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Ebrahimi OV, Borsboom D, Hoekstra RHA, Epskamp S, Ostinelli EG, Bastiaansen JA, Cipriani A. Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder. Br J Psychiatry 2024; 224:157-163. [PMID: 38584324 PMCID: PMC11039556 DOI: 10.1192/bjp.2024.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 12/14/2023] [Accepted: 01/16/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. AIMS To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. METHOD Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. RESULTS Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. CONCLUSIONS The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.
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Affiliation(s)
- Omid V. Ebrahimi
- Department of Experimental Psychology, University of Oxford, Oxford, UK; and Department of Psychology , University of Oslo, Oslo, Norway
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Ria H. A. Hoekstra
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore, Singapore, Singapore
| | - Edoardo G. Ostinelli
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Jojanneke A. Bastiaansen
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; and Friesland Mental Health Care Services, Leeuwarden, The Netherlands
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
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2
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Hoekstra RHA, Epskamp S, Nierenberg AA, Borsboom D, McNally RJ. Testing similarity in longitudinal networks: The Individual Network Invariance Test. Psychol Methods 2024:2024-71770-001. [PMID: 38602781 DOI: 10.1037/met0000638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
The comparison of idiographic network structures to determine the presence of heterogeneity is a challenging endeavor in many applied settings. Previously, researchers eyeballed idiographic networks, computed correlations, and used techniques that make use of the multilevel structure of the data (e.g., group iterative multiple model estimation and multilevel vector autoregressive) to investigate individual differences. However, these methods do not allow for testing the (in)equality of idiographic network structures directly. In this article, we propose the Individual Network Invariance Test (INIT), which we implemented in the R package INIT. INIT extends common model comparison practices in structural equation modeling to idiographic network structures to test for (in)equality between idiographic networks. In a simulation study, we evaluated the performance of INIT on both saturated and pruned idiographic network structures by inspecting the rejection rate of the χ² difference test and model selection criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Results show INIT performs adequately when t = 100 per individual. When applying INIT on saturated networks, the AIC performed best as a model selection criterion, while the BIC showed better results when applying INIT on pruned networks. In an empirical example, we highlight the possibilities of this new technique, illustrating how INIT provides researchers with a means of testing for (in)equality between idiographic network structures and within idiographic network structures over time. To conclude, recommendations for empirical researchers are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore
| | - Andrew A Nierenberg
- Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Harvard Medical School
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3
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Sijtsma K, Ellis JL, Borsboom D. Recognize the Value of the Sum Score, Psychometrics' Greatest Accomplishment. Psychometrika 2024; 89:84-117. [PMID: 38627311 DOI: 10.1007/s11336-024-09964-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Indexed: 05/02/2024]
Abstract
The sum score on a psychological test is, and should continue to be, a tool central in psychometric practice. This position runs counter to several psychometricians' belief that the sum score represents a pre-scientific conception that must be abandoned from psychometrics in favor of latent variables. First, we reiterate that the sum score stochastically orders the latent variable in a wide variety of much-used item response models. In fact, item response theory provides a mathematically based justification for the ordinal use of the sum score. Second, because discussions about the sum score often involve its reliability and estimation methods as well, we show that, based on very general assumptions, classical test theory provides a family of lower bounds several of which are close to the true reliability under reasonable conditions. Finally, we argue that eventually sum scores derive their value from the degree to which they enable predicting practically relevant events and behaviors. None of our discussion is meant to discredit modern measurement models; they have their own merits unattainable for classical test theory, but the latter model provides impressive contributions to psychometrics based on very few assumptions that seem to have become obscured in the past few decades. Their generality and practical usefulness add to the accomplishments of more recent approaches.
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Affiliation(s)
- Klaas Sijtsma
- Department of Methodology and Statistics TSB, Tilburg University, PO Box 90153, 5000LE , Tilburg, The Netherlands.
| | - Jules L Ellis
- Open University OF THE NETHERLANDS, Heerlen, The Netherlands
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4
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Chen C, Cao C, Fang R, Wang L, Borsboom D. Revealing the psychopathological pathway linking trauma to post-traumatic stress disorder: longitudinal network approach. BJPsych Open 2023; 10:e2. [PMID: 38044677 PMCID: PMC10755552 DOI: 10.1192/bjo.2023.615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 08/22/2023] [Accepted: 10/29/2023] [Indexed: 12/05/2023] Open
Abstract
The present study investigated the psychopathological processes of post-traumatic stress disorder (PTSD) following the network approach to psychopathology. The directed acyclic graph model was employed to analyse a large longitudinal data-set of Chinese children and adolescents exposed to a destructive earthquake. It was found that intrusion symptoms were first activated by trauma exposure, and subsequently activated other PTSD symptoms. The data are consistent with the idea that symptoms may form a self-sustaining dynamic network by interacting with each other to promote or maintain the chronicity of PTSD. The findings advance the current understanding about the psychopathological processes of PTSD, and inform further research and clinical practices on post-traumatic psychopathology.
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Affiliation(s)
- Chen Chen
- Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, China; and Department of Psychology, University of Chinese Academy of Sciences, China
| | - Chengqi Cao
- Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, China; and Department of Psychology, University of Chinese Academy of Sciences, China
| | - Ruojiao Fang
- Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, China; and Department of Psychology, University of Chinese Academy of Sciences, China
| | - Li Wang
- Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, China; and Department of Psychology, University of Chinese Academy of Sciences, China
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, The Netherlands
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5
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van Borkulo CD, van Bork R, Boschloo L, Kossakowski JJ, Tio P, Schoevers RA, Borsboom D, Waldorp LJ. Comparing network structures on three aspects: A permutation test. Psychol Methods 2023; 28:1273-1285. [PMID: 35404628 DOI: 10.1037/met0000476] [Citation(s) in RCA: 194] [Impact Index Per Article: 194.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
| | - Riet van Bork
- Center for Philosophy of Science, University of Pittsburgh
| | - Lynn Boschloo
- Department of Clinical, Neuro, and Developmental Psychology, VU University Amsterdam
| | | | - Pia Tio
- Department of Psychological Methods, University of Amsterdam
| | - Robert A Schoevers
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam
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6
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van der Tuin S, Hoekstra RHA, Booij SH, Oldehinkel AJ, Wardenaar KJ, van den Berg D, Borsboom D, Wigman JTW. Relating stability of individual dynamical networks to change in psychopathology. PLoS One 2023; 18:e0293200. [PMID: 37943819 PMCID: PMC10635522 DOI: 10.1371/journal.pone.0293200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 10/07/2023] [Indexed: 11/12/2023] Open
Abstract
One hypothesis flowing from the network theory of psychopathology is that symptom network structure is associated with psychopathology severity and in turn, one may expect that individual network structure changes with the level of psychopathology severity. However, this expectation has rarely been addressed directly. This study aims to examine (1) the stability of individual contemporaneous symptom networks over a one-year period and (2) whether network stability is associated with a change in psychopathology. We used daily diary data of n = 66 individuals, located along the psychosis severity continuum, from two separate 90-day periods, one year apart (t = 180). Based on the newly developed Individual Network Invariance Test (INIT) to assess symptom-network stability, participants were divided into two groups with stable and unstable networks and we tested whether these groups differed in their absolute change in psychopathology severity. The majority of the sample (n = 51, 77.3%) showed a stable network over time while most individuals showed a decrease in psychopathological severity. We found no significant association between a change in psychopathology severity and individual network stability. Our results call for further critical evaluation of the association between networks and psychopathology to optimize the implementation of clinical applications based on current methods.
