The cause of comorbidity is a puzzle, which may have its roots in the very conceptualization of what a mental disorder is 
. Although much has been learned about the genetics, neuroscience, and etiology of mental disorders, the past century of research also shows that they cannot be identified with a simple set of genetic antecedents, neural correlates, or developmental trajectories. The network hypothesis may be considered to provide an explanation for this situation.
First, consider genetics. Behavior genetic research has shown that individual differences in the liability to develop disorders are, for a large part, genetically determined (e.g., genes are estimated to be responsible for around 50 percent of the variance in the psychological traits considered in 
). However, only a minor part of the genetic variance can typically be traced to identified polymorphisms (often less than 2 percent for psychological traits; 
). This puzzle is known as the problem of missing heritability 
. In the present view, it is plausible that the strength of symptom connections in a network partly stands under genetic control, but it is unlikely that all connections in a disorder are affected by the same genes in all people. Instead, we may consider the heritability of mental disorders to arise from, e.g., the genetic transmission of intersymptom connection strength, which in turn determines global parameters of person specific networks that correspond to the vulnerability of the system with respect to external and internal shocks. If this is correct, the network model may provide an alternative explanation to missing heritability, as compared to the currently proposed hypothesis that the heritability of mental disorders result from very large numbers of polygenes with small additive effects 
. Further research should evaluate which of these hypotheses is more plausible for which disorders.
Second, consider neuroscientific research strategies into mental disorders. In the public eye, such studies have appeared to reveal what, say, depression “really is” by linking such a disorder to, for instance, a neurotransmitter imbalance. However, even though neuroscientific research has provided a wealth of empirical information about the correlates of mental disorders, no simple identifications of disorders with neural dysfunctions have been forthcoming. The network hypothesis suggests that this will remain the case, because neural properties are most likely to enter the model as mechanistic realizations of nodes and edges already in the network, or as additional nodes and edges that extend it. Such properties may contribute to the network structure importantly: for instance, many of the symptoms in the DSM-IV relate to basic homeostatic brain functions (eating, sleeping, sex, mood regulation), and it is essential to investigate their precise role in sustaining the network structure. However, just like the small world properties of the World Wide Web do not reduce to physical properties of individual webservers, mental disorders are unlikely to correspond to a single, homogeneous neural substrate.
Third, consider etiology. Some researchers have proposed that a focus on etiology may lead to a homogeneous grouping of mental disorders. In our view, this is unlikely. The giant component in the DSM-IV topology features 208*(208-1)/2
21,528 pairs of symptoms. Even if we just count the number of distinct shortest paths between any two symptoms in this topology, we obtain 129,643 distinct pathways. Although many pathways in the DSM-IV symptom space are unlikely to be active in transmitting effects, and it is probable that some pathways are more prevalent than others, it would seem unlikely that a focus on etiology could bring this number down to manageable size. Instead, the network approach predicts that individual cases of mental disorders will be highly idiosyncratic, both in the genetic and environmental determinants of the disorder, as well as in the etiological pathway by which it developed. The interesting fact is that, under the network approach, this may be the case even though population statistics relating to mental disorders are empirically stable. In this sense, the network hypothesis simultaneously accommodates the stability of population statistics in this area of research, and the idiosyncratic unpredictability of the individual person.
Thus, the network model not only yields plausible explanations for characteristic patterns in empirical psychopathology data, as shown in the research reported here; it may also illuminate the limited successes of research paths that have so far been taken. In particular, if mental disorders correspond to networks of causally coupled variables, we should not expect them to conform to a homogeneous biological, genetic, or etiological analysis. Instead, similar to current systems approaches in biology 
, research into psychopathology may profitably adopt a psychosystems approach
by investigating the inherent complexity of mental disorders 
through explicit models of the interplay between their psychological, biological, and social features that play a role in the development of psychiatric conditions, understood as clusters of causally linked properties 
Further research in this direction may be pursued along the following lines. First, it is important to augment the network structure with “positive” nodes, that is, with nodes that are known to act as protective factors against developing pathologies (e.g., coping mechanisms used to “ward off” symptoms, or situational factors that may protect certain symptoms from rising to the level of pathology). This requires the construction of an “inverse” DSM, i.e., a categorized list of anti-symptoms that protect against disorders by blocking the spreading of problems through the network. Second, research into the direction and nature of the causal effects between network nodes (i.e., symptoms and anti-symptoms) should lead to a more refined representation of the network structure. Third, it is of interest to investigate how traditional measurement models, which for instance feature higher-order factors like internalizing and externalizing 
, relate to network structures. In traditional models, these factors are most readily interpreted as latent common causes of the item responses 
, but in the network models considered here, such common causes are lacking. Our expectation is that the relevant factors can be viewed as approximately isomorphic to regions of strongly connected symptoms, but whether this is conceptually and mathematically tenable is a question for further research. Fourth, it is important to test the dynamics of the network model against real data. Experience sampling studies, or other ways to track symptom dynamics, would appear to be especially suited for this purpose. Such research could also begin to unravel intra- and interindividual differences in network structure, which would open the possibility to examine salient network characteristics of individuals known to be at risk for developing mental disorders. If such characteristics could be charted, specific targeting of the most important network components (either with medication or with psychotherapy) might offer a novel way to develop therapeutic interventions and monitor their effects.