1
|
Moroń M, Niedbała D, Mendrok M, Pach J. How complex are the associations between early maladaptive schemas and obsessive-compulsive symptoms? Commentary on Dostal and Pilkington (2023) meta-analysis. J Affect Disord 2024; 360:1-4. [PMID: 38810777 DOI: 10.1016/j.jad.2024.05.137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024]
Affiliation(s)
- Marcin Moroń
- Institute of Psychology, University of Silesia in Katowice, Poland.
| | - Dariusz Niedbała
- Department of Social Sciences, University of Silesia in Katowice, Poland.
| | - Mateusz Mendrok
- Department of Social Sciences, University of Silesia in Katowice, Poland.
| | - Janusz Pach
- Department of Social Sciences, University of Silesia in Katowice, Poland.
| |
Collapse
|
2
|
Cai H, Chen MY, Li XH, Zhang L, Su Z, Cheung T, Tang YL, Malgaroli M, Jackson T, Zhang Q, Xiang YT. A network model of depressive and anxiety symptoms: a statistical evaluation. Mol Psychiatry 2024; 29:767-781. [PMID: 38238548 PMCID: PMC11153039 DOI: 10.1038/s41380-023-02369-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 02/22/2024]
Abstract
BACKGROUND Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks. METHODS A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression. RESULTS Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model. CONCLUSION Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.
Collapse
Affiliation(s)
- Hong Cai
- Unit of medical psychology and behavior medicine, school of public health, Guangxi Medical University, Nanning, Guangxi, China
| | - Meng-Yi Chen
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China
- Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China
| | - Xiao-Hong Li
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Ling Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Zhaohui Su
- School of Public Health, Southeast University, Nanjing, China
| | - Teris Cheung
- School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yi-Lang Tang
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Atlanta VA Medical Center, Atlanta, GA, USA
| | - Matteo Malgaroli
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Todd Jackson
- Department of Psychology, University of Macau, Macao SAR, China
| | - Qinge Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Yu-Tao Xiang
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China.
- Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China.
| |
Collapse
|
3
|
Younger JW, O’Laughlin KD, Anguera JA, Bunge SA, Ferrer EE, Hoeft F, McCandliss BD, Mishra J, Rosenberg-Lee M, Gazzaley A, Uncapher MR. Better together: novel methods for measuring and modeling development of executive function diversity while accounting for unity. Front Hum Neurosci 2023; 17:1195013. [PMID: 37554411 PMCID: PMC10405287 DOI: 10.3389/fnhum.2023.1195013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/28/2023] [Indexed: 08/10/2023] Open
Abstract
INTRODUCTION Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented precise understanding of the developmental trajectory of their organization. METHODS We introduce novel methods to address challenges for both measuring and modeling EFs using an accelerated longitudinal design with a large, diverse sample of students in middle childhood (N = 1,286; ages 8 to 14). We used eight adaptive assessments hypothesized to measure three EFs, working memory, context monitoring, and interference resolution. We deployed adaptive assessments to equate EF challenge across ages and a data-driven, network analytic approach to reveal the evolving diversity of EFs while simultaneously accounting for their unity. RESULTS AND DISCUSSION Using this methodological paradigm shift brought new precision and clarity to the development of these EFs, showing these eight tasks are organized into three stable components by age 10, but refinement of composition of these components continues through at least age 14.