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Affiliation(s)
- Sara van der Tuin
- University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Centre Psychopathology and Emotion regulation, University of Groningen, Groningen, The Netherlands
| | - Ria H. A. Hoekstra
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Sanne H. Booij
- University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Centre Psychopathology and Emotion regulation, University of Groningen, Groningen, The Netherlands
- Center for Integrative Psychiatry, Lentis, Groningen, The Netherlands
| | - Albertine J. Oldehinkel
- University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Centre Psychopathology and Emotion regulation, University of Groningen, Groningen, The Netherlands
| | - Klaas J. Wardenaar
- University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Centre Psychopathology and Emotion regulation, University of Groningen, Groningen, The Netherlands
| | - David van den Berg
- Department of Clinical Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
- Department of Psychosis Research, Parnassia Psychiatric Institute, The Hague, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Johanna T. W. Wigman
- University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Centre Psychopathology and Emotion regulation, University of Groningen, Groningen, The Netherlands
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7
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de Ron J, Deserno M, Robinaugh D, Borsboom D, van der Maas HLJ. Towards a general modeling framework of resource competition in cognitive development. Child Dev 2023; 94:1432-1453. [PMID: 37501341 PMCID: PMC10848871 DOI: 10.1111/cdev.13973] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 05/11/2023] [Accepted: 05/27/2023] [Indexed: 07/29/2023]
Abstract
The current paper presents an integrated formal model of typical and atypical development based on the mechanisms of mutualism and resource competition. The mutualistic network model is extended with the dynamics of competition for limited resources, such as time and environmental factors. The proposed model generates patterns that resemble established phenomena in cognitive development: the positive manifold, developmental phases, developmental delays and lack of early indicators in atypical development, developmental regression, and "quasi-autism" caused by extreme environmental deprivation. The presented modeling framework fits a general movement towards formal theory construction in psychology. The model is easy to replicate and develop further, and we offer several avenues for future work.
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Affiliation(s)
- Jill de Ron
- Department of Psychology, University of Amsterdam, the Netherlands
| | - Marie Deserno
- Department of Psychology, University of Amsterdam, the Netherlands
| | - Donald Robinaugh
- Department of Applied Psychology, Northeastern University, USA
- Massachusetts General Hospital, USA
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, the Netherlands
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8
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Frankenhuis WE, Borsboom D, Nettle D, Roisman GI. Formalizing theories of child development: Introduction to the special section. Child Dev 2023; 94:1425-1431. [PMID: 37814543 DOI: 10.1111/cdev.14020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 10/11/2023]
Abstract
Here we introduce a Special Section of Child Development entitled "Formalizing Theories of Child Development." This Special Section features five papers that use mathematical models to advance our understanding of central questions in the study of child development. This landmark collection is timely: it signifies growing awareness that rigorous empirical bricks are not enough; we need solid theory to build the house. By stating theory in mathematical terms, formal models make concepts, assumptions, and reasoning more explicit than verbal theory does. This increases falsifiability, promotes cumulative science, and enables integration with mathematical theory in allied disciplines. The Special Section contributions cover a range of topics: the developmental origins of counting, interactions between mathematics and language development, visual exploration and word learning in infancy, referent identification by toddlers, and the emergence of typical and atypical development. All are written in an accessible manner and for a broad audience.
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Affiliation(s)
- Willem E Frankenhuis
- Evolutionary and Population Biology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
- Department of Psychology, Utrecht University, Utrecht, The Netherlands
- Max Planck Institute for the Study of Crime, Security and Law, Freiburg, Germany
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Daniel Nettle
- Institut Jean Nicod, Département d'études cognitives, École Normale Supérieure, Université PSL, EHESS, CNRS, Paris, France
| | - Glenn I Roisman
- Institute of Child Development, University of Minnesota, Minneapolis, Minnesota, USA
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9
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Burger J, Isvoranu AM, Lunansky G, Haslbeck JMB, Epskamp S, Hoekstra RHA, Fried EI, Borsboom D, Blanken TF. Reporting standards for psychological network analyses in cross-sectional data. Psychol Methods 2023; 28:806-824. [PMID: 35404629 DOI: 10.1037/met0000471] [Citation(s) in RCA: 67] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Julian Burger
- Amsterdam Centre for Urban Mental Health, University of Amsterdam
| | | | | | | | - Sacha Epskamp
- Amsterdam Centre for Urban Mental Health, University of Amsterdam
| | | | - Eiko I Fried
- Department of Clinical Psychology, Leiden University
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10
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Marsman M, Waldorp L, Borsboom D. Towards an encompassing theory of network models: Reply to Brusco, Steinley, Hoffman, Davis-Stober, and Wasserman (2019). Psychol Methods 2023; 28:757-764. [PMID: 35143218 DOI: 10.1037/met0000373] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Network models like the Ising model are increasingly used in psychological research. In a recent article published in this journal, Brusco et al. (2019) provide a critical assessment of the conditions that underlie the Ising model and the eLasso method that is commonly used to estimate it. In this commentary, we show that their main criticisms are unfounded. First, where Brusco et al. (2019) suggest that Ising models have little to do with classical network models such as random graphs, we show that they can be fruitfully connected. Second, if one makes this connection it is immediately evident that Brusco et al.'s (2019) second criticism-that the Ising model requires complete population homogeneity and does not allow for individual differences in network structure-is incorrect. In particular, we establish that if every individual has their own topology, and these individual differences instantiate a random graph model, the Ising model will hold in the population. Hence, population homogeneity is sufficient for the Ising model, but it is not necessary, as Brusco et al. (2019) suggest. Third, we address Brusco et al.'s (2019) criticism regarding the sparsity assumption that is made in common uses of the Ising model. We show that this criticism is misdirected, as it targets a particular estimation algorithm for the Ising model rather than the model itself. We also describe various established and validated approaches for estimating the Ising model for networks that violate the sparsity assumption. Finally, we outline important avenues for future research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Abstract
The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.
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Affiliation(s)
| | - Sacha Epskamp
- Department of Psychology, University of Amsterdam
- Amsterdam Centre for Urban Mental Health
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12
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Dolan CV, Borsboom D. Interpretational issues with the bifactor model: a commentary on 'Defining the p-Factor: An Empirical Test of Five Leading Theories' by Southward, Cheavens, and Coccaro. Psychol Med 2023; 53:2744-2747. [PMID: 37039112 DOI: 10.1017/s0033291723000533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
Southward, Cheavens, and Coccaro (2022, Psychological Medicine) conducted an ambitious investigation aimed at determining the nature of the general p factor of psychopathology by considering the correlation between the p factor and five candidate constructs. Generally, in this area of research, the bifactor model is preferred to the second order common factor model. In this commentary, we identify several interpretational issues concerning the bifactor model, which are based on a realistic psychometric view of latent variables. These issues may hamper the study of the nature of p factor model using the bifactor model.
