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Peng X, Hou WP, Ding YS, Wang Q, Li F, Sha S, Yu CC, Zhang XJ, Zhou FC, Wang CY. Independent effects of early life adversity on social cognitive function in patients with schizophrenia. Front Psychiatry 2024; 15:1343188. [PMID: 38505800 PMCID: PMC10948615 DOI: 10.3389/fpsyt.2024.1343188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/01/2024] [Indexed: 03/21/2024] Open
Abstract
Objective The aim of this study was to investigate the impact of early life adversity on cognitive function in patients with schizophrenia, with a focus on social cognition (SC). Methods Two groups of patients with schizophrenia were recruited and matched on sociodemographic and clinical characteristics. One group consisted of 32 patients with a history of childhood trauma (SCZ-ct), and the other group consisted of 30 patients without a history of childhood trauma (SCZ-nct). In addition, 39 healthy controls without a history of childhood trauma (HC-nct) were also recruited. The intelligence of the three groups was assessed using the Wechsler Abbreviated Scale of Intelligence (WAIS-RC) short version. The cognitive function evaluation was conducted using the MATRICS Consensus Cognitive Battery (MCCB), and early life adversity was measured using the Childhood Trauma Questionnaire-Short Form (CTQ) and Bullying Scale for Adults (BSA). Results Patients with schizophrenia endosed significantly higher scores on the CTQ (F=67.61, p<0.001) and BSA (F=9.84, p<0.001) compared to the HC-nct. Analysis of covariance (ANCOVA) and post-hoc analyses revealed that SCZ-ct (F=11.20, p<0.001) exhibited the most pronounced cognitive impairment among the three groups, as indicated in MCCB total scores and in the domain score of SC. CTQ exhibited a negative correlation with MCCB (r=-0.405, p< 0.001); SC was negatively correlated with physical abuse (PA) of CTQ (r=-0.271, p=0.030) and emotional abuse (EA) of BSA (r=-0.265, p=0.034) in the whole patient sample. Higher SC performance was significantly predicted by CT_total (Beta =-0.582, p<0.001, 95% CI -0.96-0.46), and years of education (Beta=0.260, p =0.014, 95% CI 0.20-1.75) in schizophrenia. Conclusions Besides familial trauma, schizophrenia patients appear to have a higher likelihood of experiencing bullying in their early life. These experiences seem to contribute significantly to their severe impairments in SC.
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Affiliation(s)
- Xing Peng
- School of Public Health, North China University of Science and Technology, Tangshan, China
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Wen-Peng Hou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yu-Shen Ding
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qi Wang
- Department of Psychiatry, Fengtai Mental Health Center, Beijing, China
| | - Feng Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sha Sha
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | | | - Xiu-Jun Zhang
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Fu-Chun Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Chuan-Yue Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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Sensory gating deficits and childhood trauma in the onset of first-episode schizophrenia. Asian J Psychiatr 2023; 80:103385. [PMID: 36542893 DOI: 10.1016/j.ajp.2022.103385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/09/2022] [Accepted: 09/20/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Studies have shown sensory gating deficits and severe childhood trauma in patients with schizophrenia; however, their relationship with this condition remains unclear. Here, we hypothesized that sensory gating deficits mediate the effects of childhood trauma on schizophrenia onset. METHODS We recruited 79 patients with first-episode schizophrenia (PFES) and 76 health controls (HC). The auditory conditioning (S1) and testing (S2) stimulus paradigm was used to detect P50 sensory gating. The Childhood Trauma Questionnaire (CTQ) was used to assess childhood trauma experiences. RESULTS Compared with HC, the PFES group had more severe childhood trauma experiences together with sensory gating deficits. In a partial correlation analysis, sexual abuse was negatively correlated with the P50 S2 latency, physical neglect was negatively correlated with the S1 latency, while emotional neglect was positively correlated with the S2/S1 ratio and negatively correlated with the S1-S2 difference in the PFES group. However, there was no correlation between the CTQ total and each sub-scores and P50 indicators in the HC. The S1-S2 difference was the mediator between emotional neglect and the onset of schizophrenia. CONCLUSION Childhood trauma might be associated with schizophrenia by influencing sensory gating deficits. Early intervention targeting childhood trauma might reduce the incidence of sensory gating deficits and thus schizophrenia.