Collapse
Affiliation(s)
- Jessica Wise Younger
- Neuroscape, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Kristine D. O’Laughlin
- Neuroscape, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Joaquin A. Anguera
- Neuroscape, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Silvia A. Bunge
- Department of Psychology & Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Emilio E. Ferrer
- Department of Psychology, University of California, Davis, Davis, CA, United States
| | - Fumiko Hoeft
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Department of Psychological Sciences and Brain Imaging Research Center (BIRC), University of Connecticut, Storrs, CT, United States
| | - Bruce D. McCandliss
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Jyoti Mishra
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States
- Neural Engineering & Translation Labs, University of California San Diego, La Jolla, CA, United States
| | | | - Adam Gazzaley
- Neuroscape, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Psychiatry and Physiology, University of California, San Francisco, San Francisco, CA, United States
| | - Melina R. Uncapher
- Neuroscape, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Advanced Education Research and Development Fund, Oakland, CA, United States
| |
Collapse
|
4
|
Santiago PHR, Soares GH, Smithers LG, Roberts R, Jamieson L. Psychological Network of Stress, Coping and Social Support in an Aboriginal Population. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15104. [PMID: 36429821 PMCID: PMC9690794 DOI: 10.3390/ijerph192215104] [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: 10/06/2022] [Revised: 11/04/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Over the past decades, increasing research interest has been directed towards the psychosocial factors that impact Aboriginal health, including stress, coping and social support. However, there has been no study that examined whether the behaviours, cognitions and emotions related to stress, coping and social support constitute a psychological network in an Aboriginal population and that examined its properties. To address this gap, the current study employed a new methodology, network psychometrics, to evaluate stress, coping and social support in an Aboriginal Australian population. This study conducted a secondary analysis of the South Australian Aboriginal Birth Cohort (SAABC) study, a randomised controlled trial in South Australia, which included 367 pregnant Aboriginal women at study baseline. The Gaussian Graphical Model was estimated with least absolute shrinkage and selection operator (LASSO). Node centrality was evaluated with eigencentrality, strength and bridge centrality. Network communities were investigated with the walktrap algorithm. The findings indicated that stress, coping and social support constituted a connected psychological network in an Aboriginal population. Furthermore, at the centre of the network were the troubles experienced by the Aboriginal pregnant women, bridging their perceptions of stress and coping and constituting a potential target for future interventions.
Collapse
Affiliation(s)
- Pedro Henrique Ribeiro Santiago
- Australian Research Centre for Population Oral Health (ARCPOH), Adelaide Dental School, The University of Adelaide, Adelaide 5000, Australia
- School of Public Health, The University of Adelaide, Adelaide 5005, Australia
| | - Gustavo Hermes Soares
- Australian Research Centre for Population Oral Health (ARCPOH), Adelaide Dental School, The University of Adelaide, Adelaide 5000, Australia
| | - Lisa Gaye Smithers
- School of Public Health, The University of Adelaide, Adelaide 5005, Australia
- School of Health and Society, University of Wollongong, Wollongong 2500, Australia
| | - Rachel Roberts
- School of Psychology, The University of Adelaide, Adelaide 5000, Australia
| | - Lisa Jamieson
- Australian Research Centre for Population Oral Health (ARCPOH), Adelaide Dental School, The University of Adelaide, Adelaide 5000, Australia
| |
Collapse
|
5
|
Forbush KT, Swanson TJ, Chen Y, Siew CSQ, Hagan KE, Chapa DAN, Tregarthen J, Wildes JE, Christensen KA. Generalized network psychometrics of eating-disorder psychopathology. Int J Eat Disord 2022; 55:1603-1613. [PMID: 36053836 PMCID: PMC10108623 DOI: 10.1002/eat.23801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 07/01/2022] [Accepted: 07/01/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE As network models of eating disorder (ED) psychopathology become increasingly popular in modeling symptom interconnectedness and identifying potential treatment targets, it is necessary to contextualize their performance against other methods of modeling ED psychopathology and to evaluate potential ways to optimize and capitalize on their use. To accomplish these goals, we used generalized network psychometrics to estimate and compare latent variable models and network models, as well as hybrid models. METHOD We tested the structure of the Eating Pathology Symptoms Inventory (EPSI) and Eating Disorder Examination-Questionnaire (EDE-Q) in Recovery Record, Inc. mobile phone application users (N = 6856). RESULTS Although all models fit well, results favored a hybrid latent variable and network framework, which showed that ED symptoms fit best when modeled as higher-order constructs, rather than direct symptom-to-symptom connections, and when the relationships between those constructs are described as a network. Hybrid models in which latent factors were modeled as nodes within a network showed that EPSI Purging, Binge Eating, Cognitive Restraint, Body Dissatisfaction, and Excessive Exercise had high importance in the network. EDE-Q Eating Concern and Shape Concern were also important nodes. Results showed that the EPSI network was highly stable and replicable, whereas the EDE-Q network was not. DISCUSSION Integrating latent variable and network model frameworks enables tests of centrality to identify important latent variables, such as purging, that may promote the spread of ED psychopathology throughout a network, allowing for the identification of future treatment targets.