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Affiliation(s)
- Conor V Dolan
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7-9, 1081 BT, Amsterdam, The Netherlands
| | - Denny Borsboom
- Department of Psychology, Faculty of Behavioral and Social Sciences, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018WS Amsterdam, The Netherlands
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13
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Maier M, van Dongen N, Borsboom D. Comparing theories with the Ising model of explanatory coherence. Psychol Methods 2023:2023-50323-001. [PMID: 36862460 DOI: 10.1037/met0000543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Theories are among the most important tools of science. Lewin (1943) already noted "There is nothing as practical as a good theory." Although psychologists discussed problems of theory in their discipline for a long time, weak theories are still widespread in most subfields. One possible reason for this is that psychologists lack the tools to systematically assess the quality of their theories. Thagard (1989) developed a computational model for formal theory evaluation based on the concept of explanatory coherence. However, there are possible improvements to Thagard's (1989) model and it is not available in software that psychologists typically use. Therefore, we developed a new implementation of explanatory coherence based on the Ising model. We demonstrate the capabilities of this new Ising model of Explanatory Coherence (IMEC) on several examples from psychology and other sciences. In addition, we implemented it in the R-package IMEC to assist scientists in evaluating the quality of their theories in practice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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14
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Chambon M, Dalege J, Borsboom D, Waldorp LJ, van der Maas HLJ, van Harreveld F. How compliance with behavioural measures during the initial phase of a pandemic develops over time: A longitudinal COVID-19 study. Br J Soc Psychol 2023; 62:302-321. [PMID: 36214155 PMCID: PMC9874881 DOI: 10.1111/bjso.12572] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/01/2022] [Indexed: 01/27/2023]
Abstract
In this longitudinal research, we adopt a complexity approach to examine the temporal dynamics of variables related to compliance with behavioural measures during the COVID-19 pandemic. Dutch participants (N = 2399) completed surveys with COVID-19-related variables five times over a period of 10 weeks (23 April-30 June 2020). With these data, we estimated within-person COVID-19 attitude networks containing a broad set of psychological variables and their relations. These networks display variables' predictive effects over time between measurements and contemporaneous effects during measurements. Results show (1) bidirectional effects between multiple variables relevant for compliance, forming potential feedback loops, and (2) a positive reinforcing structure between compliance, support for behavioural measures, involvement in the pandemic and vaccination intention. These results can explain why levels of these variables decreased throughout the course of the study. The reinforcing structure points towards potentially amplifying effects of interventions on these variables and might inform processes of polarization. We conclude that adopting a complexity approach might contribute to understanding protective behaviour in the initial phase of pandemics by combining different theoretical models and modelling bidirectional effects between variables. Future research could build upon this research by studying causality with interventions and including additional variables in the networks.
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Affiliation(s)
- Monique Chambon
- National Institute for Public Health and the Environment (RIVM)BilthovenThe Netherlands,Department of PsychologyUniversity of AmsterdamAmsterdamThe Netherlands
| | | | - Denny Borsboom
- Department of PsychologyUniversity of AmsterdamAmsterdamThe Netherlands
| | | | | | - Frenk van Harreveld
- National Institute for Public Health and the Environment (RIVM)BilthovenThe Netherlands,Department of PsychologyUniversity of AmsterdamAmsterdamThe Netherlands
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15
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Deserno MK, Fuhrmann D, Begeer S, Borsboom D, Geurts HM, Kievit RA. Longitudinal development of language and fine motor skills is correlated, but not coupled, in a childhood atypical cohort. Autism 2023; 27:133-144. [PMID: 35470698 PMCID: PMC9806469 DOI: 10.1177/13623613221086448] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
LAY ABSTRACT More and more members of the autistic community and the research field are moving away from the idea that there will be a single biological or cognitive explanation for autistic characteristics. However, little is known about the complex dynamic processes that could explain why early difficulties in the language and motor domain often go hand-in-hand. We here study how language and motor skills develop simultaneously in the British Autism Study of Infant Siblings cohort of infants, and compare the way they are linked between children with and without developmental delays. Our results suggest that improvements in one domain go hand-in-hand with improvements in the other in both groups and show no compelling evidence for group differences in how motor skills relate to language and vice versa. We did observe a larger diversity in motor and language skills at 6 months, and because we found the motor and language development to be tightly linked, this suggests that even very small early impairments can result in larger developmental delays in later childhood. Greater variability at baseline, combined with very strong correlations between the slopes, suggests that dynamic processes may amplify small differences between individuals at 6months to result into large individual differences in autism symptomatology at 36 months.
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Affiliation(s)
- Marie K Deserno
- Dr. Leo Kannerhuis and REACH-AUT, The
Netherlands,University of Amsterdam, The
Netherlands,Max Planck Institute for Human
Development, Germany,Marie K Deserno, Max Planck Institute for
Human Development, Postbus 15933, Amsterdam, 1001 NK, The Netherlands.
| | | | | | | | - Hilde M Geurts
- Dr. Leo Kannerhuis and REACH-AUT, The
Netherlands,University of Amsterdam, The
Netherlands
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16
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Abstract
Over the past decade, there has been a surge of empirical research investigating mental disorders as complex systems. In this article, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the abductive formal theory construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
| | - Oisín Ryan
- Department of Methodology and Statistics
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17
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Abstract
Over the past decade, there has been a surge of empirical research investigating mental disorders as complex systems. In this article, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the abductive formal theory construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
| | - Oisín Ryan
- Department of Methodology and Statistics
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18
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Chambon M, Dalege J, Waldorp LJ, Van der Maas HLJ, Borsboom D, van Harreveld F. Tailored interventions into broad attitude networks towards the COVID-19 pandemic. PLoS One 2022; 17:e0276439. [PMID: 36301880 PMCID: PMC9612523 DOI: 10.1371/journal.pone.0276439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 10/07/2022] [Indexed: 11/29/2022] Open
Abstract
This study examines how broad attitude networks are affected by tailored interventions aimed at variables selected based on their connectiveness with other variables. We first computed a broad attitude network based on a large-scale cross-sectional COVID-19 survey (N = 6,093). Over a period of approximately 10 weeks, participants were invited five times to complete this survey, with the third and fifth wave including interventions aimed at manipulating specific variables in the broad COVID-19 attitude network. Results suggest that targeted interventions that yield relatively strong effects on variables central to a broad attitude network have downstream effects on connected variables, which can be partially explained by the variables the interventions were aimed at. We conclude that broad attitude network structures can reveal important relations between variables that can help to design new interventions.
