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A Network View on Psychiatric Disorders: Network Clusters of Symptoms as Elementary Syndromes of Psychopathology

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  • Rutger Goekoop
  • Jaap G Goekoop

Abstract

Introduction: The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. Aim: To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. Methods: 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique. Results: In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "DEPRESSION", "MANIA", “ANXIETY”, "PSYCHOSIS", "RETARDATION", and "BEHAVIORAL DISORGANIZATION". Network metrics were used to quantify the continuities between the elementary syndromes. Conclusion: We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a ‘Psychopathology Web’. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.

Suggested Citation

  • Rutger Goekoop & Jaap G Goekoop, 2014. "A Network View on Psychiatric Disorders: Network Clusters of Symptoms as Elementary Syndromes of Psychopathology," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-47, November.
    • Handle: RePEc:plo:pone00:0112734
    DOI: 10.1371/journal.pone.0112734
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    References listed on IDEAS

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    1. Rutger Goekoop & Jaap G Goekoop & H Steven Scholte, 2012. "The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-18, December.
    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    3. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    4. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
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    1. Michael A. Freeman & Paige J. Staudenmaier & Mackenzie R. Zisser & Lisa Abdilova Andresen, 2019. "The prevalence and co-occurrence of psychiatric conditions among entrepreneurs and their families," Small Business Economics, Springer, vol. 53(2), pages 323-342, August.
    2. Simon Foster & Meichun Mohler-Kuo, 2020. "The proportion of non-depressed subjects in a study sample strongly affects the results of psychometric analyses of depression symptoms," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-13, July.
    3. Maarten Bak & Marjan Drukker & Laila Hasmi & Jim van Os, 2016. "An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-15, September.

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