Collapse
Affiliation(s)
- Kelsie T. Forbush
- Department of Psychology, University of Kansas, Lawrence, Kansas, USA
| | - Trevor J. Swanson
- Department of Psychology, University of Kansas, Lawrence, Kansas, USA
| | - Yiyang Chen
- Department of Psychology, University of Kansas, Lawrence, Kansas, USA
| | - Cynthia S. Q. Siew
- Department of Psychology, National University of Singapore, Singapore, Singapore
| | - Kelsey E. Hagan
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | | | | | - Jennifer E. Wildes
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois, USA
| | - Kara A. Christensen
- Department of Psychology, University of Kansas, Lawrence, Kansas, USA
- Department of Psychology, University of Nevada Las Vegas, Las Vegas, Nevada, USA
| |
Collapse
|
6
|
Punzi C, Petti M, Tieri P. Network-based methods for psychometric data of eating disorders: A systematic review. PLoS One 2022; 17:e0276341. [PMID: 36315522 PMCID: PMC9621460 DOI: 10.1371/journal.pone.0276341] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 10/04/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Network science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected symptoms. One category of mental disorders, relevant for their severity, incidence and multifaceted structure, is that of eating disorders (EDs), serious disturbances that negatively affect a person's eating behavior. AIMS We aimed to review the corpus of psychometric network analysis methods by scrutinizing a large sample of network-based studies that exploit psychometric data related to EDs. A particular focus is given to the description of the methodologies for network estimation, network description and network stability analysis providing also a review of the statistical software packages currently used to carry out each phase of the network estimation and analysis workflow. Moreover, we try to highlight aspects with potential clinical impact such as core symptoms, influences of external factors, comorbidities, and related changes in network structure and connectivity across both time and subpopulations. METHODS A systematic search was conducted (February 2022) on three different literature databases to identify 57 relevant research articles. The exclusion criteria comprehended studies not based on psychometric data, studies not using network analysis, studies with different aims or not focused on ED, and review articles. RESULTS Almost all the selected 57 papers employed the same analytical procedures implemented in a collection of R packages specifically designed for psychometric network analysis and are mostly based on cross-sectional data retrieved from structured psychometric questionnaires, with just few exemptions of panel data. Most of them used the same techniques for all phases of their analysis. In particular, a pervasive use of the Gaussian Graphical Model with LASSO regularization was registered for in network estimation step. Among the clinically relevant results, we can include the fact that all papers found strong symptom interconnections between specific and nonspecific ED symptoms, suggesting that both types should therefore be addressed by clinical treatment. CONCLUSIONS We here presented the largest and most comprehensive review to date about psychometric network analysis methods. Although these methods still need solid validation in the clinical setting, they have already been able to show many strengths and important results, as well as great potentials and perspectives, which have been analyzed here to provide suggestions on their use and their possible improvement.
Collapse
Affiliation(s)
- Clara Punzi
- Data Science MSc Program, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- DIAG Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
- * E-mail:
| | - Paolo Tieri
- Data Science MSc Program, Sapienza University of Rome, Rome, Italy
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| |
Collapse
|
7
|
Höltge J, Cowden RG, Lee MT, Bechara AO, Joynt S, Kamble S, Khalanskyi VV, Shtanko L, Kurniati NMT, Tymchenko S, Voytenko VL, McNeely E, VanderWeele TJ. A systems perspective on human flourishing: Exploring cross-country similarities and differences of a multisystemic flourishing network. THE JOURNAL OF POSITIVE PSYCHOLOGY 2022. [DOI: 10.1080/17439760.2022.2093784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- J. Höltge
- Resilience Research Centre, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Psychology, University of Hawaii at Manoa, Honolulu, HI, USA
| | - R. G. Cowden
- Human Flourishing Program, Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
| | - Matthew T. Lee
- Human Flourishing Program, Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
| | - A. O. Bechara
- Department of Psychology, Universidad Del Sinú, Montería, Colombia
| | - S. Joynt
- Department of Practical and Missional Theology, University of the Free, Bloemfontein, South Africa
| | - S. Kamble
- Department of Psychology, Karnatak University, Dharwad, India