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Affiliation(s)
- Monique Chambon
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- * E-mail:
| | - Jonas Dalege
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Lourens J. Waldorp
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Frenk van Harreveld
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
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19
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Affiliation(s)
- Denny Borsboom
- Department of PsychologyUniversity of AmsterdamAmsterdamthe Netherlands
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20
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van Bork R, Rhemtulla M, Sijtsma K, Borsboom D. A causal theory of error scores. Psychol Methods 2022:2022-83043-001. [PMID: 35878074 DOI: 10.1037/met0000521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In modern test theory, response variables are a function of a common latent variable that represents the measured attribute, and error variables that are unique to the response variables. While considerable thought goes into the interpretation of latent variables in these models (e.g., validity research), the interpretation of error variables is typically left implicit (e.g., describing error variables as residuals). Yet, many psychometric assumptions are essentially assumptions about error and thus being able to reason about psychometric models requires the ability to reason about errors. We propose a causal theory of error as a framework that enables researchers to reason about errors in terms of the data-generating mechanism. In this framework, the error variable reflects myriad causes that are specific to an item and, together with the latent variable, determine the scores on that item. We distinguish two types of item-specific causes: characteristic variables that differ between people (e.g., familiarity with words used in the item), and circumstance variables that vary over occasions in which the item is administered (e.g., a distracting noise). We show that different assumptions about these unique causes (a) imply different psychometric models; (b) have different implications for the chance experiment that makes these models probabilistic models; and (c) have different consequences for item bias, local homogeneity, and reliability coefficient α and the test-retest correlation. The ability to reason about the causes that produce error variance puts researchers in a better position to motivate modeling choices. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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21
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22
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Abstract
When it originated in the late 19th century, psychometrics was a field with both a scientific and a social mission: Psychometrics provided new methods for research into individual differences and at the same time considered these methods a means of creating a new social order. In contrast, contemporary psychometrics-because of its highly technical nature and its limited involvement in substantive psychological research-has created the impression of being a value-free discipline. In this article, we develop a contrasting characterization of contemporary psychometrics as a value-laden discipline. We expose four such values: that individual differences are quantitative (rather than qualitative), that measurement should be objective in a specific sense, that test items should be fair, and that the utility of a model is more important than its truth. Our goal is not to criticize psychometrics for supporting these values but rather to bring them into the open and to show that they are not inevitable and are in need of systematic evaluation.
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Affiliation(s)
| | | | - Anna Alexandrova
- Department of History and Philosophy of Science, King’s College, University of Cambridge
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23
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Roefs A, Fried EI, Kindt M, Martijn C, Elzinga B, Evers AW, Wiers RW, Borsboom D, Jansen A. A new science of mental disorders: Using personalised, transdiagnostic, dynamical systems to understand, model, diagnose and treat psychopathology. Behav Res Ther 2022; 153:104096. [DOI: 10.1016/j.brat.2022.104096] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 03/29/2022] [Accepted: 04/08/2022] [Indexed: 12/18/2022]
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24
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Dekker MM, Blanken TF, Dablander F, Ou J, Borsboom D, Panja D. Quantifying agent impacts on contact sequences in social interactions. Sci Rep 2022; 12:3483. [PMID: 35241710 PMCID: PMC8894368 DOI: 10.1038/s41598-022-07384-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 02/10/2022] [Indexed: 01/12/2023] Open
Abstract
Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions—since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time—analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called contact sequence centrality, which quantifies the impact of an individual on the contact sequences, reflecting the individual’s behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential ‘behavioral super-spreaders’. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions.
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Affiliation(s)
- Mark M Dekker
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands. .,Centre for Complex Systems Studies, Utrecht University, Minnaertgebouw, Leuvenlaan 4, 3584 CE, Utrecht, The Netherlands.
| | - Tessa F Blanken
- Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 VZ, Amsterdam, The Netherlands
| | - Fabian Dablander
- Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 VZ, Amsterdam, The Netherlands
| | - Jiamin Ou
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands.,Department of Sociology, Utrecht University, Padualaan 14, 3584 CH, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 VZ, Amsterdam, The Netherlands
| | - Debabrata Panja
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands.,Centre for Complex Systems Studies, Utrecht University, Minnaertgebouw, Leuvenlaan 4, 3584 CE, Utrecht, The Netherlands
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25
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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.
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Affiliation(s)
- Denny Borsboom
- Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands
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26
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Dablander F, Heesterbeek H, Borsboom D, Drake JM. Overlapping timescales obscure early warning signals of the second COVID-19 wave. Proc Biol Sci 2022; 289:20211809. [PMID: 35135355 PMCID: PMC8825995 DOI: 10.1098/rspb.2021.1809] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/13/2022] [Indexed: 11/12/2022] Open
Abstract
Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.
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Affiliation(s)
- Fabian Dablander
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Hans Heesterbeek
- Department of Population Health Sciences, Utrecht University, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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27
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Abstract
Psychotic and autistic symptoms are related to social functioning in individuals with psychotic disorders (PD). The present study used a network approach to (1) evaluate the interactions between autistic symptoms, psychotic symptoms, and social functioning, and (2) investigate whether relations are similar in individuals with and without PD. We estimated an undirected network model in a sample of 504 PD, 572 familial risk for psychosis (FR), and 337 typical comparisons (TC), with a mean age of 34.9 years. Symptoms were assessed with the Autism Spectrum Quotient (AQ; 5 nodes) and the Community Assessment of Psychic Experiences (CAPE; 9 nodes). Social functioning was measured with the Social Functioning Scale (SFS; 7 nodes). We identified statistically significant differences between the FR and PD samples in global strength (P < .001) and network structure (P < .001). Our results show autistic symptoms (social interaction nodes) are negatively and more closely related to social functioning (withdrawal, interpersonal behavior) than psychotic symptoms. More and stronger connections between nodes were observed for the PD network than for FR and TC networks, while the latter 2 were similar in density (P = .11) and network structure (P = .19). The most central items in strength for PD were bizarre experiences, social skills, and paranoia. In conclusion, specific autistic symptoms are negatively associated with social functioning across the psychosis spectrum, but in the PD network symptoms may reinforce each other more easily. These findings emphasize the need for increased clinical awareness of comorbid autistic symptoms in psychotic individuals.
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Affiliation(s)
- Adela-Maria Isvoranu
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Tim Ziermans
- Department of Psychology, Brain and Cognition, Dutch Autism and ADHD Research Center (d’Arc), University of Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Frederike Schirmbeck
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Arkin Mental Health Care, Amsterdam, The Netherlands
| | - Denny Borsboom
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Hilde M Geurts
- Department of Psychology, Brain and Cognition, Dutch Autism and ADHD Research Center (d’Arc), University of Amsterdam, Amsterdam, The Netherlands
| | - Lieuwe de Haan
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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28
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Bathelt J, Geurts HM, Borsboom D. More than the sum of its parts: Merging network psychometrics and
network neuroscience with application in autism. Netw Neurosci 2021; 6:445-466. [PMID: 35733421 PMCID: PMC9207995 DOI: 10.1162/netn_a_00222] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/08/2021] [Indexed: 11/05/2022] Open
Abstract
Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. In this paper, we introduce a methodology for combining network psychometrics and network neuroscience. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10–21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates. The article introduces a method to combine common practices in network psychometrics and network neuroimaging. Namely, we use the unique variance in behavioural measures as regressors to identify unique neural correlates. This enables the description of brain-level and behavioural-level data into a joint network while keeping the dimensionality of the results manageable and interpretable. We illustrate this approach by showing the network of autistic traits and their correlates in resting-state functional connectivity.
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Affiliation(s)
- Joe Bathelt
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey TW20 0EX, United Kingdom
- Department of Psychology, University of Amsterdam
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29
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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: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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30
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Lunansky G, Naberman J, van Borkulo CD, Chen C, Wang L, Borsboom D. Intervening on psychopathology networks: Evaluating intervention targets through simulations. Methods 2021; 204:29-37. [PMID: 34793976 DOI: 10.1016/j.ymeth.2021.11.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/30/2021] [Accepted: 11/11/2021] [Indexed: 01/16/2023] Open
Abstract
Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods.
