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      Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

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          Abstract

          Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network , in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network , in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.

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          Most cited references41

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          A new method for constructing networks from binary data

          Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.
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            Generalized Network Psychometrics: Combining Network and Latent Variable Models

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              Mental Disorders as Causal Systems: A Network Approach to Posttraumatic Stress Disorder

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                Author and article information

                Journal
                Clin Psychol Sci
                Clin Psychol Sci
                CPX
                spcpx
                Clinical Psychological Science
                SAGE Publications (Sage CA: Los Angeles, CA )
                2167-7026
                2167-7034
                19 January 2018
                May 2018
                : 6
                : 3
                : 416-427
                Affiliations
                [1 ]Department of Psychological Methods, University of Amsterdam
                [2 ]Department of Psychiatry, Interdisciplinary Center for Psychopathology and Emotion Regulation, University Medical Center Groningen, University of Groningen
                [3 ]Neuroimaging Center, Department of Neuroscience, University of Groningen, University Medical Center Groningen
                [4 ]Department of Methodology and Statistics, School of Social and Behavioral Sciences, Tilburg University
                Author notes
                [*]Sacha Epskamp, Department of Psychology, Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands E-mail: sacha.epskamp@ 123456gmail.com
                Author information
                https://orcid.org/0000-0003-4884-8118
                Article
                10.1177_2167702617744325
                10.1177/2167702617744325
                5952299
                29805918
                2e45df72-a2fb-4d66-b07a-65f6133f71cc
                © The Author(s) 2018

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 19 March 2017
                : 25 October 2017
                Categories
                Theoretical/Methodological/Review Article
                Custom metadata
                open-data
                open-materials

                causality,depression,psychotherapy,longitudinal methods,network analysis

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