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Table 1 DSM-IV-(Text Revision) criteria for deli...

Fig. 1 Estimated marginal means of HTQ DSM-IV.

Figure 3 Diagnostic criteria for depression based...

Box 1 DSM-IV diagnoses included in the ProCEED...

Figure 1 Flowchart.£ 28 patients both had Health ...

Figure 2 Autism diagnostic criteria used by state...

Figure 13. Raster DSM produced by node 1 through to...

Figure 1 This image shows the variant diagnostic ...

Figure 1 Receiver operating curve of the AUDIT us...

Figure 2 Receiver operating curve of the AUDIT us...

Figure 2: The DSM-IV symptom space.Symptoms are represented as nodes and connected by an edge whenever they figure in the same disorder. Color of nodes represents the DSM-IV chapter in which they occur most often.

Image Text (High Precision): 10000 Anxiety Disorders Mental Mood Sleep Symptom adjustment adolescence amnesia chapters childhood classified cognitive condition conditions control delirium dementia diagnosed dissociative due eating elsewhere equally gender general identity impulse infancy medical multiple personality psychotic related schizophrenia sexual substance usually

Other Images from "The Small World of Psychopathology":


Figure 1 The difference between the existing view...

Figure 2 The DSM-IV symptom space.Symptoms are re...

Figure 3 Small-world-ness indices (SWI).Density d...

Figure 4 The giant component and its degree distr...

Figure 5 Average shortest path length and comorbi...

Figure 6 The simulation of MDE and GAD and its re...

Figure 7 Densities of simulation results of origi...

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Abstract

BackgroundMental 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).Principal FindingsWe 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.ConclusionsIn 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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