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Affiliation(s)
- Gabriela Lunansky
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands.
| | - Jasper Naberman
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands
| | - Claudia D van Borkulo
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands; Centre for Urban Mental Health, University of Amsterdam, The Netherlands
| | - Chen Chen
- Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Li Wang
- Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands
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31
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van der Wal JM, van Borkulo CD, Deserno MK, Breedvelt JJF, Lees M, Lokman JC, Borsboom D, Denys D, van Holst RJ, Smidt MP, Stronks K, Lucassen PJ, van Weert JCM, Sloot PMA, Bockting CL, Wiers RW. Advancing urban mental health research: from complexity science to actionable targets for intervention. Lancet Psychiatry 2021; 8:991-1000. [PMID: 34627532 DOI: 10.1016/s2215-0366(21)00047-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/27/2021] [Accepted: 01/28/2021] [Indexed: 12/30/2022]
Abstract
Urbanisation and common mental disorders (CMDs; ie, depressive, anxiety, and substance use disorders) are increasing worldwide. In this Review, we discuss how urbanicity and risk of CMDs relate to each other and call for a complexity science approach to advance understanding of this interrelationship. We did an ecological analysis using data on urbanicity and CMD burden in 191 countries. We found a positive, non-linear relationship with a higher CMD prevalence in more urbanised countries, particularly for anxiety disorders. We also did a review of meta-analytic studies on the association between urban factors and CMD risk. We identified factors relating to the ambient, physical, and social urban environment and showed differences per diagnosis of CMDs. We argue that factors in the urban environment are likely to operate as a complex system and interact with each other and with individual city inhabitants (including their psychological and neurobiological characteristics) to shape mental health in an urban context. These interactions operate on various timescales and show feedback loop mechanisms, rendering system behaviour characterised by non-linearity that is hard to predict over time. We present a conceptual framework for future urban mental health research that uses a complexity science approach. We conclude by discussing how complexity science methodology (eg, network analyses, system-dynamic modelling, and agent-based modelling) could enable identification of actionable targets for treatment and policy, aimed at decreasing CMD burdens in an urban context.
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Affiliation(s)
- Junus M van der Wal
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Department of Psychiatry, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, Netherlands; Department of Public Health, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, Netherlands
| | - Claudia D van Borkulo
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Marie K Deserno
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands; Centre for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Josefien J F Breedvelt
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; National Centre for Social Research, London, UK; Department of Psychiatry, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, Netherlands
| | - Mike Lees
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - John C Lokman
- Department of Psychiatry, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, Netherlands
| | - Denny Borsboom
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Damiaan Denys
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Department of Psychiatry, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, Netherlands
| | - Ruth J van Holst
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Department of Psychiatry, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, Netherlands
| | - Marten P Smidt
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Karien Stronks
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Department of Public Health, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, Netherlands
| | - Paul J Lucassen
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Julia C M van Weert
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Amsterdam School of Communication Research/ASCoR, University of Amsterdam, Amsterdam, Netherlands
| | - Peter M A Sloot
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Institute for Advanced Study, University of Amsterdam, Amsterdam, Netherlands; National Centre for Cognitive Science, ITMO University, St Petersburg, Russia
| | - Claudi L Bockting
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Department of Psychiatry, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, Netherlands.
| | - Reinout W Wiers
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands; Department of Developmental Psychology, University of Amsterdam, Amsterdam, Netherlands
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32
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von Klipstein L, Borsboom D, Arntz A. The exploratory value of cross-sectional partial correlation networks: Predicting relationships between change trajectories in borderline personality disorder. PLoS One 2021; 16:e0254496. [PMID: 34329316 PMCID: PMC8323921 DOI: 10.1371/journal.pone.0254496] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 06/28/2021] [Indexed: 02/04/2023] Open
Abstract
Objective Within the network approach to psychopathology, cross-sectional partial correlation networks have frequently been used to estimate relationships between symptoms. The resulting relationships have been used to generate hypotheses about causal links between symptoms. In order to justify such exploratory use of partial correlation networks, one needs to assume that the between-subjects relationships in the network approximate systematic within-subjects relationships, which are in turn the results of some within-subjects causal mechanism. If this assumption holds, relationships in the network should be mirrored by relationships between symptom changes; if links in networks approximate systematic within-subject relationships, change in a symptom should relate to change in connected symptoms. Method To investigate this implication, we combined longitudinal data on the Borderline Personality Disorder Severity Index from four samples of borderline personality disorder patients (N = 683). We related parameters from baseline partial correlation networks of symptoms to relationships between change trajectories of these symptoms. Results Across multiple levels of analysis, our results showed that parameters from baseline partial correlation networks are strongly predictive of relationships between change trajectories. Conclusions By confirming its implication, our results support the idea that cross-sectional partial correlation networks hold a relevant amount of information about systematic within-subjects relationships and thereby have exploratory value to generate hypotheses about the causal dynamics between symptoms.
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Affiliation(s)
- Lino von Klipstein
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- * E-mail:
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Arnoud Arntz
- Department of Clinical Psychology, University of Amsterdam, Amsterdam, The Netherlands
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Blanken TF, Bathelt J, Deserno MK, Voge L, Borsboom D, Douw L. Connecting brain and behavior in clinical neuroscience: A network approach. Neurosci Biobehav Rev 2021; 130:81-90. [PMID: 34324918 DOI: 10.1016/j.neubiorev.2021.07.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/14/2021] [Accepted: 07/23/2021] [Indexed: 11/16/2022]
Abstract
In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.
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Affiliation(s)
- Tessa F Blanken
- Department of Psychological Methods, University of Amsterdam, 1018 WT, Amsterdam, the Netherlands.
| | - Joe Bathelt
- Royal Holloway, University of London, Department of Psychology, Egham, Surrey, TW20 0EX, United Kingdom
| | - Marie K Deserno
- Max Planck Institute for Human Development, 14195, Berlin, Germany
| | - Lily Voge
- Department of Anatomy and Neurosciences, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HZ, Amsterdam, the Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, 1018 WT, Amsterdam, the Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HZ, Amsterdam, the Netherlands; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusets General Hospital, Boston, MA, 02129, USA
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Tanis CC, Leach NM, Geiger SJ, Nauta FH, Dablander F, van Harreveld F, de Wit S, Kanters G, Knoppers J, Markus DAW, Bouten RRM, Oostvogel QH, Boersma MJ, van der Steenhoven MV, Borsboom D, Blanken TF. Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic. Sci Data 2021; 8:179. [PMID: 34267219 PMCID: PMC8282783 DOI: 10.1038/s41597-021-00971-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/10/2021] [Indexed: 11/17/2022] Open
Abstract
In the absence of a vaccine, social distancing behaviour is pivotal to mitigate COVID-19 virus spread. In this large-scale behavioural experiment, we gathered data during Smart Distance Lab: The Art Fair (n = 839) between August 28 and 30, 2020 in Amsterdam, the Netherlands. We varied walking directions (bidirectional, unidirectional, and no directions) and supplementary interventions (face mask and buzzer to alert visitors of 1.5 metres distance). We captured visitors’ movements using cameras, registered their contacts (defined as within 1.5 metres) using wearable sensors, and assessed their attitudes toward COVID-19 as well as their experience during the event using questionnaires. We also registered environmental measures (e.g., humidity). In this paper, we describe this unprecedented, multi-modal experimental data set on social distancing, including psychological, behavioural, and environmental measures. The data set is available on figshare and in a MySQL database. It can be used to gain insight into (attitudes toward) behavioural interventions promoting social distancing, to calibrate pedestrian models, and to inform new studies on behavioural interventions. Measurement(s) | Proximity • Movement • Attitudes and beliefs relating to COVID-19 • Indoor environment measures | Technology Type(s) | Ultra-wideband technology • Camera Device • questionnaire • environment sensor | Factor Type(s) | Walking direction • Wearing of face masks • Proximity buzzer | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Environment | public exhibition | Sample Characteristic - Location | Kingdom of the Netherlands |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14312180
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Affiliation(s)
- Charlotte C Tanis
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands.
