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Liang S, Huang Z, Wang Y, Wu Y, Chen Z, Zhang Y, Guo W, Zhao Z, Ford SD, Palaniyappan L, Li T. Using a longitudinal network structure to subgroup depressive symptoms among adolescents. BMC Psychol 2024; 12:46. [PMID: 38268052 PMCID: PMC10807250 DOI: 10.1186/s40359-024-01537-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/11/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Network modeling has been proposed as an effective approach to examine complex associations among antecedents, mediators and symptoms. This study aimed to investigate whether the severity of depressive symptoms affects the multivariate relationships among symptoms and mediating factors over a 2-year longitudinal follow-up. METHODS We recruited a school-based cohort of 1480 primary and secondary school students over four semesters from January 2020 to December 2021. The participants (n = 1145) were assessed at four time points (ages 10-13 years old at baseline). Based on a cut-off score of 5 on the 9-item Patient Health Questionnaire at each time point, the participants were categorized into the non-depressive symptom (NDS) and depressive symptom (DS) groups. We conducted network analysis to investigate the symptom-to-symptom influences in these two groups over time. RESULTS The global network metrics did not differ statistically between the NDS and DS groups at four time points. However, network connection strength varied with symptom severity. The edge weights between learning anxiety and social anxiety were prominently in the NDS group over time. The central factors for NDS and DS were oversensitivity and impulsivity (3 out of 4 time points), respectively. Moreover, both node strength and closeness were stable over time in both groups. CONCLUSIONS Our study suggests that interrelationships among symptoms and contributing factors are generally stable in adolescents, but a higher severity of depressive symptoms may lead to increased stability in these relationships.
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Affiliation(s)
- Sugai Liang
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, 305 Tianmushan Road, 310013, Hangzhou, China
| | - Zejun Huang
- Hangzhou Institute of Educational Science, 310003, Hangzhou, China
| | - Yiquan Wang
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, 305 Tianmushan Road, 310013, Hangzhou, China
| | - Yue Wu
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, 305 Tianmushan Road, 310013, Hangzhou, China
| | - Zhiyu Chen
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, 305 Tianmushan Road, 310013, Hangzhou, China
| | - Yamin Zhang
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, 305 Tianmushan Road, 310013, Hangzhou, China
| | - Wanjun Guo
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, 305 Tianmushan Road, 310013, Hangzhou, China
| | - Zhenqing Zhao
- Hangzhou Vocational & Technical College, 310018, Hangzhou, China
| | - Sabrina D Ford
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, H4H1R3, Montreal, Canada
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, H4H1R3, Montreal, Canada.
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, N6A5K8, London, Canada.
- Department of Medical Biophysics, Western University, N6A5K8, London, Canada.
| | - Tao Li
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, 305 Tianmushan Road, 310013, Hangzhou, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 310000, Hangzhou, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, 310063, Hangzhou, China.
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Moffa G, Kuipers J, Kuipers E, McManus S, Bebbington P. Sexual abuse and psychotic phenomena: a directed acyclic graph analysis of affective symptoms using English national psychiatric survey data. Psychol Med 2023; 53:7817-7826. [PMID: 37485689 PMCID: PMC10755243 DOI: 10.1017/s003329172300185x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 06/08/2023] [Accepted: 06/13/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Sexual abuse and bullying are associated with poor mental health in adulthood. We previously established a clear relationship between bullying and symptoms of psychosis. Similarly, we would expect sexual abuse to be linked to the emergence of psychotic symptoms, through effects on negative affect. METHOD We analysed English data from the Adult Psychiatric Morbidity Surveys, carried out in 2007 (N = 5954) and 2014 (N = 5946), based on representative national samples living in private households. We used probabilistic graphical models represented by directed acyclic graphs (DAGs). We obtained measures of persecutory ideation and auditory hallucinosis from the Psychosis Screening Questionnaire, and identified affective symptoms using the Clinical Interview Schedule. We included cannabis consumption and sex as they may determine the relationship between symptoms. We constrained incoming edges to sexual abuse and bullying to respect temporality. RESULTS In the DAG analyses, contrary to our expectations, paranoia appeared early in the cascade of relationships, close to the abuse variables, and generally lying upstream of affective symptoms. Paranoia was consistently directly antecedent to hallucinations, but also indirectly so, via non-psychotic symptoms. Hallucinosis was also the endpoint of pathways involving non-psychotic symptoms. CONCLUSIONS Via worry, sexual abuse and bullying appear to drive a range of affective symptoms, and in some people, these may encourage the emergence of hallucinations. The link between adverse experiences and paranoia is much more direct. These findings have implications for managing distressing outcomes. In particular, worry may be a salient target for intervention in psychosis.