| | - V. V. Khalanskyi
- Department of Economic Cybernetics, Finance and Management, Ukrainian Institute of Arts and Sciences, Bucha, Ukraine
| | - L. Shtanko
- Department of Economic Cybernetics, Finance and Management, Ukrainian Institute of Arts and Sciences, Bucha, Ukraine
| | | | - S. Tymchenko
- REALIS Center for Education and Research, Kyiv, Ukraine
| | - V. L. Voytenko
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA
| | - E. McNeely
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - T. J. VanderWeele
- Human Flourishing Program, Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA
| |
Collapse
|
8
|
Betz LT, Penzel N, Kambeitz J. A network approach to relationships between cannabis use characteristics and psychopathology in the general population. Sci Rep 2022; 12:7163. [PMID: 35504926 PMCID: PMC9065088 DOI: 10.1038/s41598-022-11092-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 04/08/2022] [Indexed: 11/27/2022] Open
Abstract
Cannabis use characteristics, such as earlier initiation and frequent use, have been associated with an increased risk for developing psychotic experiences and psychotic disorders. However, little is known how these characteristics relate to specific aspects of sub-clinical psychopathology in the general population. Here, we explore the relationships between cannabis use characteristics and psychopathology in a large general population sample (N = 2,544, mean age 29.2 years, 47% women) by employing a network approach. This allows for the identification of unique associations between two cannabis use characteristics (lifetime cumulative frequency of cannabis use, age of cannabis use initiation), and specific psychotic experiences and affective symptoms, while controlling for early risk factors (childhood trauma, urban upbringing). We found particularly pronounced unique positive associations between frequency of cannabis use and specific delusional experiences (persecutory delusions and thought broadcasting). Age of cannabis use initiation was negatively related to visual hallucinatory experiences and irritability, implying that these experiences become more likely the earlier use is initiated. Earlier initiation, but not lifetime frequency of cannabis use, was related to early risk factors. These findings suggest that cannabis use characteristics may contribute differentially to risk for specific psychotic experiences and affective symptoms in the general population.
Collapse
Affiliation(s)
- Linda T Betz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.
| | - Nora Penzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| |
Collapse
|
9
|
Marsman M, Rhemtulla M. Guest Editors' Introduction to The Special Issue "Network Psychometrics in Action": Methodological Innovations Inspired by Empirical Problems. PSYCHOMETRIKA 2022; 87:1-11. [PMID: 35397084 PMCID: PMC9021145 DOI: 10.1007/s11336-022-09861-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Maarten Marsman
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
- University of Amsterdam, Psychological Methods, Nieuwe Achtergracht 129B, PO Box 15906, 1001 NK, Amsterdam, The Netherlands.
| | - Mijke Rhemtulla
- Department of Psychology, University of California at Davis, Davis, California, USA
| |
Collapse
|
10
|
Abstract
This commentary reflects on the articles included in the Psychometrika Special Issue on Network Psychometrics in Action. The contributions to the special issue are related to several possible future paths for research in this area. These include the development of models to analyze and represent interventions, improvement in exploratory and inferential techniques in network psychometrics, the articulation of psychometric theories in addition to psychometric models, and extensions of network modeling to novel data sources. Finally, network psychometrics is part of a larger movement in psychology that revolves around the analysis of human beings as complex systems, and it is timely that psychometricians start extending their rich modeling tradition to improve and extend the analysis of systems in psychology.
Collapse
Affiliation(s)
- Denny Borsboom
- Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands
| |
Collapse
|
11
|
Haslbeck JMB. Estimating group differences in network models using moderation analysis. Behav Res Methods 2022; 54:522-540. [PMID: 34291432 DOI: 10.31234/osf.io/926pv] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2021] [Indexed: 05/24/2023]
Abstract
Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications, the goal is to compare such network models across groups. In this paper, I introduce a method to estimate group differences in network models that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups for all parameters within a single model and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.
Collapse
Affiliation(s)
- Jonas M B Haslbeck
- Psychological Methods Group, University of Amsterdam, Amsterdam, Netherlands.