| | - Nina M Leach
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands
| | - Sandra J Geiger
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands
| | - Floor H Nauta
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands
| | - Fabian Dablander
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands
| | - Frenk van Harreveld
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands.,National Institute for Public Health and the Environment (RIVM), Bilthoven, 3721, MA, the Netherlands
| | - Sanne de Wit
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands
| | | | - Jop Knoppers
- Centillien B.V., Mierlo, 5731, SG, the Netherlands
| | | | - Rick R M Bouten
- Focus Technologies B.V., Eindhoven, 5657, EW, the Netherlands
| | | | | | | | - Denny Borsboom
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands
| | - Tessa F Blanken
- University of Amsterdam, Department of Psychology, Amsterdam, 1018, WS, the Netherlands.
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35
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Blanken TF, Borsboom D, Penninx BW, Van Someren EJ. Network outcome analysis identifies difficulty initiating sleep as a primary target for prevention of depression: a 6-year prospective study. Sleep 2021; 43:5650354. [PMID: 31789381 PMCID: PMC7215262 DOI: 10.1093/sleep/zsz288] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/30/2019] [Indexed: 01/01/2023] Open
Abstract
Study Objectives Major depressive disorder (MDD) is the leading cause of disability worldwide. Its high recurrence rate calls for prevention of first-onset MDD. Although meta-analysis suggested insomnia as the strongest modifiable risk factor, previous studies insufficiently addressed that insomnia might also occur as a residual symptom of unassessed prior depression, or as a comorbid complaint secondary to other depression risks. Methods In total, 768 participants from the Netherlands Study of Depression and Anxiety who were free from current and lifetime MDD were followed-up for four repeated assessments, spanning 6 years in total. We performed separate Cox proportional hazard analyses to evaluate whether baseline insomnia severity, short-sleep duration, and individual insomnia complaints prospectively predicted first-onset MDD during follow-up. The novel method of network outcome analysis (NOA) allowed us to sort out whether there is any direct predictive value of individual insomnia complaints among several other complaints that are associated with insomnia. Results Over 6-year follow-up, 141 (18.4%) were diagnosed with first-onset MDD. Insomnia severity but not sleep duration predicted first-onset MDD (HR = 1.11, 95% CI: 1.07–1.15), and this was driven solely by the insomnia complaint difficulty initiating sleep (DIS) (HR = 1.10, 95% CI: 1.04–1.16). NOA likewise identified DIS only to directly predict first-onset MDD, independent of four other associated depression complaints. Conclusions We showed prospectively that DIS is a risk factor for first-onset MDD. Among the different other insomnia symptoms, the specific treatment of DIS might be the most sensible target to combat the global burden of depression through prevention.
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Affiliation(s)
- Tessa F Blanken
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Brenda Wjh Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Eus Jw Van Someren
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.,Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.,Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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36
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Lunansky G, van Borkulo CD, Haslbeck JMB, van der Linden MA, Garay CJ, Etchevers MJ, Borsboom D. The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors. Front Psychiatry 2021; 12:640658. [PMID: 33815173 PMCID: PMC8012560 DOI: 10.3389/fpsyt.2021.640658] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/22/2021] [Indexed: 12/27/2022] Open
Abstract
Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.
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Affiliation(s)
- Gabriela Lunansky
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Claudia D. van Borkulo
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands
| | - Jonas M. B. Haslbeck
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Max A. van der Linden
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Cristian J. Garay
- Faculty of Psychology, University of Buenos Aires, Buenos Aires, Argentina
| | | | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
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Abstract
Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research, this is often implausible. In this article, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct MNMs and propose an ℓ1-regularized nodewise regression approach to estimate them. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification.
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Wijsen LD, Borsboom D. Perspectives on Psychometrics Interviews with 20 Past Psychometric Society Presidents. Psychometrika 2021; 86:327-343. [PMID: 33770319 PMCID: PMC8035107 DOI: 10.1007/s11336-021-09752-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 03/13/2020] [Indexed: 06/12/2023]
Abstract
In this article, we present the findings of an oral history project on the past, present, and future of psychometrics, as obtained through structured interviews with twenty past Psychometric Society presidents. Perspectives on how psychometrics should be practiced vary strongly. Some presidents are psychology-oriented, whereas others have a more mathematical or statistical approach. The originally strong relationship between psychometrics and psychology has weakened, and contemporary psychometrics has become a diverse and multifaceted discipline. The presidents are confident psychometrics will continue to be relevant but believe psychometrics needs to become better at selling its strong points to relevant research areas. We recommend for psychometrics to cherish its plurality and make its goals and priorities explicit.
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Affiliation(s)
- Lisa D Wijsen
- Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018, WS, Amsterdam, The Netherlands.
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018, WS, Amsterdam, The Netherlands
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van Bork R, Rhemtulla M, Waldorp LJ, Kruis J, Rezvanifar S, Borsboom D. Latent Variable Models and Networks: Statistical Equivalence and Testability. Multivariate Behav Res 2021; 56:175-198. [PMID: 31617420 DOI: 10.1080/00273171.2019.1672515] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Networks are gaining popularity as an alternative to latent variable models for representing psychological constructs. Whereas latent variable approaches introduce unobserved common causes to explain the relations among observed variables, network approaches posit direct causal relations between observed variables. While these approaches lead to radically different understandings of the psychological constructs of interest, recent articles have established mathematical equivalences that hold between network models and latent variable models. We argue that the fact that for any model from one class there is an equivalent model from the other class does not mean that both models are equally plausible accounts of the data-generating mechanism. In many cases the constraints that are meaningful in one framework translate to constraints in the equivalent model that lack a clear interpretation in the other framework. Finally, we discuss three diverging predictions for the relation between zero-order correlations and partial correlations implied by sparse network models and unidimensional factor models. We propose a test procedure that compares the likelihoods of these models in light of these diverging implications. We use an empirical example to illustrate our argument.
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Isvoranu AM, Abdin E, Chong SA, Vaingankar J, Borsboom D, Subramaniam M. Extended network analysis: from psychopathology to chronic illness. BMC Psychiatry 2021; 21:119. [PMID: 33639891 PMCID: PMC7913444 DOI: 10.1186/s12888-021-03128-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 02/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Understanding complex associations between psychopathology and chronic illness is instrumental in facilitating both research and treatment progress. The current study is the first and only network-based study to provide such an encompassing view of unique associations between a multitude of mental and physical health-related domains. METHODS The current analyses were based on the Singapore Mental Health Study, a cross-sectional study of adult Singapore residents. The study sample consisted of 6616 respondents, of which 49.8% were male and 50.2% female. A network structure was constructed to examine associations between psychopathology, alcohol use, gambling, major chronic conditions, and functioning. RESULTS The network structure identified what we have labeled a Cartesian graph: a network visibly split into a psychopathological domain and a physical health domain. The borders between these domains were fuzzy and bridged by various cross-domain associations, with functioning items playing an important role in bridging chronic conditions to psychopathology. CONCLUSIONS Current results deliver a comprehensive overview of the complex relation between psychopathology, functioning, and chronic illness, highlighting potential pathways to comorbidity.