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Affiliation(s)
- Giusi Moffa
- University of Basel, Basel, Switzerland
- University College London, London, UK
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, Eidgenossische Technische Hochschule Zurich, Basel, Switzerland
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Sun Y, Zhang Y, Lu Z, Yan H, Guo L, Liao Y, Lu T, Wang L, Li J, Li W, Yang Y, Yu H, Lv L, Zhang D, Bi W, Yue W. Longitudinal Network Analysis Reveals Interactive Change of Schizophrenia Symptoms During Acute Antipsychotic Treatment. Schizophr Bull 2023; 49:208-217. [PMID: 36179110 PMCID: PMC9810008 DOI: 10.1093/schbul/sbac131] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND AND HYPOTHESIS Complex schizophrenia symptoms were recently conceptualized as interactive symptoms within a network system. However, it remains unknown how a schizophrenia network changed during acute antipsychotic treatment. The present study aimed to evaluate the interactive change of schizophrenia symptoms under seven antipsychotics from individual time series. STUDY DESIGN Data on 3030 schizophrenia patients were taken from a multicenter randomized clinical trial and used to estimate the partial correlation cross-sectional networks and longitudinal random slope networks based on multivariate multilevel model. Thirty symptoms assessed by The Positive and Negative Syndrome Scale clustered the networks. STUDY RESULTS Five stable communities were detected in cross-sectional networks and random slope networks that describe symptoms change over time. Delusions, emotional withdrawal, and lack of spontaneity and flow of conversation featured as central symptoms, and conceptual disorganization, hostility, uncooperativeness, and difficulty in abstract thinking featured as bridge symptoms, all showing high centrality in the random slope network. Acute antipsychotic treatment changed the network structure (M-test = 0.116, P < .001) compared to baseline, and responsive subjects showed lower global strength after treatment (11.68 vs 14.18, S-test = 2.503, P < .001) compared to resistant subjects. Central symptoms and bridge symptoms kept higher centrality across random slope networks of different antipsychotics. Quetiapine treatment network showed improvement in excitement symptoms, the one featured as both central and bridge symptom. CONCLUSION Our findings revealed the central symptoms, bridge symptoms, cochanging features, and individualized features under different antipsychotics of schizophrenia. This brings implications for future targeted drug development and search for pathophysiological mechanisms.
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Affiliation(s)
- Yaoyao Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Yuyanan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Zhe Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Liangkun Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Yundan Liao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Tianlan Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Lifang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Jun Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
| | - Wenqiang Li
- Henan Key Lab of Biological Psychiatry, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, P. R. China
| | - Yongfeng Yang
- Henan Key Lab of Biological Psychiatry, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, P. R. China
| | - Hao Yu
- Department of Psychiatry, Jining Medical University, Jining, Shandong, P. R. China
| | - Luxian Lv
- Henan Key Lab of Biological Psychiatry, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, P. R. China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, P. R. China
| | - Wenjian Bi
- Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, P. R. China
| | - Weihua Yue
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, P. R. China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P. R. China
- Henan Key Lab of Biological Psychiatry, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, P. R. China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, P. R. China
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Nguyen MT, Tran BT, Nguyen TG, Phan MT, Luong TTT, Le DD. Self-control as an important factor affecting the online learning readiness of Vietnamese medical and health students during the COVID-19 pandemic: a network analysis. JOURNAL OF EDUCATIONAL EVALUATION FOR HEALTH PROFESSIONS 2022; 19:22. [PMID: 36002389 PMCID: PMC9582298 DOI: 10.3352/jeehp.2022.19.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
PURPOSE The current study aimed to use network analysis to investigate medical and health students’ readiness for online learning during the coronavirus disease 2019 (COVID-19) pandemic at the University of Medicine and Pharmacy, Hue University. METHODS A questionnaire survey on the students’ readiness for online learning was performed using a Google Form from May 13 to June 22, 2021. In total, 1,377 completed responses were eligible for analysis out of 1,411 participants. The network structure was estimated for readiness scales with 6 factors: computer skills, internet skills, online communication, motivation, self-control, and self-learning. Data were fitted using a Gaussian graphical model with the extended Bayesian information criterion. RESULTS In 1,377 students, a network structure was identified with 6 nodes and no isolated nodes. The top 3 partial correlations were similar in networks for the overall sample and subgroups of gender and grade levels. The self-control node was the strongest for the connection to others, with the highest nodal strength. The change of nodal strength was greatest in online communication for both gender and grade levels. The correlation stability coefficient for nodal strength was achieved for all networks. CONCLUSION These findings indicated that self-control was the most important factor in students’ readiness network structures for online learning. Therefore, self-control needs to be encouraged during online learning to improve the effectiveness of achieving online learning outcomes for students.
