Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011;6(11):e27407.
doi: 10.1371/journal.pone.0027407. Epub 2011 Nov 17.

The Small World of Psychopathology

Free PMC article

The Small World of Psychopathology

Denny Borsboom et al. PLoS One. .
Free PMC article


Background: 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).

Principal findings: 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.

Conclusions: 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.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Figure 1
Figure 1. The difference between the existing view on comorbidity (top) versus the network approach (bottom) for two fictitious persons, Alice (left) and Bob (right).
In both figures, the red node represents an external life event; green nodes core MDE symptoms; turquoise nodes core GAD symptoms; purple nodes bridge symptoms (i.e., symptoms that are part of both MDE and GAD). Edges between nodes represent pathways between symptoms. The light green edges represent possible pathways; the thicker and dark green edges the pathways taken by Alice and Bob respectively. roma = break-up of romantic relationship; jobl = job loss; mWei = weight problems; mInt = loss of interest; mRep = self-reproach; mDep = depressed mood; mSui = (thoughts of) suicide; bSle = sleep problems; bFat = fatigue; bCon = concentration problems; bMot = psychomotor problems; gAnx = chronic anxiety; gEve = anxiety about more than one event; gCtr = no control over anxiety; gMus = muscle tension; gIrr = irritable.
Figure 2
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.
Figure 3
Figure 3. Small-world-ness indices (SWI).
Density distributions of the SWI's of 10000 random networks (in black), and of 10000 permutation model networks (in blue). The vertical red line marks the observed SWI of the giant component. Dotted vertical lines indicate the respective mean SWI.
Figure 4
Figure 4. The giant component and its degree distribution.
In the top left and bottom right parts of the figure, node size is proportional to the centrality of the node: the more central, the larger the node. The represented centrality measure is based on random walk betweenness . That is, we averaged the number of times that a node was part of a path between two other nodes chosen during consecutive random walks. The top left part represents the giant component while the bottom right highlights the four most central symptoms. The top right and bottom left parts of the figure show the fit of two functions on the degree distribution: logistic (to assess power law property; left bottom) and exponential (to assess exponential decay; top right). The x-as represents the (log) degree while the y-axis represents the probability that a node chosen uniformly at random has a degree larger than k.
Figure 5
Figure 5. Average shortest path length and comorbidity.
Left y-axis represents comorbidity; right y-axis average shortest path length. Abbreviations: MDE = Major Depressive Episode; DYS = Dysthymia; AGPH = Agoraphobia; SOP = Social Phobia; SIP = Simple Phobia; GAD = Generalized Anxiety Disorder; APD = Antisocial Personality Disorder.
Figure 6
Figure 6. The simulation of MDE and GAD and its results.
The left part of the figure shows core MDE (blue nodes), core GAD (red nodes) and bridge symptoms (purple nodes). The middle part of the figure represents the implied structure of the simulated network: comorbidity arises through connections via bridge symptoms. There are no direct connections between core MDE and core GAD symptoms. The right part of the figure displays the results of the simulations. The x-axis represents the number of replications of the simulation. The y-axis represents 1) odds: odds ratio of diagnoses as measure of comorbidity, 2) alpha: Cronbach's α, 3) MDE: prevalence of MDE and 4) GAD: prevalence of GAD.
Figure 7
Figure 7. Densities of simulation results of original vs random parameter values.
Top to bottom: Prevalence of MDE, Prevalence of GAD, Odds ratio, Cronbach' alpha. Densities of networks resulting from original (random) parameter values are shown in blue (red).

Similar articles

See all similar articles

Cited by 101 articles

See all "Cited by" articles


    1. Kessler RC, Chiu W, Demler O, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):617–627. - PMC - PubMed
    1. Albert U, Rosso G, Maina G, Bogetto F. Impact of anxiety disorder comorbidity on quality of life in euthymic bipolar disorder patients: Differences between bipolar I and II subtypes. J Affect Disorders. 2008;105:297–303. - PubMed
    1. Schoevers RA, Deeg DJH, Van Tilburg W, Beekman ATF. Anxiety and depression: Co-occurrence and longitudinal patterns in elderly patients. Am J Geriat Psychia. 2005;13:31–39. - PubMed
    1. Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: Results from the National Comorbidity Survey. Arch Gen Psychiatry. 1994;51(1):8–19. - PubMed
    1. Merikangas KR, Mehta RL, Molnar BE, Walters EE, Swendsen JD, et al. Comorbidity of substance use disorders with mood and anxiety disorders: Results of the International Consortium in Psychiatric Epidemiology. Addict Behav. 1998;23:893–907. - PubMed

Publication types

MeSH terms