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Affiliation(s)
- Adela-Maria Isvoranu
- Department of Psychology, Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129B, 1018 WT, Amsterdam, The Netherlands.
| | - Edimansyah Abdin
- Research Division, Institute of Mental Health, Singapore, Singapore
| | - Siow Ann Chong
- Research Division, Institute of Mental Health, Singapore, Singapore
| | | | - Denny Borsboom
- Department of Psychology, Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129B, 1018 WT, Amsterdam, The Netherlands
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Aalbers G, Engels T, Haslbeck JMB, Borsboom D, Arntz A. The network structure of schema modes. Clin Psychol Psychother 2021; 28:1065-1078. [PMID: 33606318 PMCID: PMC8596577 DOI: 10.1002/cpp.2577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 11/16/2020] [Accepted: 02/10/2021] [Indexed: 11/10/2022]
Abstract
A fundamental question in psychotherapy is whether interventions should target client problems (i.e., problem-focused approaches) or client strengths (i.e., strength-focused approaches). In this study, we first propose to address this question from a network perspective on schema modes (i.e., healthy or dysfunctional patterns of co-occurring emotions, cognitions, and behaviours). From this perspective, schema modes mutually influence each other (e.g., healthy modes reduce dysfunctional modes). Recent evidence suggests that changes in modes that are strongly associated to other modes (i.e., central modes) could be associated with greater treatment effects. We therefore suggest research should investigate the relative centrality of healthy and dysfunctional modes. To make an exploratory start, we investigated the cross-sectional network structure of schema modes in a clinical (comprising individuals diagnosed with paranoid, narcissistic, histrionic, and Cluster C personality disorders) and non-clinical sample. Results showed that, in both samples, the Healthy Adult was significantly less central than several dysfunctional modes (e.g., Undisciplined Child and Abandoned and Abused Child). Although our study cannot draw causal conclusions, this finding could suggest that weakening dysfunctional modes (compared to strengthening the Healthy Adult) might be more effective in decreasing other dysfunctional modes. Our study further indicates that several schema modes are negatively associated, which could suggest that decreasing one might increase another. Finally, the Healthy Adult was among the modes that most strongly discriminated between clinical and non-clinical individuals. Longitudinal and experimental research into the network structure of schema modes is required to further clarify the relative influence of schema modes.
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Affiliation(s)
- George Aalbers
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
| | - Tiarah Engels
- Department of Social Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Jonas M B Haslbeck
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands
| | - Arnoud Arntz
- Department of Clinical Psychology, University of Amsterdam, Amsterdam, Netherlands
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Ong HL, Isvoranu AM, Schirmbeck F, McGuire P, Valmaggia L, Kempton MJ, van der Gaag M, Riecher-Rössler A, Bressan RA, Barrantes-Vidal N, Nelson B, Amminger GP, McGorry P, Pantelis C, Krebs MO, Nordentoft M, Glenthøj B, Ruhrmann S, Sachs G, Rutten BPF, van Os J, de Haan L, Borsboom D. Obsessive-Compulsive Symptoms and Other Symptoms of the At-risk Mental State for Psychosis: A Network Perspective. Schizophr Bull 2021; 47:1018-1028. [PMID: 33595089 PMCID: PMC8266672 DOI: 10.1093/schbul/sbaa187] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The high prevalence of obsessive-compulsive symptoms (OCS) among subjects at Ultra-High Risk (UHR) for psychosis is well documented. However, the network structure spanning the relations between OCS and symptoms of the at risk mental state for psychosis as assessed with the Comprehensive Assessment of At Risk Mental States (CAARMS) has not yet been investigated. This article aimed to use a network approach to investigate the associations between OCS and CAARMS symptoms in a large sample of individuals with different levels of risk for psychosis. METHOD Three hundred and forty-one UHR and 66 healthy participants were included, who participated in the EU-GEI study. Data analysis consisted of constructing a network of CAARMS symptoms, investigating central items in the network, and identifying the shortest pathways between OCS and positive symptoms. RESULTS Strong associations between OCS and anxiety, social isolation and blunted affect were identified. Depression was the most central symptom in terms of the number of connections, and anxiety was a key item in bridging OCS to other symptoms. Shortest paths between OCS and positive symptoms revealed that unusual thought content and perceptual abnormalities were connected mainly via anxiety, while disorganized speech was connected via blunted affect and cognitive change. CONCLUSIONS Findings provide valuable insight into the central role of depression and the potential connective component of anxiety between OCS and other symptoms of the network. Interventions specifically aimed to reduce affective symptoms might be crucial for the development and prospective course of symptom co-occurrence.
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Affiliation(s)
- Hui Lin Ong
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands
| | - Adela-Maria Isvoranu
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands,To whom correspondence should be addressed; Department of Psychology, Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129B, 1018 WT Amsterdam, the Netherlands; tel: +31 (0)20 8913639,
| | - Frederike Schirmbeck
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands,Arkin, Institute for Mental Health, Amsterdam, the Netherlands
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England
| | - Lucia Valmaggia
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England
| | - Mark van der Gaag
- Amsterdam Public Mental Health Research Institute, Department of Clinical Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Rodrigo A Bressan
- LiNC-Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Neus Barrantes-Vidal
- Departament de Psicologia Clínica i de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain,Fundació Sanitària Sant Pere Claver, Spanish Mental Health Research Network (CIBERSAM), Spain
| | - Barnaby Nelson
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Patrick McGorry
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Carlton South, Victoria, Australia
| | - Marie-Odile Krebs
- University of Paris, GHU-Paris, Sainte-Anne, C’JAAD, Inserm U1266, Institut de Psychiatrie (CNRS 3557), Paris, France
| | - Merete Nordentoft
- Mental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Birte Glenthøj
- Centre for Neuropsychiatric Schizophrenia Research (CNSR) & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Gabriele Sachs
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Jim van Os
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands,Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Lieuwe de Haan
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands,Arkin, Institute for Mental Health, Amsterdam, the Netherlands
| | - Denny Borsboom
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands
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Borsboom D, van der Maas HLJ, Dalege J, Kievit RA, Haig BD. Theory Construction Methodology: A Practical Framework for Building Theories in Psychology. Perspect Psychol Sci 2021; 16:756-766. [PMID: 33593167 DOI: 10.1177/1745691620969647] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
This article aims to improve theory formation in psychology by developing a practical methodology for constructing explanatory theories: theory construction methodology (TCM). TCM is a sequence of five steps. First, the theorist identifies a domain of empirical phenomena that becomes the target of explanation. Second, the theorist constructs a prototheory, a set of theoretical principles that putatively explain these phenomena. Third, the prototheory is used to construct a formal model, a set of model equations that encode explanatory principles. Fourth, the theorist investigates the explanatory adequacy of the model by formalizing its empirical phenomena and assessing whether it indeed reproduces these phenomena. Fifth, the theorist studies the overall adequacy of the theory by evaluating whether the identified phenomena are indeed reproduced faithfully and whether the explanatory principles are sufficiently parsimonious and substantively plausible. We explain TCM with an example taken from research on intelligence (the mutualism model of intelligence), in which key elements of the method have been successfully implemented. We discuss the place of TCM in the larger scheme of scientific research and propose an outline for a university curriculum that can systematically educate psychologists in the process of theory formation.
