, Jining, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The neural mechanisms that underpin cognitive impairments in patients with schizophrenia remain unclear. Previous studies have typically treated patients as a homogeneous group, despite the existence of distinct symptom presentations between deficit and non-deficit subtypes. This approach has been found to be inadequate, necessitating separate investigation. Methods: This study was conducted at Daizhuang Hospital in Jining City, China, from January 2022 to October 2023. The study sample comprised 30 healthy controls, 19 patients with deficit schizophrenia, and 19 patients with non-deficit schizophrenia, all aged between 18 and 45 years. Cognitive abilities were evaluated using a change detection task. The NeuroScan EEG/ERP System, comprising 64 channels and utilising standard 10-20 electrode placements, was employed to record EEG signals. The multiscale entropy and sample entropy of the EEG signals were calculated. Results: The healthy controls demonstrated superior task performance compared to both the non-deficit (p < 0.001) and deficit groups(p < 0.001). Significant differences in multiscale entropy between the three groups were observed at multiple electrode sites. In the task state, there are significant differences in the sample entropy of the β frequency band among the three groups of subjects. Under simple conditions of difficulty, the performance of the healthy controls exhibited a positive correlation with alpha band sample entropy(r = 0.372) and a negative correlation with beta band sample entropy (r = -0.411). Deficit patients demonstrated positive correlations with alpha band sample entropy (r = 0.370), whereas non-deficit patients exhibited negative correlations with both alpha and beta band sample entropy (r = -0.451, r = -0.362). Under difficult conditions of difficulty, the performance of healthy controls demonstrated a positive correlation with beta band sample entropy (r = 0.486). Deficit patients exhibited a positive correlation with alpha band sample entropy (r = 0.351), while non-deficit patients demonstrated a negative correlation with beta band sample entropy (r = -0.331). Conclusion: The results of this study indicate that cognitive impairment in specific subtypes of schizophrenia may have distinct physiological underpinnings, underscoring the need for further investigation.

Indexed as

EEG complexityfrequency bandssample entropyschizophreniaworking memory

Identifiers

PMID39975949
PMCPMC11835803

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