| |
Collapse
|
12
|
Stefanovic M, Ehring T, Wittekind CE, Kleim B, Rohde J, Krüger-Gottschalk A, Knaevelsrud C, Rau H, Schäfer I, Schellong J, Dyer A, Takano K. Comparing PTSD symptom networks in type I vs. type II trauma survivors. Eur J Psychotraumatol 2022; 13:2114260. [PMID: 36186163 PMCID: PMC9518442 DOI: 10.1080/20008066.2022.2114260] [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] [Indexed: 11/10/2022] Open
Abstract
Background: Network analysis has gained increasing attention as a new framework to study complex associations between symptoms of post-traumatic stress disorder (PTSD). A number of studies have been published to investigate symptom networks on different sets of symptoms in different populations, and the findings have been inconsistent. Objective: We aimed to extend previous research by testing whether differences in PTSD symptom networks can be found in survivors of type I (single event; sudden and unexpected, high levels of acute threat) vs. type II (repeated and/or protracted; anticipated) trauma (with regard to their index trauma). Method: Participants were trauma-exposed individuals with elevated levels of PTSD symptomatology, most of whom (94%) were undergoing assessment in preparation for PTSD treatment in several treatment centres in Germany and Switzerland (n = 286 with type I and n = 187 with type II trauma). We estimated Bayesian Gaussian graphical models for each trauma group and explored group differences in the symptom network. Results: First, for both trauma types, our analyses identified the edges that were repeatedly reported in previous network studies. Second, there was decisive evidence that the two networks were generated from different multivariate normal distributions, i.e. the networks differed on a global level. Third, explorative edge-wise comparisons showed moderate or strong evidence for specific 12 edges. Edges which emerged as especially important in distinguishing the networks were between intrusions and flashbacks, highlighting the stronger positive association in the group of type II trauma survivors compared to type I survivors. Flashbacks showed a similar pattern of results in the associations with detachment and sleep problems (type II > type I). Conclusion: Our findings suggest that trauma type contributes to the heterogeneity in the symptom network. Future research on PTSD symptom networks should include this variable in the analyses to reduce heterogeneity.
Collapse
Affiliation(s)
| | - Thomas Ehring
- Department of Psychology, LMU Munich, Munich, Germany
| | | | - Birgit Kleim
- Department of Psychology, University of Zurich, Zurich, Switzerland.,Outpatient Centre for Specific Psychotherapy, Psychiatric University Hospital, Zurich, Switzerland
| | - Judith Rohde
- Outpatient Centre for Specific Psychotherapy, Psychiatric University Hospital, Zurich, Switzerland
| | | | - Christine Knaevelsrud
- Department of Clinical Psychology and Psychotherapy, Free University Berlin, Berlin, Germany
| | - Heinrich Rau
- Psychotrauma Centre, German Armed Forces Hospital Berlin, Berlin, Germany
| | - Ingo Schäfer
- Department of Psychiatry and Psychotherapy, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Julia Schellong
- Department of Psychotherapy and Psychosomatic Medicine, Technical University Dresden, Dresden, Germany
| | - Anne Dyer
- Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | | |
Collapse
|
13
|
Bringmann LF, Albers C, Bockting C, Borsboom D, Ceulemans E, Cramer A, Epskamp S, Eronen MI, Hamaker E, Kuppens P, Lutz W, McNally RJ, Molenaar P, Tio P, Voelkle MC, Wichers M. Psychopathological networks: Theory, methods and practice. Behav Res Ther 2021; 149:104011. [PMID: 34998034 DOI: 10.1016/j.brat.2021.104011] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 11/05/2021] [Accepted: 11/27/2021] [Indexed: 12/19/2022]
Abstract
In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.
Collapse
Affiliation(s)
- Laura F Bringmann
- University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), P.O. Box 30.001 (CC72), 9700 RB, Groningen, the Netherlands; University of Groningen, Faculty of Behavioural and Social Sciences, Department of Psychometrics and Statistics, Grote Kruisstraat 2/1, 9712 TS, Groningen, the Netherlands.
| | - Casper Albers
- University of Groningen, Faculty of Behavioural and Social Sciences, Department of Psychometrics and Statistics, Grote Kruisstraat 2/1, 9712 TS, Groningen, the Netherlands
| | - Claudi Bockting
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam UMC, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - Eva Ceulemans
- KU Leuven, Faculty of Psychology and Educational Sciences, Leuven, Belgium
| | - Angélique Cramer
- RIVM National Institute for Public Health and the Environment, the Netherlands
| | - Sacha Epskamp
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - Markus I Eronen
- Department of Theoretical Philosophy, University of Groningen, the Netherlands
| | - Ellen Hamaker
- Department of Methodology and Statistics, Utrecht University, the Netherlands
| | - Peter Kuppens
- KU Leuven, Faculty of Psychology and Educational Sciences, Leuven, Belgium
| | - Wolfgang Lutz
- Department of Psychology, University of Trier, Germany
| | | | - Peter Molenaar
- Department of Human Development and Family Studies, The Pennsylvania State University, USA
| | - Pia Tio
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Department of Methodology and Statistics, Tilburg University, Tilburg, the Netherlands
| | - Manuel C Voelkle
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marieke Wichers
- University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), P.O. Box 30.001 (CC72), 9700 RB, Groningen, the Netherlands
| |
Collapse
|