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Affiliation(s)
| | | | | | - Rogier A Kievit
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center.,Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge
| | - Brian D Haig
- Department of Psychology, University of Canterbury
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Deserno MK, Borsboom D, Begeer S, van Bork R, Hinne M, Geurts HM. Highways to happiness for autistic adults? Perceived causal relations among clinicians. PLoS One 2020; 15:e0243298. [PMID: 33320901 PMCID: PMC7737981 DOI: 10.1371/journal.pone.0243298] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 11/18/2020] [Indexed: 11/19/2022] Open
Abstract
The network approach to psychological phenomena advances our understanding of the interrelations between autism and well-being. We use the Perceived Causal Relations methodology in order to (i) identify perceived causal pathways in the well-being system, (ii) validate networks based on self-report data, and (iii) quantify and integrate clinical expertise in autism research. Trained clinicians served as raters (N = 29) completing 374 cause-effects ratings of 34 variables on well-being and symptomatology. A subgroup (N = 16) of raters chose intervention targets in the resulting network which we found to match the respective centrality of nodes. Clinicians' perception of causal relations was similar to the interrelatedness found in self-reported client data (N = 323). We present a useful tool for translating clinical expertise into quantitative information enabling future research to integrate this in scientific studies.
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Affiliation(s)
- Marie K. Deserno
- Dr. Leo Kannerhuis and REACH-AUT, Doorwerth, The Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Sander Begeer
- Section Clinical Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Riet van Bork
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Max Hinne
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Hilde M. Geurts
- Dr. Leo Kannerhuis and REACH-AUT, Doorwerth, The Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
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Lunansky G, van Borkulo C, Borsboom D. Personality, Resilience, and Psychopathology: A Model for the Interaction between Slow and Fast Network Processes in the Context of Mental Health. Eur J Pers 2020. [DOI: 10.1002/per.2263] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviours, and environments). Here, we connect these theoretical strands in an overarching personality–resilience–psychopathology model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item ‘I often feel blue’ measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom ‘depressed mood’ in the psychopathology network. Conversely, events at the level of the fast–evolving psychopathology network (e.g. a depressive episode) can influence the slow–evolving personality variables (e.g. by increasing feelings of worthlessness). We apply the theory to neuroticism and major depressive disorder. Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive simulation tool. Implications for research into the relationship between personality and psychopathology are discussed. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
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Affiliation(s)
- Gabriela Lunansky
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Claudia van Borkulo
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
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Isvoranu AM, Guloksuz S, Epskamp S, van Os J, Borsboom D. Toward incorporating genetic risk scores into symptom networks of psychosis. Psychol Med 2020; 50:636-643. [PMID: 30867074 PMCID: PMC7093319 DOI: 10.1017/s003329171900045x] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 02/14/2019] [Accepted: 02/18/2019] [Indexed: 11/16/2022]
Abstract
BACKGROUND Psychosis spectrum disorder is a heterogeneous, multifactorial clinical phenotype, known to have a high heritability, only a minor portion of which can be explained by molecular measures of genetic variation. This study proposes that the identification of genetic variation underlying psychotic disorder may have suffered due to issues in the psychometric conceptualization of the phenotype. Here we aim to open a new line of research into the genetics of mental disorders by explicitly incorporating genes into symptom networks. Specifically, we investigate whether links between a polygenic risk score (PRS) for schizophrenia and measures of psychosis proneness can be identified in a network model. METHODS We analyzed data from n = 2180 subjects (controls, patients diagnosed with a non-affective psychotic disorder, and the first-degree relatives of the patients). A network structure was computed to examine associations between the 42 symptoms of the Community Assessment of Psychic Experiences (CAPE) and the PRS for schizophrenia. RESULTS The resulting network shows that the PRS is directly connected to the spectrum of positive and depressive symptoms, with the items conspiracy and no future being more often located on predictive pathways from PRS to other symptoms. CONCLUSIONS To our knowledge, the current exploratory study provides a first application of the network framework to the field of behavior genetics research. This allows for a novel outlook on the investigation of the relations between genome-wide association study-based PRSs and symptoms of mental disorders, by focusing on the dependencies among variables.
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Affiliation(s)
- Adela-Maria Isvoranu
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Sacha Epskamp
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Jim van Os
- Utrecht University Medical Centre, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
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Abstract
Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect-program theories, constructionist theories, and appraisal theories. Integrating these broad classes of theories into a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we delineate properties of emotions stated by emotion theories and investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow (a) identifying distinct emotions (central in affect-program theories), (b) between- and within-person variations of emotions (central in constructionist theories), and (c) causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models-in which emotions are conceptualized as unobservable causes or consequences of emotion components-cannot account for all properties. Conversely, a psychometric network model-in which emotions are conceptualized as systems of causally interacting emotion components-accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.
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Affiliation(s)
- Jens Lange
- Psychology Research Institute, University of
Amsterdam
| | - Jonas Dalege
- Psychology Research Institute, University of
Amsterdam
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49
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Abstract
The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.
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Affiliation(s)
- Donald J. Robinaugh
- Massachusetts General Hospital, Department of Psychiatry
- Harvard Medical School
| | | | - Emma R. Toner
- Massachusetts General Hospital, Department of Psychiatry
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Miers AC, Weeda WD, Blöte AW, Cramer AOJ, Borsboom D, Westenberg PM. A cross-sectional and longitudinal network analysis approach to understanding connections among social anxiety components in youth. J Abnorm Psychol 2019; 129:82-91. [PMID: 31697140 DOI: 10.1037/abn0000484] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
As proposed in a prominent developmental model, social anxiety has different manifestations: social fear, shy temperament, anxious cognitions, and avoidance of social situations. Drawing from this model, we used the network approach to psychopathology to gain a detailed understanding of specific social anxiety components and their associations. The current article investigated (a) how social anxiety components are interconnected within a network, and (b) the consistency of the network over time, in a community sample of children and adolescents. Data from 3 waves of a longitudinal study were used. At Time 1 (T1) the total sample comprised 331 participants (Mage = 13.34 years); at Time 3 (T3) there were 236 participants (Mage = 17.48 years). Social anxiety components were assessed with self-report questionnaires. Networks of 15 nodes (i.e., components) were estimated. Network analysis of T1 components revealed 4 communities: cognitive, social-emotional, avoidance of performance, and avoidance of interaction situations. There were no direct connections between the cognitive and behavioral communities; social-emotional nodes appeared to act as bridge components between the 2 communities. A similar pattern of component associations and communities was found in the T2 and T3 networks, and the longitudinal network incorporating node change trajectories. Networks were estimated on group-level observational data and conclusions about cause-effect relationships are tentative. Although the sample size decreased across the 3 waves, the reliability of parameter estimates were minimally affected. Findings attest to the potential value of applying the network approach to investigate the pattern of associations among social anxiety components in youth. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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