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Tian Q, Yang NB, Fan Y, Dong F, Bo QJ, Zhou FC, Zhang JC, Li L, Yin GZ, Wang CY, Fan M. Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features. Front Psychiatry 2022; 13:810362. [PMID: 35449564 PMCID: PMC9016153 DOI: 10.3389/fpsyt.2022.810362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 02/21/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND The search for a method that utilizes biomarkers to identify patients with schizophrenia from healthy individuals has occupied researchers for decades. However, no single indicator can be employed to achieve the good in clinical practice. We aim to develop a comprehensive machine learning pipeline based on neurocognitive and electrophysiological combined features for distinguishing schizophrenia patients from healthy people. METHODS In the present study, 69 patients with schizophrenia and 50 healthy controls participated. Neurocognitive (contains seven specific domains of cognition) and electrophysiological [prepulse inhibition, electroencephalography (EEG) power spectrum, detrended fluctuation analysis, and fractal dimension (FD)] features were collected, all these features were taken together to generate the identification models of schizophrenia by applying logistics, random forest, and extreme gradient boosting algorithm. The classification capabilities of these models were also evaluated. RESULTS Both the neurocognitive and electrophysiological feature sets showed a good classification effect with the highest accuracy greater than 85% and AUC greater than 90%. Specifically, the performances of the combined neurocognitive and electrophysiological feature sets achieved the highest accuracy of 93.28% and AUC of 97.91%. The extreme gradient boosting algorithm as a whole presented more stably and precisely in classification efficiency. CONCLUSION The highest classification accuracy of 93.28% by combination of neurocognitive and electrophysiological features shows that both measurements are appropriate indicators to be used in discriminating schizophrenia patients and healthy individuals. Also, among three algorithms, extreme gradient boosting had better classified performances than logistics and random forest algorithms.
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Affiliation(s)
- Qing Tian
- Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Ministry of Science and Technology, Beijing, China.,Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, The Institute of Mental Health, Suzhou, China.,Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Center of Schizophrenia, Capital Medical University, Beijing, China
| | - Ning-Bo Yang
- Department of Psychiatry, First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
| | - Yu Fan
- Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, The Institute of Mental Health, Suzhou, China.,Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Center of Schizophrenia, Capital Medical University, Beijing, China
| | - Fang Dong
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Center of Schizophrenia, Capital Medical University, Beijing, China
| | - Qi-Jing Bo
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Center of Schizophrenia, Capital Medical University, Beijing, China
| | - Fu-Chun Zhou
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Center of Schizophrenia, Capital Medical University, Beijing, China
| | - Ji-Cong Zhang
- Beijing Advanced Innovation Centre for Biomedical Engineering, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, The School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Liang Li
- Department of Psychology, Peking University, Beijing, China
| | - Guang-Zhong Yin
- Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, The Institute of Mental Health, Suzhou, China
| | - Chuan-Yue Wang
- Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Center of Schizophrenia, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ming Fan
- Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Ministry of Science and Technology, Beijing, China.,Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing, China
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San-Martin R, Castro LA, Menezes PR, Fraga FJ, Simões PW, Salum C. Meta-Analysis of Sensorimotor Gating Deficits in Patients With Schizophrenia Evaluated by Prepulse Inhibition Test. Schizophr Bull 2020; 46:1482-1497. [PMID: 32506125 PMCID: PMC8061122 DOI: 10.1093/schbul/sbaa059] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Prepulse inhibition (PPI) of startle is an operational measure of sensorimotor gating that is often impaired in patients with schizophrenia. Despite the large number of studies, there is considerable variation in PPI outcomes reported. We conducted a systematic review and meta-analysis investigating PPI impairment in patients with schizophrenia compared with healthy control subjects, and examined possible explanations for the variation in results between studies. Major databases were screened for observational studies comparing healthy subjects and patients with schizophrenia for the prepulse and pulse intervals of 60 and 120 ms as primary outcomes, ie, PPI-60 and PPI-120. Standardized mean difference (SMD) and 95% confidence intervals (CI) were extracted and pooled using random effects models. We then estimated the mean effect size of these measures with random effects meta-analyses and evaluated potential PPI heterogeneity moderators, using sensitivity analysis and meta-regressions. Sixty-seven primary studies were identified, with 3685 healthy and 4290 patients with schizophrenia. The schizophrenia group showed reduction in sensorimotor gating for both PPI-60 (SMD = -0.50, 95% CI = [-0.61, -0.39]) and PPI-120 (SMD = -0.44, 95% CI = [-0.54, -0.33]). The sensitivity and meta-regression analysis showed that sample size, gender proportion, imbalance for gender, source of control group, and study continent were sources of heterogeneity (P < .05) for both PPI-60 and PPI-120 outcomes. Our findings confirm a global sensorimotor gating deficit in schizophrenia patients, with overall moderate effect size for PPI-60 and PPI-120. Methodological consistency should decrease the high level of heterogeneity of PPI results between studies.
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Affiliation(s)
- Rodrigo San-Martin
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Leonardo Andrade Castro
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Paulo Rossi Menezes
- Department of Preventive Medicine, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Population Mental Health Research Center, Universidade de São Paulo, São Paulo, Brazil
| | - Francisco José Fraga
- Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, Santo André, Brazil
| | - Priscyla Waleska Simões
- Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, Santo André, Brazil
| | - Cristiane Salum
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil
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