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Affiliation(s)
- Minh Tu Nguyen
- Office for Undergraduate Training, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Binh Thang Tran
- Faculty of Public Health, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Thanh Gia Nguyen
- Faculty of Public Health, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Minh Tri Phan
- University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | | | - Dinh Duong Le
- Faculty of Public Health, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
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Dal Santo F, Fonseca-Pedrero E, García-Portilla MP, González-Blanco L, Sáiz PA, Galderisi S, Giordano GM, Bobes J. Searching for bridges between psychopathology and real-world functioning in first-episode psychosis: A network analysis from the OPTiMiSE trial. Eur Psychiatry 2022; 65:e33. [PMID: 35686446 PMCID: PMC9251819 DOI: 10.1192/j.eurpsy.2022.25] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Network analysis has been used to explore the interplay between psychopathology and functioning in psychosis, but no study has used dedicated statistical techniques to focus on the bridge symptoms connecting these domains. The current study aims to estimate the network of depressive, negative, and positive symptoms, general psychopathology, and real-world functioning in people with first-episode schizophrenia or schizophreniform disorder, focusing on bridge nodes. Methods Baseline data from the OPTiMiSE trial were analyzed. The sample included 446 participants (age 40.0 ± 10.9 years, 70% males). The network was estimated with a Gaussian graphical model, using scores on individual items of the positive and negative syndrome scale (PANSS), the Calgary depression scale for schizophrenia, and the personal and social performance scale. Stability, strength centrality, expected influence (EI), predictability, and bridge centrality statistics were computed. The top 20% scoring nodes on bridge strength were selected as bridge nodes. Results Nodes from different rating scales assessing similar psychopathological and functioning constructs tended to cluster together in the estimated network. The most central nodes (EI) were Delusions, Emotional Withdrawal, Depression, and Depressed Mood. Bridge nodes included Depression, Conceptual Disorganization, Active Social Avoidance, Delusions, Stereotyped Thinking, Poor Impulse Control, Guilty Feelings, Unusual Thought Content, and Hostility. Most of the bridge nodes belonged to the general psychopathology subscale of the PANSS. Depression (G6) was the bridge node with the highest value. Conclusions The current study provides novel insights for understanding the complex phenotype of psychotic disorders and the mechanisms underlying the development and maintenance of comorbidity and functional impairment after psychosis onset.
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Affiliation(s)
- Francesco Dal Santo
- Área de Psiquiatría, Universidad de Oviedo, Oviedo, Spain.,Servicio de Salud del Principado de Asturias (SESPA), Oviedo, Spain.,Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain.,Instituto de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain
| | - Eduardo Fonseca-Pedrero
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Educational Sciences, University of La Rioja, Logroño, Spain
| | - María Paz García-Portilla
- Área de Psiquiatría, Universidad de Oviedo, Oviedo, Spain.,Servicio de Salud del Principado de Asturias (SESPA), Oviedo, Spain.,Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain.,Instituto de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Leticia González-Blanco
- Área de Psiquiatría, Universidad de Oviedo, Oviedo, Spain.,Servicio de Salud del Principado de Asturias (SESPA), Oviedo, Spain.,Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain.,Instituto de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Pilar A Sáiz
- Área de Psiquiatría, Universidad de Oviedo, Oviedo, Spain.,Servicio de Salud del Principado de Asturias (SESPA), Oviedo, Spain.,Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain.,Instituto de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | - Julio Bobes
- Área de Psiquiatría, Universidad de Oviedo, Oviedo, Spain.,Servicio de Salud del Principado de Asturias (SESPA), Oviedo, Spain.,Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain.,Instituto de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
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