The Small World of Psychopathology

Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
PLoS ONE (Impact Factor: 3.53). 11/2011; 6(11):e27407. DOI: 10.1371/journal.pone.0027407
Source: PubMed

ABSTRACT Mental disorders are highly comorbid: people having one disorder are likely to have another as well. We explain empirical comorbidity patterns based on a network model of psychiatric symptoms, derived from an analysis of symptom overlap in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV).
We show that a) half of the symptoms in the DSM-IV network are connected, b) the architecture of these connections conforms to a small world structure, featuring a high degree of clustering but a short average path length, and c) distances between disorders in this structure predict empirical comorbidity rates. Network simulations of Major Depressive Episode and Generalized Anxiety Disorder show that the model faithfully reproduces empirical population statistics for these disorders.
In the network model, mental disorders are inherently complex. This explains the limited successes of genetic, neuroscientific, and etiological approaches to unravel their causes. We outline a psychosystems approach to investigate the structure and dynamics of mental disorders.

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Available from: Angelique Cramer, Aug 16, 2015
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    • "One important drawback of this model is that it does not assume associations between the individual symptoms, whereas this does seem plausible (e.g. sleep problems leading directly to concentration problems (Borsboom et al. 2011). It has been suggested that the structure of psychopathology may be better described as a complex network of components that interact in dynamic and nonlinear ways both at biological (Buckholtz & Meyer-Lindenberg, 2012) and psychological (Kendler et al. 2011) levels. "
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    ABSTRACT: It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques. A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states ('cheerful', 'content', 'down', 'insecure' and 'suspicious') were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters. Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics. Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.
    Psychological Medicine 03/2015; 45(11):1-13. DOI:10.1017/S0033291715000331 · 5.43 Impact Factor
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    • "Related work has shown that the DSM symptom network con - forms to what can be called a small world structure : the DSM features a host of interrelated symptoms , and symptoms are strongly connected both within and across diagnoses . This means that one can " travel " from any symptom to any other symptom in just a few jumps ( Borsboom et al . , 2011 ; Goekoop and Goekoop , 2014 ) , a perspective that offers new possibilities for comorbidity research ."
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    ABSTRACT: Major depression (MD) is a highly heterogeneous diagnostic category. Diverse symptoms such as sad mood, anhedonia, and fatigue are routinely added to an unweighted sum-score, and cutoffs are used to distinguish between depressed participants and healthy controls. Researchers then investigate outcome variables like MD risk factors, biomarkers, and treatment response in such samples. These practices presuppose that (1) depression is a discrete condition, and that (2) symptoms are interchangeable indicators of this latent disorder. Here I review these two assumptions, elucidate their historical roots, show how deeply engrained they are in psychological and psychiatric research, and document that they contrast with evidence. Depression is not a consistent syndrome with clearly demarcated boundaries, and depression symptoms are not interchangeable indicators of an underlying disorder. Current research practices lump individuals with very different problems into one category, which has contributed to the remarkably slow progress in key research domains such as the development of efficacious antidepressants or the identification of biomarkers for depression. The recently proposed network framework offers an alternative to the problematic assumptions. MD is not understood as a distinct condition, but as heterogeneous symptom cluster that substantially overlaps with other syndromes such as anxiety disorders. MD is not framed as an underlying disease with a number of equivalent indicators, but as a network of symptoms that have direct causal influence on each other: insomnia can cause fatigue which then triggers concentration and psychomotor problems. This approach offers new opportunities for constructing an empirically based classification system and has broad implications for future research.
    Frontiers in Psychology 03/2015; 6(306):1-11. DOI:10.3389/fpsyg.2015.00309 · 2.80 Impact Factor
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    • "For example, the fatigue symptom in a Major Depressive Episode (MDE) could causally influence the concentration problems in Generalized Anxiety Disorder (GAD). Thus, there are individualized pathways to comorbidity between disorders (Borsboom et al., 2011). Given the controversy regarding whether symptoms are manifest or latent-level constructs (McFarland and Malta, 2010), the idea that the network approach does not completely contradict the latent-level approach (Humphry and McGrane, 2010), and possibility of incorporating the symptom and latent-level approaches (Molenaar, 2010), necessitates consideration of the latent-level approach. "
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    ABSTRACT: Existing literature indicates significant comorbidity between posttraumatic stress disorder (PTSD) and major depression. We examined whether PTSD's dysphoria and mood/cognitions factors, conceptualized by the empirically supported four-factor DSM-5 PTSD models, account for PTSD's inherent relationship with depression. We hypothesized that depression's somatic and non-somatic factors would be more related to PTSD's dysphoria and mood/cognitions factors than other PTSD model factors. Further, we hypothesized that PTSD's arousal would significantly mediate relations between PTSD's dysphoria and somatic/non-somatic depression. Using 181 trauma-exposed primary care patients, confirmatory factor analyses (CFA) indicated a well-fitting DSM-5 PTSD dysphoria model, DSM-5 numbing model and two-factor depression model. Both somatic and non-somatic depression factors were more related to PTSD's dysphoria and mood/cognitions factors than to re-experiencing and avoidance factors; non-somatic depression was more related to PTSD's dysphoria than PTSD's arousal factor. PTSD's arousal did not mediate the relationship between PTSD's dysphoria and somatic/non-somatic depression. Implications are discussed.
    10/2013; 215(1). DOI:10.1016/j.psychres.2013.10.015
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